From cb5fcb4953a9b0a181479fc4b434c64123280497 Mon Sep 17 00:00:00 2001 From: somyasaxena01 <140182178+somyasaxena01@users.noreply.github.com> Date: Thu, 30 May 2024 10:56:55 +0530 Subject: [PATCH 01/76] Added stacks.md --- contrib/ds-algorithms/stacks.md | 131 ++++++++++++++++++++++++++++++++ 1 file changed, 131 insertions(+) create mode 100644 contrib/ds-algorithms/stacks.md diff --git a/contrib/ds-algorithms/stacks.md b/contrib/ds-algorithms/stacks.md new file mode 100644 index 0000000..f9f662b --- /dev/null +++ b/contrib/ds-algorithms/stacks.md @@ -0,0 +1,131 @@ +# STACKS IN PYTHON +In Data Structures and Algorithms, a stack is a linear data structure that complies with the Last In, First Out (LIFO) rule. It works by use of two fundamental techniques: *PUSH* which inserts an element on top of the stack and *POP* which takes out the topmost element.This concept is similar to a stack of plates in a cafeteria. Stacks are usually used for handling function calls, expression evaluation, and parsing in programming. Indeed, they are efficient in managing memory as well as tracking program state. + +**POINTS TO BE REMEMBERED :-** +- A stack is a collection of data items that can be accessed at only one end, called *TOP*. +- Items can be inserted and deleted in a stack only at the *TOP*. +- The last item inserted in a stack is the first one to be deleted. +- Therefore, a stack is called a **Last-In-First-Out (LIFO)** data structure. + +## REAL LIFE EXAMPLES OF STACKS + +**PILE OF BOOKS** - Suppose a set of books are placed one over the other in a pile. When you remove books from the pile, the topmost book will be removed first. Similarly, when you have to add a book to the pile, the book will be placed at the top of the file. + +**PILE OF PLATES** - The first plate begins the pile. The second plate is placed on the top of the first plate and the third plate is placed on the top of the second plate, and so on. In general, if you want to add a plate to the pile, you can keep it on the top of the pile. Similarly, if you want to remove a plate, you can remove the plate from the top of the pile. + +**BANGLES IN A HAND** - When a person wears bangles, the last bangle worn is the first one to be removed. + +## APPLICATIONS OF STACKS + +Stacks are widely used in Computer Science: +- *Function call* management +- Maintaining the *UNDO* list for the application +- Web browser *history management* +- Evaluating expressions +- Checking the nesting of parentheses in an expression +- *Backtracking* algorithms (Recursion) + +Understanding these applications is essential for Software Development. + +## OPERATIONS ON A STACK + +Key operations on a stack include: +- **PUSH** - It is the process of inserting a new element on the top of a stack. +- **OVERFLOW** - A situation when we are pushing an item in a stack that is full. +- **POP** - It is the process of deleting an element from the top of a stack. +- **UNDERFLOW** - A situation when we are popping item from an empty stack. +- **PEEK** - It is the process of getting the most recent value of stack *(i.e. the value at the top of the stack)* +- **ISEMPTY** - It is the function which return true if stack is empty else false. +- **SHOW** -Displaying stack items. + +## IMPLEMENTING STACKS IN PYTHON + +```python +def isEmpty(S): + + if len(S) == 0: + return True + + else: + + return False + +def Push(S, item): + S.append(item) + +def Pop(S): + + if isEmpty(S): + return "Underflow" + + else: + val = S.pop() + return val + +def Peek(S): + + if isEmpