From 741bb87351bc533ddace81823ed506aa63188a59 Mon Sep 17 00:00:00 2001 From: Krishna Kaushik <131583096+kRiShNa-429407@users.noreply.github.com> Date: Fri, 17 May 2024 23:11:26 +0530 Subject: [PATCH 01/72] Update index.md --- contrib/pandas/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/pandas/index.md b/contrib/pandas/index.md index 5c0f2b4..f2a3fa7 100644 --- a/contrib/pandas/index.md +++ b/contrib/pandas/index.md @@ -1,3 +1,4 @@ # List of sections - [Pandas Series Vs NumPy ndarray](pandas_series_vs_numpy_ndarray.md) +- [Pandas Introduction and Dataframes in Pandas](Introduction_to_Pandas_Library_and_DataFrames.md) From 35baa355cb23b9f7dad9818ebc5cbe80fff35e84 Mon Sep 17 00:00:00 2001 From: Krishna Kaushik <131583096+kRiShNa-429407@users.noreply.github.com> Date: Fri, 17 May 2024 23:11:53 +0530 Subject: [PATCH 02/72] Add files via upload --- ...uction_to_Pandas_Library_and_DataFrames.md | 395 ++++++++++++++++++ 1 file changed, 395 insertions(+) create mode 100644 contrib/pandas/Introduction_to_Pandas_Library_and_DataFrames.md diff --git a/contrib/pandas/Introduction_to_Pandas_Library_and_DataFrames.md b/contrib/pandas/Introduction_to_Pandas_Library_and_DataFrames.md new file mode 100644 index 0000000..5e7dcd3 --- /dev/null +++ b/contrib/pandas/Introduction_to_Pandas_Library_and_DataFrames.md @@ -0,0 +1,395 @@ +# Introduction_to_Pandas_Library_and_DataFrames + + +> Content Creator - Krishna Kaushik + +**As you have learnt Python Programming , now it's time for some applications.** + +- Machine Learning and Data Science is the emerging field of today's time , to work in this this field your first step should be `Data Science` as Machine Learning is all about data. +- To begin with Data Science your first tool will be `Pandas Library`. + +## Introduction of Pandas Library + +Pandas is a data analysis and manipulation tool, built on top of the python programming language. Pandas got its name from the term Panel data (‘Pa’ from Panel and ‘da’ from data). Panel data is a data which have rows and columns in it like excel spreadsheets, csv files etc. + +**To use Pandas, first we’ve to import it.** + +## Why pandas? + +* Pandas provides a simple-to-use but very capable set of functions that you can use on your data. +* It is also associate with other machine learning libraries , so it is important to learn it. + +* For example - It is highly used to transform tha data which will be use by machine learning model during the training. + + +```python +# Importing the pandas +import pandas as pd +``` + +*To import any module in Python use “import 'module name' ” command, I used “pd” as pandas abbreviation because we don’t need to type pandas every time only type “pd” to use pandas.* + + +```python +# To check available pandas version +print(f"Pandas Version is : {pd.__version__}") +``` + + Pandas Version is : 2.1.4 + + +## Understanding Pandas data types + +Pandas has two main data types : `Series` and `DataFrames` + +* `pandas.Series` is a 1-dimensional column of data. +* `pandas.DataFrames` is 2 -dimensional data table having rows and columns. + +### 1. Series datatype + +**To creeate a series you can use `pd.Series()` and passing a python list inside()**. + +Note: S in Series is capital if you use small s it will give you an error. + +> Let's create a series + + + +```python +# Creating a series of car companies +cars = pd.Series(["Honda","Audi","Thar","BMW"]) +cars +``` + + + + + 0 Honda + 1 Audi + 2 Thar + 3 BMW + dtype: object + + + +The above code creates a Series of cars companies the name of series is “cars” the code “pd.Series([“Honda” , “Audi” , “Thar”, "BMW"])” means Hey! pandas (pd) create a Series of cars named "Honda" , "Audi" , "Thar" and "BMW". + +The default index of a series is 0,1,2….(Remember it starts from 0) + +To change the index of any series set the “index” parameter accordingly. It takes the list of index values: + + +```python +cars = pd.Series(["Honda","Audi","Thar","BMW"],index = ["A" , "B" , "C" ,"D"]) +cars +``` + + + + + A Honda + B Audi + C Thar + D BMW + dtype: object + + + +You can see that the index has been changed from numbers to A, B ,C and D. + +And the mentioned ‘dtype’ tells us about the type of data we have in the series. + +### 2. DataFrames datatype + +DataFrame contains rows and columns like a csv file have. + +You can also create a DataFrame by using `pd.DataFrame()` and passing it a Python dictionary. + + +```python +# Let's create +cars_with_colours = pd.DataFrame({"Cars" : ["BMW","Audi","Thar","Honda"], + "Colour" : ["Black","White","Red","Green"]}) +cars_with_colours +``` + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
CarsColour
0BMWBlack
1AudiWhite
2TharRed
3HondaGreen
+
+ + + +The dictionary key is the `column name` and value are the `column data`. + +*You can also create a DataFrame with the help of series.* + + +```python +# Let's create two series +students = pd.Series(["Ram","Mohan","Krishna","Shivam"]) +age = pd.Series([19,20,21,24]) + +students +``` + + + + + 0 Ram + 1 Mohan + 2 Krishna + 3 Shivam + dtype: object + + + + +```python +age +``` + + + + + 0 19 + 1 20 + 2 21 + 3 24 + dtype: int64 + + + + +```python +# Now let's create a dataframe with the help of above series +# pass the series name to the dictionary value + +record = pd.DataFrame({"Student_Name":students , + "Age" :age}) +record +``` + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Student_NameAge
0Ram19
1Mohan20
2Krishna21
3Shivam24
+
+ + + + +```python +# To print the list of columns names +record.columns +``` + + + + + Index(['Student_Name', 'Age'], dtype='object') + + + +### Describe Data + +**The good news is that pandas has many built-in functions which allow you to quickly get information about a DataFrame.** +Let's explore the `record` dataframe + +#### 1. Use `.dtypes` to find what datatype a column contains + + +```python +record.dtypes +``` + + + + + Student_Name object + Age int64 + dtype: object + + + +#### 2. use `.describe()` for statistical overview. + + +```python +record.describe() # It only display the results for numeric data +``` + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Age
count4.000000
mean21.000000
std2.160247
min19.000000
25%19.750000
50%20.500000
75%21.750000
max24.000000
+
+ + + +#### 3. Use `.info()` to find information about the dataframe + + +```python +record.info() +``` + + + RangeIndex: 4 entries, 0 to 3 + Data columns (total 2 columns): + # Column Non-Null Count Dtype + --- ------ -------------- ----- + 0 Student_Name 4 non-null object + 1 Age 4 non-null int64 + dtypes: int64(1), object(1) + memory usage: 196.0+ bytes + + + +```python + +``` From 6bee5482ad8b0b83273f1a8f6c3ddc8aee483d3c Mon Sep 17 00:00:00 2001 From: Krishna Kaushik <131583096+kRiShNa-429407@users.noreply.github.com> Date: Fri, 17 May 2024 23:14:01 +0530 Subject: [PATCH 03/72] Update Introduction_to_Pandas_Library_and_DataFrames.md --- ...uction_to_Pandas_Library_and_DataFrames.md | 54 ------------------- 1 file changed, 54 deletions(-) diff --git a/contrib/pandas/Introduction_to_Pandas_Library_and_DataFrames.md b/contrib/pandas/Introduction_to_Pandas_Library_and_DataFrames.md index 5e7dcd3..809a155 100644 --- a/contrib/pandas/Introduction_to_Pandas_Library_and_DataFrames.md +++ b/contrib/pandas/Introduction_to_Pandas_Library_and_DataFrames.md @@ -112,24 +112,6 @@ cars_with_colours = pd.DataFrame({"Cars" : ["BMW","Audi","Thar","Honda"], "Colour" : ["Black","White","Red","Green"]}) cars_with_colours ``` - - - - -
- @@ -214,24 +196,6 @@ record = pd.DataFrame({"Student_Name":students , "Age" :age}) record ``` - - - - -
-
@@ -307,24 +271,6 @@ record.dtypes ```python record.describe() # It only display the results for numeric data ``` - - - - -
-
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Thamine","female",,1,0,"2665",14.4542,,"C",,, +3,0,"Zakarian, Mr. Mapriededer","male",26.5,0,0,"2656",7.2250,,"C",,"304", +3,0,"Zakarian, Mr. Ortin","male",27,0,0,"2670",7.2250,,"C",,, +3,0,"Zimmerman, Mr. Leo","male",29,0,0,"315082",7.8750,,"S",,, From d1e1fc5c9d637395e32235e45891698478929700 Mon Sep 17 00:00:00 2001 From: Krishna Kaushik <131583096+kRiShNa-429407@users.noreply.github.com> Date: Sat, 18 May 2024 22:37:38 +0530 Subject: [PATCH 05/72] Add files via upload --- .../Importing_and_Exporting_Data_in_Pandas.md | 291 ++++++++++++++++++ 1 file changed, 291 insertions(+) create mode 100644 contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md diff --git a/contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md b/contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md new file mode 100644 index 0000000..a6949d2 --- /dev/null +++ b/contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md @@ -0,0 +1,291 @@ +# Importing_and_Exporting_Data_in_Pandas + +>Created by Krishna Kaushik + +- **Now we're able to create `Series` and `DataFrames` in pandas, but we usually do not do this , in practice we import the data which is in the form of .csv (Comma Seperated Values) , a spreadsheet file or something similar.** + +- *Good news is that pandas allows for easy importing of data like this through functions such as ``pd.read_csv()`` and ``pd.read_excel()`` for Microsoft Excel files.* + +## 1. Importing from a Google sheet to a pandas dataframe + +*Let's say that you wanted to get the information from Google Sheet document into a pandas DataFrame.*. + +*You could export it as a .csv file and then import it using ``pd.read_csv()``.* + +*In this case, the exported .csv file is called `Titanic.csv`* + + +```python +## Importing Titanic Data set +import pandas as pd + +titanic_df= pd.read_csv("https://raw.githubusercontent.com/kRiShNa-429407/learn-python/main/contrib/pandas/Titanic.csv") +titanic_df +``` + + + + +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
pclasssurvivednamesexagesibspparchticketfarecabinembarkedboatbodyhome.dest
011Allen, Miss. Elisabeth Waltonfemale29.000024160211.3375B5S2NaNSt Louis, MO
111Allison, Master. Hudson Trevormale0.9212113781151.5500C22 C26S11NaNMontreal, PQ / Chesterville, ON
210Allison, Miss. Helen Lorainefemale2.0012113781151.5500C22 C26SNaNNaNMontreal, PQ / Chesterville, ON
310Allison, Mr. Hudson Joshua Creightonmale30.0012113781151.5500C22 C26SNaN135.0Montreal, PQ / Chesterville, ON
410Allison, Mrs. Hudson J C (Bessie Waldo Daniels)female25.0012113781151.5500C22 C26SNaNNaNMontreal, PQ / Chesterville, ON
.............................................
130430Zabour, Miss. Hilenifemale14.5010266514.4542NaNCNaN328.0NaN
130530Zabour, Miss. ThaminefemaleNaN10266514.4542NaNCNaNNaNNaN
130630Zakarian, Mr. Mapriededermale26.500026567.2250NaNCNaN304.0NaN
130730Zakarian, Mr. Ortinmale27.000026707.2250NaNCNaNNaNNaN
130830Zimmerman, Mr. Leomale29.00003150827.8750NaNSNaNNaNNaN
+

1309 rows × 14 columns

+
+ + + +The dataset I am using here for your reference is taken from the same repository i.e ``learn-python`` (https://raw.githubusercontent.com/kRiShNa-429407/learn-python/main/contrib/pandas/Titanic.csv) I uploaded it you can use it from there. + +**Now we've got the same data from the Google Spreadsheet , but now available as ``pandas DataFrame`` which means we can now apply all pandas functionality over it.** + +#### Note: The quiet important thing i am telling is that ``pd.read_csv()`` takes the location of the file (which is in your current working directory) or the hyperlink of the dataset from the other source. + +#### But if you want to import the data from Github you can't directly use its link , you have to first convert it to raw by clicking on the raw button present in the repo . + +#### Also you can't use the data directly from `Kaggle` you have to use ``kaggle API`` + +## 2. The Anatomy of DataFrame + +**Different functions use different labels for different things, and can get a little confusing.** + +- Rows are refer as ``axis=0`` +- columns are refer as ``axis=1`` + +## 3. Exporting Data + +**OK, so after you've made a few changes to your data, you might want to export it and save it so someone else can access the changes.** + +**pandas allows you to export ``DataFrame's`` to ``.csv`` format using ``.to_csv()``, or to a spreadsheet format using .to_excel().** + +### Exporting a dataframe to a CSV + +**We haven't made any changes yet to the ``titanic_df`` DataFrame but let's try to export it.** + + +```python +#Export the titanic_df DataFrame to csv +titanic_df.to_csv("exported_titanic.csv") +``` + +Running this will save a file called ``exported_titanic.csv`` to the current folder. From bf4dcff08b1c47b0f15a63634879390156d2f995 Mon Sep 17 00:00:00 2001 From: Krishna Kaushik <131583096+kRiShNa-429407@users.noreply.github.com> Date: Sat, 18 May 2024 22:39:54 +0530 Subject: [PATCH 06/72] Update index.md --- contrib/pandas/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/pandas/index.md b/contrib/pandas/index.md index f2a3fa7..84871dd 100644 --- a/contrib/pandas/index.md +++ b/contrib/pandas/index.md @@ -2,3 +2,4 @@ - [Pandas Series Vs NumPy ndarray](pandas_series_vs_numpy_ndarray.md) - [Pandas Introduction and Dataframes in Pandas](Introduction_to_Pandas_Library_and_DataFrames.md) +- [Importing and Exportin data in pandas](Importing_and_Exporting_Data_in_Pandas.md) From 849f5abc6e072a7f96aec848fb8d0c5658518be8 Mon Sep 17 00:00:00 2001 From: Krishna Kaushik <131583096+kRiShNa-429407@users.noreply.github.com> Date: Sat, 18 May 2024 22:40:30 +0530 Subject: [PATCH 07/72] Update Importing_and_Exporting_Data_in_Pandas.md --- .../Importing_and_Exporting_Data_in_Pandas.md | 18 ------------------ 1 file changed, 18 deletions(-) diff --git a/contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md b/contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md index a6949d2..4d0ffad 100644 --- a/contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md +++ b/contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md @@ -22,24 +22,6 @@ import pandas as pd titanic_df= pd.read_csv("https://raw.githubusercontent.com/kRiShNa-429407/learn-python/main/contrib/pandas/Titanic.csv") titanic_df ``` - - - - -
- From f012e8c481de8f98a8a32e822268602bfe93f6b6 Mon Sep 17 00:00:00 2001 From: Labqari Date: Thu, 23 May 2024 15:38:35 +0530 Subject: [PATCH 08/72] adding flask --- contrib/advanced-python/index.md | 1 + ...duction-to-flask-a-python-web-framework.md | 440 ++++++++++++++++++ 2 files changed, 441 insertions(+) create mode 100644 contrib/advanced-python/introduction-to-flask-a-python-web-framework.md diff --git a/contrib/advanced-python/index.md b/contrib/advanced-python/index.md index 5ea5081..027f989 100644 --- a/contrib/advanced-python/index.md +++ b/contrib/advanced-python/index.md @@ -1,3 +1,4 @@ # List of sections - [Decorators/\*args/**kwargs](decorator-kwargs-args.md) +- [Decorators/\*args/**kwargs](introduction-to-flask-a-python-web-framework.md) diff --git a/contrib/advanced-python/introduction-to-flask-a-python-web-framework.md b/contrib/advanced-python/introduction-to-flask-a-python-web-framework.md new file mode 100644 index 0000000..957f39b --- /dev/null +++ b/contrib/advanced-python/introduction-to-flask-a-python-web-framework.md @@ -0,0 +1,440 @@ +Sure, here's the guide without Markdown formatting: + +--- + +# Introduction to Flask: A Python Web Framework + +## Table of Contents +1. Introduction +2. Prerequisites +3. Setting Up Your Environment +4. Creating Your First Flask Application + - Project Structure + - Hello World Application +5. Routing +6. Templates and Static Files + - Jinja2 Templating Engine + - Serving Static Files +7. Working with Forms + - Handling Form Data +8. Database Integration + - Setting Up SQLAlchemy + - Performing CRUD Operations +9. Error Handling +10. Testing Your Application +11. Deploying Your Flask Application + - Using Gunicorn + - Deploying to Render +12. Conclusion +13. Further Reading and Resources + +--- + +## 1. Introduction +Flask is a lightweight WSGI web application framework in Python. It is designed with simplicity and flexibility in mind, allowing developers to create web applications with minimal setup. Flask was created by Armin Ronacher as part of the Pocoo project and has gained popularity for its ease of use and extensive documentation. + +## 2. Prerequisites +Before starting with Flask, ensure you have the following: +- Basic knowledge of Python. +- Understanding of web development concepts (HTML, CSS, JavaScript). +- Python installed on your machine (version 3.6 or higher). +- pip (Python package installer) installed. + +## 3. Setting Up Your Environment +1. **Install Python**: Download and install Python from python.org. +2. **Create a Virtual Environment**: + ``` + python -m venv venv + ``` +3. **Activate the Virtual Environment**: + - On Windows: + ``` + venv\Scripts\activate + ``` + - On macOS/Linux: + ``` + source venv/bin/activate + ``` +4. **Install Flask**: + ``` + pip install Flask + ``` + +## 4. Creating Your First Flask Application +### Project Structure +A typical Flask project structure might look like this: +``` +my_flask_app/ + app/ + __init__.py + routes.py + templates/ + static/ + venv/ + run.py +``` + +### Hello World Application +1. **Create a Directory for Your Project**: + ``` + mkdir my_flask_app + cd my_flask_app + ``` +2. **Initialize the Application**: + - Create `app/__init__.py`: + ```python + from flask import Flask + + def create_app(): + app = Flask(__name__) + + with app.app_context(): + from . import routes + return app + ``` + - Create `run.py`: + ```python + from app import create_app + + app = create_app() + + if __name__ == "__main__": + app.run(debug=True) + ``` + - Create `app/routes.py`: + ```python + from flask import current_app as app + + @app.route('/') + def hello_world(): + return 'Hello, World!' + ``` + +3. **Run the Application**: + ``` + python run.py + ``` + Navigate to `http://127.0.0.1:5000` in your browser to see "Hello, World!". + +## 5. Routing +In Flask, routes are defined using the `@app.route` decorator. Here's an example of different routes: + +```python +from flask import Flask + +app = Flask(__name__) + +@app.route('/') +def home(): + return 'Home Page' + +@app.route('/about') +def about(): + return 'About Page' + +@app.route('/user/') +def show_user_profile(username): + return f'User: {username}' +``` + +- **Explanation**: + - The `@app.route('/')` decorator binds the URL `'/'` to the `home` function, which returns 'Home Page'. + - The `@app.route('/about')` decorator binds the URL `/about` to the `about` function. + - The `@app.route('/user/')` decorator binds the URL `/user/` to the `show_user_profile` function, capturing the part of the URL as the `username` variable. + +## 6. Templates and Static Files +### Jinja2 Templating Engine +Jinja2 is Flask's templating engine. Templates are HTML files that can include dynamic content. + +- **Create a Template**: + - `app/templates/index.html`: + ```html + + + + {{ title }} + + +

{{ heading }}

+

{{ content }}

+ + + ``` + +- **Render the Template**: + ```python + from flask import Flask, render_template + + app = Flask(__name__) + + @app.route('/') + def home(): + return render_template('index.html', title='Home', heading='Welcome to Flask', content='This is a Flask application.') + ``` + +### Serving Static Files +Static files like CSS, JavaScript, and images are placed in the `static` directory. + +- **Create Static Files**: + - `app/static/style.css`: + ```css + body { + font-family: Arial, sans-serif; + } + ``` + +- **Include Static Files in Templates**: + ```html + + + + {{ title }} + + + +

{{ heading }}

+

{{ content }}

+ + + ``` + +## 7. Working with Forms +### Handling Form Data +Forms are used to collect user input. Flask provides utilities to handle form submissions. + +- **Create a Form**: + - `app/templates/form.html`: + ```html + + + + Form + + +
+ + + + + + + ``` + +- **Handle Form Submission**: + ```python + from flask import Flask, request, render_template + + app = Flask(__name__) + + @app.route('/form') + def form(): + return render_template('form.html') + + @app.route('/submit', methods=['POST']) + def submit(): + name = request.form['name'] + return f'Hello, {name}!' + ``` + +- **Explanation**: + - The `@app.route('/form')` route renders the form. + - The `@app.route('/submit', methods=['POST'])` route handles the form submission and displays the input name. + +## 8. Database Integration +### Setting Up SQLAlchemy +SQLAlchemy is an ORM that allows you to interact with databases using Python objects. + +- **Install SQLAlchemy**: + ``` + pip install flask_sqlalchemy + ``` + +- **Configure SQLAlchemy**: + - `app/__init__.py`: + ```python + from flask import Flask + from flask_sqlalchemy import SQLAlchemy + + db = SQLAlchemy() + + def create_app(): + app = Flask(__name__) + app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///site.db' + db.init_app(app) + return app + ``` + +### Performing CRUD Operations +Define models and perform CRUD operations. + +- **Define a Model**: + - `app/models.py`: + ```python + from app import db + + class User(db.Model): + id = db.Column(db.Integer, primary key=True) + username = db.Column(db.String(80), unique=True, nullable=False) + + def __repr__(self): + return f'' + ``` + +- **Create the Database**: + ```python + from app import create_app, db + from app.models import User + + app = create_app() + with app.app_context(): + db.create_all() + ``` + +- **Perform CRUD Operations**: + ```python + from app import db + from app.models import User + + # Create + new_user = User(username='new_user') + db.session.add(new_user) + db.session.commit() + + # Read + user = User.query.first() + + # Update + user.username = 'updated_user' + db.session.commit() + + # Delete + db.session.delete(user) + db.session.commit() + ``` + +## 9. Error Handling +Error handling in Flask can be managed by defining error handlers for different HTTP status codes. + +- **Define an Error Handler**: + ```python + from flask import Flask, render_template + + app = Flask(__name__) + + @app.errorhandler(404) + def page_not_found(e): + return render_template('404.html'), 404 + + @app.errorhandler(500) + def internal_server_error(e): + return render_template('500.html'), 500 + ``` + + - **Create Error Pages**: + `app/templates/404.html`: + + + + + Page Not Found + + +

404 - Page Not Found

+

The page you are looking for does not exist.

+ + + + + + - **app/templates/500.html:** + + + + + Internal Server Error + + +

500 - Internal Server Error

+

Something went wrong on our end. Please try again later.

+ + + + +## 10. Testing Your Application +Flask applications can be tested using Python's built-in `unittest` framework. + +- **Write a Test Case**: + - `tests/test_app.py`: + ```python + import unittest + from app import create_app + + class BasicTestCase(unittest.TestCase): + def setUp(self): + self.app = create_app() + self.app.config['TESTING'] = True + self.client = self.app.test_client() + + def test_home(self): + response = self.client.get('/') + self.assertEqual(response.status_code, 200) + self.assertIn(b'Hello, World!', response.data) + + if __name__ == '__main__': + unittest.main() + ``` + + - **Run the Tests**: + ``` + python -m unittest discover -s tests + ``` + +## 11. Deploying Your Flask Application +### Using Gunicorn +Gunicorn is a Python WSGI HTTP Server for UNIX. It’s a pre-fork worker model, meaning that it forks multiple worker processes to handle requests. + +- **Install Gunicorn**: + ``` + pip install gunicorn + ``` + +- **Run Your Application with Gunicorn**: + ``` + gunicorn -w 4 run:app + ``` + +### Deploying to Render +Render is a cloud platform for deploying web applications. + +- **Create a `requirements.txt` File**: + ``` + Flask + gunicorn + flask_sqlalchemy + ``` + +- **Create a `render.yaml` File**: + ```yaml + services: + - type: web + name: my-flask-app + env: python + plan: free + buildCommand: pip install -r requirements.txt + startCommand: gunicorn -w 4 run:app + ``` + +- **Deploy Your Application**: + 1. Push your code to a Git repository. + 2. Sign in to Render and create a new Web Service. + 3. Connect your repository and select the branch to deploy. + 4. Render will automatically use the `render.yaml` file to configure and deploy your application. + +## 12. Conclusion +Flask is a powerful and flexible framework for building web applications in Python. It offers simplicity and ease of use, making it a great choice for both beginners and experienced developers. This guide covered the basics of setting up a Flask application, routing, templating, working with forms, integrating databases, error handling, testing, and deployment. + +## 13. Further Reading and Resources +- Flask Documentation: https://flask.palletsprojects.com/en/latest/ +- Jinja2 Documentation: https://jinja.palletsprojects.com/en/latest/ +- SQLAlchemy Documentation: https://docs.sqlalchemy.org/en/latest/ +- Render Documentation: https://render.com/docs \ No newline at end of file From cdb57900d40905a8cb0685bba807e4a6484475d2 Mon Sep 17 00:00:00 2001 From: Krishna Kaushik <131583096+kRiShNa-429407@users.noreply.github.com> Date: Fri, 24 May 2024 21:11:33 +0530 Subject: [PATCH 09/72] Delete contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md --- .../Importing_and_Exporting_Data_in_Pandas.md | 273 ------------------ 1 file changed, 273 deletions(-) delete mode 100644 contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md diff --git a/contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md b/contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md deleted file mode 100644 index 4d0ffad..0000000 --- a/contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md +++ /dev/null @@ -1,273 +0,0 @@ -# Importing_and_Exporting_Data_in_Pandas - ->Created by Krishna Kaushik - -- **Now we're able to create `Series` and `DataFrames` in pandas, but we usually do not do this , in practice we import the data which is in the form of .csv (Comma Seperated Values) , a spreadsheet file or something similar.** - -- *Good news is that pandas allows for easy importing of data like this through functions such as ``pd.read_csv()`` and ``pd.read_excel()`` for Microsoft Excel files.* - -## 1. Importing from a Google sheet to a pandas dataframe - -*Let's say that you wanted to get the information from Google Sheet document into a pandas DataFrame.*. - -*You could export it as a .csv file and then import it using ``pd.read_csv()``.* - -*In this case, the exported .csv file is called `Titanic.csv`* - - -```python -## Importing Titanic Data set -import pandas as pd - -titanic_df= pd.read_csv("https://raw.githubusercontent.com/kRiShNa-429407/learn-python/main/contrib/pandas/Titanic.csv") -titanic_df -``` -
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
pclasssurvivednamesexagesibspparchticketfarecabinembarkedboatbodyhome.dest
011Allen, Miss. Elisabeth Waltonfemale29.000024160211.3375B5S2NaNSt Louis, MO
111Allison, Master. Hudson Trevormale0.9212113781151.5500C22 C26S11NaNMontreal, PQ / Chesterville, ON
210Allison, Miss. Helen Lorainefemale2.0012113781151.5500C22 C26SNaNNaNMontreal, PQ / Chesterville, ON
310Allison, Mr. Hudson Joshua Creightonmale30.0012113781151.5500C22 C26SNaN135.0Montreal, PQ / Chesterville, ON
410Allison, Mrs. Hudson J C (Bessie Waldo Daniels)female25.0012113781151.5500C22 C26SNaNNaNMontreal, PQ / Chesterville, ON
.............................................
130430Zabour, Miss. Hilenifemale14.5010266514.4542NaNCNaN328.0NaN
130530Zabour, Miss. ThaminefemaleNaN10266514.4542NaNCNaNNaNNaN
130630Zakarian, Mr. Mapriededermale26.500026567.2250NaNCNaN304.0NaN
130730Zakarian, Mr. Ortinmale27.000026707.2250NaNCNaNNaNNaN
130830Zimmerman, Mr. Leomale29.00003150827.8750NaNSNaNNaNNaN
-

1309 rows × 14 columns

-
- - - -The dataset I am using here for your reference is taken from the same repository i.e ``learn-python`` (https://raw.githubusercontent.com/kRiShNa-429407/learn-python/main/contrib/pandas/Titanic.csv) I uploaded it you can use it from there. - -**Now we've got the same data from the Google Spreadsheet , but now available as ``pandas DataFrame`` which means we can now apply all pandas functionality over it.** - -#### Note: The quiet important thing i am telling is that ``pd.read_csv()`` takes the location of the file (which is in your current working directory) or the hyperlink of the dataset from the other source. - -#### But if you want to import the data from Github you can't directly use its link , you have to first convert it to raw by clicking on the raw button present in the repo . - -#### Also you can't use the data directly from `Kaggle` you have to use ``kaggle API`` - -## 2. The Anatomy of DataFrame - -**Different functions use different labels for different things, and can get a little confusing.** - -- Rows are refer as ``axis=0`` -- columns are refer as ``axis=1`` - -## 3. Exporting Data - -**OK, so after you've made a few changes to your data, you might want to export it and save it so someone else can access the changes.** - -**pandas allows you to export ``DataFrame's`` to ``.csv`` format using ``.to_csv()``, or to a spreadsheet format using .to_excel().** - -### Exporting a dataframe to a CSV - -**We haven't made any changes yet to the ``titanic_df`` DataFrame but let's try to export it.** - - -```python -#Export the titanic_df DataFrame to csv -titanic_df.to_csv("exported_titanic.csv") -``` - -Running this will save a file called ``exported_titanic.csv`` to the current folder. From bd52a3a43bf558f158b3bf2880d8212a93ad8a83 Mon Sep 17 00:00:00 2001 From: Krishna Kaushik <131583096+kRiShNa-429407@users.noreply.github.com> Date: Fri, 24 May 2024 21:12:23 +0530 Subject: [PATCH 10/72] Delete contrib/pandas/Titanic.csv --- contrib/pandas/Titanic.csv | 1310 ------------------------------------ 1 file changed, 1310 deletions(-) delete mode 100644 contrib/pandas/Titanic.csv diff --git a/contrib/pandas/Titanic.csv b/contrib/pandas/Titanic.csv deleted file mode 100644 index f8d49dc..0000000 --- a/contrib/pandas/Titanic.csv +++ /dev/null @@ -1,1310 +0,0 @@ -"pclass","survived","name","sex","age","sibsp","parch","ticket","fare","cabin","embarked","boat","body","home.dest" -1,1,"Allen, Miss. Elisabeth Walton","female",29,0,0,"24160",211.3375,"B5","S","2",,"St Louis, MO" -1,1,"Allison, Master. Hudson Trevor","male",0.92,1,2,"113781",151.5500,"C22 C26","S","11",,"Montreal, PQ / Chesterville, ON" -1,0,"Allison, Miss. Helen Loraine","female",2,1,2,"113781",151.5500,"C22 C26","S",,,"Montreal, PQ / Chesterville, ON" -1,0,"Allison, Mr. Hudson Joshua Creighton","male",30,1,2,"113781",151.5500,"C22 C26","S",,"135","Montreal, PQ / Chesterville, ON" -1,0,"Allison, Mrs. Hudson J C (Bessie Waldo Daniels)","female",25,1,2,"113781",151.5500,"C22 C26","S",,,"Montreal, PQ / Chesterville, ON" -1,1,"Anderson, Mr. Harry","male",48,0,0,"19952",26.5500,"E12","S","3",,"New York, NY" -1,1,"Andrews, Miss. Kornelia Theodosia","female",63,1,0,"13502",77.9583,"D7","S","10",,"Hudson, NY" -1,0,"Andrews, Mr. Thomas Jr","male",39,0,0,"112050",0.0000,"A36","S",,,"Belfast, NI" -1,1,"Appleton, Mrs. Edward Dale (Charlotte Lamson)","female",53,2,0,"11769",51.4792,"C101","S","D",,"Bayside, Queens, NY" -1,0,"Artagaveytia, Mr. Ramon","male",71,0,0,"PC 17609",49.5042,,"C",,"22","Montevideo, Uruguay" -1,0,"Astor, Col. John Jacob","male",47,1,0,"PC 17757",227.5250,"C62 C64","C",,"124","New York, NY" -1,1,"Astor, Mrs. John Jacob (Madeleine Talmadge Force)","female",18,1,0,"PC 17757",227.5250,"C62 C64","C","4",,"New York, NY" -1,1,"Aubart, Mme. Leontine Pauline","female",24,0,0,"PC 17477",69.3000,"B35","C","9",,"Paris, France" -1,1,"Barber, Miss. Ellen ""Nellie""","female",26,0,0,"19877",78.8500,,"S","6",, -1,1,"Barkworth, Mr. Algernon Henry Wilson","male",80,0,0,"27042",30.0000,"A23","S","B",,"Hessle, Yorks" -1,0,"Baumann, Mr. John D","male",,0,0,"PC 17318",25.9250,,"S",,,"New York, NY" -1,0,"Baxter, Mr. Quigg Edmond","male",24,0,1,"PC 17558",247.5208,"B58 B60","C",,,"Montreal, PQ" -1,1,"Baxter, Mrs. James (Helene DeLaudeniere Chaput)","female",50,0,1,"PC 17558",247.5208,"B58 B60","C","6",,"Montreal, PQ" -1,1,"Bazzani, Miss. 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Thamine","female",,1,0,"2665",14.4542,,"C",,, -3,0,"Zakarian, Mr. Mapriededer","male",26.5,0,0,"2656",7.2250,,"C",,"304", -3,0,"Zakarian, Mr. Ortin","male",27,0,0,"2670",7.2250,,"C",,, -3,0,"Zimmerman, Mr. Leo","male",29,0,0,"315082",7.8750,,"S",,, From f8f8686d6e127743dcfcc017422a2d701b5abe12 Mon Sep 17 00:00:00 2001 From: Krishna Kaushik <131583096+kRiShNa-429407@users.noreply.github.com> Date: Fri, 24 May 2024 21:12:48 +0530 Subject: [PATCH 11/72] Update index.md --- contrib/pandas/index.md | 1 - 1 file changed, 1 deletion(-) diff --git a/contrib/pandas/index.md b/contrib/pandas/index.md index d194bfa..c578ade 100644 --- a/contrib/pandas/index.md +++ b/contrib/pandas/index.md @@ -2,7 +2,6 @@ - [Pandas Series Vs NumPy ndarray](pandas_series_vs_numpy_ndarray.md) - [Pandas Introduction and Dataframes in Pandas](Introduction_to_Pandas_Library_and_DataFrames.md) -- [Importing and Exportin data in pandas](Importing_and_Exporting_Data_in_Pandas.md) - [Pandas Descriptive Statistics](Descriptive_Statistics.md) - [Group By Functions with Pandas](GroupBy_Functions_Pandas.md) - [Excel using Pandas DataFrame](excel_with_pandas.md) From ab6428fd59dfae627ff94613cc55b19472b6c500 Mon Sep 17 00:00:00 2001 From: Krishna Kaushik <131583096+kRiShNa-429407@users.noreply.github.com> Date: Fri, 24 May 2024 21:40:43 +0530 Subject: [PATCH 12/72] Update Introduction_to_Pandas_Library_and_DataFrames.md --- ...uction_to_Pandas_Library_and_DataFrames.md | 147 +++--------------- 1 file changed, 25 insertions(+), 122 deletions(-) diff --git a/contrib/pandas/Introduction_to_Pandas_Library_and_DataFrames.md b/contrib/pandas/Introduction_to_Pandas_Library_and_DataFrames.md index 809a155..3552437 100644 --- a/contrib/pandas/Introduction_to_Pandas_Library_and_DataFrames.md +++ b/contrib/pandas/Introduction_to_Pandas_Library_and_DataFrames.md @@ -1,8 +1,5 @@ # Introduction_to_Pandas_Library_and_DataFrames - -> Content Creator - Krishna Kaushik - **As you have learnt Python Programming , now it's time for some applications.** - Machine Learning and Data Science is the emerging field of today's time , to work in this this field your first step should be `Data Science` as Machine Learning is all about data. @@ -110,42 +107,15 @@ You can also create a DataFrame by using `pd.DataFrame()` and passing it a Pytho # Let's create cars_with_colours = pd.DataFrame({"Cars" : ["BMW","Audi","Thar","Honda"], "Colour" : ["Black","White","Red","Green"]}) -cars_with_colours +print(cars_with_colours) ``` - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
CarsColour
0BMWBlack
1AudiWhite
2TharRed
3HondaGreen
- - + Cars Colour + 0 BMW Black + 1 Audi White + 2 Thar Red + 3 Honda Green + The dictionary key is the `column name` and value are the `column data`. @@ -194,42 +164,15 @@ age record = pd.DataFrame({"Student_Name":students , "Age" :age}) -record +print(record) ``` - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Student_NameAge
0Ram19
1Mohan20
2Krishna21
3Shivam24
- - + Student_Name Age + 0 Ram 19 + 1 Mohan 20 + 2 Krishna 21 + 3 Shivam 24 + ```python @@ -269,53 +212,19 @@ record.dtypes ```python -record.describe() # It only display the results for numeric data +print(record.describe()) # It only display the results for numeric data ``` - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Age
count4.000000
mean21.000000
std2.160247
min19.000000
25%19.750000
50%20.500000
75%21.750000
max24.000000
- - + Age + count 4.000000 + mean 21.000000 + std 2.160247 + min 19.000000 + 25% 19.750000 + 50% 20.500000 + 75% 21.750000 + max 24.000000 + #### 3. Use `.info()` to find information about the dataframe @@ -333,9 +242,3 @@ record.info() 1 Age 4 non-null int64 dtypes: int64(1), object(1) memory usage: 196.0+ bytes - - - -```python - -``` From 5ccb3f2b0fc0650b26eba3b99fc7f780df6a0fe8 Mon Sep 17 00:00:00 2001 From: Pranshu shah <97401387+shahpranshu27@users.noreply.github.com> Date: Fri, 24 May 2024 22:11:05 +0530 Subject: [PATCH 13/72] create lambda-function.md --- contrib/advanced-python/index.md | 1 + contrib/advanced-python/lambda-function.md | 88 ++++++++++++++++++++++ 2 files changed, 89 insertions(+) create mode 100644 contrib/advanced-python/lambda-function.md diff --git a/contrib/advanced-python/index.md b/contrib/advanced-python/index.md index 5ea5081..b884dde 100644 --- a/contrib/advanced-python/index.md +++ b/contrib/advanced-python/index.md @@ -1,3 +1,4 @@ # List of sections - [Decorators/\*args/**kwargs](decorator-kwargs-args.md) +- [Lambda Function](lambda-function.md) diff --git a/contrib/advanced-python/lambda-function.md b/contrib/advanced-python/lambda-function.md new file mode 100644 index 0000000..93a5330 --- /dev/null +++ b/contrib/advanced-python/lambda-function.md @@ -0,0 +1,88 @@ +# Lambda Function + +Lambda functions in Python are small, anonymous functions that can be created on-the-fly. They are defined using the `lambda` keyword instead of the `def` keyword used for regular functions. Lambda functions are typically used for simple tasks where a full-blown function definition is not necessary. + +Here's an example of a lambda function that adds two numbers: + +```python +add = lambda x, y: x + y +print(add(3, 5)) # Output: 8 +``` + +The above lambda function is equivalent to the following regular function: + +```python +def add(x, y): + return x + y + +print(add(3, 5)) # Output: 8 +``` + +The difference between a regular function and a lambda function lies mainly in syntax and usage. Here are some key distinctions: + +1. **Syntax**: Lambda functions are defined using the `lambda` keyword, followed by parameters and a colon, while regular functions use the `def` keyword, followed by the function name, parameters, and a colon. + +2. **Name**: Lambda functions are anonymous; they do not have a name like regular functions. Regular functions are defined with a name. + +3. **Complexity**: Lambda functions are suitable for simple, one-liner tasks. They are not meant for complex operations or tasks that require multiple lines of code. Regular functions can handle more complex logic and can contain multiple statements and lines of code. + +4. **Usage**: Lambda functions are often used in situations where a function is needed as an argument to another function (e.g., sorting, filtering, mapping), or when you want to write concise code without defining a separate function. + +Lambda functions are used primarily for convenience and brevity in situations where a full function definition would be overkill or too cumbersome. They are handy for tasks that require a small, one-time function and can improve code readability when used judiciously. + +## Use Cases + +1. **Sorting**: Lambda functions are often used as key functions for sorting lists, dictionaries, or other data structures based on specific criteria. For example: + + ```python + students = [ + {"name": "Alice", "age": 20}, + {"name": "Bob", "age": 18}, + {"name": "Charlie", "age": 22} + ] + sorted_students = sorted(students, key=lambda x: x["age"]) + ``` + +2. **Filtering**: Lambda functions can be used with filter() to selectively include elements from a collection based on a condition. For instance: + + ```python + numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] + even_numbers = list(filter(lambda x: x % 2 == 0, numbers)) + ``` + +3. **Mapping**: Lambda functions are useful with map() to apply a transformation to each element of a collection. For example: + + ```python + numbers = [1, 2, 3, 4, 5] + squared_numbers = list(map(lambda x: x**2, numbers)) + ``` + +4. **Event Handling**: In GUI programming or event-driven systems, lambda functions can be used as event handlers to execute specific actions when an event occurs. For instance: + + ```python + button.clicked.connect(lambda: self.on_button_click(argument)) + ``` + +5. **Callback Functions**: Lambda functions can be passed as callback functions to other functions, especially when a simple operation needs to be performed in response to an event. For example: + + ```python + def process_data(data, callback): + # Process data + result = ... + # Execute callback function + callback(result) + + process_data(data, lambda x: print("Result:", x)) + ``` + +6. **Anonymous Functions in Higher-Order Functions**: Lambda functions are commonly used with higher-order functions such as reduce(), which applies a rolling computation to sequential pairs of values in a list. For example: + + ```python + from functools import reduce + numbers = [1, 2, 3, 4, 5] + sum_of_numbers = reduce(lambda x, y: x + y, numbers) + ``` + +These are just a few examples of how lambda functions can be applied in Python to simplify code and make it more expressive. They are particularly useful in situations where a small, one-time function is needed and defining a separate named function would be excessive. + +In conclusion, **lambda functions** in Python offer a concise and powerful way to handle simple tasks without the need for full function definitions. Their versatility, especially in scenarios like sorting, filtering, and event handling, makes them valuable tools for improving code readability and efficiency. By mastering lambda functions, you can enhance your Python programming skills and tackle various tasks with elegance and brevity. \ No newline at end of file From 534475bf7a9e19bd4cd432f6ec4e54f5ae825b4d Mon Sep 17 00:00:00 2001 From: manishh12 Date: Fri, 24 May 2024 22:57:17 +0530 Subject: [PATCH 14/72] Added types of optimizers issue#527 --- .../machine-learning/Types_of_optimizers.md | 357 ++++++++++++++++++ contrib/machine-learning/index.md | 1 + 2 files changed, 358 insertions(+) create mode 100644 contrib/machine-learning/Types_of_optimizers.md diff --git a/contrib/machine-learning/Types_of_optimizers.md b/contrib/machine-learning/Types_of_optimizers.md new file mode 100644 index 0000000..7d0a617 --- /dev/null +++ b/contrib/machine-learning/Types_of_optimizers.md @@ -0,0 +1,357 @@ +Sure, here's a more detailed explanation for each optimizer, including the mathematical formulation, intuition, advantages, and disadvantages, along with the Python implementation. + +--- + +# Optimizers in Machine Learning + +Optimizers are algorithms or methods used to change the attributes of your neural network such as weights and learning rate in order to reduce the losses. Optimization algorithms help to minimize (or maximize) an objective function (also called a loss function) which is simply a mathematical function dependent on the model's internal learnable parameters which are used in computing the target values from the set of features. + +## Types of Optimizers + +### 1. Gradient Descent + +**Explanation:** +Gradient Descent is the simplest and most commonly used optimization algorithm. It works by iteratively updating the model parameters in the opposite direction of the gradient of the objective function with respect to the parameters. The idea is to find the minimum of a function by taking steps proportional to the negative of the gradient of the function at the current point. + +**Mathematical Formulation:** + +The update rule for the parameter vector θ in gradient descent is represented by the equation: + +- \(theta_new = theta_old - alpha * gradient/) + +Where: +- theta_old is the old parameter vector. +- theta_new is the updated parameter vector. +- alpha is the learning rate. +- gradient is the gradient of the objective function with respect to the parameters. + + +**Intuition:** +- At each iteration, we calculate the gradient of the cost function. +- The parameters are updated in the opposite direction of the gradient. +- The size of the step is controlled by the learning rate \( \alpha \). + +**Advantages:** +- Simple to implement. +- Suitable for convex problems. + +**Disadvantages:** +- Can be slow for large datasets. +- May get stuck in local minima for non-convex problems. +- Requires careful tuning of the learning rate. + +**Python Implementation:** +```python +import numpy as np + +def gradient_descent(X, y, lr=0.01, epochs=1000): + m, n = X.shape + theta = np.zeros(n) + for epoch in range(epochs): + gradient = np.dot(X.T, (np.dot(X, theta) - y)) / m + theta -= lr * gradient + return theta +``` + +### 2. Stochastic Gradient Descent (SGD) + +**Explanation:** +SGD is a variation of gradient descent where we use only one training example to calculate the gradient and update the parameters. This introduces noise into the parameter updates, which can help to escape local minima but may cause the loss to fluctuate. + +**Mathematical Formulation:** + +- \(theta = theta - alpha * dJ(theta; x_i, y_i) / d(theta)/) + +\( x_i, y_i \) are a single training example and its target. + +**Intuition:** +- At each iteration, a random training example is selected. +- The gradient is calculated and the parameters are updated for this single example. +- This process is repeated for a specified number of epochs. + +**Advantages:** +- Faster updates compared to batch gradient descent. +- Can handle large datasets. +- Helps to escape local minima due to the noise in updates. + +**Disadvantages:** +- Loss function may fluctuate. +- Requires more iterations to converge. + +**Python Implementation:** +```python +def stochastic_gradient_descent(X, y, lr=0.01, epochs=1000): + m, n = X.shape + theta = np.zeros(n) + for epoch in range(epochs): + for i in range(m): + rand_index = np.random.randint(0, m) + xi = X[rand_index:rand_index+1] + yi = y[rand_index:rand_index+1] + gradient = np.dot(xi.T, (np.dot(xi, theta) - yi)) + theta -= lr * gradient + return theta +``` + +### 3. Mini-Batch Gradient Descent + +**Explanation:** +Mini-Batch Gradient Descent is a variation where instead of a single training example or the whole dataset, a mini-batch of examples is used to compute the gradient. This reduces the variance of the parameter updates, leading to more stable convergence. + +**Mathematical Formulation:** + +- theta = theta - alpha * (1/k) * sum(dJ(theta; x_i, y_i) / d(theta)) + +Where: +- \( k \) is the batch size. + +**Intuition:** +- At each iteration, a mini-batch of training examples is selected. +- The gradient is calculated for this mini-batch. +- The parameters are updated based on the average gradient of the mini-batch. + +**Advantages:** +- More stable updates compared to SGD. +- Faster convergence than batch gradient descent. +- Efficient on large datasets. + +**Disadvantages:** +- Requires tuning of batch size. +- Computationally more expensive than SGD per iteration. + +**Python Implementation:** +```python +def mini_batch_gradient_descent(X, y, lr=0.01, epochs=1000, batch_size=32): + m, n = X.shape + theta = np.zeros(n) + for epoch in range(epochs): + indices = np.random.permutation(m) + X_shuffled = X[indices] + y_shuffled = y[indices] + for i in range(0, m, batch_size): + X_i = X_shuffled[i:i+batch_size] + y_i = y_shuffled[i:i+batch_size] + gradient = np.dot(X_i.T, (np.dot(X_i, theta) - y_i)) / batch_size + theta -= lr * gradient + return theta +``` + +### 4. Momentum + +**Explanation:** +Momentum helps accelerate gradient vectors in the right directions, thus leading to faster converging. It accumulates a velocity vector in directions of persistent reduction in the objective function, which helps to smooth the path towards the minimum. + +**Mathematical Formulation:** + +- v_t = gamma * v_{t-1} + alpha * dJ(theta) / d(theta) + +- theta = theta - v_t + +where: + +- \( v_t \) is the velocity. +- \( \gamma \) is the momentum term, typically set between 0.9 and 0.99. + +**Intuition:** +- At each iteration, the gradient is calculated. +- The velocity is updated based on the current gradient and the previous velocity. +- The parameters are updated based on the velocity. + +**Advantages:** +- Faster convergence. +- Reduces oscillations in the parameter updates. + +**Disadvantages:** +- Requires tuning of the momentum term. + +**Python Implementation:** +```python +def momentum_gradient_descent(X, y, lr=0.01, epochs=1000, gamma=0.9): + m, n = X.shape + theta = np.zeros(n) + v = np.zeros(n) + for epoch in range(epochs): + gradient = np.dot(X.T, (np.dot(X, theta) - y)) / m + v = gamma * v + lr * gradient + theta -= v + return theta +``` + +### 5. Nesterov Accelerated Gradient (NAG) + +**Explanation:** +NAG is a variant of the gradient descent with momentum. It looks ahead by a step and calculates the gradient at that point, thus providing more accurate updates. This method helps to correct the overshooting problem seen in standard momentum. + +**Mathematical Formulation:** + +- v_t = gamma * v_{t-1} + alpha * dJ(theta - gamma * v_{t-1}) / d(theta) + +- theta = theta - v_t + + +**Intuition:** +- At each iteration, the parameters are temporarily updated using the previous velocity. +- The gradient is calculated at this lookahead position. +- The velocity and parameters are then updated based on this gradient. + +**Advantages:** +- More accurate updates compared to standard momentum. +- Faster convergence. + +**Disadvantages:** +- Requires tuning of the momentum term. + +**Python Implementation:** +```python +def nesterov_accelerated_gradient(X, y, lr=0.01, epochs=1000, gamma=0.9): + m, n = X.shape + theta = np.zeros(n) + v = np.zeros(n) + for epoch in range(epochs): + lookahead_theta = theta - gamma * v + gradient = np.dot(X.T, (np.dot(X, lookahead_theta) - y)) / m + v = gamma * v + lr * gradient + theta -= v + return theta +``` + +### 6. AdaGrad + +**Explanation:** +AdaGrad adapts the learning rate to the parameters, performing larger updates for infrequent and smaller updates for frequent parameters. It scales the learning rate inversely proportional to the square root of the sum of all historical squared values of the gradient. + +**Mathematical Formulation:** + +- G_t = G_{t-1} + (dJ(theta) / d(theta)) ⊙ (dJ(theta) / d(theta)) + +- theta = theta - (alpha / sqrt(G_t + epsilon)) * (dJ(theta) / d(theta)) + +Where: +- \( G_t \) is the sum of squares of the gradients up to time step \( t \). +- \( \epsilon \) is a small constant to avoid division by zero. + +**Intuition:** +- Accumulates the sum of the squares of the gradients for each parameter. +- Uses this accumulated + + sum to scale the learning rate. +- Parameters with large gradients in the past have smaller learning rates. + +**Advantages:** +- Effective for sparse data. +- Automatically adjusts learning rate. + +**Disadvantages:** +- Learning rate decreases continuously, which can lead to premature convergence. + +**Python Implementation:** +```python +def adagrad(X, y, lr=0.01, epochs=1000, epsilon=1e-8): + m, n = X.shape + theta = np.zeros(n) + G = np.zeros(n) + for epoch in range(epochs): + gradient = np.dot(X.T, (np.dot(X, theta) - y)) / m + G += gradient**2 + adjusted_lr = lr / (np.sqrt(G) + epsilon) + theta -= adjusted_lr * gradient + return theta +``` + +### 7. RMSprop + +**Explanation:** +RMSprop modifies AdaGrad to perform well in non-convex settings by using a moving average of squared gradients to scale the learning rate. It helps to keep the learning rate in check, especially in the presence of noisy gradients. + +**Mathematical Formulation:** + +E[g^2]_t = beta * E[g^2]_{t-1} + (1 - beta) * (dJ(theta) / d(theta)) ⊙ (dJ(theta) / d(theta)) + +theta = theta - (alpha / sqrt(E[g^2]_t + epsilon)) * (dJ(theta) / d(theta)) + +Where: +- \( E[g^2]_t \) is the exponentially decaying average of past squared gradients. +- \( \beta \) is the decay rate. + +**Intuition:** +- Keeps a running average of the squared gradients. +- Uses this average to scale the learning rate. +- Parameters with large gradients have their learning rates reduced. + +**Advantages:** +- Effective for non-convex problems. +- Reduces oscillations in parameter updates. + +**Disadvantages:** +- Requires tuning of the decay rate. + +**Python Implementation:** +```python +def rmsprop(X, y, lr=0.01, epochs=1000, beta=0.9, epsilon=1e-8): + m, n = X.shape + theta = np.zeros(n) + E_g = np.zeros(n) + for epoch in range(epochs): + gradient = np.dot(X.T, (np.dot(X, theta) - y)) / m + E_g = beta * E_g + (1 - beta) * gradient**2 + adjusted_lr = lr / (np.sqrt(E_g) + epsilon) + theta -= adjusted_lr * gradient + return theta +``` + +### 8. Adam + +**Explanation:** +Adam (Adaptive Moment Estimation) combines the advantages of both RMSprop and AdaGrad by keeping an exponentially decaying average of past gradients and past squared gradients. + +**Mathematical Formulation:** + +- m_t = beta1 * m_{t-1} + (1 - beta1) * (dJ(theta) / d(theta)) + +- v_t = beta2 * v_{t-1} + (1 - beta2) * ((dJ(theta) / d(theta))^2) + +- hat_m_t = m_t / (1 - beta1^t) + +- hat_v_t = v_t / (1 - beta2^t) + +- theta = theta - (alpha * hat_m_t) / (sqrt(hat_v_t) + epsilon) + +Where: +- \( m_t \) is the first moment (mean) of the gradient. +- \( v_t \) is the second moment (uncentered variance) of the gradient. +- \( \beta_1, \beta_2 \) are the decay rates for the moment estimates. + +**Intuition:** +- Keeps track of both the mean and the variance of the gradients. +- Uses these to adaptively scale the learning rate. +- Provides a balance between AdaGrad and RMSprop. + +**Advantages:** +- Efficient for large datasets. +- Well-suited for non-convex optimization. +- Handles sparse gradients well. + +**Disadvantages:** +- Requires careful tuning of hyperparameters. +- Can be computationally intensive. + +**Python Implementation:** +```python +def adam(X, y, lr=0.01, epochs=1000, beta1=0.9, beta2=0.999, epsilon=1e-8): + m, n = X.shape + theta = np.zeros(n) + m_t = np.zeros(n) + v_t = np.zeros(n) + for epoch in range(1, epochs+1): + gradient = np.dot(X.T, (np.dot(X, theta) - y)) / m + m_t = beta1 * m_t + (1 - beta1) * gradient + v_t = beta2 * v_t + (1 - beta2) * gradient**2 + m_t_hat = m_t / (1 - beta1**epoch) + v_t_hat = v_t / (1 - beta2**epoch) + theta -= lr * m_t_hat / (np.sqrt(v_t_hat) + epsilon) + return theta +``` + +These implementations are basic examples of how these optimizers can be implemented in Python using NumPy. In practice, libraries like TensorFlow and PyTorch provide highly optimized and more sophisticated implementations of these and other optimization algorithms. + +--- \ No newline at end of file diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index 45235e4..ce75c70 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -7,3 +7,4 @@ - [Support Vector Machine Algorithm](support-vector-machine.md) - [Artificial Neural Network from the Ground Up](ArtificialNeuralNetwork.md) - [TensorFlow.md](tensorFlow.md) +- [Types of optimizers](Types_of_optimizers.md) From a51621da8aabd6601923b20eab801707713b5bef Mon Sep 17 00:00:00 2001 From: manishh12 Date: Fri, 24 May 2024 23:01:05 +0530 Subject: [PATCH 15/72] updated readme issue#527 --- contrib/machine-learning/Types_of_optimizers.md | 2 -- 1 file changed, 2 deletions(-) diff --git a/contrib/machine-learning/Types_of_optimizers.md b/contrib/machine-learning/Types_of_optimizers.md index 7d0a617..e941597 100644 --- a/contrib/machine-learning/Types_of_optimizers.md +++ b/contrib/machine-learning/Types_of_optimizers.md @@ -1,7 +1,5 @@ -Sure, here's a more detailed explanation for each optimizer, including the mathematical formulation, intuition, advantages, and disadvantages, along with the Python implementation. --- - # Optimizers in Machine Learning Optimizers are algorithms or methods used to change the attributes of your neural network such as weights and learning rate in order to reduce the losses. Optimization algorithms help to minimize (or maximize) an objective function (also called a loss function) which is simply a mathematical function dependent on the model's internal learnable parameters which are used in computing the target values from the set of features. From 7d1c8f449a297e0c4d2ec6ef4e902971077286b7 Mon Sep 17 00:00:00 2001 From: Lingamuneni Santhosh Siddhardha <103999924+Santhosh-Siddhardha@users.noreply.github.com> Date: Sat, 25 May 2024 00:38:57 +0530 Subject: [PATCH 16/72] Create sorting_numpy_array.md Added Introduction Added sort method Added argsort method Added lexsort method --- contrib/numpy/sorting_numpy_array.md | 104 +++++++++++++++++++++++++++ 1 file changed, 104 insertions(+) create mode 100644 contrib/numpy/sorting_numpy_array.md diff --git a/contrib/numpy/sorting_numpy_array.md b/contrib/numpy/sorting_numpy_array.md new file mode 100644 index 0000000..65e9c25 --- /dev/null +++ b/contrib/numpy/sorting_numpy_array.md @@ -0,0 +1,104 @@ +# Sorting NumPy Arrays +- Sorting arrays is a common operation in data manipulation and analysis. +- NumPy provides various functions to sort arrays efficiently. +- The primary methods are `numpy.sort`,`numpy.argsort`, and `numpy.lexsort` + +### 1. numpy.sort() + +The `numpy.sort` function returns a sorted copy of an array. + +#### Syntax : + +```python +numpy.sort(arr, axis=-1, kind=None, order=None) +``` +- **arr** : Array to be sorted. +- **axis** : Axis along which to sort. (By Default is -1) +- **kind** : Sorting algorithm. Options are 'quicksort', 'mergesort', 'heapsort', and 'stable'. (By Default 'quicksort') +- **order** : When arr is an array with fields defined, this argument specifies which fields to compare first. + +#### Example : + +```python +import numpy as np + +arr = np.array([1,7,0,4,6]) +sarr = np.sort(arr) +print(sarr) +``` + +**Output** : +```python +[0 1 4 6 7] +``` + +### 2. numpy.argsort() + +The `numpy.argsort` function returns the indices that would sort an array. Using those indices you can sort the array. + +#### Syntax : + +```python +numpy.argsort(a, axis=-1, kind=None, order=None) +``` +- **arr** : Array to be sorted. +- **axis** : Axis along which to sort. (By Default is -1) +- **kind** : Sorting algorithm. Options are 'quicksort', 'mergesort', 'heapsort', and 'stable'. (By Default 'quicksort') +- **order** : When arr is an array with fields defined, this argument specifies which fields to compare first. + +#### Example : + +```python +import numpy as np + +arr = np.array([2.1,7,4.2,4.3,6]) +indices = np.argsort(arr) +print(indices) +s_arr = arr[indices] +print(s_arr) +``` + +**Output** : +```python +[0 2 3 4 1] +[2.1 4.2 4.3 6. 7. ] +``` + +### 3. np.lexsort() + +The np.lexsort function performs an indirect stable sort using a sequence of keys. + +#### Syntax : + +```python +numpy.lexsort(keys, axis=-1) +``` +- **keys**: Sequence of arrays to sort by. The last key is the primary sort key. +- **axis**: Axis to be indirectly sorted.(By Default -1) + +#### Example : + +```python +import numpy as np + +a = np.array([5,4,3,2]) +b = np.array(['a','d','c','b']) +indices = np.lexsort((a,b)) +print(indices) + +s_arr = a[indices] +print(s_arr) + +s_arr = b[indices] +print(s_arr) +``` + +**Output** : +```python +[0 3 2 1] +[2 3 4 5] +['a' 'b' 'c' 'd'] +``` + +NumPy provides powerful and flexible functions for sorting arrays, including `np.sort`, `np.argsort`, and `np.lexsort`. +These functions support sorting along different axes, using various algorithms, and sorting by multiple keys, making them suitable for a wide range of data manipulation tasks. From 366743436cc8905d9b2163015eb27827114f14e9 Mon Sep 17 00:00:00 2001 From: Lingamuneni Santhosh Siddhardha <103999924+Santhosh-Siddhardha@users.noreply.github.com> Date: Sat, 25 May 2024 00:41:11 +0530 Subject: [PATCH 17/72] Update index.md Added sorting numpy array section --- contrib/numpy/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/numpy/index.md b/contrib/numpy/index.md index 18ed17a..4b4a2c8 100644 --- a/contrib/numpy/index.md +++ b/contrib/numpy/index.md @@ -7,3 +7,4 @@ - [Operations on Arrays in NumPy](operations-on-arrays.md) - [Loading Arrays from Files](loading_arrays_from_files.md) - [Saving Numpy Arrays into FIles](saving_numpy_arrays_to_files.md) +- [Sorting NumPy Arrays](sorting_numpy_array.md) From cc4fa4a47c0802f5bd50456358a888ac6212f459 Mon Sep 17 00:00:00 2001 From: Arihant Yadav <147732947+arihunter-18@users.noreply.github.com> Date: Sat, 25 May 2024 03:07:10 +0530 Subject: [PATCH 18/72] Add files via upload --- contrib/database/intro_mysql_queries.md | 371 ++++++++++++++++++++++++ 1 file changed, 371 insertions(+) create mode 100644 contrib/database/intro_mysql_queries.md diff --git a/contrib/database/intro_mysql_queries.md b/contrib/database/intro_mysql_queries.md new file mode 100644 index 0000000..b955ead --- /dev/null +++ b/contrib/database/intro_mysql_queries.md @@ -0,0 +1,371 @@ +# Introduction to MySQL Queries +MySQL is a widely-used open-source relational database management system (RDBMS) that utilizes SQL (Structured Query Language) for managing and querying data. In Python, the **mysql-connector-python** library allows you to connect to MySQL databases and execute SQL queries, providing a way to interact with the database from within a Python program. + +## Prerequisites +* Python and MySQL Server must be installed and configured. +* The library: **mysql-connector-python** must be installed. + +## Establishing connection with server +To establish a connection with the MySQL server, you need to import the **mysql.connector** module and create a connection object using the **connect()** function by providing the prompt server details as mentioned. + +```python +import mysql.connector + +con = mysql.connector.connect( +host ="localhost", +user ="root", +passwd ="12345" +) + +print((con.is_connected())) +``` +Having established a connection with the server, you get the following output : +``` +True +``` +## Creating a Database [CREATE] +To create a database, you need to execute the **CREATE DATABASE** query. The following code snippet demonstrates how to create a database named **GSSOC**. +```python +import mysql.connector + +# Establish the connection +conn = mysql.connector.connect( + host="localhost", + user="root", + password="12345" +) + +# Create a cursor object +cursor = conn.cursor() + +# Execute the query to show databases +cursor.execute("SHOW DATABASES") + +# Fetch and print the databases +databases = cursor.fetchall() +for database in databases: + print(database[0]) + +# Execute the query to create database GSSOC +cursor.execute("CREATE DATABASE GSSOC") + +print("\nAfter creation of the database\n") + +# Execute the query to show databases +cursor.execute("SHOW DATABASES") +# Fetch and print the databases +databases = cursor.fetchall() +for database in databases: + print(database[0]) + +cursor.close() +conn.close() +``` +You can observe in the output below, after execution of the query a new database named **GSSOC** has been created. +#### Output: +``` +information_schema +mysql +performance_schema +sakila +sys +world + +After creation of the database + +gssoc +information_schema +mysql +performance_schema +sakila +sys +world +``` +## Creating a Table in the Database [CREATE] +Now, we will create a table in the database. We will create a table named **example_table** in the database **GSSOC**. We will execute **CREATE TABLE** query and provide the fields for the table as mentioned in the code below: +```python +import mysql.connector + +# Establish the connection +conn = mysql.connector.connect( + host="localhost", + user="root", + password="12345" +) +# Create a cursor object +cursor = conn.cursor() + +# Execute the query to show tables +cursor.execute("USE GSSOC") +cursor.execute("SHOW TABLES") + +# Fetch and print the tables +tables = cursor.fetchall() +print("Before creation of table\n") +for table in tables: + print(table[0]) + +create_table_query = """ +CREATE TABLE example_table ( + name VARCHAR(255) NOT NULL, + age INT NOT NULL, + email VARCHAR(255) +) +""" +# Execute the query +cursor.execute(create_table_query) + +# Commit the changes +conn.commit() + +print("\nAfter creation of Table\n") +# Execute the query to show tables in GSSOC +cursor.execute("SHOW TABLES") + +# Fetch and print the tables +tables = cursor.fetchall() +for table in tables: + print(table[0]) + +cursor.close() +conn.close() +``` +#### Output: +``` +Before creation of table + + +After creation of Table + +example_table +``` +## Inserting Data [INSERT] +To insert data in an existing table, the **INSERT INTO** query is used, followed by the name of the table in which the data needs to be inserted. The following code demonstrates the insertion of multiple records in the table by **executemany()**. +```python +import mysql.connector + +# Establish the connection +conn = mysql.connector.connect( + host="localhost", + user="root", + password="12345" +) +# Create a cursor object +cursor = conn.cursor() +cursor.execute("USE GSSOC") +# SQL query to insert data +insert_data_query = """ +INSERT INTO example_table (name, age, email) +VALUES (%s, %s, %s) +""" + +# Data to be inserted +data_to_insert = [ + ("John Doe", 28, "john.doe@example.com"), + ("Jane Smith", 34, "jane.smith@example.com"), + ("Sam Brown", 22, "sam.brown@example.com") +] + +# Execute the query for each data entry +cursor.executemany(insert_data_query, data_to_insert) + +conn.commit() +cursor.close() +conn.close() +``` +## Displaying Data [SELECT] +To display the data from a table, the **SELECT** query is used. The following code demonstrates the display of data from the table. +```python +import mysql.connector + +# Establish the connection +conn = mysql.connector.connect( + host="localhost", + user="root", + password="12345" +) +# Create a cursor object +cursor = conn.cursor() +cursor.execute("USE GSSOC") + +# SQL query to display data +display_data_query = "SELECT * FROM example_table" + +# Execute the query for each data entry +cursor.execute(display_data_query) + +# Fetch all the rows +rows = cursor.fetchall() + +# Print the column names +column_names = [desc[0] for desc in cursor.description] +print(column_names) + +# Print the rows +for row in rows: + print(row) + +cursor.close() +conn.close() +``` +#### Output : +``` +['name', 'age', 'email'] +('John Doe', 28, 'john.doe@example.com') +('Jane Smith', 34, 'jane.smith@example.com') +('Sam Brown', 22, 'sam.brown@example.com') +``` +## Updating Data [UPDATE] +To update data in the table, **UPDATE** query is used. In the following code, we will be updating the email and age of the record where the name is John Doe. +```python +import mysql.connector + +# Establish the connection +conn = mysql.connector.connect( + host="localhost", + user="root", + password="12345" +) +# Create a cursor object +cursor = conn.cursor() +cursor.execute("USE GSSOC") + +# SQL query to display data +display_data_query = "SELECT * FROM example_table" + +# SQL Query to update data of John Doe +update_data_query = """ +UPDATE example_table +SET age = %s, email = %s +WHERE name = %s +""" + +# Data to be updated +data_to_update = (30, "new.email@example.com", "John Doe") + +# Execute the query +cursor.execute(update_data_query, data_to_update) + +# Commit the changes +conn.commit() + +# Execute the query for each data entry +cursor.execute(display_data_query) + +# Fetch all the rows +rows = cursor.fetchall() + +# Print the column names +column_names = [desc[0] for desc in cursor.description] +print(column_names) + +# Print the rows +for row in rows: + print(row) + +cursor.close() +conn.close() +``` +#### Output: +``` +['name', 'age', 'email'] +('John Doe', 30, 'new.email@example.com') +('Jane Smith', 34, 'jane.smith@example.com') +('Sam Brown', 22, 'sam.brown@example.com') +``` + +## Deleting Data [DELETE] +In this segment, we will Delete the record named "John Doe" using the **DELETE** and **WHERE** statements in the query. The following code explains the same and the observe the change in output. +```python +import mysql.connector + +# Establish the connection +conn = mysql.connector.connect( + host="localhost", + user="root", + password="12345" +) +# Create a cursor object +cursor = conn.cursor() +cursor.execute("USE GSSOC") + +# SQL query to display data +display_data_query = "SELECT * FROM example_table" + +# SQL query to delete data +delete_data_query = "DELETE FROM example_table WHERE name = %s" + +# Data to be deleted +data_to_delete = ("John Doe",) + +# Execute the query +cursor.execute(delete_data_query, data_to_delete) + +# Commit the changes +conn.commit() + +# Execute the query for each data entry +cursor.execute(display_data_query) + +# Fetch all the rows +rows = cursor.fetchall() + +# Print the column names +column_names = [desc[0] for desc in cursor.description] +print(column_names) + +# Print the rows +for row in rows: + print(row) + +cursor.close() +conn.close() +``` +#### Output: +``` +['name', 'age', 'email'] +('Jane Smith', 34, 'jane.smith@example.com') +('Sam Brown', 22, 'sam.brown@example.com') +``` +## Deleting the Table/Database [DROP] +For deleting a table, you can use the **DROP** query in the following manner: +```python +import mysql.connector + +# Establish the connection +conn = mysql.connector.connect( + host="localhost", + user="root", + password="12345" +) +# Create a cursor object +cursor = conn.cursor() +cursor.execute("USE GSSOC") + +# SQL query to delete the table +delete_table_query = "DROP TABLE IF EXISTS example_table" + +# Execute the query +cursor.execute(delete_table_query) + +# Verify the table deletion +cursor.execute("SHOW TABLES LIKE 'example_table'") +result = cursor.fetchone() + +cursor.close() +conn.close() + +if result: + print("Table deletion failed.") +else: + print("Table successfully deleted.") +``` +#### Output: +``` +Table successfully deleted. +``` +Similarly, you can delete the database also by using the **DROP** and accordingly changing the query to be executed. + + + + From fd5c4ec3345a48c46eff8ed0db3826b6b6845227 Mon Sep 17 00:00:00 2001 From: Arihant Yadav <147732947+arihunter-18@users.noreply.github.com> Date: Sat, 25 May 2024 03:09:38 +0530 Subject: [PATCH 19/72] Add files via upload --- contrib/database/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/database/index.md b/contrib/database/index.md index 82596a2..56cd85b 100644 --- a/contrib/database/index.md +++ b/contrib/database/index.md @@ -1,3 +1,3 @@ # List of sections -- [Section title](filename.md) +- [Introduction to MySQL and Queries](intro_mysql_queries.md) From 91c57b20b772613d779c2bbd963385d2458f6773 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sat, 25 May 2024 04:58:13 +0530 Subject: [PATCH 20/72] Rename Introduction_to_Pandas_Library_and_DataFrames.md to introduction.md --- ...uction_to_Pandas_Library_and_DataFrames.md => introduction.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/pandas/{Introduction_to_Pandas_Library_and_DataFrames.md => introduction.md} (100%) diff --git a/contrib/pandas/Introduction_to_Pandas_Library_and_DataFrames.md b/contrib/pandas/introduction.md similarity index 100% rename from contrib/pandas/Introduction_to_Pandas_Library_and_DataFrames.md rename to contrib/pandas/introduction.md From 6ec6fd6f409f96d26afb7b0b6554da02a1dfc74d Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sat, 25 May 2024 04:58:34 +0530 Subject: [PATCH 21/72] Update index.md --- contrib/pandas/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/pandas/index.md b/contrib/pandas/index.md index c578ade..2874431 100644 --- a/contrib/pandas/index.md +++ b/contrib/pandas/index.md @@ -1,7 +1,7 @@ # List of sections +- [Pandas Introduction and Dataframes in Pandas](introduction.md) - [Pandas Series Vs NumPy ndarray](pandas_series_vs_numpy_ndarray.md) -- [Pandas Introduction and Dataframes in Pandas](Introduction_to_Pandas_Library_and_DataFrames.md) - [Pandas Descriptive Statistics](Descriptive_Statistics.md) - [Group By Functions with Pandas](GroupBy_Functions_Pandas.md) - [Excel using Pandas DataFrame](excel_with_pandas.md) From 54eec70ca9001137fb9d80d442c3fd7d31f7b1ce Mon Sep 17 00:00:00 2001 From: Pranshu shah <97401387+shahpranshu27@users.noreply.github.com> Date: Sat, 25 May 2024 07:25:26 +0530 Subject: [PATCH 22/72] create map-function.md --- contrib/advanced-python/index.md | 1 + contrib/advanced-python/map-function.md | 54 +++++++++++++++++++++++++ 2 files changed, 55 insertions(+) create mode 100644 contrib/advanced-python/map-function.md diff --git a/contrib/advanced-python/index.md b/contrib/advanced-python/index.md index 880b393..e1ff216 100644 --- a/contrib/advanced-python/index.md +++ b/contrib/advanced-python/index.md @@ -5,3 +5,4 @@ - [Regular Expressions in Python](regular_expressions.md) - [JSON module](json-module.md) - [OOPs](OOPs.md) +- [Map Function](map-function.md) diff --git a/contrib/advanced-python/map-function.md b/contrib/advanced-python/map-function.md new file mode 100644 index 0000000..be035d0 --- /dev/null +++ b/contrib/advanced-python/map-function.md @@ -0,0 +1,54 @@ +The `map()` function in Python is a built-in function used for applying a given function to each item of an iterable (like a list, tuple, or dictionary) and returning a new iterable with the results. It's a powerful tool for transforming data without the need for explicit loops. Let's break down its syntax, explore examples, and discuss various use cases. + +### Syntax: + +```python +map(function, iterable1, iterable2, ...) +``` + +- `function`: The function to apply to each item in the iterables. +- `iterable1`, `iterable2`, ...: One or more iterable objects whose items will be passed as arguments to `function`. + +### Examples: + +#### Example 1: Doubling the values in a list + +```python +# Define the function +def double(x): + return x * 2 + +# Apply the function to each item in the list using map +original_list = [1, 2, 3, 4, 5] +doubled_list = list(map(double, original_list)) +print(doubled_list) # Output: [2, 4, 6, 8, 10] +``` + +#### Example 2: Converting temperatures from Celsius to Fahrenheit + +```python +# Define the function +def celsius_to_fahrenheit(celsius): + return (celsius * 9/5) + 32 + +# Apply the function to each Celsius temperature using map +celsius_temperatures = [0, 10, 20, 30, 40] +fahrenheit_temperatures = list(map(celsius_to_fahrenheit, celsius_temperatures)) +print(fahrenheit_temperatures) # Output: [32.0, 50.0, 68.0, 86.0, 104.0] +``` + +### Use Cases: + +1. **Data Transformation**: When you need to apply a function to each item of a collection and obtain the transformed values, `map()` is very handy. + +2. **Parallel Processing**: In some cases, `map()` can be utilized in parallel processing scenarios, especially when combined with `multiprocessing` or `concurrent.futures`. + +3. **Cleaning and Formatting Data**: It's often used in data processing pipelines for tasks like converting data types, normalizing values, or applying formatting functions. + +4. **Functional Programming**: In functional programming paradigms, `map()` is frequently used along with other functional constructs like `filter()` and `reduce()` for concise and expressive code. + +5. **Generating Multiple Outputs**: You can use `map()` to generate multiple outputs simultaneously by passing multiple iterables. The function will be applied to corresponding items in the iterables. + +6. **Lazy Evaluation**: In Python 3, `map()` returns an iterator rather than a list. This means it's memory efficient and can handle large datasets without loading everything into memory at once. + +Remember, while `map()` is powerful, it's essential to balance its use with readability and clarity. Sometimes, a simple loop might be more understandable than a `map()` call. \ No newline at end of file From 9680263fa2e36061cf81665b6c1e2a6560e8e77b Mon Sep 17 00:00:00 2001 From: Krishna Kaushik <131583096+kRiShNa-429407@users.noreply.github.com> Date: Sat, 25 May 2024 08:19:34 +0530 Subject: [PATCH 23/72] Create readme.md --- contrib/pandas/Datasets/readme.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 contrib/pandas/Datasets/readme.md diff --git a/contrib/pandas/Datasets/readme.md b/contrib/pandas/Datasets/readme.md new file mode 100644 index 0000000..ea2255c --- /dev/null +++ b/contrib/pandas/Datasets/readme.md @@ -0,0 +1 @@ +## This folder contains all the Datasets used in the content. From 1b654fd3abd8a6a940f99e367e5a4ca0d67e9a44 Mon Sep 17 00:00:00 2001 From: Krishna Kaushik <131583096+kRiShNa-429407@users.noreply.github.com> Date: Sat, 25 May 2024 08:19:56 +0530 Subject: [PATCH 24/72] Add files via upload --- contrib/pandas/Datasets/Titanic.csv | 1310 +++++++++++++++++++++++++++ 1 file changed, 1310 insertions(+) create mode 100644 contrib/pandas/Datasets/Titanic.csv diff --git a/contrib/pandas/Datasets/Titanic.csv b/contrib/pandas/Datasets/Titanic.csv new file mode 100644 index 0000000..f8d49dc --- /dev/null +++ b/contrib/pandas/Datasets/Titanic.csv @@ -0,0 +1,1310 @@ +"pclass","survived","name","sex","age","sibsp","parch","ticket","fare","cabin","embarked","boat","body","home.dest" +1,1,"Allen, Miss. Elisabeth Walton","female",29,0,0,"24160",211.3375,"B5","S","2",,"St Louis, MO" +1,1,"Allison, Master. Hudson Trevor","male",0.92,1,2,"113781",151.5500,"C22 C26","S","11",,"Montreal, PQ / Chesterville, ON" +1,0,"Allison, Miss. 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Thamine","female",,1,0,"2665",14.4542,,"C",,, +3,0,"Zakarian, Mr. Mapriededer","male",26.5,0,0,"2656",7.2250,,"C",,"304", +3,0,"Zakarian, Mr. Ortin","male",27,0,0,"2670",7.2250,,"C",,, +3,0,"Zimmerman, Mr. Leo","male",29,0,0,"315082",7.8750,,"S",,, From f4b24fdfd85416c3fdceec43d5425a94a4cf3bc3 Mon Sep 17 00:00:00 2001 From: Krishna Kaushik <131583096+kRiShNa-429407@users.noreply.github.com> Date: Sat, 25 May 2024 08:24:05 +0530 Subject: [PATCH 25/72] Add files via upload --- .../Importing_and_Exporting_Data_in_Pandas.md | 103 ++++++++++++++++++ 1 file changed, 103 insertions(+) create mode 100644 contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md diff --git a/contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md b/contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md new file mode 100644 index 0000000..bb490b9 --- /dev/null +++ b/contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md @@ -0,0 +1,103 @@ +# Importing_and_Exporting_Data_in_Pandas + +>Created by Krishna Kaushik + +- **Now we're able to create `Series` and `DataFrames` in pandas, but we usually do not do this , in practice we import the data which is in the form of .csv (Comma Seperated Values) , a spreadsheet file or something similar.** + +- *Good news is that pandas allows for easy importing of data like this through functions such as ``pd.read_csv()`` and ``pd.read_excel()`` for Microsoft Excel files.* + +## 1. Importing from a Google sheet to a pandas dataframe + +*Let's say that you wanted to get the information from Google Sheet document into a pandas DataFrame.*. + +*You could export it as a .csv file and then import it using ``pd.read_csv()``.* + +*In this case, the exported .csv file is called `Titanic.csv`* + + +```python +## Importing Titanic Data set +import pandas as pd + +titanic_df= pd.read_csv("https://raw.githubusercontent.com/kRiShNa-429407/learn-python/main/contrib/pandas/Datasets/Titanic.csv") +print(titanic_df) +``` + + pclass survived name \ + 0 1 1 Allen, Miss. Elisabeth Walton + 1 1 1 Allison, Master. Hudson Trevor + 2 1 0 Allison, Miss. Helen Loraine + 3 1 0 Allison, Mr. Hudson Joshua Creighton + 4 1 0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) + ... ... ... ... + 1304 3 0 Zabour, Miss. Hileni + 1305 3 0 Zabour, Miss. Thamine + 1306 3 0 Zakarian, Mr. Mapriededer + 1307 3 0 Zakarian, Mr. Ortin + 1308 3 0 Zimmerman, Mr. Leo + + sex age sibsp parch ticket fare cabin embarked boat \ + 0 female 29.00 0 0 24160 211.3375 B5 S 2 + 1 male 0.92 1 2 113781 151.5500 C22 C26 S 11 + 2 female 2.00 1 2 113781 151.5500 C22 C26 S NaN + 3 male 30.00 1 2 113781 151.5500 C22 C26 S NaN + 4 female 25.00 1 2 113781 151.5500 C22 C26 S NaN + ... ... ... ... ... ... ... ... ... ... + 1304 female 14.50 1 0 2665 14.4542 NaN C NaN + 1305 female NaN 1 0 2665 14.4542 NaN C NaN + 1306 male 26.50 0 0 2656 7.2250 NaN C NaN + 1307 male 27.00 0 0 2670 7.2250 NaN C NaN + 1308 male 29.00 0 0 315082 7.8750 NaN S NaN + + body home.dest + 0 NaN St Louis, MO + 1 NaN Montreal, PQ / Chesterville, ON + 2 NaN Montreal, PQ / Chesterville, ON + 3 135.0 Montreal, PQ / Chesterville, ON + 4 NaN Montreal, PQ / Chesterville, ON + ... ... ... + 1304 328.0 NaN + 1305 NaN NaN + 1306 304.0 NaN + 1307 NaN NaN + 1308 NaN NaN + + [1309 rows x 14 columns] + + +The dataset I am using here for your reference is taken from the same repository i.e ``learn-python`` (https://raw.githubusercontent.com/kRiShNa-429407/learn-python/main/contrib/pandas/Datasets/Titanic.csv) I uploaded it in the Datasets folder,you can use it from there. + +You can also place the filename with its path in `pd.read_csv()`. + +**Now we've got the same data from the Google Spreadsheet , but now available as ``pandas DataFrame`` which means we can now apply all pandas functionality over it.** + +#### Note: The quiet important thing i am telling is that ``pd.read_csv()`` takes the location of the file (which is in your current working directory) or the hyperlink of the dataset from the other source. + +#### But if you want to import the data from Github you can't directly use its link , you have to first convert it to raw by clicking on the raw button present in the repo . + +#### Also you can't use the data directly from `Kaggle` you have to use ``kaggle API`` + +## 2. The Anatomy of DataFrame + +**Different functions use different labels for different things, and can get a little confusing.** + +- Rows are refer as ``axis=0`` +- columns are refer as ``axis=1`` + +## 3. Exporting Data + +**OK, so after you've made a few changes to your data, you might want to export it and save it so someone else can access the changes.** + +**pandas allows you to export ``DataFrame's`` to ``.csv`` format using ``.to_csv()``, or to a spreadsheet format using .to_excel().** + +### Exporting a dataframe to a CSV + +**We haven't made any changes yet to the ``titanic_df`` DataFrame but let's try to export it.** + + +```python +#Export the titanic_df DataFrame to csv +titanic_df.to_csv("exported_titanic.csv") +``` + +Running this will save a file called ``exported_titanic.csv`` to the current folder. From 43a1b5a652b74852b2a17cd169d1412cf5114fb0 Mon Sep 17 00:00:00 2001 From: rohit Date: Sat, 25 May 2024 12:12:28 +0530 Subject: [PATCH 26/72] Added PyTorch.md --- contrib/machine-learning/PyTorch.md | 113 ++++++++++++++++++++++++++++ contrib/machine-learning/index.md | 1 + 2 files changed, 114 insertions(+) create mode 100644 contrib/machine-learning/PyTorch.md diff --git a/contrib/machine-learning/PyTorch.md b/contrib/machine-learning/PyTorch.md new file mode 100644 index 0000000..7de853b --- /dev/null +++ b/contrib/machine-learning/PyTorch.md @@ -0,0 +1,113 @@ +# PyTorch: A Comprehensive Overview + +## Introduction +PyTorch is an open-source deep learning framework developed by Facebook's AI Research lab. It provides a flexible and efficient platform for building and deploying machine learning models. PyTorch is known for its dynamic computational graph, ease of use, and strong support for GPU acceleration. + +## Key Features +- **Dynamic Computational Graphs**: PyTorch's dynamic computation graph (or define-by-run) allows you to change the network architecture during runtime. This feature makes debugging and experimenting with different model architectures easier. +- **GPU Acceleration**: PyTorch supports CUDA, enabling efficient computation on GPUs. +- **Extensive Libraries and Tools**: PyTorch has a rich ecosystem of libraries and tools such as torchvision for computer vision, torchtext for natural language processing, and more. +- **Community Support**: PyTorch has a large and active community, providing extensive resources, tutorials, and forums for support. + +## Installation +To install PyTorch, you can use pip: + +```sh +pip install torch torchvision +``` + +For detailed installation instructions, including GPU support, visit the [official PyTorch installation guide](https://pytorch.org/get-started/locally/). + +## Basic Usage + +### Tensors +Tensors are the fundamental building blocks in PyTorch. They are similar to NumPy arrays but can run on GPUs. + +```python +import torch + +# Creating a tensor +x = torch.tensor([1.0, 2.0, 3.0]) +print(x) + +# Performing basic operations +y = torch.tensor([4.0, 5.0, 6.0]) +z = x + y +print(z) +``` + +### Autograd +Autograd is PyTorch's automatic differentiation engine that powers neural network training. It tracks operations on tensors to automatically compute gradients. + +```python +# Requires gradient +x = torch.tensor([1.0, 2.0, 3.0], requires_grad=True) + +# Perform operations +y = x ** 2 +z = y.sum() + +# Compute gradients +z.backward() +print(x.grad) +``` + +### Building Neural Networks +PyTorch provides the `torch.nn` module to build neural networks. + +```python +import torch +import torch.nn as nn +import torch.optim as optim + +# Define a simple neural network +class SimpleNN(nn.Module): + def __init__(self): + super(SimpleNN, self).__init__() + self.fc1 = nn.Linear(3, 1) + + def forward(self, x): + x = self.fc1(x) + return x + +# Create the network, define the criterion and optimizer +model = SimpleNN() +criterion = nn.MSELoss() +optimizer = optim.SGD(model.parameters(), lr=0.01) + +# Dummy input and target +inputs = torch.tensor([[1.0, 2.0, 3.0]]) +targets = torch.tensor([[0.5]]) + +# Forward pass +outputs = model(inputs) +loss = criterion(outputs, targets) + +# Backward pass and optimization +loss.backward() +optimizer.step() + +print(f'Loss: {loss.item()}') +``` + +## When to Use PyTorch +### Use PyTorch When: +1. **Research and Development**: PyTorch's dynamic computation graph makes it ideal for experimentation and prototyping. +2. **Computer Vision and NLP**: With extensive libraries like torchvision and torchtext, PyTorch is well-suited for these domains. +3. **Custom Operations**: If your work involves custom layers or operations, PyTorch provides the flexibility to implement and integrate them easily. +4. **Community and Ecosystem**: If you prefer a strong community support and extensive third-party resources, PyTorch is a good choice. + +### Consider Alternatives When: +1. **Production Deployment**: While PyTorch has made strides in deployment (e.g., TorchServe), TensorFlow's TensorFlow Serving is more mature for large-scale deployment. +2. **Static Graphs**: If your model architecture doesn't change frequently and you prefer static computation graphs, TensorFlow might be more suitable. +3. **Multi-Language Support**: If you need integration with languages other than Python (e.g., Java, JavaScript), TensorFlow offers better support. + +## Conclusion +PyTorch is a powerful and flexible deep learning framework that caters to both researchers and practitioners. Its ease of use, dynamic computation graph, and strong community support make it an excellent choice for many machine learning tasks. However, for certain production scenarios or specific requirements, alternatives like TensorFlow may be more appropriate. + +## Additional Resources +- [PyTorch Official Documentation](https://pytorch.org/docs/stable/index.html) +- [PyTorch Tutorials](https://pytorch.org/tutorials/) +- [PyTorch Forum](https://discuss.pytorch.org/) + +Feel free to explore and experiment with PyTorch to harness the full potential of this versatile framework! \ No newline at end of file diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index 45235e4..edb0860 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -7,3 +7,4 @@ - [Support Vector Machine Algorithm](support-vector-machine.md) - [Artificial Neural Network from the Ground Up](ArtificialNeuralNetwork.md) - [TensorFlow.md](tensorFlow.md) +- [PyTorch.md](PyTorch.md) From ce0fc1208d986f166487a35bfcbe8fcfd47c1f61 Mon Sep 17 00:00:00 2001 From: Ramya Korupolu <104009248+Ramya-korupolu@users.noreply.github.com> Date: Sat, 25 May 2024 12:34:32 +0530 Subject: [PATCH 27/72] Update sorting-algorithms.md --- contrib/ds-algorithms/sorting-algorithms.md | 159 ++++++++++++++++++++ 1 file changed, 159 insertions(+) diff --git a/contrib/ds-algorithms/sorting-algorithms.md b/contrib/ds-algorithms/sorting-algorithms.md index 6423b4b..25bee49 100644 --- a/contrib/ds-algorithms/sorting-algorithms.md +++ b/contrib/ds-algorithms/sorting-algorithms.md @@ -327,3 +327,162 @@ print("Sorted array:", arr) # Output: [3, 9, 10, 27, 38, 43, 82]

+ +# 5. Insertion Sort + +Insertion sort is a straightforward and efficient sorting algorithm for small datasets. It builds the final sorted array one element at a time. It is much like sorting playing cards in your hands: you take one card at a time and insert it into its correct position among the already sorted cards. + +**Algorithm Overview:** +- **Start from the Second Element:** Begin with the second element, assuming the first element is already sorted. +- **Compare with Sorted Subarray:** Take the current element and compare it with elements in the sorted subarray (the part of the array before the current element). +- **Insert in Correct Position:** Shift all elements in the sorted subarray that are greater than the current element to one position ahead. Insert the current element into its correct position. +- **Repeat Until End:** Repeat this process for all elements in the array. + +## Example with Visualization +Let's sort the list `[5, 3, 8, 1, 2]` using insertion sort. + +**Step-by-Step Visualization:** +**Initial List:** `[5, 3, 8, 1, 2]` + +1. **Pass 1:** + - Current element: 3 + - Compare 3 with 5, move 5 to the right: `[5, 5, 8, 1, 2]` + - Insert 3 in its correct position: `[3, 5, 8, 1, 2]` + +2. **Pass 2:** + - Current element: 8 + - 8 is already in the correct position: `[3, 5, 8, 1, 2]` + +3. **Pass 3:** + - Current element: 1 + - Compare 1 with 8, move 8 to the right: `[3, 5, 8, 8, 2]` + - Compare 1 with 5, move 5 to the right: `[3, 5, 5, 8, 2]` + - Compare 1 with 3, move 3 to the right: `[3, 3, 5, 8, 2]` + - Insert 1 in its correct position: `[1, 3, 5, 8, 2]` + +4. **Pass 4:** + - Current element: 2 + - Compare 2 with 8, move 8 to the right: `[1, 3, 5, 8, 8]` + - Compare 2 with 5, move 5 to the right: `[1, 3, 5, 5, 8]` + - Compare 2 with 3, move 3 to the right: `[1, 3, 3, 5, 8]` + - Insert 2 in its correct position: `[1, 2, 3, 5, 8]` + +## Insertion Sort Code in Python + + +```python + +def insertion_sort(arr): + # Traverse from 1 to len(arr) + for i in range(1, len(arr)): + key = arr[i] + # Move elements of arr[0..i-1], that are greater than key, + # to one position ahead of their current position + j = i - 1 + while j >= 0 and key < arr[j]: + arr[j + 1] = arr[j] + j -= 1 + arr[j + 1] = key + +# Example usage +arr = [5, 3, 8, 1, 2] +insertion_sort(arr) +print("Sorted array:", arr) # Output: [1, 2, 3, 5, 8] +``` +## Complexity Analysis + - **Worst Case:** `𝑂(𝑛^2)` comparisons and swaps. This occurs when the array is in reverse order. + - **Best Case:** `𝑂(𝑛)` comparisons and `𝑂(1)` swaps. This happens when the array is already sorted. + - **Average Case:** `𝑂(𝑛^2)` comparisons and swaps. This is the expected number of comparisons and swaps over all possible input sequences. + + +
+
+
+ +# 6. Heap Sort + +Heap Sort is an efficient comparison-based sorting algorithm that uses a binary heap data structure. It divides its input into a sorted and an unsorted region and iteratively shrinks the unsorted region by extracting the largest (or smallest) element and moving it to the sorted region. + +**Algorithm Overview:** +- **Build a Max Heap:** Convert the array into a max heap, a complete binary tree where the value of each node is greater than or equal to the values of its children. +- **Heapify:** Ensure that the subtree rooted at each node satisfies the max heap property. This process is called heapify. +- **Extract Maximum:** Swap the root (the maximum element) with the last element of the heap and reduce the heap size by one. Restore the max heap property by heapifying the root. +- **Repeat:** Continue extracting the maximum element and heapifying until the entire array is sorted. + +## Example with Visualization + +Let's sort the list `[5, 3, 8, 1, 2]` using heap sort. + +1. **Build Max Heap:** + - Initial array: `[5, 3, 8, 1, 2]` + - Start heapifying from the last non-leaf node. + - Heapify at index 1: `[5, 3, 8, 1, 2]` (no change, children are already less than the parent) + - Heapify at index 0: `[8, 3, 5, 1, 2]` (swap 5 and 8 to make 8 the root) + +2. **Heapify Process:** + - Heapify at index 0: `[8, 3, 5, 1, 2]` (no change needed, already a max heap) + +3. **Extract Maximum:** + - Swap root with the last element: `[2, 3, 5, 1, 8]` + - Heapify at index 0: `[5, 3, 2, 1, 8]` (swap 2 and 5) + +4. **Repeat Extraction:** + - Swap root with the second last element: `[1, 3, 2, 5, 8]` + - Heapify at index 0: `[3, 1, 2, 5, 8]` (swap 1 and 3) + - Swap root with the third last element: `[2, 1, 3, 5, 8]` + - Heapify at index 0: `[2, 1, 3, 5, 8]` (no change needed) + - Swap root with the fourth last element: `[1, 2, 3, 5, 8]` + +After all extractions, the array is sorted: `[1, 2, 3, 5, 8]`. + +## Heap Sort Code in Python + +```python +def heapify(arr, n, i): + largest = i # Initialize largest as root + left = 2 * i + 1 # left child index + right = 2 * i + 2 # right child index + + # See if left child of root exists and is greater than root + if left < n and arr[largest] < arr[left]: + largest = left + + # See if right child of root exists and is greater than root + if right < n and arr[largest] < arr[right]: + largest = right + + # Change root, if needed + if largest != i: + arr[i], arr[largest] = arr[largest], arr[i] # swap + + # Heapify the root. + heapify(arr, n, largest) + +def heap_sort(arr): + n = len(arr) + + # Build a maxheap. + for i in range(n // 2 - 1, -1, -1): + heapify(arr, n, i) + + # One by one extract elements + for i in range(n - 1, 0, -1): + arr[i], arr[0] = arr[0], arr[i] # swap + heapify(arr, i, 0) + +# Example usage +arr = [5, 3, 8, 1, 2] +heap_sort(arr) +print("Sorted array:", arr) # Output: [1, 2, 3, 5, 8] +``` + +## Complexity Analysis + - **Worst Case:** `𝑂(𝑛log𝑛)`. Building the heap takes `𝑂(𝑛)` time, and each of the 𝑛 element extractions takes `𝑂(log𝑛)` time. + - **Best Case:** `𝑂(𝑛log𝑛)`. Even if the array is already sorted, heap sort will still build the heap and perform the extractions. + - **Average Case:** `𝑂(𝑛log𝑛)`. Similar to the worst-case, the overall complexity remains `𝑂(𝑛log𝑛)` because each insertion and deletion in a heap takes `𝑂(log𝑛)` time, and these operations are performed 𝑛 times. + +
+
+
+ + From 7db982f3a3b94320c5638f7925cfa64054d1bd51 Mon Sep 17 00:00:00 2001 From: Labqari Date: Sat, 25 May 2024 12:50:18 +0530 Subject: [PATCH 28/72] Adding Flask --- contrib/web-scrapping/index.md | 1 + ...duction-to-flask-a-python-web-framework.md | 440 ++++++++++++++++++ 2 files changed, 441 insertions(+) create mode 100644 contrib/web-scrapping/introduction-to-flask-a-python-web-framework.md diff --git a/contrib/web-scrapping/index.md b/contrib/web-scrapping/index.md index 82596a2..a78be3d 100644 --- a/contrib/web-scrapping/index.md +++ b/contrib/web-scrapping/index.md @@ -1,3 +1,4 @@ # List of sections - [Section title](filename.md) +- [Introduction-to-flask-a-python-web-framework](introduction-to-flask-a-python-web-framework.md) diff --git a/contrib/web-scrapping/introduction-to-flask-a-python-web-framework.md b/contrib/web-scrapping/introduction-to-flask-a-python-web-framework.md new file mode 100644 index 0000000..957f39b --- /dev/null +++ b/contrib/web-scrapping/introduction-to-flask-a-python-web-framework.md @@ -0,0 +1,440 @@ +Sure, here's the guide without Markdown formatting: + +--- + +# Introduction to Flask: A Python Web Framework + +## Table of Contents +1. Introduction +2. Prerequisites +3. Setting Up Your Environment +4. Creating Your First Flask Application + - Project Structure + - Hello World Application +5. Routing +6. Templates and Static Files + - Jinja2 Templating Engine + - Serving Static Files +7. Working with Forms + - Handling Form Data +8. Database Integration + - Setting Up SQLAlchemy + - Performing CRUD Operations +9. Error Handling +10. Testing Your Application +11. Deploying Your Flask Application + - Using Gunicorn + - Deploying to Render +12. Conclusion +13. Further Reading and Resources + +--- + +## 1. Introduction +Flask is a lightweight WSGI web application framework in Python. It is designed with simplicity and flexibility in mind, allowing developers to create web applications with minimal setup. Flask was created by Armin Ronacher as part of the Pocoo project and has gained popularity for its ease of use and extensive documentation. + +## 2. Prerequisites +Before starting with Flask, ensure you have the following: +- Basic knowledge of Python. +- Understanding of web development concepts (HTML, CSS, JavaScript). +- Python installed on your machine (version 3.6 or higher). +- pip (Python package installer) installed. + +## 3. Setting Up Your Environment +1. **Install Python**: Download and install Python from python.org. +2. **Create a Virtual Environment**: + ``` + python -m venv venv + ``` +3. **Activate the Virtual Environment**: + - On Windows: + ``` + venv\Scripts\activate + ``` + - On macOS/Linux: + ``` + source venv/bin/activate + ``` +4. **Install Flask**: + ``` + pip install Flask + ``` + +## 4. Creating Your First Flask Application +### Project Structure +A typical Flask project structure might look like this: +``` +my_flask_app/ + app/ + __init__.py + routes.py + templates/ + static/ + venv/ + run.py +``` + +### Hello World Application +1. **Create a Directory for Your Project**: + ``` + mkdir my_flask_app + cd my_flask_app + ``` +2. **Initialize the Application**: + - Create `app/__init__.py`: + ```python + from flask import Flask + + def create_app(): + app = Flask(__name__) + + with app.app_context(): + from . import routes + return app + ``` + - Create `run.py`: + ```python + from app import create_app + + app = create_app() + + if __name__ == "__main__": + app.run(debug=True) + ``` + - Create `app/routes.py`: + ```python + from flask import current_app as app + + @app.route('/') + def hello_world(): + return 'Hello, World!' + ``` + +3. **Run the Application**: + ``` + python run.py + ``` + Navigate to `http://127.0.0.1:5000` in your browser to see "Hello, World!". + +## 5. Routing +In Flask, routes are defined using the `@app.route` decorator. Here's an example of different routes: + +```python +from flask import Flask + +app = Flask(__name__) + +@app.route('/') +def home(): + return 'Home Page' + +@app.route('/about') +def about(): + return 'About Page' + +@app.route('/user/') +def show_user_profile(username): + return f'User: {username}' +``` + +- **Explanation**: + - The `@app.route('/')` decorator binds the URL `'/'` to the `home` function, which returns 'Home Page'. + - The `@app.route('/about')` decorator binds the URL `/about` to the `about` function. + - The `@app.route('/user/')` decorator binds the URL `/user/` to the `show_user_profile` function, capturing the part of the URL as the `username` variable. + +## 6. Templates and Static Files +### Jinja2 Templating Engine +Jinja2 is Flask's templating engine. Templates are HTML files that can include dynamic content. + +- **Create a Template**: + - `app/templates/index.html`: + ```html + + + + {{ title }} + + +

{{ heading }}

+

{{ content }}

+ + + ``` + +- **Render the Template**: + ```python + from flask import Flask, render_template + + app = Flask(__name__) + + @app.route('/') + def home(): + return render_template('index.html', title='Home', heading='Welcome to Flask', content='This is a Flask application.') + ``` + +### Serving Static Files +Static files like CSS, JavaScript, and images are placed in the `static` directory. + +- **Create Static Files**: + - `app/static/style.css`: + ```css + body { + font-family: Arial, sans-serif; + } + ``` + +- **Include Static Files in Templates**: + ```html + + + + {{ title }} + + + +

{{ heading }}

+

{{ content }}

+ + + ``` + +## 7. Working with Forms +### Handling Form Data +Forms are used to collect user input. Flask provides utilities to handle form submissions. + +- **Create a Form**: + - `app/templates/form.html`: + ```html + + + + Form + + +
+ + + +
+ + + ``` + +- **Handle Form Submission**: + ```python + from flask import Flask, request, render_template + + app = Flask(__name__) + + @app.route('/form') + def form(): + return render_template('form.html') + + @app.route('/submit', methods=['POST']) + def submit(): + name = request.form['name'] + return f'Hello, {name}!' + ``` + +- **Explanation**: + - The `@app.route('/form')` route renders the form. + - The `@app.route('/submit', methods=['POST'])` route handles the form submission and displays the input name. + +## 8. Database Integration +### Setting Up SQLAlchemy +SQLAlchemy is an ORM that allows you to interact with databases using Python objects. + +- **Install SQLAlchemy**: + ``` + pip install flask_sqlalchemy + ``` + +- **Configure SQLAlchemy**: + - `app/__init__.py`: + ```python + from flask import Flask + from flask_sqlalchemy import SQLAlchemy + + db = SQLAlchemy() + + def create_app(): + app = Flask(__name__) + app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///site.db' + db.init_app(app) + return app + ``` + +### Performing CRUD Operations +Define models and perform CRUD operations. + +- **Define a Model**: + - `app/models.py`: + ```python + from app import db + + class User(db.Model): + id = db.Column(db.Integer, primary key=True) + username = db.Column(db.String(80), unique=True, nullable=False) + + def __repr__(self): + return f'' + ``` + +- **Create the Database**: + ```python + from app import create_app, db + from app.models import User + + app = create_app() + with app.app_context(): + db.create_all() + ``` + +- **Perform CRUD Operations**: + ```python + from app import db + from app.models import User + + # Create + new_user = User(username='new_user') + db.session.add(new_user) + db.session.commit() + + # Read + user = User.query.first() + + # Update + user.username = 'updated_user' + db.session.commit() + + # Delete + db.session.delete(user) + db.session.commit() + ``` + +## 9. Error Handling +Error handling in Flask can be managed by defining error handlers for different HTTP status codes. + +- **Define an Error Handler**: + ```python + from flask import Flask, render_template + + app = Flask(__name__) + + @app.errorhandler(404) + def page_not_found(e): + return render_template('404.html'), 404 + + @app.errorhandler(500) + def internal_server_error(e): + return render_template('500.html'), 500 + ``` + + - **Create Error Pages**: + `app/templates/404.html`: + + + + + Page Not Found + + +

404 - Page Not Found

+

The page you are looking for does not exist.

+ + + + + + - **app/templates/500.html:** + + + + + Internal Server Error + + +

500 - Internal Server Error

+

Something went wrong on our end. Please try again later.

+ + + + +## 10. Testing Your Application +Flask applications can be tested using Python's built-in `unittest` framework. + +- **Write a Test Case**: + - `tests/test_app.py`: + ```python + import unittest + from app import create_app + + class BasicTestCase(unittest.TestCase): + def setUp(self): + self.app = create_app() + self.app.config['TESTING'] = True + self.client = self.app.test_client() + + def test_home(self): + response = self.client.get('/') + self.assertEqual(response.status_code, 200) + self.assertIn(b'Hello, World!', response.data) + + if __name__ == '__main__': + unittest.main() + ``` + + - **Run the Tests**: + ``` + python -m unittest discover -s tests + ``` + +## 11. Deploying Your Flask Application +### Using Gunicorn +Gunicorn is a Python WSGI HTTP Server for UNIX. It’s a pre-fork worker model, meaning that it forks multiple worker processes to handle requests. + +- **Install Gunicorn**: + ``` + pip install gunicorn + ``` + +- **Run Your Application with Gunicorn**: + ``` + gunicorn -w 4 run:app + ``` + +### Deploying to Render +Render is a cloud platform for deploying web applications. + +- **Create a `requirements.txt` File**: + ``` + Flask + gunicorn + flask_sqlalchemy + ``` + +- **Create a `render.yaml` File**: + ```yaml + services: + - type: web + name: my-flask-app + env: python + plan: free + buildCommand: pip install -r requirements.txt + startCommand: gunicorn -w 4 run:app + ``` + +- **Deploy Your Application**: + 1. Push your code to a Git repository. + 2. Sign in to Render and create a new Web Service. + 3. Connect your repository and select the branch to deploy. + 4. Render will automatically use the `render.yaml` file to configure and deploy your application. + +## 12. Conclusion +Flask is a powerful and flexible framework for building web applications in Python. It offers simplicity and ease of use, making it a great choice for both beginners and experienced developers. This guide covered the basics of setting up a Flask application, routing, templating, working with forms, integrating databases, error handling, testing, and deployment. + +## 13. Further Reading and Resources +- Flask Documentation: https://flask.palletsprojects.com/en/latest/ +- Jinja2 Documentation: https://jinja.palletsprojects.com/en/latest/ +- SQLAlchemy Documentation: https://docs.sqlalchemy.org/en/latest/ +- Render Documentation: https://render.com/docs \ No newline at end of file From 365cc3a62f07d3b013529bca18590f0041524fee Mon Sep 17 00:00:00 2001 From: Labqari Date: Sat, 25 May 2024 13:04:32 +0530 Subject: [PATCH 29/72] flask commit --- contrib/advanced-python/index.md | 1 - ...duction-to-flask-a-python-web-framework.md | 440 ------------------ 2 files changed, 441 deletions(-) delete mode 100644 contrib/advanced-python/introduction-to-flask-a-python-web-framework.md diff --git a/contrib/advanced-python/index.md b/contrib/advanced-python/index.md index 027f989..5ea5081 100644 --- a/contrib/advanced-python/index.md +++ b/contrib/advanced-python/index.md @@ -1,4 +1,3 @@ # List of sections - [Decorators/\*args/**kwargs](decorator-kwargs-args.md) -- [Decorators/\*args/**kwargs](introduction-to-flask-a-python-web-framework.md) diff --git a/contrib/advanced-python/introduction-to-flask-a-python-web-framework.md b/contrib/advanced-python/introduction-to-flask-a-python-web-framework.md deleted file mode 100644 index 957f39b..0000000 --- a/contrib/advanced-python/introduction-to-flask-a-python-web-framework.md +++ /dev/null @@ -1,440 +0,0 @@ -Sure, here's the guide without Markdown formatting: - ---- - -# Introduction to Flask: A Python Web Framework - -## Table of Contents -1. Introduction -2. Prerequisites -3. Setting Up Your Environment -4. Creating Your First Flask Application - - Project Structure - - Hello World Application -5. Routing -6. Templates and Static Files - - Jinja2 Templating Engine - - Serving Static Files -7. Working with Forms - - Handling Form Data -8. Database Integration - - Setting Up SQLAlchemy - - Performing CRUD Operations -9. Error Handling -10. Testing Your Application -11. Deploying Your Flask Application - - Using Gunicorn - - Deploying to Render -12. Conclusion -13. Further Reading and Resources - ---- - -## 1. Introduction -Flask is a lightweight WSGI web application framework in Python. It is designed with simplicity and flexibility in mind, allowing developers to create web applications with minimal setup. Flask was created by Armin Ronacher as part of the Pocoo project and has gained popularity for its ease of use and extensive documentation. - -## 2. Prerequisites -Before starting with Flask, ensure you have the following: -- Basic knowledge of Python. -- Understanding of web development concepts (HTML, CSS, JavaScript). -- Python installed on your machine (version 3.6 or higher). -- pip (Python package installer) installed. - -## 3. Setting Up Your Environment -1. **Install Python**: Download and install Python from python.org. -2. **Create a Virtual Environment**: - ``` - python -m venv venv - ``` -3. **Activate the Virtual Environment**: - - On Windows: - ``` - venv\Scripts\activate - ``` - - On macOS/Linux: - ``` - source venv/bin/activate - ``` -4. **Install Flask**: - ``` - pip install Flask - ``` - -## 4. Creating Your First Flask Application -### Project Structure -A typical Flask project structure might look like this: -``` -my_flask_app/ - app/ - __init__.py - routes.py - templates/ - static/ - venv/ - run.py -``` - -### Hello World Application -1. **Create a Directory for Your Project**: - ``` - mkdir my_flask_app - cd my_flask_app - ``` -2. **Initialize the Application**: - - Create `app/__init__.py`: - ```python - from flask import Flask - - def create_app(): - app = Flask(__name__) - - with app.app_context(): - from . import routes - return app - ``` - - Create `run.py`: - ```python - from app import create_app - - app = create_app() - - if __name__ == "__main__": - app.run(debug=True) - ``` - - Create `app/routes.py`: - ```python - from flask import current_app as app - - @app.route('/') - def hello_world(): - return 'Hello, World!' - ``` - -3. **Run the Application**: - ``` - python run.py - ``` - Navigate to `http://127.0.0.1:5000` in your browser to see "Hello, World!". - -## 5. Routing -In Flask, routes are defined using the `@app.route` decorator. Here's an example of different routes: - -```python -from flask import Flask - -app = Flask(__name__) - -@app.route('/') -def home(): - return 'Home Page' - -@app.route('/about') -def about(): - return 'About Page' - -@app.route('/user/') -def show_user_profile(username): - return f'User: {username}' -``` - -- **Explanation**: - - The `@app.route('/')` decorator binds the URL `'/'` to the `home` function, which returns 'Home Page'. - - The `@app.route('/about')` decorator binds the URL `/about` to the `about` function. - - The `@app.route('/user/')` decorator binds the URL `/user/` to the `show_user_profile` function, capturing the part of the URL as the `username` variable. - -## 6. Templates and Static Files -### Jinja2 Templating Engine -Jinja2 is Flask's templating engine. Templates are HTML files that can include dynamic content. - -- **Create a Template**: - - `app/templates/index.html`: - ```html - - - - {{ title }} - - -

{{ heading }}

-

{{ content }}

- - - ``` - -- **Render the Template**: - ```python - from flask import Flask, render_template - - app = Flask(__name__) - - @app.route('/') - def home(): - return render_template('index.html', title='Home', heading='Welcome to Flask', content='This is a Flask application.') - ``` - -### Serving Static Files -Static files like CSS, JavaScript, and images are placed in the `static` directory. - -- **Create Static Files**: - - `app/static/style.css`: - ```css - body { - font-family: Arial, sans-serif; - } - ``` - -- **Include Static Files in Templates**: - ```html - - - - {{ title }} - - - -

{{ heading }}

-

{{ content }}

- - - ``` - -## 7. Working with Forms -### Handling Form Data -Forms are used to collect user input. Flask provides utilities to handle form submissions. - -- **Create a Form**: - - `app/templates/form.html`: - ```html - - - - Form - - -
- - - -
- - - ``` - -- **Handle Form Submission**: - ```python - from flask import Flask, request, render_template - - app = Flask(__name__) - - @app.route('/form') - def form(): - return render_template('form.html') - - @app.route('/submit', methods=['POST']) - def submit(): - name = request.form['name'] - return f'Hello, {name}!' - ``` - -- **Explanation**: - - The `@app.route('/form')` route renders the form. - - The `@app.route('/submit', methods=['POST'])` route handles the form submission and displays the input name. - -## 8. Database Integration -### Setting Up SQLAlchemy -SQLAlchemy is an ORM that allows you to interact with databases using Python objects. - -- **Install SQLAlchemy**: - ``` - pip install flask_sqlalchemy - ``` - -- **Configure SQLAlchemy**: - - `app/__init__.py`: - ```python - from flask import Flask - from flask_sqlalchemy import SQLAlchemy - - db = SQLAlchemy() - - def create_app(): - app = Flask(__name__) - app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///site.db' - db.init_app(app) - return app - ``` - -### Performing CRUD Operations -Define models and perform CRUD operations. - -- **Define a Model**: - - `app/models.py`: - ```python - from app import db - - class User(db.Model): - id = db.Column(db.Integer, primary key=True) - username = db.Column(db.String(80), unique=True, nullable=False) - - def __repr__(self): - return f'' - ``` - -- **Create the Database**: - ```python - from app import create_app, db - from app.models import User - - app = create_app() - with app.app_context(): - db.create_all() - ``` - -- **Perform CRUD Operations**: - ```python - from app import db - from app.models import User - - # Create - new_user = User(username='new_user') - db.session.add(new_user) - db.session.commit() - - # Read - user = User.query.first() - - # Update - user.username = 'updated_user' - db.session.commit() - - # Delete - db.session.delete(user) - db.session.commit() - ``` - -## 9. Error Handling -Error handling in Flask can be managed by defining error handlers for different HTTP status codes. - -- **Define an Error Handler**: - ```python - from flask import Flask, render_template - - app = Flask(__name__) - - @app.errorhandler(404) - def page_not_found(e): - return render_template('404.html'), 404 - - @app.errorhandler(500) - def internal_server_error(e): - return render_template('500.html'), 500 - ``` - - - **Create Error Pages**: - `app/templates/404.html`: - - - - - Page Not Found - - -

404 - Page Not Found

-

The page you are looking for does not exist.

- - - - - - - **app/templates/500.html:** - - - - - Internal Server Error - - -

500 - Internal Server Error

-

Something went wrong on our end. Please try again later.

- - - - -## 10. Testing Your Application -Flask applications can be tested using Python's built-in `unittest` framework. - -- **Write a Test Case**: - - `tests/test_app.py`: - ```python - import unittest - from app import create_app - - class BasicTestCase(unittest.TestCase): - def setUp(self): - self.app = create_app() - self.app.config['TESTING'] = True - self.client = self.app.test_client() - - def test_home(self): - response = self.client.get('/') - self.assertEqual(response.status_code, 200) - self.assertIn(b'Hello, World!', response.data) - - if __name__ == '__main__': - unittest.main() - ``` - - - **Run the Tests**: - ``` - python -m unittest discover -s tests - ``` - -## 11. Deploying Your Flask Application -### Using Gunicorn -Gunicorn is a Python WSGI HTTP Server for UNIX. It’s a pre-fork worker model, meaning that it forks multiple worker processes to handle requests. - -- **Install Gunicorn**: - ``` - pip install gunicorn - ``` - -- **Run Your Application with Gunicorn**: - ``` - gunicorn -w 4 run:app - ``` - -### Deploying to Render -Render is a cloud platform for deploying web applications. - -- **Create a `requirements.txt` File**: - ``` - Flask - gunicorn - flask_sqlalchemy - ``` - -- **Create a `render.yaml` File**: - ```yaml - services: - - type: web - name: my-flask-app - env: python - plan: free - buildCommand: pip install -r requirements.txt - startCommand: gunicorn -w 4 run:app - ``` - -- **Deploy Your Application**: - 1. Push your code to a Git repository. - 2. Sign in to Render and create a new Web Service. - 3. Connect your repository and select the branch to deploy. - 4. Render will automatically use the `render.yaml` file to configure and deploy your application. - -## 12. Conclusion -Flask is a powerful and flexible framework for building web applications in Python. It offers simplicity and ease of use, making it a great choice for both beginners and experienced developers. This guide covered the basics of setting up a Flask application, routing, templating, working with forms, integrating databases, error handling, testing, and deployment. - -## 13. Further Reading and Resources -- Flask Documentation: https://flask.palletsprojects.com/en/latest/ -- Jinja2 Documentation: https://jinja.palletsprojects.com/en/latest/ -- SQLAlchemy Documentation: https://docs.sqlalchemy.org/en/latest/ -- Render Documentation: https://render.com/docs \ No newline at end of file From 1496dac821b26a46cd5bd2432af95b052cc43494 Mon Sep 17 00:00:00 2001 From: Manish kumar gupta <97523900+manishh12@users.noreply.github.com> Date: Sat, 25 May 2024 16:10:18 +0530 Subject: [PATCH 30/72] Updated maths formulas --- .../machine-learning/Types_of_optimizers.md | 74 ++++++++++--------- 1 file changed, 38 insertions(+), 36 deletions(-) diff --git a/contrib/machine-learning/Types_of_optimizers.md b/contrib/machine-learning/Types_of_optimizers.md index e941597..ae2759d 100644 --- a/contrib/machine-learning/Types_of_optimizers.md +++ b/contrib/machine-learning/Types_of_optimizers.md @@ -6,6 +6,8 @@ Optimizers are algorithms or methods used to change the attributes of your neura ## Types of Optimizers + + ### 1. Gradient Descent **Explanation:** @@ -15,19 +17,20 @@ Gradient Descent is the simplest and most commonly used optimization algorithm. The update rule for the parameter vector θ in gradient descent is represented by the equation: -- \(theta_new = theta_old - alpha * gradient/) +- $$\theta_{\text{new}} = \theta_{\text{old}} - \alpha \cdot \nabla J(\theta)$$ Where: -- theta_old is the old parameter vector. -- theta_new is the updated parameter vector. -- alpha is the learning rate. -- gradient is the gradient of the objective function with respect to the parameters. +- θold is the old parameter vector. +- θnew is the updated parameter vector. +- alpha(α) is the learning rate. +- ∇J(θ) is the gradient of the objective function with respect to the parameters. + **Intuition:** - At each iteration, we calculate the gradient of the cost function. - The parameters are updated in the opposite direction of the gradient. -- The size of the step is controlled by the learning rate \( \alpha \). +- The size of the step is controlled by the learning rate α. **Advantages:** - Simple to implement. @@ -58,9 +61,10 @@ SGD is a variation of gradient descent where we use only one training example to **Mathematical Formulation:** -- \(theta = theta - alpha * dJ(theta; x_i, y_i) / d(theta)/) +- $$θ = θ - α \cdot \frac{∂J (θ; xᵢ, yᵢ)}{∂θ}$$ -\( x_i, y_i \) are a single training example and its target. + +- xᵢ, yᵢ are a single training example and its target. **Intuition:** - At each iteration, a random training example is selected. @@ -98,7 +102,8 @@ Mini-Batch Gradient Descent is a variation where instead of a single training ex **Mathematical Formulation:** -- theta = theta - alpha * (1/k) * sum(dJ(theta; x_i, y_i) / d(theta)) +- $$θ = θ - α \cdot \frac{1}{k} \sum_{i=1}^{k} \frac{∂J (θ; xᵢ, yᵢ)}{∂θ}$$ + Where: - \( k \) is the batch size. @@ -141,14 +146,13 @@ Momentum helps accelerate gradient vectors in the right directions, thus leading **Mathematical Formulation:** -- v_t = gamma * v_{t-1} + alpha * dJ(theta) / d(theta) - -- theta = theta - v_t +- $$v_t = γ \cdot v_{t-1} + α \cdot ∇J(θ)$$ +- $$θ = θ - v_t$$ where: - \( v_t \) is the velocity. -- \( \gamma \) is the momentum term, typically set between 0.9 and 0.99. +- γ is the momentum term, typically set between 0.9 and 0.99. **Intuition:** - At each iteration, the gradient is calculated. @@ -182,9 +186,11 @@ NAG is a variant of the gradient descent with momentum. It looks ahead by a step **Mathematical Formulation:** -- v_t = gamma * v_{t-1} + alpha * dJ(theta - gamma * v_{t-1}) / d(theta) +- $$v_t = γv_{t-1} + α \cdot ∇J(θ - γ \cdot v_{t-1})$$ + +- $$θ = θ - v_t$$ + -- theta = theta - v_t **Intuition:** @@ -220,13 +226,13 @@ AdaGrad adapts the learning rate to the parameters, performing larger updates fo **Mathematical Formulation:** -- G_t = G_{t-1} + (dJ(theta) / d(theta)) ⊙ (dJ(theta) / d(theta)) +- $$G_t = G_{t-1} + (∂J(θ)/∂θ)^2$$ -- theta = theta - (alpha / sqrt(G_t + epsilon)) * (dJ(theta) / d(theta)) +- $$θ = θ - \frac{α}{\sqrt{G_t + ε}} \cdot ∇J(θ)$$ Where: -- \( G_t \) is the sum of squares of the gradients up to time step \( t \). -- \( \epsilon \) is a small constant to avoid division by zero. +- \(G_t\) is the sum of squares of the gradients up to time step \( t \). +- ε is a small constant to avoid division by zero. **Intuition:** - Accumulates the sum of the squares of the gradients for each parameter. @@ -263,13 +269,13 @@ RMSprop modifies AdaGrad to perform well in non-convex settings by using a movin **Mathematical Formulation:** -E[g^2]_t = beta * E[g^2]_{t-1} + (1 - beta) * (dJ(theta) / d(theta)) ⊙ (dJ(theta) / d(theta)) +- E[g²]ₜ = βE[g²]ₜ₋₁ + (1 - β)(∂J(θ) / ∂θ)² -theta = theta - (alpha / sqrt(E[g^2]_t + epsilon)) * (dJ(theta) / d(theta)) +- $$θ = θ - \frac{α}{\sqrt{E[g^2]_t + ε}} \cdot ∇J(θ)$$ Where: -- \( E[g^2]_t \) is the exponentially decaying average of past squared gradients. -- \( \beta \) is the decay rate. +- \( E[g^2]_t \) is the exponentially decaying average of past squared gradients. +- β is the decay rate. **Intuition:** - Keeps a running average of the squared gradients. @@ -304,20 +310,16 @@ Adam (Adaptive Moment Estimation) combines the advantages of both RMSprop and Ad **Mathematical Formulation:** -- m_t = beta1 * m_{t-1} + (1 - beta1) * (dJ(theta) / d(theta)) - -- v_t = beta2 * v_{t-1} + (1 - beta2) * ((dJ(theta) / d(theta))^2) - -- hat_m_t = m_t / (1 - beta1^t) - -- hat_v_t = v_t / (1 - beta2^t) - -- theta = theta - (alpha * hat_m_t) / (sqrt(hat_v_t) + epsilon) +- $$m_t = β_1m_{t-1} + (1 - β_1)(∂J(θ)/∂θ)$$ +- $$v_t = β_2v_{t-1} + (1 - β_2)(∂J(θ)/∂θ)^2$$ +- $$\hat{m}_t = \frac{m_t}{1 - β_1^t}$$ +- $$\hat{v}_t = \frac{v_t}{1 - β_2^t}$$ +- $$θ = θ - \frac{α\hat{m}_t}{\sqrt{\hat{v}_t} + ε}$$ Where: -- \( m_t \) is the first moment (mean) of the gradient. -- \( v_t \) is the second moment (uncentered variance) of the gradient. -- \( \beta_1, \beta_2 \) are the decay rates for the moment estimates. +- \( mt \) is the first moment (mean) of the gradient. +- \( vt \) is the second moment (uncentered variance) of the gradient. +- β_1.β_2 are the decay rates for the moment estimates. **Intuition:** - Keeps track of both the mean and the variance of the gradients. @@ -352,4 +354,4 @@ def adam(X, y, lr=0.01, epochs=1000, beta1=0.9, beta2=0.999, epsilon=1e-8): These implementations are basic examples of how these optimizers can be implemented in Python using NumPy. In practice, libraries like TensorFlow and PyTorch provide highly optimized and more sophisticated implementations of these and other optimization algorithms. ---- \ No newline at end of file +--- From 534eaca30dce735f3be6034aea0b2e9376f2643e Mon Sep 17 00:00:00 2001 From: Krishna Kaushik <131583096+kRiShNa-429407@users.noreply.github.com> Date: Sat, 25 May 2024 19:39:50 +0530 Subject: [PATCH 31/72] Add files via upload --- contrib/pandas/Datasets/car-sales.csv | 11 +++++++++++ 1 file changed, 11 insertions(+) create mode 100644 contrib/pandas/Datasets/car-sales.csv diff --git a/contrib/pandas/Datasets/car-sales.csv b/contrib/pandas/Datasets/car-sales.csv new file mode 100644 index 0000000..63a63bf --- /dev/null +++ b/contrib/pandas/Datasets/car-sales.csv @@ -0,0 +1,11 @@ +Make,Colour,Odometer (KM),Doors,Price +Toyota,White,150043,4,"$4,000.00" +Honda,Red,87899,4,"$5,000.00" +Toyota,Blue,32549,3,"$7,000.00" +BMW,Black,11179,5,"$22,000.00" +Nissan,White,213095,4,"$3,500.00" +Toyota,Green,99213,4,"$4,500.00" +Honda,Blue,45698,4,"$7,500.00" +Honda,Blue,54738,4,"$7,000.00" +Toyota,White,60000,4,"$6,250.00" +Nissan,White,31600,4,"$9,700.00" \ No newline at end of file From 5fd2169b54ec03e88b3d4d645cef209c8feab85e Mon Sep 17 00:00:00 2001 From: Krishna Kaushik <131583096+kRiShNa-429407@users.noreply.github.com> Date: Sat, 25 May 2024 19:40:22 +0530 Subject: [PATCH 32/72] Delete contrib/pandas/Datasets/Titanic.csv --- contrib/pandas/Datasets/Titanic.csv | 1310 --------------------------- 1 file changed, 1310 deletions(-) delete mode 100644 contrib/pandas/Datasets/Titanic.csv diff --git a/contrib/pandas/Datasets/Titanic.csv b/contrib/pandas/Datasets/Titanic.csv deleted file mode 100644 index f8d49dc..0000000 --- a/contrib/pandas/Datasets/Titanic.csv +++ /dev/null @@ -1,1310 +0,0 @@ -"pclass","survived","name","sex","age","sibsp","parch","ticket","fare","cabin","embarked","boat","body","home.dest" -1,1,"Allen, Miss. Elisabeth Walton","female",29,0,0,"24160",211.3375,"B5","S","2",,"St Louis, MO" -1,1,"Allison, Master. Hudson Trevor","male",0.92,1,2,"113781",151.5500,"C22 C26","S","11",,"Montreal, PQ / Chesterville, ON" -1,0,"Allison, Miss. Helen Loraine","female",2,1,2,"113781",151.5500,"C22 C26","S",,,"Montreal, PQ / Chesterville, ON" -1,0,"Allison, Mr. Hudson Joshua Creighton","male",30,1,2,"113781",151.5500,"C22 C26","S",,"135","Montreal, PQ / Chesterville, ON" -1,0,"Allison, Mrs. Hudson J C (Bessie Waldo Daniels)","female",25,1,2,"113781",151.5500,"C22 C26","S",,,"Montreal, PQ / Chesterville, ON" -1,1,"Anderson, Mr. Harry","male",48,0,0,"19952",26.5500,"E12","S","3",,"New York, NY" -1,1,"Andrews, Miss. Kornelia Theodosia","female",63,1,0,"13502",77.9583,"D7","S","10",,"Hudson, NY" -1,0,"Andrews, Mr. Thomas Jr","male",39,0,0,"112050",0.0000,"A36","S",,,"Belfast, NI" -1,1,"Appleton, Mrs. Edward Dale (Charlotte Lamson)","female",53,2,0,"11769",51.4792,"C101","S","D",,"Bayside, Queens, NY" -1,0,"Artagaveytia, Mr. Ramon","male",71,0,0,"PC 17609",49.5042,,"C",,"22","Montevideo, Uruguay" -1,0,"Astor, Col. John Jacob","male",47,1,0,"PC 17757",227.5250,"C62 C64","C",,"124","New York, NY" -1,1,"Astor, Mrs. John Jacob (Madeleine Talmadge Force)","female",18,1,0,"PC 17757",227.5250,"C62 C64","C","4",,"New York, NY" -1,1,"Aubart, Mme. Leontine Pauline","female",24,0,0,"PC 17477",69.3000,"B35","C","9",,"Paris, France" -1,1,"Barber, Miss. Ellen ""Nellie""","female",26,0,0,"19877",78.8500,,"S","6",, -1,1,"Barkworth, Mr. Algernon Henry Wilson","male",80,0,0,"27042",30.0000,"A23","S","B",,"Hessle, Yorks" -1,0,"Baumann, Mr. John D","male",,0,0,"PC 17318",25.9250,,"S",,,"New York, NY" -1,0,"Baxter, Mr. Quigg Edmond","male",24,0,1,"PC 17558",247.5208,"B58 B60","C",,,"Montreal, PQ" -1,1,"Baxter, Mrs. James (Helene DeLaudeniere Chaput)","female",50,0,1,"PC 17558",247.5208,"B58 B60","C","6",,"Montreal, PQ" -1,1,"Bazzani, Miss. Albina","female",32,0,0,"11813",76.2917,"D15","C","8",, -1,0,"Beattie, Mr. Thomson","male",36,0,0,"13050",75.2417,"C6","C","A",,"Winnipeg, MN" -1,1,"Beckwith, Mr. Richard Leonard","male",37,1,1,"11751",52.5542,"D35","S","5",,"New York, NY" -1,1,"Beckwith, Mrs. Richard Leonard (Sallie Monypeny)","female",47,1,1,"11751",52.5542,"D35","S","5",,"New York, NY" -1,1,"Behr, Mr. Karl Howell","male",26,0,0,"111369",30.0000,"C148","C","5",,"New York, NY" -1,1,"Bidois, Miss. 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Thamine","female",,1,0,"2665",14.4542,,"C",,, -3,0,"Zakarian, Mr. Mapriededer","male",26.5,0,0,"2656",7.2250,,"C",,"304", -3,0,"Zakarian, Mr. Ortin","male",27,0,0,"2670",7.2250,,"C",,, -3,0,"Zimmerman, Mr. Leo","male",29,0,0,"315082",7.8750,,"S",,, From 255d6092bc304c46e471f324b28e6e6d1ab49790 Mon Sep 17 00:00:00 2001 From: Krishna Kaushik <131583096+kRiShNa-429407@users.noreply.github.com> Date: Sat, 25 May 2024 19:44:31 +0530 Subject: [PATCH 33/72] Update Importing_and_Exporting_Data_in_Pandas.md --- .../Importing_and_Exporting_Data_in_Pandas.md | 63 +++++-------------- 1 file changed, 17 insertions(+), 46 deletions(-) diff --git a/contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md b/contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md index bb490b9..a4bc1be 100644 --- a/contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md +++ b/contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md @@ -12,60 +12,31 @@ *You could export it as a .csv file and then import it using ``pd.read_csv()``.* -*In this case, the exported .csv file is called `Titanic.csv`* +*In this case, the exported .csv file is called `car-sales.csv`* ```python ## Importing Titanic Data set import pandas as pd -titanic_df= pd.read_csv("https://raw.githubusercontent.com/kRiShNa-429407/learn-python/main/contrib/pandas/Datasets/Titanic.csv") -print(titanic_df) +car_sales_df= pd.read_csv("https://raw.githubusercontent.com/kRiShNa-429407/learn-python/main/contrib/pandas/Datasets/car-sales.csv") +print(car_sales_df) ``` - pclass survived name \ - 0 1 1 Allen, Miss. Elisabeth Walton - 1 1 1 Allison, Master. Hudson Trevor - 2 1 0 Allison, Miss. Helen Loraine - 3 1 0 Allison, Mr. Hudson Joshua Creighton - 4 1 0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) - ... ... ... ... - 1304 3 0 Zabour, Miss. Hileni - 1305 3 0 Zabour, Miss. Thamine - 1306 3 0 Zakarian, Mr. Mapriededer - 1307 3 0 Zakarian, Mr. Ortin - 1308 3 0 Zimmerman, Mr. Leo - - sex age sibsp parch ticket fare cabin embarked boat \ - 0 female 29.00 0 0 24160 211.3375 B5 S 2 - 1 male 0.92 1 2 113781 151.5500 C22 C26 S 11 - 2 female 2.00 1 2 113781 151.5500 C22 C26 S NaN - 3 male 30.00 1 2 113781 151.5500 C22 C26 S NaN - 4 female 25.00 1 2 113781 151.5500 C22 C26 S NaN - ... ... ... ... ... ... ... ... ... ... - 1304 female 14.50 1 0 2665 14.4542 NaN C NaN - 1305 female NaN 1 0 2665 14.4542 NaN C NaN - 1306 male 26.50 0 0 2656 7.2250 NaN C NaN - 1307 male 27.00 0 0 2670 7.2250 NaN C NaN - 1308 male 29.00 0 0 315082 7.8750 NaN S NaN - - body home.dest - 0 NaN St Louis, MO - 1 NaN Montreal, PQ / Chesterville, ON - 2 NaN Montreal, PQ / Chesterville, ON - 3 135.0 Montreal, PQ / Chesterville, ON - 4 NaN Montreal, PQ / Chesterville, ON - ... ... ... - 1304 328.0 NaN - 1305 NaN NaN - 1306 304.0 NaN - 1307 NaN NaN - 1308 NaN NaN - - [1309 rows x 14 columns] + Make Colour Odometer (KM) Doors Price + 0 Toyota White 150043 4 $4,000.00 + 1 Honda Red 87899 4 $5,000.00 + 2 Toyota Blue 32549 3 $7,000.00 + 3 BMW Black 11179 5 $22,000.00 + 4 Nissan White 213095 4 $3,500.00 + 5 Toyota Green 99213 4 $4,500.00 + 6 Honda Blue 45698 4 $7,500.00 + 7 Honda Blue 54738 4 $7,000.00 + 8 Toyota White 60000 4 $6,250.00 + 9 Nissan White 31600 4 $9,700.00 -The dataset I am using here for your reference is taken from the same repository i.e ``learn-python`` (https://raw.githubusercontent.com/kRiShNa-429407/learn-python/main/contrib/pandas/Datasets/Titanic.csv) I uploaded it in the Datasets folder,you can use it from there. +The dataset I am using here for your reference is taken from the same repository i.e ``learn-python`` (https://raw.githubusercontent.com/kRiShNa-429407/learn-python/main/contrib/pandas/Datasets/car-sales.csv) I uploaded it in the Datasets folder,you can use it from there. You can also place the filename with its path in `pd.read_csv()`. @@ -92,12 +63,12 @@ You can also place the filename with its path in `pd.read_csv()`. ### Exporting a dataframe to a CSV -**We haven't made any changes yet to the ``titanic_df`` DataFrame but let's try to export it.** +**We haven't made any changes yet to the ``car_sales_df`` DataFrame but let's try to export it.** ```python #Export the titanic_df DataFrame to csv -titanic_df.to_csv("exported_titanic.csv") +car_sales_df.to_csv("exported_car_sales_df.csv") ``` Running this will save a file called ``exported_titanic.csv`` to the current folder. From cb52a821a21faa902dc53ee012732a8232ffd7f0 Mon Sep 17 00:00:00 2001 From: Krishna Kaushik <131583096+kRiShNa-429407@users.noreply.github.com> Date: Sat, 25 May 2024 19:45:29 +0530 Subject: [PATCH 34/72] Update index.md --- contrib/pandas/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/pandas/index.md b/contrib/pandas/index.md index 2874431..a2d826c 100644 --- a/contrib/pandas/index.md +++ b/contrib/pandas/index.md @@ -5,3 +5,4 @@ - [Pandas Descriptive Statistics](Descriptive_Statistics.md) - [Group By Functions with Pandas](GroupBy_Functions_Pandas.md) - [Excel using Pandas DataFrame](excel_with_pandas.md) +- [Importing and Exporting Data in Pandas](Importing_and_Exporting_Data_in_Pandas.md) From 577df2c86e89ea943be4d9ac7415b88006181c2a Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sat, 25 May 2024 20:46:11 +0530 Subject: [PATCH 35/72] Update car-sales.csv --- contrib/pandas/Datasets/car-sales.csv | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/pandas/Datasets/car-sales.csv b/contrib/pandas/Datasets/car-sales.csv index 63a63bf..81e534a 100644 --- a/contrib/pandas/Datasets/car-sales.csv +++ b/contrib/pandas/Datasets/car-sales.csv @@ -8,4 +8,4 @@ Toyota,Green,99213,4,"$4,500.00" Honda,Blue,45698,4,"$7,500.00" Honda,Blue,54738,4,"$7,000.00" Toyota,White,60000,4,"$6,250.00" -Nissan,White,31600,4,"$9,700.00" \ No newline at end of file +Nissan,White,31600,4,"$9,700.00" From 14d806ff7052ac1eace6a200abde29c4aaf06562 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sat, 25 May 2024 21:01:18 +0530 Subject: [PATCH 36/72] Update and rename Importing_and_Exporting_Data_in_Pandas.md to import-export.md --- .../Importing_and_Exporting_Data_in_Pandas.md | 74 ------------------- contrib/pandas/import-export.md | 46 ++++++++++++ 2 files changed, 46 insertions(+), 74 deletions(-) delete mode 100644 contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md create mode 100644 contrib/pandas/import-export.md diff --git a/contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md b/contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md deleted file mode 100644 index a4bc1be..0000000 --- a/contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md +++ /dev/null @@ -1,74 +0,0 @@ -# Importing_and_Exporting_Data_in_Pandas - ->Created by Krishna Kaushik - -- **Now we're able to create `Series` and `DataFrames` in pandas, but we usually do not do this , in practice we import the data which is in the form of .csv (Comma Seperated Values) , a spreadsheet file or something similar.** - -- *Good news is that pandas allows for easy importing of data like this through functions such as ``pd.read_csv()`` and ``pd.read_excel()`` for Microsoft Excel files.* - -## 1. Importing from a Google sheet to a pandas dataframe - -*Let's say that you wanted to get the information from Google Sheet document into a pandas DataFrame.*. - -*You could export it as a .csv file and then import it using ``pd.read_csv()``.* - -*In this case, the exported .csv file is called `car-sales.csv`* - - -```python -## Importing Titanic Data set -import pandas as pd - -car_sales_df= pd.read_csv("https://raw.githubusercontent.com/kRiShNa-429407/learn-python/main/contrib/pandas/Datasets/car-sales.csv") -print(car_sales_df) -``` - - Make Colour Odometer (KM) Doors Price - 0 Toyota White 150043 4 $4,000.00 - 1 Honda Red 87899 4 $5,000.00 - 2 Toyota Blue 32549 3 $7,000.00 - 3 BMW Black 11179 5 $22,000.00 - 4 Nissan White 213095 4 $3,500.00 - 5 Toyota Green 99213 4 $4,500.00 - 6 Honda Blue 45698 4 $7,500.00 - 7 Honda Blue 54738 4 $7,000.00 - 8 Toyota White 60000 4 $6,250.00 - 9 Nissan White 31600 4 $9,700.00 - - -The dataset I am using here for your reference is taken from the same repository i.e ``learn-python`` (https://raw.githubusercontent.com/kRiShNa-429407/learn-python/main/contrib/pandas/Datasets/car-sales.csv) I uploaded it in the Datasets folder,you can use it from there. - -You can also place the filename with its path in `pd.read_csv()`. - -**Now we've got the same data from the Google Spreadsheet , but now available as ``pandas DataFrame`` which means we can now apply all pandas functionality over it.** - -#### Note: The quiet important thing i am telling is that ``pd.read_csv()`` takes the location of the file (which is in your current working directory) or the hyperlink of the dataset from the other source. - -#### But if you want to import the data from Github you can't directly use its link , you have to first convert it to raw by clicking on the raw button present in the repo . - -#### Also you can't use the data directly from `Kaggle` you have to use ``kaggle API`` - -## 2. The Anatomy of DataFrame - -**Different functions use different labels for different things, and can get a little confusing.** - -- Rows are refer as ``axis=0`` -- columns are refer as ``axis=1`` - -## 3. Exporting Data - -**OK, so after you've made a few changes to your data, you might want to export it and save it so someone else can access the changes.** - -**pandas allows you to export ``DataFrame's`` to ``.csv`` format using ``.to_csv()``, or to a spreadsheet format using .to_excel().** - -### Exporting a dataframe to a CSV - -**We haven't made any changes yet to the ``car_sales_df`` DataFrame but let's try to export it.** - - -```python -#Export the titanic_df DataFrame to csv -car_sales_df.to_csv("exported_car_sales_df.csv") -``` - -Running this will save a file called ``exported_titanic.csv`` to the current folder. diff --git a/contrib/pandas/import-export.md b/contrib/pandas/import-export.md new file mode 100644 index 0000000..23d1ad8 --- /dev/null +++ b/contrib/pandas/import-export.md @@ -0,0 +1,46 @@ +# Importing and Exporting Data in Pandas + +## Importing Data from a CSV + +We can create `Series` and `DataFrame` in pandas, but often we have to import the data which is in the form of `.csv` (Comma Separated Values), a spreadsheet file or similar tabular data file format. + +`pandas` allows for easy importing of this data using functions such as `read_csv()` and `read_excel()` for Microsoft Excel files. + +*Note: In case you want to get the information from a **Google Sheet** you can export it as a .csv file.* + +The `read_csv()` function can be used to import a CSV file into a pandas DataFrame. The path can be a file system path or a URL where the CSV is available. + +```python +import pandas as pd + +car_sales_df= pd.read_csv("Datasets/car-sales.csv") +print(car_sales_df) +``` + +``` + Make Colour Odometer (KM) Doors Price + 0 Toyota White 150043 4 $4,000.00 + 1 Honda Red 87899 4 $5,000.00 + 2 Toyota Blue 32549 3 $7,000.00 + 3 BMW Black 11179 5 $22,000.00 + 4 Nissan White 213095 4 $3,500.00 + 5 Toyota Green 99213 4 $4,500.00 + 6 Honda Blue 45698 4 $7,500.00 + 7 Honda Blue 54738 4 $7,000.00 + 8 Toyota White 60000 4 $6,250.00 + 9 Nissan White 31600 4 $9,700.00 +``` + +You can find the dataset used above in the `Datasets` folder. + +*Note: If you want to import the data from Github you can't directly use its link, you have to first obtain the raw file URL by clicking on the raw button present in the repo* + +## Exporting Data to a CSV + +`pandas` allows you to export `DataFrame` to `.csv` format using `.to_csv()`, or to a Excel spreadsheet using `.to_excel()`. + +```python +car_sales_df.to_csv("exported_car_sales.csv") +``` + +Running this will save a file called ``exported_car_sales.csv`` to the current folder. From 0db944c7900aa2e72c0df64af1049864dbcb5541 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sat, 25 May 2024 21:01:48 +0530 Subject: [PATCH 37/72] Update index.md --- contrib/pandas/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/pandas/index.md b/contrib/pandas/index.md index a2d826c..bf677cf 100644 --- a/contrib/pandas/index.md +++ b/contrib/pandas/index.md @@ -5,4 +5,4 @@ - [Pandas Descriptive Statistics](Descriptive_Statistics.md) - [Group By Functions with Pandas](GroupBy_Functions_Pandas.md) - [Excel using Pandas DataFrame](excel_with_pandas.md) -- [Importing and Exporting Data in Pandas](Importing_and_Exporting_Data_in_Pandas.md) +- [Importing and Exporting Data in Pandas](import-export.md) From 18b4c52c96d8d74a79816fb96e0b7789f1b3ba11 Mon Sep 17 00:00:00 2001 From: Krishna Kaushik <131583096+kRiShNa-429407@users.noreply.github.com> Date: Sun, 26 May 2024 02:23:42 +0530 Subject: [PATCH 38/72] Add files via upload --- contrib/pandas/Datasets/car-sales-missing-data.csv | 11 +++++++++++ 1 file changed, 11 insertions(+) create mode 100644 contrib/pandas/Datasets/car-sales-missing-data.csv diff --git a/contrib/pandas/Datasets/car-sales-missing-data.csv b/contrib/pandas/Datasets/car-sales-missing-data.csv new file mode 100644 index 0000000..e34cd5f --- /dev/null +++ b/contrib/pandas/Datasets/car-sales-missing-data.csv @@ -0,0 +1,11 @@ +Make,Colour,Odometer,Doors,Price +Toyota,White,150043,4,"$4,000" +Honda,Red,87899,4,"$5,000" +Toyota,Blue,,3,"$7,000" +BMW,Black,11179,5,"$22,000" +Nissan,White,213095,4,"$3,500" +Toyota,Green,,4,"$4,500" +Honda,,,4,"$7,500" +Honda,Blue,,4, +Toyota,White,60000,, +,White,31600,4,"$9,700" \ No newline at end of file From 76fadaa2fd3edf6d17dd96e3d9d1aa13dd88de43 Mon Sep 17 00:00:00 2001 From: Vinay Date: Sun, 26 May 2024 12:09:47 +0530 Subject: [PATCH 39/72] this is the new change --- contrib/Linklist/linklist.md | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 contrib/Linklist/linklist.md diff --git a/contrib/Linklist/linklist.md b/contrib/Linklist/linklist.md new file mode 100644 index 0000000..e69de29 From 05e14ba89825fe857c9b3ec1333e0f288161d83e Mon Sep 17 00:00:00 2001 From: Vinay Date: Sun, 26 May 2024 13:26:15 +0530 Subject: [PATCH 40/72] New change --- contrib/{Linklist/linklist.md => ds-algorithms/Linked-list.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/{Linklist/linklist.md => ds-algorithms/Linked-list.md} (100%) diff --git a/contrib/Linklist/linklist.md b/contrib/ds-algorithms/Linked-list.md similarity index 100% rename from contrib/Linklist/linklist.md rename to contrib/ds-algorithms/Linked-list.md From b2a16691059d9b0cff7b544a779299b1722708b1 Mon Sep 17 00:00:00 2001 From: Vinay Date: Sun, 26 May 2024 14:09:10 +0530 Subject: [PATCH 41/72] this is commit --- contrib/ds-algorithms/Linked-list.md | 181 +++++++++++++++++++++++++++ contrib/ds-algorithms/index.md | 1 + 2 files changed, 182 insertions(+) diff --git a/contrib/ds-algorithms/Linked-list.md b/contrib/ds-algorithms/Linked-list.md index e69de29..d3d7ec1 100644 --- a/contrib/ds-algorithms/Linked-list.md +++ b/contrib/ds-algorithms/Linked-list.md @@ -0,0 +1,181 @@ +# Linked List Data Structure + +Link list is a linear data Structure which can be defined as collection of objects called nodes that are randomly stored in the memory. +A node contains two types of metadata i.e. data stored at that particular address and the pointer which contains the address of the next node in the memory. +The last node of the list contains pointer to the null. + +## Why use linked list over array? + +From the beginning, we are using array data structure to organize the group of elements that are stored individually in the memory. +However, there are some advantage and disadvantage of array which should be known to decide which data structure will used throughout the program. + +limitations + +1. The size of array must be known in advance before using it in the program. +2. Increasing size of the array is a time taking process. It is almost impossible to expand the size of the array at run time. +3. All the elements in the array need to be contiguously stored in the memory. Inserting any element in the array needs shifting of all its predecessors. + +So we introduce a new data structure to overcome these limitations. + +Linked list is used because, +1. It allocates the memory dynamically. All the nodes of linked list are non-contiguously stored in the memory and linked together with the help of pointers. +2. Sizing is no longer a problem since we do not need to define its size at the time of declaration. List grows as per the program's demand and limited to the available memory space. + +Let's code something + +The smallest Unit: Node + +class Node: + def __init__(self, data): + self.data = data # Assigns the given data to the node + self.next = None # Initialize the next attribute to null + +Now, we will see the types of linked list. + +There are mainly four types of linked list, +1. Singly Link list +2. Doubly link list +3. Circular link list +4. Doubly circular link list + + +## 1. Singly linked list. + +Simply think it is a chain of nodes in which each node remember(contains) the addresses of it next node. + +### Creating a linked list class + +class LinkedList: + def __init__(self): + self.head = None # Initialize head as None + +### Inserting a new node at the beginning of a linked list + + def insertAtBeginning(self, new_data): + new_node = Node(new_data) # Create a new node + new_node.next = self.head # Next for new node becomes the current head + self.head = new_node # Head now points to the new node + +### Inserting a new node at the end of a linked list + + def insertAtEnd(self, new_data): + new_node = Node(new_data) # Create a new node + if self.head is None: + self.head = new_node # If the list is empty, make the new node the head + return + last = self.head + while last.next: # Otherwise, traverse the list to find the last node + last = last.next + last.next = new_node # Make the new node the next node of the last node + +### Inserting a new node at the middle of a linked list + + def insertAtPosition(self, data, position): + new_node = Node(data) + if position <= 0: #check if position is valid or not + print("Position should be greater than 0") + return + if position == 1: + new_node.next = self.head + self.head = new_node + return + current_node = self.head + current_position = 1 + while current_node and current_position < position - 1: #Iterating to behind of the postion. + current_node = current_node.next + current_position += 1 + if not current_node: #Check if Position is out of bound or not + print("Position is out of bounds") + return + new_node.next = current_node.next #connect the intermediate node + current_node.next = new_node + +### Printing the Linked list + + def printList(self): + temp = self.head # Start from the head of the list + while temp: + print(temp.data,end=' ') # Print the data in the current node + temp = temp.next # Move to the next node + print() # Ensures the output is followed by a new line + + +Lets complete the code and create a linked list. + +Connect all the code. + +if __name__ == '__main__': + llist = LinkedList() + + # Insert words at the beginning + llist.insertAtBeginning(4) # <4> + llist.insertAtBeginning(3) # <3> 4 + llist.insertAtBeginning(2) # <2> 3 4 + llist.insertAtBeginning(1) # <1> 2 3 4 + + # Insert a word at the end + llist.insertAtEnd(10) # 1 2 3 4 <10> + llist.insertAtEnd(7) # 1 2 3 4 10 <7> + + #Insert at a random position + llist.insertAtPosition(9,4) ## 1 2 3 <9> 4 10 7 + # Print the list + llist.printList() + + + + output: + 1 2 3 9 4 10 7 + + +### Deleting a node from the beginning of a linked list +check the list is empty otherwise shift the head to next node. + +def deleteFromBeginning(self): + if self.head is None: + return "The list is empty" # If the list is empty, return this string + self.head = self.head.next # Otherwise, remove the head by making the next node the new head + +### Deleting a node from the end of a linked list + +def deleteFromEnd(self): + if self.head is None: + return "The list is empty" + if self.head.next is None: + self.head = None # If there's only one node, remove the head by making it None + return + temp = self.head + while temp.next.next: # Otherwise, go to the second-last node + temp = temp.next + temp.next = None # Remove the last node by setting the next pointer of the second-last node to None + + +### Search in a linked list + +def search(self, value): + current = self.head # Start with the head of the list + position = 0 # Counter to keep track of the position + while current: # Traverse the list + if current.data == value: # Compare the list's data to the search value + return f"Value '{value}' found at position {position}" # Print the value if a match is found + current = current.next + position += 1 + return f"Value '{value}' not found in the list" + + + + + + + + + + + + + + + + + + diff --git a/contrib/ds-algorithms/index.md b/contrib/ds-algorithms/index.md index 666bcfd..4b6e20d 100644 --- a/contrib/ds-algorithms/index.md +++ b/contrib/ds-algorithms/index.md @@ -8,3 +8,4 @@ - [Searching Algorithms](searching-algorithms.md) - [Greedy Algorithms](greedy-algorithms.md) - [Dynamic Programming](dynamic-programming.md) +- [Linked list](Linked-list.md) From d2a25f16cca049ed1f1b06cd5d25a677144f4ea4 Mon Sep 17 00:00:00 2001 From: Vinay Sagar <64737008+vinay-sagar123@users.noreply.github.com> Date: Sun, 26 May 2024 14:13:10 +0530 Subject: [PATCH 42/72] Update Linked-list.md new change --- contrib/ds-algorithms/Linked-list.md | 39 ++++++++++++++-------------- 1 file changed, 19 insertions(+), 20 deletions(-) diff --git a/contrib/ds-algorithms/Linked-list.md b/contrib/ds-algorithms/Linked-list.md index d3d7ec1..a660b1c 100644 --- a/contrib/ds-algorithms/Linked-list.md +++ b/contrib/ds-algorithms/Linked-list.md @@ -104,28 +104,27 @@ Lets complete the code and create a linked list. Connect all the code. -if __name__ == '__main__': - llist = LinkedList() - - # Insert words at the beginning - llist.insertAtBeginning(4) # <4> - llist.insertAtBeginning(3) # <3> 4 - llist.insertAtBeginning(2) # <2> 3 4 - llist.insertAtBeginning(1) # <1> 2 3 4 - - # Insert a word at the end - llist.insertAtEnd(10) # 1 2 3 4 <10> - llist.insertAtEnd(7) # 1 2 3 4 10 <7> - - #Insert at a random position - llist.insertAtPosition(9,4) ## 1 2 3 <9> 4 10 7 - # Print the list - llist.printList() + if __name__ == '__main__': + llist = LinkedList() + + # Insert words at the beginning + llist.insertAtBeginning(4) # <4> + llist.insertAtBeginning(3) # <3> 4 + llist.insertAtBeginning(2) # <2> 3 4 + llist.insertAtBeginning(1) # <1> 2 3 4 + + # Insert a word at the end + llist.insertAtEnd(10) # 1 2 3 4 <10> + llist.insertAtEnd(7) # 1 2 3 4 10 <7> + + #Insert at a random position + llist.insertAtPosition(9,4) ## 1 2 3 <9> 4 10 7 + # Print the list + llist.printList() - - output: - 1 2 3 9 4 10 7 +## output: +1 2 3 9 4 10 7 ### Deleting a node from the beginning of a linked list From 4a0576633fc3e06900b2c34e134c0ac5a77de8d1 Mon Sep 17 00:00:00 2001 From: Vinay Sagar <64737008+vinay-sagar123@users.noreply.github.com> Date: Sun, 26 May 2024 14:14:13 +0530 Subject: [PATCH 43/72] Update Linked-list.md change2 --- contrib/ds-algorithms/Linked-list.md | 62 ++++++++++++++-------------- 1 file changed, 31 insertions(+), 31 deletions(-) diff --git a/contrib/ds-algorithms/Linked-list.md b/contrib/ds-algorithms/Linked-list.md index a660b1c..e3abc9a 100644 --- a/contrib/ds-algorithms/Linked-list.md +++ b/contrib/ds-algorithms/Linked-list.md @@ -25,10 +25,10 @@ Let's code something The smallest Unit: Node -class Node: - def __init__(self, data): - self.data = data # Assigns the given data to the node - self.next = None # Initialize the next attribute to null + class Node: + def __init__(self, data): + self.data = data # Assigns the given data to the node + self.next = None # Initialize the next attribute to null Now, we will see the types of linked list. @@ -45,9 +45,9 @@ Simply think it is a chain of nodes in which each node remember(contains) the ad ### Creating a linked list class -class LinkedList: - def __init__(self): - self.head = None # Initialize head as None + class LinkedList: + def __init__(self): + self.head = None # Initialize head as None ### Inserting a new node at the beginning of a linked list @@ -130,37 +130,37 @@ Connect all the code. ### Deleting a node from the beginning of a linked list check the list is empty otherwise shift the head to next node. -def deleteFromBeginning(self): - if self.head is None: - return "The list is empty" # If the list is empty, return this string - self.head = self.head.next # Otherwise, remove the head by making the next node the new head + def deleteFromBeginning(self): + if self.head is None: + return "The list is empty" # If the list is empty, return this string + self.head = self.head.next # Otherwise, remove the head by making the next node the new head ### Deleting a node from the end of a linked list -def deleteFromEnd(self): - if self.head is None: - return "The list is empty" - if self.head.next is None: - self.head = None # If there's only one node, remove the head by making it None - return - temp = self.head - while temp.next.next: # Otherwise, go to the second-last node - temp = temp.next - temp.next = None # Remove the last node by setting the next pointer of the second-last node to None + def deleteFromEnd(self): + if self.head is None: + return "The list is empty" + if self.head.next is None: + self.head = None # If there's only one node, remove the head by making it None + return + temp = self.head + while temp.next.next: # Otherwise, go to the second-last node + temp = temp.next + temp.next = None # Remove the last node by setting the next pointer of the second-last node to None ### Search in a linked list -def search(self, value): - current = self.head # Start with the head of the list - position = 0 # Counter to keep track of the position - while current: # Traverse the list - if current.data == value: # Compare the list's data to the search value - return f"Value '{value}' found at position {position}" # Print the value if a match is found - current = current.next - position += 1 - return f"Value '{value}' not found in the list" - + def search(self, value): + current = self.head # Start with the head of the list + position = 0 # Counter to keep track of the position + while current: # Traverse the list + if current.data == value: # Compare the list's data to the search value + return f"Value '{value}' found at position {position}" # Print the value if a match is found + current = current.next + position += 1 + return f"Value '{value}' not found in the list" + From b2264d4e6fe3dfe738bc321fe3e6edd946fb9f7e Mon Sep 17 00:00:00 2001 From: Vinay Date: Sun, 26 May 2024 14:22:13 +0530 Subject: [PATCH 44/72] COMM3 --- contrib/ds-algorithms/Linked-list.md | 25 ++++++++++++++++++------- 1 file changed, 18 insertions(+), 7 deletions(-) diff --git a/contrib/ds-algorithms/Linked-list.md b/contrib/ds-algorithms/Linked-list.md index e3abc9a..147527b 100644 --- a/contrib/ds-algorithms/Linked-list.md +++ b/contrib/ds-algorithms/Linked-list.md @@ -25,10 +25,12 @@ Let's code something The smallest Unit: Node + ```python class Node: def __init__(self, data): self.data = data # Assigns the given data to the node self.next = None # Initialize the next attribute to null + ``` Now, we will see the types of linked list. @@ -44,20 +46,24 @@ There are mainly four types of linked list, Simply think it is a chain of nodes in which each node remember(contains) the addresses of it next node. ### Creating a linked list class - + ```python class LinkedList: def __init__(self): self.head = None # Initialize head as None + ``` ### Inserting a new node at the beginning of a linked list + ```python def insertAtBeginning(self, new_data): new_node = Node(new_data) # Create a new node new_node.next = self.head # Next for new node becomes the current head self.head = new_node # Head now points to the new node + ``` ### Inserting a new node at the end of a linked list +```python def insertAtEnd(self, new_data): new_node = Node(new_data) # Create a new node if self.head is None: @@ -67,9 +73,10 @@ Simply think it is a chain of nodes in which each node remember(contains) the ad while last.next: # Otherwise, traverse the list to find the last node last = last.next last.next = new_node # Make the new node the next node of the last node - +``` ### Inserting a new node at the middle of a linked list +```python def insertAtPosition(self, data, position): new_node = Node(data) if position <= 0: #check if position is valid or not @@ -89,21 +96,23 @@ Simply think it is a chain of nodes in which each node remember(contains) the ad return new_node.next = current_node.next #connect the intermediate node current_node.next = new_node - +``` ### Printing the Linked list +```python def printList(self): temp = self.head # Start from the head of the list while temp: print(temp.data,end=' ') # Print the data in the current node temp = temp.next # Move to the next node print() # Ensures the output is followed by a new line - +``` Lets complete the code and create a linked list. Connect all the code. +```python if __name__ == '__main__': llist = LinkedList() @@ -121,7 +130,7 @@ Connect all the code. llist.insertAtPosition(9,4) ## 1 2 3 <9> 4 10 7 # Print the list llist.printList() - +``` ## output: 1 2 3 9 4 10 7 @@ -129,14 +138,15 @@ Connect all the code. ### Deleting a node from the beginning of a linked list check the list is empty otherwise shift the head to next node. - +```python def deleteFromBeginning(self): if self.head is None: return "The list is empty" # If the list is empty, return this string self.head = self.head.next # Otherwise, remove the head by making the next node the new head - +``` ### Deleting a node from the end of a linked list +```python def deleteFromEnd(self): if self.head is None: return "The list is empty" @@ -147,6 +157,7 @@ check the list is empty otherwise shift the head to next node. while temp.next.next: # Otherwise, go to the second-last node temp = temp.next temp.next = None # Remove the last node by setting the next pointer of the second-last node to None + ``` ### Search in a linked list From a1dd770b20179f859dc669fdda163bc7c01a96a2 Mon Sep 17 00:00:00 2001 From: Vinay Sagar <64737008+vinay-sagar123@users.noreply.github.com> Date: Sun, 26 May 2024 14:24:19 +0530 Subject: [PATCH 45/72] Update Linked-list.md chnage3 --- contrib/ds-algorithms/Linked-list.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/contrib/ds-algorithms/Linked-list.md b/contrib/ds-algorithms/Linked-list.md index 147527b..541410c 100644 --- a/contrib/ds-algorithms/Linked-list.md +++ b/contrib/ds-algorithms/Linked-list.md @@ -25,12 +25,12 @@ Let's code something The smallest Unit: Node - ```python +```python class Node: def __init__(self, data): self.data = data # Assigns the given data to the node self.next = None # Initialize the next attribute to null - ``` +``` Now, we will see the types of linked list. @@ -46,20 +46,20 @@ There are mainly four types of linked list, Simply think it is a chain of nodes in which each node remember(contains) the addresses of it next node. ### Creating a linked list class - ```python +```python class LinkedList: def __init__(self): self.head = None # Initialize head as None - ``` +``` ### Inserting a new node at the beginning of a linked list - ```python +```python def insertAtBeginning(self, new_data): new_node = Node(new_data) # Create a new node new_node.next = self.head # Next for new node becomes the current head self.head = new_node # Head now points to the new node - ``` +``` ### Inserting a new node at the end of a linked list From beba33d28736209440c9d9cb4784426a9d38ce9b Mon Sep 17 00:00:00 2001 From: Vinay Sagar <64737008+vinay-sagar123@users.noreply.github.com> Date: Sun, 26 May 2024 14:24:51 +0530 Subject: [PATCH 46/72] Update Linked-list.md change4 --- contrib/ds-algorithms/Linked-list.md | 18 ------------------ 1 file changed, 18 deletions(-) diff --git a/contrib/ds-algorithms/Linked-list.md b/contrib/ds-algorithms/Linked-list.md index 541410c..9e51ab5 100644 --- a/contrib/ds-algorithms/Linked-list.md +++ b/contrib/ds-algorithms/Linked-list.md @@ -171,21 +171,3 @@ check the list is empty otherwise shift the head to next node. current = current.next position += 1 return f"Value '{value}' not found in the list" - - - - - - - - - - - - - - - - - - From c3b01f340fc1948f8d6f03d4115d822bb07221f7 Mon Sep 17 00:00:00 2001 From: Vinay Date: Sun, 26 May 2024 14:32:08 +0530 Subject: [PATCH 47/72] commit4 --- contrib/ds-algorithms/Linked-list.md | 47 ++++++++++++++++++++++------ 1 file changed, 38 insertions(+), 9 deletions(-) diff --git a/contrib/ds-algorithms/Linked-list.md b/contrib/ds-algorithms/Linked-list.md index 147527b..194cd92 100644 --- a/contrib/ds-algorithms/Linked-list.md +++ b/contrib/ds-algorithms/Linked-list.md @@ -157,11 +157,11 @@ check the list is empty otherwise shift the head to next node. while temp.next.next: # Otherwise, go to the second-last node temp = temp.next temp.next = None # Remove the last node by setting the next pointer of the second-last node to None - ``` +``` ### Search in a linked list - +```python def search(self, value): current = self.head # Start with the head of the list position = 0 # Counter to keep track of the position @@ -171,14 +171,43 @@ check the list is empty otherwise shift the head to next node. current = current.next position += 1 return f"Value '{value}' not found in the list" +``` + +```python + if __name__ == '__main__': + llist = LinkedList() + + # Insert words at the beginning + llist.insertAtBeginning(4) # <4> + llist.insertAtBeginning(3) # <3> 4 + llist.insertAtBeginning(2) # <2> 3 4 + llist.insertAtBeginning(1) # <1> 2 3 4 - - - - - - - + # Insert a word at the end + llist.insertAtEnd(10) # 1 2 3 4 <10> + llist.insertAtEnd(7) # 1 2 3 4 10 <7> + + #Insert at a random position + llist.insertAtPosition(9,4) # 1 2 3 <9> 4 10 7 + llist.insertAtPositon(56,4) # 1 2 3 <56> 9 4 10 7 + + #delete at the beginning + llist.deleteFromBeginning() # 2 3 56 9 4 10 7 + + #delete at the end + llist.deleteFromEnd() # 2 3 56 9 4 10 + # Print the list + llist.printList() +``` + + + +## Real Life uses of Linked List +Music Player – Songs in the music player are linked to the previous and next songs. So you can play songs either from starting or ending of the list. +GPS navigation systems- Linked lists can be used to store and manage a list of locations and routes, allowing users to easily navigate to their desired destination. +Task Scheduling- Operating systems use linked lists to manage task scheduling, where each process waiting to be executed is represented as a node in the list. +Speech Recognition- Speech recognition software uses linked lists to represent the possible phonetic pronunciations of a word, where each possible pronunciation is represented as a node in the list. +and more.... From c91b657e2c186a509cd0e32a3cf398d30c79ab6b Mon Sep 17 00:00:00 2001 From: Vinay Date: Sun, 26 May 2024 14:36:28 +0530 Subject: [PATCH 48/72] hello --- contrib/ds-algorithms/Linked-list.md | 3 --- 1 file changed, 3 deletions(-) diff --git a/contrib/ds-algorithms/Linked-list.md b/contrib/ds-algorithms/Linked-list.md index 5febd22..0ec285d 100644 --- a/contrib/ds-algorithms/Linked-list.md +++ b/contrib/ds-algorithms/Linked-list.md @@ -171,7 +171,6 @@ check the list is empty otherwise shift the head to next node. current = current.next position += 1 return f"Value '{value}' not found in the list" -<<<<<<< HEAD ``` ```python @@ -219,5 +218,3 @@ and more.... -======= ->>>>>>> beba33d28736209440c9d9cb4784426a9d38ce9b From 297d68fadbc26f0f752c3272c9bc04094882b7fe Mon Sep 17 00:00:00 2001 From: Vinay Date: Sun, 26 May 2024 14:38:31 +0530 Subject: [PATCH 49/72] comm6 --- contrib/ds-algorithms/Linked-list.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/contrib/ds-algorithms/Linked-list.md b/contrib/ds-algorithms/Linked-list.md index 0ec285d..007674f 100644 --- a/contrib/ds-algorithms/Linked-list.md +++ b/contrib/ds-algorithms/Linked-list.md @@ -204,9 +204,13 @@ check the list is empty otherwise shift the head to next node. ## Real Life uses of Linked List Music Player – Songs in the music player are linked to the previous and next songs. So you can play songs either from starting or ending of the list. +
GPS navigation systems- Linked lists can be used to store and manage a list of locations and routes, allowing users to easily navigate to their desired destination. +
Task Scheduling- Operating systems use linked lists to manage task scheduling, where each process waiting to be executed is represented as a node in the list. +
Speech Recognition- Speech recognition software uses linked lists to represent the possible phonetic pronunciations of a word, where each possible pronunciation is represented as a node in the list. +
and more.... From e8a1d62febf2195d49d0207f76cd82591fa24c31 Mon Sep 17 00:00:00 2001 From: Vinay Date: Sun, 26 May 2024 14:40:43 +0530 Subject: [PATCH 50/72] commit7 --- contrib/ds-algorithms/Linked-list.md | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/contrib/ds-algorithms/Linked-list.md b/contrib/ds-algorithms/Linked-list.md index 007674f..95350fd 100644 --- a/contrib/ds-algorithms/Linked-list.md +++ b/contrib/ds-algorithms/Linked-list.md @@ -199,17 +199,19 @@ check the list is empty otherwise shift the head to next node. # Print the list llist.printList() ``` +##Output +2 3 56 9 4 10 ## Real Life uses of Linked List -Music Player – Songs in the music player are linked to the previous and next songs. So you can play songs either from starting or ending of the list. +1. Music Player – Songs in the music player are linked to the previous and next songs. So you can play songs either from starting or ending of the list.
-GPS navigation systems- Linked lists can be used to store and manage a list of locations and routes, allowing users to easily navigate to their desired destination. +2. GPS navigation systems- Linked lists can be used to store and manage a list of locations and routes, allowing users to easily navigate to their desired destination.
-Task Scheduling- Operating systems use linked lists to manage task scheduling, where each process waiting to be executed is represented as a node in the list. +3. Task Scheduling- Operating systems use linked lists to manage task scheduling, where each process waiting to be executed is represented as a node in the list.
-Speech Recognition- Speech recognition software uses linked lists to represent the possible phonetic pronunciations of a word, where each possible pronunciation is represented as a node in the list. +4. Speech Recognition- Speech recognition software uses linked lists to represent the possible phonetic pronunciations of a word, where each possible pronunciation is represented as a node in the list.
and more.... From 7e979edff8636325b197246275db488e13952347 Mon Sep 17 00:00:00 2001 From: Vinay Date: Sun, 26 May 2024 14:41:38 +0530 Subject: [PATCH 51/72] commit8 --- contrib/ds-algorithms/Linked-list.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/contrib/ds-algorithms/Linked-list.md b/contrib/ds-algorithms/Linked-list.md index 95350fd..424a8fb 100644 --- a/contrib/ds-algorithms/Linked-list.md +++ b/contrib/ds-algorithms/Linked-list.md @@ -199,7 +199,8 @@ check the list is empty otherwise shift the head to next node. # Print the list llist.printList() ``` -##Output +## Output: + 2 3 56 9 4 10 From 4c51b5916e64e1e6f64fa2987905e9e98d9a0efd Mon Sep 17 00:00:00 2001 From: Vinay Date: Sun, 26 May 2024 14:42:37 +0530 Subject: [PATCH 52/72] c10 --- contrib/ds-algorithms/Linked-list.md | 4 ---- 1 file changed, 4 deletions(-) diff --git a/contrib/ds-algorithms/Linked-list.md b/contrib/ds-algorithms/Linked-list.md index 424a8fb..1c4511e 100644 --- a/contrib/ds-algorithms/Linked-list.md +++ b/contrib/ds-algorithms/Linked-list.md @@ -207,13 +207,9 @@ check the list is empty otherwise shift the head to next node. ## Real Life uses of Linked List 1. Music Player – Songs in the music player are linked to the previous and next songs. So you can play songs either from starting or ending of the list. -
2. GPS navigation systems- Linked lists can be used to store and manage a list of locations and routes, allowing users to easily navigate to their desired destination. -
3. Task Scheduling- Operating systems use linked lists to manage task scheduling, where each process waiting to be executed is represented as a node in the list. -
4. Speech Recognition- Speech recognition software uses linked lists to represent the possible phonetic pronunciations of a word, where each possible pronunciation is represented as a node in the list. -
and more.... From 45e8763b5a25403f8a3eba79cf0e8a7c0fa46879 Mon Sep 17 00:00:00 2001 From: Vinay Date: Sun, 26 May 2024 14:53:04 +0530 Subject: [PATCH 53/72] com11 --- contrib/ds-algorithms/Linked-list.md | 31 ++++++++++++++++++---------- 1 file changed, 20 insertions(+), 11 deletions(-) diff --git a/contrib/ds-algorithms/Linked-list.md b/contrib/ds-algorithms/Linked-list.md index 1c4511e..11f4016 100644 --- a/contrib/ds-algorithms/Linked-list.md +++ b/contrib/ds-algorithms/Linked-list.md @@ -2,7 +2,8 @@ Link list is a linear data Structure which can be defined as collection of objects called nodes that are randomly stored in the memory. A node contains two types of metadata i.e. data stored at that particular address and the pointer which contains the address of the next node in the memory. -The last node of the list contains pointer to the null. + +The last element in a linked list features a null pointer. ## Why use linked list over array? @@ -11,15 +12,15 @@ However, there are some advantage and disadvantage of array which should be know limitations -1. The size of array must be known in advance before using it in the program. -2. Increasing size of the array is a time taking process. It is almost impossible to expand the size of the array at run time. -3. All the elements in the array need to be contiguously stored in the memory. Inserting any element in the array needs shifting of all its predecessors. +1. Before an array can be utilized in a program, its size must be established in advance. +2. Expanding an array's size is a lengthy process and is almost impossible to achieve during runtime. +3. Array elements must be stored in contiguous memory locations. To insert an element, all subsequent elements must be shifted So we introduce a new data structure to overcome these limitations. Linked list is used because, -1. It allocates the memory dynamically. All the nodes of linked list are non-contiguously stored in the memory and linked together with the help of pointers. -2. Sizing is no longer a problem since we do not need to define its size at the time of declaration. List grows as per the program's demand and limited to the available memory space. +1. Dynamic Memory Management: Linked lists allocate memory dynamically, meaning nodes can be located anywhere in memory and are connected through pointers, rather than being stored contiguously. +2. Adaptive Sizing: There is no need to predefine the size of a linked list. It can expand or contract during runtime, adapting to the program's requirements within the constraints of the available memory. Let's code something @@ -206,11 +207,19 @@ check the list is empty otherwise shift the head to next node. ## Real Life uses of Linked List -1. Music Player – Songs in the music player are linked to the previous and next songs. So you can play songs either from starting or ending of the list. -2. GPS navigation systems- Linked lists can be used to store and manage a list of locations and routes, allowing users to easily navigate to their desired destination. -3. Task Scheduling- Operating systems use linked lists to manage task scheduling, where each process waiting to be executed is represented as a node in the list. -4. Speech Recognition- Speech recognition software uses linked lists to represent the possible phonetic pronunciations of a word, where each possible pronunciation is represented as a node in the list. -and more.... + + +Here are a few practical applications of linked lists in various fields: + +1. **Music Player**: In a music player, songs are often linked to the previous and next tracks. This allows for seamless navigation between songs, enabling you to play tracks either from the beginning or the end of the playlist. This is akin to a doubly linked list where each song node points to both the previous and the next song, enhancing the flexibility of song selection. + +2. **GPS Navigation Systems**: Linked lists can be highly effective for managing lists of locations and routes in GPS navigation systems. Each location or waypoint can be represented as a node, making it easy to add or remove destinations and to navigate smoothly from one location to another. This is similar to how you might plan a road trip, plotting stops along the way in a flexible, dynamic manner. + +3. **Task Scheduling**: Operating systems utilize linked lists to manage task scheduling. Each process waiting to be executed is represented as a node in a linked list. This organization allows the system to efficiently keep track of which processes need to be run, enabling fair and systematic scheduling of tasks. Think of it like a to-do list where each task is a node, and the system executes tasks in a structured order. + +4. **Speech Recognition**: Speech recognition software uses linked lists to represent possible phonetic pronunciations of words. Each potential pronunciation is a node, allowing the software to dynamically explore different pronunciation paths as it processes spoken input. This method helps in accurately recognizing and understanding speech by considering multiple possibilities in a flexible manner, much like evaluating various potential meanings in a conversation. + +These examples illustrate how linked lists provide a flexible, dynamic data structure that can be adapted to a wide range of practical applications, making them a valuable tool in both software development and real-world problem-solving. 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It's designed for high performance and handles thousands of requests per second . +2. **Easy to use 😃:** FastAPI is designed to be intuitive and easy to use, especially for developers familiar with Python. It uses standard Python type hints for request and response validation, making it easy to understand and write code. +3. **Automatic Interactive API Documentation generation 🤩:** FastAPI automatically generates interactive API documentation (Swagger UI or ReDoc) based on your code and type annotations. Swagger UI also allows you to test API endpoints. +4. **Asynchronous Support 🔁:** FastAPI fully supports asynchronous programming, allowing you to write asynchronous code with async/await syntax. This enables handling high-concurrency scenarios and improves overall performance. + +Now, lets get hands-on with FastAPI. + + +## Installation + +Make sure that you have python version 3.7 or greater. + +Then, simply open your command shell and give the following command. + +```bash + pip install fastapi +``` +After this, you need to install uvicorn. uvicorn is an ASGI server on which we will be running our API. + +```bash + pip install uvicorn +``` + + + +## Making First API + +After successful installation we will be moving towards making an API and seeing how to use it. + +Firstly, the first thing in an API is its root/index page which is sent as response when API is called. + +Follow the given steps to make your first FastAPI🫨 + +First, lets import FastAPI to get things started. + +```python +from fastapi import FastAPI +app = FastAPI() +``` +Now, we will write the ``GET`` method for the root of the API. As you have already seen, the GET method is ``HTTP request`` method used to fetch data from a source. In web development, it is primarily used to *retrieve data* from server. + +The root of the app is ``"/"`` When the API will be called, response will be generated by on this url: ```localhost:8000``` + +### GET method +Following is the code to write GET method which will be calling API. + +When the API is called, the ``read_root()`` function will be hit and the JSON response will be returned which will be shown on your web browser. + +```python +@app.get("/") +def read_root(): + return {"Hello": "World"} + +``` + +Tadaaa! you have made your first FastAPI! Now lets run it! + +### Running Server and calling API + +Open your terminal and give following command: +```bash +uvicorn myapi:app --reload +``` +Here, ``myapi`` is the name of your API which is name of your python file. ``app`` is the name you have given to your API in assignment ``app = FastAPI()`` + +After running this command, uvicorn server will be live and you can access your API. + +As right now we have only written root ``GET`` method, only its corresponding response will be displayed. + +On running this API, we get the response in JSON form: + +```json +{ + "Hello": "World" +} +``` +## Path Parameters +Path parameters are a way to send variables to an API endpoint so that an operation may be perfomed on it. + +This feature is particularly useful for defining routes that need to operate on resources identified by unique identifiers, such as user IDs, product IDs, or any other unique value. + +### Example +Lets take an example to make it understandable. + + +Assume that we have some Students 🧑‍🎓 in our class and we have saved their data in form of dictionary in our API (in practical scenarios they will be saved in a database and API will query database). +So we have a student dictionary that looks something like this: + +```python +students = { + 1: { + "name": "John", + "age": 17, + "class": "year 12" + }, + 2: { + "name": "Jane", + "age": 16, + "class": "year 11" + }, + 3: { + "name": "Alice", + "age": 17, + "class": "year 12" + } +} +``` +Here, keys are ``student_id``. + +Let's say user wants the data of the student whose ID is 2. Here, we will take ID as **path parameter** from the user and return the data of that ID. + + +Lets see how it will be done! + +```python +@app.get("/students/{student_id}") +def read_student(student_id: int): + return students[student_id] +``` +Here is the explanatory breakdown of the method: + +- ``/students`` is the URL of students endpoint in API. +- ``{student_id}`` is the path parameter, which is a dynamic variable the user will give to fetch the record of a particular student. +- ``def read_student(student_id: int)`` is the signature of function which takes the student_id we got from path parameter. Its type is defined as ``int`` as our ID will be an integer. +**Note that there will be automatic type checking of the parameter. If it is not same as type defined in method, an Error response ⛔ will be generated.** + +- ``return students[student_id]`` will return the data of required student from dictionary. + +When the user passes the URL ``http://127.0.0.1:8000/students/1`` the data of student with student_id=1 is fetched and displayed. +In this case following output will be displayed: + +```json +{ + "name": "John", + "age": 17, + "class": "year 12" +} +``` + +## Query Parameters +Query parameters in FastAPI allow you to pass data to your API endpoints via the URL's query string. This is useful for filtering, searching, and other operations that do not fit well with the path parameters. + +Query parameters are specified after the ``?`` symbol in the URL and are typically used for optional parameters. + +### Example +Lets continue the example of students to understand the query parameters. + +Assume that we want to search students by name. In this case, we will be sending datat in query parameter which will be read by our method and respective result will be returned. + +Lets see the method: + +```python +@app.get("/get-by-name") +def read_student(name: str): + for student_id in students: + if students[student_id]["name"] == name: + return students[student_id] + return {"Error": "Student not found"} +``` +Here is the explanatory breakdown of this process: + +- ``/get-by-name`` is the URL of the endpoint. After this URL, client will enter the query parameter(s). +- ``http://127.0.0.1:8000/get-by-name?name=Jane`` In this URL, ``name=Jane`` is the query parameter. It means that user needs to search the student whose name is Jane. When you hit this URL, ``read_student(name:str)`` method is called and respective response is returned. + +In this case, the output will be: +```json +{ + "name": "Jane", + "age": 16, + "class": "year 11" +} +``` +If we pass a name that doesn't exist in dictionary, Error response will be returned. + +## POST Method +The ``POST`` method in FastAPI is used to **create resources** or submit data to an API endpoint. This method typically involves sending data in the request body, which the server processes to create or modify resources. + +**⛔ In case of ``GET`` method, sent data is part of URL, but in case of ``POST`` metohod, sent data is part of request body.** + +### Example +Again continuing with the example of student. Now, lets assume we need to add student. Following is the ``POST`` method to do this: + +```python +@app.post("/create-student/{student_id}") +def create_student(student_id: int, student: dict): + if student_id in students: + return {"Error": "Student exists"} + students[student_id] = student + return students +``` +Here is the explanation of process: + +- ``/create-student/{student_id}`` shows that only student_id will be part of URL, rest of the data will be sent in request body. +- Data in the request body will be in JSON format and will be received in ``student: dict`` +- Data sent in JSON format is given as: +```json +{ +"name":"Seerat", +"age":22, +"class":"8 sem" + +} +``` +*Note:* I have used Swagger UI to send data in request body to test my ``POST`` method but you may use any other API tesing tool like Postman etc. + +- This new student will be added in the dictionary, and if operation is successful, new dictionary will be returned as response. + +Following is the output of this ``POST`` method call: + +```json +{ + "1": { + "name": "John", + "age": 17, + "class": "year 12" + }, + "2": { + "name": "Jane", + "age": 16, + "class": "year 11" + }, + "3": { + "name": "Alice", + "age": 17, + "class": "year 12" + }, + "4": { + "name": "Seerat", + "age": 22, + "class": "8 sem" + } +} +``` + +## PUT Method +The ``PUT`` method in FastAPI is used to **update** existing resources or create resources if they do not already exist. It is one of the standard HTTP methods and is idempotent, meaning that multiple identical requests should have the same effect as a single request. + +### Example +Let's update the record of a student. + +```python +@app.put("/update-student/{student_id}") +def update_student(student_id: int, student: dict): + if student_id not in students: + return {"Error": "Student does not exist"} + students[student_id] = student + return students +``` +``PUT`` method is nearly same as ``POST`` method but ``PUT`` is indempotent while ``POST`` is not. + +The given method will update an existing student record and if student doesnt exist, it'll send error response. + +## Additional Content + +### Swagger UI + +Swagger UI automatically generates UI for API tesing. Just write ``/docs`` with the URL and UI mode of Swagger UI will be launched. + +Following Screenshot shows the Swagger UI +![App Screenshot](/assets/image.png) + +Here is how I tested ``POST`` method in UI: +![Screenshot](/assets/image2.png) + +That's all for FastAPI for now.... Happy Learning! \ No newline at end of file diff --git a/contrib/api-development/index.md b/contrib/api-development/index.md index 75c702a..7278907 100644 --- a/contrib/api-development/index.md +++ b/contrib/api-development/index.md @@ -1,3 +1,4 @@ # List of sections - [API Methods](api-methods.md) +- [FastAPI](fast-api.md) \ No newline at end of file From 8dc01e263549680c0cda00b53d49f9d33133a603 Mon Sep 17 00:00:00 2001 From: Krishna Kaushik <131583096+kRiShNa-429407@users.noreply.github.com> Date: Sun, 26 May 2024 21:48:11 +0530 Subject: [PATCH 55/72] Add files via upload --- contrib/pandas/Handling_Missing_Values.md | 273 ++++++++++++++++++++++ 1 file changed, 273 insertions(+) create mode 100644 contrib/pandas/Handling_Missing_Values.md diff --git a/contrib/pandas/Handling_Missing_Values.md b/contrib/pandas/Handling_Missing_Values.md new file mode 100644 index 0000000..7207d81 --- /dev/null +++ b/contrib/pandas/Handling_Missing_Values.md @@ -0,0 +1,273 @@ +# Handling Missing Values in Pandas + +**Upuntil now we're working on complete data i.e not having any missing values. But in real life it is the one of the main problem.** + +*Many datasets arrive with missing data either because it exists and was not collected or it never existed.* + +In Pandas missing data is represented by two values: + +* `None` : None is simply is `keyword` refer as empty or none. +* `NaN` : Acronym for `Not a Number`. + +**There are several useful functions for detecting, removing, and replacing null values in Pandas DataFrame :** + +1. isnull() +2. notnull() +3. dropna() +4. fillna() +5. replace() + +## 2. Checking for missing values using `isnull()` and `notnull()` + +Let's import pandas and our fancy car-sales dataset having some missing values. + + +```python +import pandas as pd +``` + + +```python +car_sales_missing_df = pd.read_csv("https://raw.githubusercontent.com/kRiShNa-429407/learn-python/main/contrib/pandas/Datasets/car-sales-missing-data.csv") +print(car_sales_missing_df) +``` + + Make Colour Odometer Doors Price + 0 Toyota White 150043.0 4.0 $4,000 + 1 Honda Red 87899.0 4.0 $5,000 + 2 Toyota Blue NaN 3.0 $7,000 + 3 BMW Black 11179.0 5.0 $22,000 + 4 Nissan White 213095.0 4.0 $3,500 + 5 Toyota Green NaN 4.0 $4,500 + 6 Honda NaN NaN 4.0 $7,500 + 7 Honda Blue NaN 4.0 NaN + 8 Toyota White 60000.0 NaN NaN + 9 NaN White 31600.0 4.0 $9,700 + + + +```python +## Using isnull() + +print(car_sales_missing_df.isnull()) +``` + + Make Colour Odometer Doors Price + 0 False False False False False + 1 False False False False False + 2 False False True False False + 3 False False False False False + 4 False False False False False + 5 False False True False False + 6 False True True False False + 7 False False True False True + 8 False False False True True + 9 True False False False False + + +Note here: +* `True` means for `NaN` values +* `False` means for no `Nan` values + +If we want to find the number of missing values in each column use `isnull().sum()`. + + +```python +print(car_sales_missing_df.isnull().sum()) +``` + + Make 1 + Colour 1 + Odometer 4 + Doors 1 + Price 2 + dtype: int64 + + +You can also check presense of null values in a single column. + + +```python +print(car_sales_missing_df["Odometer"].isnull()) +``` + + 0 False + 1 False + 2 True + 3 False + 4 False + 5 True + 6 True + 7 True + 8 False + 9 False + Name: Odometer, dtype: bool + + + +```python +## using notnull() + +print(car_sales_missing_df.notnull()) +``` + + Make Colour Odometer Doors Price + 0 True True True True True + 1 True True True True True + 2 True True False True True + 3 True True True True True + 4 True True True True True + 5 True True False True True + 6 True False False True True + 7 True True False True False + 8 True True True False False + 9 False True True True True + + +Note here: +* `True` means no `NaN` values +* `False` means for `NaN` values + +#### A little note here : `isnull()` means having null values so it gives boolean `True` for NaN values. And `notnull()` means having no null values so it gives `True` for no NaN value. + +## 2. Filling missing values using `fillna()`, `replace()`. + + +```python +## Filling missing values with a single value using `fillna` +print(car_sales_missing_df.fillna(0)) +``` + + Make Colour Odometer Doors Price + 0 Toyota White 150043.0 4.0 $4,000 + 1 Honda Red 87899.0 4.0 $5,000 + 2 Toyota Blue 0.0 3.0 $7,000 + 3 BMW Black 11179.0 5.0 $22,000 + 4 Nissan White 213095.0 4.0 $3,500 + 5 Toyota Green 0.0 4.0 $4,500 + 6 Honda 0 0.0 4.0 $7,500 + 7 Honda Blue 0.0 4.0 0 + 8 Toyota White 60000.0 0.0 0 + 9 0 White 31600.0 4.0 $9,700 + + + +```python +## Filling missing values with the previous value using `ffill()` +print(car_sales_missing_df.ffill()) +``` + + Make Colour Odometer Doors Price + 0 Toyota White 150043.0 4.0 $4,000 + 1 Honda Red 87899.0 4.0 $5,000 + 2 Toyota Blue 87899.0 3.0 $7,000 + 3 BMW Black 11179.0 5.0 $22,000 + 4 Nissan White 213095.0 4.0 $3,500 + 5 Toyota Green 213095.0 4.0 $4,500 + 6 Honda Green 213095.0 4.0 $7,500 + 7 Honda Blue 213095.0 4.0 $7,500 + 8 Toyota White 60000.0 4.0 $7,500 + 9 Toyota White 31600.0 4.0 $9,700 + + + +```python +## illing null value with the next ones using 'bfill()' +print(car_sales_missing_df.bfill()) +``` + + Make Colour Odometer Doors Price + 0 Toyota White 150043.0 4.0 $4,000 + 1 Honda Red 87899.0 4.0 $5,000 + 2 Toyota Blue 11179.0 3.0 $7,000 + 3 BMW Black 11179.0 5.0 $22,000 + 4 Nissan White 213095.0 4.0 $3,500 + 5 Toyota Green 60000.0 4.0 $4,500 + 6 Honda Blue 60000.0 4.0 $7,500 + 7 Honda Blue 60000.0 4.0 $9,700 + 8 Toyota White 60000.0 4.0 $9,700 + 9 NaN White 31600.0 4.0 $9,700 + + +#### Filling a null values using `replace()` method + +**Now we are going to replace the all Nan value in the data frame with -125 value** + +*For this we will need numpy also* + + +```python +import numpy as np +``` + + +```python +print(car_sales_missing_df.replace(to_replace = np.nan, value = -125) ) +``` + + Make Colour Odometer Doors Price + 0 Toyota White 150043.0 4.0 $4,000 + 1 Honda Red 87899.0 4.0 $5,000 + 2 Toyota Blue -125.0 3.0 $7,000 + 3 BMW Black 11179.0 5.0 $22,000 + 4 Nissan White 213095.0 4.0 $3,500 + 5 Toyota Green -125.0 4.0 $4,500 + 6 Honda -125 -125.0 4.0 $7,500 + 7 Honda Blue -125.0 4.0 -125 + 8 Toyota White 60000.0 -125.0 -125 + 9 -125 White 31600.0 4.0 $9,700 + + +## 3. Dropping missing values using `dropna()` + +**In order to drop a null values from a dataframe, we used `dropna()` function this function drop Rows/Columns of datasets with Null values in different ways.** + +#### Dropping rows with at least 1 null value. + + +```python +print(car_sales_missing_df.dropna(axis = 0)) ##Now we drop rows with at least one Nan value (Null value) +``` + + Make Colour Odometer Doors Price + 0 Toyota White 150043.0 4.0 $4,000 + 1 Honda Red 87899.0 4.0 $5,000 + 3 BMW Black 11179.0 5.0 $22,000 + 4 Nissan White 213095.0 4.0 $3,500 + + +#### Dropping rows if all values in that row are missing. + + +```python +print(car_sales_missing_df.dropna(how = 'all',axis = 0)) ## If not have leave the row as it is +``` + + Make Colour Odometer Doors Price + 0 Toyota White 150043.0 4.0 $4,000 + 1 Honda Red 87899.0 4.0 $5,000 + 2 Toyota Blue NaN 3.0 $7,000 + 3 BMW Black 11179.0 5.0 $22,000 + 4 Nissan White 213095.0 4.0 $3,500 + 5 Toyota Green NaN 4.0 $4,500 + 6 Honda NaN NaN 4.0 $7,500 + 7 Honda Blue NaN 4.0 NaN + 8 Toyota White 60000.0 NaN NaN + 9 NaN White 31600.0 4.0 $9,700 + + +#### Dropping columns with at least 1 null value + + +```python +print(car_sales_missing_df.dropna(axis = 1)) +``` + + Empty DataFrame + Columns: [] + Index: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] + + +Now we drop a columns which have at least 1 missing values. + +**Here the dataset becomes empty after dropna() because each column as atleast 1 null value so it remove that columns resulting in an empty dataframe.** From eeef1dc21c19e1275c92fbbb6923b81d0a0635d0 Mon Sep 17 00:00:00 2001 From: Krishna Kaushik <131583096+kRiShNa-429407@users.noreply.github.com> Date: Sun, 26 May 2024 21:50:20 +0530 Subject: [PATCH 56/72] Update index.md --- contrib/pandas/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/pandas/index.md b/contrib/pandas/index.md index bf677cf..894be52 100644 --- a/contrib/pandas/index.md +++ b/contrib/pandas/index.md @@ -6,3 +6,4 @@ - [Group By Functions with Pandas](GroupBy_Functions_Pandas.md) - [Excel using Pandas DataFrame](excel_with_pandas.md) - [Importing and Exporting Data in Pandas](import-export.md) +- [Handling Missing Values in Pandas](Handling_Missing_Values.md) From be7781b1e35a22cfcbc853bf8cb2c31304308e15 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Mon, 27 May 2024 08:16:52 +0530 Subject: [PATCH 57/72] Rename Handling_Missing_Values.md to handling-missing-values.md --- .../{Handling_Missing_Values.md => handling-missing-values.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/pandas/{Handling_Missing_Values.md => handling-missing-values.md} (100%) diff --git a/contrib/pandas/Handling_Missing_Values.md b/contrib/pandas/handling-missing-values.md similarity index 100% rename from contrib/pandas/Handling_Missing_Values.md rename to contrib/pandas/handling-missing-values.md From ad6fd6b6233140c0e128cdd424409247921d1b6f Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Mon, 27 May 2024 08:17:16 +0530 Subject: [PATCH 58/72] Update index.md --- contrib/pandas/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/pandas/index.md b/contrib/pandas/index.md index 894be52..c71c324 100644 --- a/contrib/pandas/index.md +++ b/contrib/pandas/index.md @@ -6,4 +6,4 @@ - [Group By Functions with Pandas](GroupBy_Functions_Pandas.md) - [Excel using Pandas DataFrame](excel_with_pandas.md) - [Importing and Exporting Data in Pandas](import-export.md) -- [Handling Missing Values in Pandas](Handling_Missing_Values.md) +- [Handling Missing Values in Pandas](handling-missing-values.md) From 5d15c73a87290afdf5cf8928f92e4f315b6e4c74 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Mon, 27 May 2024 08:17:31 +0530 Subject: [PATCH 59/72] Update car-sales-missing-data.csv --- contrib/pandas/Datasets/car-sales-missing-data.csv | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/pandas/Datasets/car-sales-missing-data.csv b/contrib/pandas/Datasets/car-sales-missing-data.csv index e34cd5f..21a3157 100644 --- a/contrib/pandas/Datasets/car-sales-missing-data.csv +++ b/contrib/pandas/Datasets/car-sales-missing-data.csv @@ -8,4 +8,4 @@ Toyota,Green,,4,"$4,500" Honda,,,4,"$7,500" Honda,Blue,,4, Toyota,White,60000,, -,White,31600,4,"$9,700" \ No newline at end of file +,White,31600,4,"$9,700" From 8c95bb1de779e455c53c52bd22622b8a533d63c4 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Mon, 27 May 2024 08:21:10 +0530 Subject: [PATCH 60/72] Update handling-missing-values.md --- contrib/pandas/handling-missing-values.md | 37 +++++++++-------------- 1 file changed, 14 insertions(+), 23 deletions(-) diff --git a/contrib/pandas/handling-missing-values.md b/contrib/pandas/handling-missing-values.md index 7207d81..da6c377 100644 --- a/contrib/pandas/handling-missing-values.md +++ b/contrib/pandas/handling-missing-values.md @@ -1,34 +1,28 @@ # Handling Missing Values in Pandas -**Upuntil now we're working on complete data i.e not having any missing values. But in real life it is the one of the main problem.** - -*Many datasets arrive with missing data either because it exists and was not collected or it never existed.* +In real life, many datasets arrive with missing data either because it exists and was not collected or it never existed. In Pandas missing data is represented by two values: * `None` : None is simply is `keyword` refer as empty or none. * `NaN` : Acronym for `Not a Number`. -**There are several useful functions for detecting, removing, and replacing null values in Pandas DataFrame :** +There are several useful functions for detecting, removing, and replacing null values in Pandas DataFrame: -1. isnull() -2. notnull() -3. dropna() -4. fillna() -5. replace() +1. `isnull()` +2. `notnull()` +3. `dropna()` +4. `fillna()` +5. `replace()` ## 2. Checking for missing values using `isnull()` and `notnull()` Let's import pandas and our fancy car-sales dataset having some missing values. - ```python import pandas as pd -``` - -```python -car_sales_missing_df = pd.read_csv("https://raw.githubusercontent.com/kRiShNa-429407/learn-python/main/contrib/pandas/Datasets/car-sales-missing-data.csv") +car_sales_missing_df = pd.read_csv("Datasets/car-sales-missing-data.csv") print(car_sales_missing_df) ``` @@ -128,7 +122,7 @@ Note here: * `True` means no `NaN` values * `False` means for `NaN` values -#### A little note here : `isnull()` means having null values so it gives boolean `True` for NaN values. And `notnull()` means having no null values so it gives `True` for no NaN value. +`isnull()` means having null values so it gives boolean `True` for NaN values. And `notnull()` means having no null values so it gives `True` for no NaN value. ## 2. Filling missing values using `fillna()`, `replace()`. @@ -191,18 +185,15 @@ print(car_sales_missing_df.bfill()) #### Filling a null values using `replace()` method -**Now we are going to replace the all Nan value in the data frame with -125 value** +Now we are going to replace the all `NaN` value in the data frame with -125 value -*For this we will need numpy also* +For this we will also need numpy ```python import numpy as np -``` - -```python -print(car_sales_missing_df.replace(to_replace = np.nan, value = -125) ) +print(car_sales_missing_df.replace(to_replace = np.nan, value = -125)) ``` Make Colour Odometer Doors Price @@ -220,7 +211,7 @@ print(car_sales_missing_df.replace(to_replace = np.nan, value = -125) ) ## 3. Dropping missing values using `dropna()` -**In order to drop a null values from a dataframe, we used `dropna()` function this function drop Rows/Columns of datasets with Null values in different ways.** +In order to drop a null values from a dataframe, we used `dropna()` function this function drop Rows/Columns of datasets with Null values in different ways. #### Dropping rows with at least 1 null value. @@ -270,4 +261,4 @@ print(car_sales_missing_df.dropna(axis = 1)) Now we drop a columns which have at least 1 missing values. -**Here the dataset becomes empty after dropna() because each column as atleast 1 null value so it remove that columns resulting in an empty dataframe.** +Here the dataset becomes empty after `dropna()` because each column as atleast 1 null value so it remove that columns resulting in an empty dataframe. From 02081e65791893aa1075b0d2a12a1b731e40a5d6 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Mon, 27 May 2024 08:23:06 +0530 Subject: [PATCH 61/72] Update fast-api.md --- contrib/api-development/fast-api.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/contrib/api-development/fast-api.md b/contrib/api-development/fast-api.md index 52966d6..b67c56e 100644 --- a/contrib/api-development/fast-api.md +++ b/contrib/api-development/fast-api.md @@ -281,9 +281,9 @@ The given method will update an existing student record and if student doesnt ex Swagger UI automatically generates UI for API tesing. Just write ``/docs`` with the URL and UI mode of Swagger UI will be launched. Following Screenshot shows the Swagger UI -![App Screenshot](/assets/image.png) +![App Screenshot](assets/image.png) Here is how I tested ``POST`` method in UI: -![Screenshot](/assets/image2.png) +![Screenshot](assets/image2.png) -That's all for FastAPI for now.... Happy Learning! \ No newline at end of file +That's all for FastAPI for now.... Happy Learning! From 1c6166e03c00415a836f784659b9016f91695dc3 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Mon, 27 May 2024 08:38:32 +0530 Subject: [PATCH 62/72] Rename Linked-list.md to linked-list.md --- contrib/ds-algorithms/{Linked-list.md => linked-list.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/ds-algorithms/{Linked-list.md => linked-list.md} (100%) diff --git a/contrib/ds-algorithms/Linked-list.md b/contrib/ds-algorithms/linked-list.md similarity index 100% rename from contrib/ds-algorithms/Linked-list.md rename to contrib/ds-algorithms/linked-list.md From fd0c10df6bb1e5c3d11e42861db95dc212aa9777 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Mon, 27 May 2024 08:39:03 +0530 Subject: [PATCH 63/72] Update linked-list.md --- contrib/ds-algorithms/linked-list.md | 10 ---------- 1 file changed, 10 deletions(-) diff --git a/contrib/ds-algorithms/linked-list.md b/contrib/ds-algorithms/linked-list.md index 11f4016..ddbc6d5 100644 --- a/contrib/ds-algorithms/linked-list.md +++ b/contrib/ds-algorithms/linked-list.md @@ -220,13 +220,3 @@ Here are a few practical applications of linked lists in various fields: 4. **Speech Recognition**: Speech recognition software uses linked lists to represent possible phonetic pronunciations of words. Each potential pronunciation is a node, allowing the software to dynamically explore different pronunciation paths as it processes spoken input. This method helps in accurately recognizing and understanding speech by considering multiple possibilities in a flexible manner, much like evaluating various potential meanings in a conversation. These examples illustrate how linked lists provide a flexible, dynamic data structure that can be adapted to a wide range of practical applications, making them a valuable tool in both software development and real-world problem-solving. - - - - - - - - - - From 76df5a6a9fb679418444caed6e233c456f19991d Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Mon, 27 May 2024 08:39:22 +0530 Subject: [PATCH 64/72] Update index.md --- contrib/ds-algorithms/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/ds-algorithms/index.md b/contrib/ds-algorithms/index.md index 4b6e20d..c61ca0b 100644 --- a/contrib/ds-algorithms/index.md +++ b/contrib/ds-algorithms/index.md @@ -8,4 +8,4 @@ - [Searching Algorithms](searching-algorithms.md) - [Greedy Algorithms](greedy-algorithms.md) - [Dynamic Programming](dynamic-programming.md) -- [Linked list](Linked-list.md) +- [Linked list](linked-list.md) From 8f0a0865d944d30d121fb4c14122cd8c431e4bf9 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Mon, 27 May 2024 08:40:56 +0530 Subject: [PATCH 65/72] Update index.md --- contrib/machine-learning/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index edb0860..ce42538 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -7,4 +7,4 @@ - [Support Vector Machine Algorithm](support-vector-machine.md) - [Artificial Neural Network from the Ground Up](ArtificialNeuralNetwork.md) - [TensorFlow.md](tensorFlow.md) -- [PyTorch.md](PyTorch.md) +- [PyTorch.md](pytorch.md) From d9129ccde313a1b3ef56d102da5abb5ba09da076 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Mon, 27 May 2024 08:41:13 +0530 Subject: [PATCH 66/72] Rename PyTorch.md to pytorch.md --- contrib/machine-learning/{PyTorch.md => pytorch.md} | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename contrib/machine-learning/{PyTorch.md => pytorch.md} (99%) diff --git a/contrib/machine-learning/PyTorch.md b/contrib/machine-learning/pytorch.md similarity index 99% rename from contrib/machine-learning/PyTorch.md rename to contrib/machine-learning/pytorch.md index 7de853b..ccfee21 100644 --- a/contrib/machine-learning/PyTorch.md +++ b/contrib/machine-learning/pytorch.md @@ -110,4 +110,4 @@ PyTorch is a powerful and flexible deep learning framework that caters to both r - [PyTorch Tutorials](https://pytorch.org/tutorials/) - [PyTorch Forum](https://discuss.pytorch.org/) -Feel free to explore and experiment with PyTorch to harness the full potential of this versatile framework! \ No newline at end of file +Feel free to explore and experiment with PyTorch to harness the full potential of this versatile framework! From 8761dcec7a3b333b8562724b77c740a25e7b4012 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Mon, 27 May 2024 08:49:39 +0530 Subject: [PATCH 67/72] Rename introduction-to-flask-a-python-web-framework.md to flask.md --- ...introduction-to-flask-a-python-web-framework.md => flask.md} | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename contrib/web-scrapping/{introduction-to-flask-a-python-web-framework.md => flask.md} (99%) diff --git a/contrib/web-scrapping/introduction-to-flask-a-python-web-framework.md b/contrib/web-scrapping/flask.md similarity index 99% rename from contrib/web-scrapping/introduction-to-flask-a-python-web-framework.md rename to contrib/web-scrapping/flask.md index 957f39b..fbd1829 100644 --- a/contrib/web-scrapping/introduction-to-flask-a-python-web-framework.md +++ b/contrib/web-scrapping/flask.md @@ -437,4 +437,4 @@ Flask is a powerful and flexible framework for building web applications in Pyth - Flask Documentation: https://flask.palletsprojects.com/en/latest/ - Jinja2 Documentation: https://jinja.palletsprojects.com/en/latest/ - SQLAlchemy Documentation: https://docs.sqlalchemy.org/en/latest/ -- Render Documentation: https://render.com/docs \ No newline at end of file +- Render Documentation: https://render.com/docs From 2acbfeab38ba07eccb00c4e3281f9ff60a8f53f9 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Mon, 27 May 2024 08:50:06 +0530 Subject: [PATCH 68/72] Update flask.md --- contrib/web-scrapping/flask.md | 4 ---- 1 file changed, 4 deletions(-) diff --git a/contrib/web-scrapping/flask.md b/contrib/web-scrapping/flask.md index fbd1829..d65da02 100644 --- a/contrib/web-scrapping/flask.md +++ b/contrib/web-scrapping/flask.md @@ -1,7 +1,3 @@ -Sure, here's the guide without Markdown formatting: - ---- - # Introduction to Flask: A Python Web Framework ## Table of Contents From b0e24b769f157a3776512305ac10ffab0d3f8ae7 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Mon, 27 May 2024 08:50:47 +0530 Subject: [PATCH 69/72] Update index.md --- contrib/web-scrapping/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/web-scrapping/index.md b/contrib/web-scrapping/index.md index a78be3d..276014e 100644 --- a/contrib/web-scrapping/index.md +++ b/contrib/web-scrapping/index.md @@ -1,4 +1,4 @@ # List of sections - [Section title](filename.md) -- [Introduction-to-flask-a-python-web-framework](introduction-to-flask-a-python-web-framework.md) +- [Introduction to Flask](flask.md) From dcf0065e1216c106657115354c0d90c5b242c079 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Mon, 27 May 2024 09:04:27 +0530 Subject: [PATCH 70/72] Update sorting-algorithms.md --- contrib/ds-algorithms/sorting-algorithms.md | 69 +++++++++------------ 1 file changed, 29 insertions(+), 40 deletions(-) diff --git a/contrib/ds-algorithms/sorting-algorithms.md b/contrib/ds-algorithms/sorting-algorithms.md index 6423b4b..09eccd7 100644 --- a/contrib/ds-algorithms/sorting-algorithms.md +++ b/contrib/ds-algorithms/sorting-algorithms.md @@ -2,14 +2,15 @@ In computer science, a sorting algorithm takes a collection of items and arranges them in a specific order. This order is usually determined by comparing the items using a defined rule. -## Real Life Example of Sorting +Real Life Example of Sorting + - Sorting a deck of cards - Sorting names in alphabetical order - Sorting a list of items, etc. -# Some common sorting techniques +Some common sorting techniques: -# 1. Bubble Sort +## 1. Bubble Sort Bubble sort is a basic sorting technique that iteratively steps through a list, comparing neighboring elements. If elements are out of order, it swaps them. While easy to understand, bubble sort becomes inefficient for large datasets due to its slow execution time. @@ -21,7 +22,7 @@ Bubble sort is a basic sorting technique that iteratively steps through a list, - **Repeating Until Sorted:** The algorithm continues making passes through the list until no more swaps are needed. This indicates the entire list is sorted. -## Bubble Sort Code in Python +### Bubble Sort Code in Python ```python def bubble_sort(arr): @@ -35,7 +36,8 @@ arr = [5, 3, 8, 1, 2] bubble_sort(arr) print("Sorted array:", arr) # Output: [1, 2, 3, 5, 8] ``` -## Example with Visualization + +### Example with Visualization Let's sort the list `[5, 3, 8, 1, 2]` using bubble sort. @@ -53,17 +55,13 @@ Let's sort the list `[5, 3, 8, 1, 2]` using bubble sort. - Comparing neighbors: `[1, 2, 3, 5, 8]` - No swapping needed, the list is already sorted. -## Complexity Analysis +### Complexity Analysis - **Worst Case:** `O(n^2)` comparisons and swaps. This happens when the list is in reverse order, and we need to make maximum swaps. - **Best Case:** `O(n)` comparisons. This occurs when the list is already sorted, but we still need O(n^2) swaps because of the nested loops. - **Average Case:** `O(n^2)` comparisons and swaps. This is the expected number of comparisons and swaps over all possible input sequences. -
-

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- -# 2. Selection Sort +## 2. Selection Sort Selection sort is a simple sorting algorithm that divides the input list into two parts: a sorted sublist and an unsorted sublist. The algorithm repeatedly finds the smallest (or largest, depending on sorting order) element from the unsorted sublist and moves it to the sorted sublist. It's not efficient for large datasets but performs better than bubble sort due to fewer swaps. @@ -73,7 +71,7 @@ Selection sort is a simple sorting algorithm that divides the input list into tw - **Expanding the Sorted Sublist:** As elements are moved to the sorted sublist, it expands until all elements are sorted. - **Repeating Until Sorted:** The process continues until the entire list is sorted. -## Example with Visualization +### Example with Visualization Let's sort the list `[5, 3, 8, 1, 2]` using selection sort. @@ -97,7 +95,7 @@ Let's sort the list `[5, 3, 8, 1, 2]` using selection sort. - Find the minimum: `5` - No swapping needed, the list is already sorted. -## Selection Sort Code in Python +### Selection Sort Code in Python ```python def selection_sort(arr): @@ -114,15 +112,13 @@ selection_sort(arr) print("Sorted array:", arr) # Output: [1, 2, 3, 5, 8] ``` -## Complexity Analysis +### Complexity Analysis + - **Worst Case**: `O(n^2)` comparisons and O(n) swaps. This occurs when the list is in reverse order, and we need to make maximum comparisons and swaps. - **Best Case**: `O(n^2)` comparisons and O(n) swaps. This happens when the list is in sorted order, but the algorithm still needs to iterate through all elements for comparisons. - **Average Case**: `O(n^2)` comparisons and O(n) swaps. This is the expected number of comparisons and swaps over all possible input sequences. -
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-# 3. Quick Sort +## 3. Quick Sort Quick sort is a popular divide-and-conquer sorting algorithm known for its efficiency on average. It works by selecting a 'pivot' element from the array and partitioning the other elements into two sub-arrays according to whether they are less than or greater than the pivot. The sub-arrays are then recursively sorted. **Algorithm Overview:** @@ -131,29 +127,24 @@ Quick sort is a popular divide-and-conquer sorting algorithm known for its effic - **Recursion:** Apply the above steps recursively to the sub-arrays formed by partitioning until the base case is reached. The base case is usually when the size of the sub-array becomes 0 or 1, indicating it is already sorted. - **Base Case:** If the sub-array size becomes 0 or 1, it is already sorted. -## Example with Visualization +### Example with Visualization Let's sort the list `[5, 3, 8, 1, 2]` using quick sort. 1. **Initial Array:** `[5, 3, 8, 1, 2]` - 2. **Choose Pivot:** Let's choose the last element, `2`, as the pivot. - 3. **Partitioning:** - We'll partition the array around the pivot `2`. All elements less than `2` will be placed to its left, and all elements greater than `2` will be placed to its right. - - After partitioning, the array becomes `[1, 2, 5, 3, 8]`. The pivot element, `2`, is now in its correct sorted position. - 4. **Recursion:** - Now, we recursively sort the sub-arrays `[1]` and `[5, 3, 8]`. - For the sub-array `[5, 3, 8]`, we choose `8` as the pivot and partition it. - After partitioning, the sub-array becomes `[3, 5, 8]`. The pivot element, `8`, is now in its correct sorted position. - - 5. **Concatenation:** - Concatenating the sorted sub-arrays `[1]`, `[2]`, `[3, 5, 8]`, we get the final sorted array `[1, 2, 3, 5, 8]`. -## Quick Sort Code in Python (Iterative) +### Quick Sort Code in Python (Iterative) + ```python def partition(arr, low, high): pivot = arr[high] @@ -180,7 +171,8 @@ quick_sort_iterative(arr) print("Sorted array:", arr) # Output: [3, 9, 10, 27, 38, 43, 82] ``` -## Quick Sort Code in Python (Recursive) + +### Quick Sort Code in Python (Recursive) ```python def quick_sort(arr): @@ -196,17 +188,15 @@ arr = [5, 3, 8, 1, 2] sorted_arr = quick_sort(arr) print("Sorted array:", sorted_arr) # Output: [1, 2, 3, 5, 8] ``` -## Complexity Analysis + +### Complexity Analysis - **Worst Case**: The worst-case time complexity of quick sort is `O(n^2)`. This occurs when the pivot selection consistently results in unbalanced partitioning, such as choosing the smallest or largest element as the pivot. -**Best Case**: The best-case time complexity is `O(n log n)`. This happens when the pivot selection leads to well-balanced partitioning, halving the array size in each recursive call. - **Average Case**: The average-case time complexity is `O(n log n)`. This is the expected time complexity when the pivot selection results in reasonably balanced partitioning across recursive calls. - **Space Complexity**: Quick sort has an `O(log n)` space complexity for the recursion stack, as it recursively sorts sub-arrays. -
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-# 4. Merge Sort +## 4. Merge Sort Merge sort is a divide-and-conquer algorithm that recursively divides the input list into smaller sublists until each sublist contains only one element. Then, it repeatedly merges adjacent sublists while maintaining the sorted order until there is only one sublist remaining, which represents the sorted list. @@ -214,7 +204,7 @@ Merge sort is a divide-and-conquer algorithm that recursively divides the input - **Divide:** Split the input list into smaller sublists recursively until each sublist contains only one element. - **Merge:** Repeatedly merge adjacent sublists while maintaining the sorted order until there is only one sublist remaining, which represents the sorted list. -## Example with Visualization +### Example with Visualization Let's sort the list `[38, 27, 43, 3, 9, 82, 10]` using merge sort. @@ -233,7 +223,7 @@ Let's sort the list `[38, 27, 43, 3, 9, 82, 10]` using merge sort. 3. **Final Sorted List:** - `[3, 9, 10, 27, 38, 43, 82]` -## Merge Sort Code in Python (Iterative) +### Merge Sort Code in Python (Iterative) ```python def merge_sort_iterative(arr): @@ -280,7 +270,9 @@ arr = [38, 27, 43, 3, 9, 82, 10] merge_sort_iterative(arr) print("Sorted array:", arr) # Output: [3, 9, 10, 27, 38, 43, 82] ``` -## Merge Sort Code in Python (Recursive) + +### Merge Sort Code in Python (Recursive) + ```python def merge_sort(arr): if len(arr) > 1: @@ -320,10 +312,7 @@ arr = [38, 27, 43, 3, 9, 82, 10] merge_sort(arr) print("Sorted array:", arr) # Output: [3, 9, 10, 27, 38, 43, 82] ``` -## Complexity Analysis + +### Complexity Analysis - **Time Complexity**: `O(n log n)` for all cases. Merge sort always divides the list into halves until each sublist contains only one element, and then merges them back together, resulting in O(n log n) time complexity. - **Space Complexity**: `O(n)` auxiliary space. In the iterative version, merge sort uses additional space for creating temporary sublists during merging operations. - -
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From 9d35a7fd1827eebce44a08660ed7f9e5cc4496b4 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Mon, 27 May 2024 09:36:18 +0530 Subject: [PATCH 71/72] Rename sorting_numpy_array.md to sorting-array.md --- contrib/numpy/{sorting_numpy_array.md => sorting-array.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/numpy/{sorting_numpy_array.md => sorting-array.md} (100%) diff --git a/contrib/numpy/sorting_numpy_array.md b/contrib/numpy/sorting-array.md similarity index 100% rename from contrib/numpy/sorting_numpy_array.md rename to contrib/numpy/sorting-array.md From 8c756d0534fda5ba190575b64df40290058eb239 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Mon, 27 May 2024 09:36:37 +0530 Subject: [PATCH 72/72] Update index.md --- contrib/numpy/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/numpy/index.md b/contrib/numpy/index.md index 4b4a2c8..b2d459a 100644 --- a/contrib/numpy/index.md +++ b/contrib/numpy/index.md @@ -7,4 +7,4 @@ - [Operations on Arrays in NumPy](operations-on-arrays.md) - [Loading Arrays from Files](loading_arrays_from_files.md) - [Saving Numpy Arrays into FIles](saving_numpy_arrays_to_files.md) -- [Sorting NumPy Arrays](sorting_numpy_array.md) +- [Sorting NumPy Arrays](sorting-array.md)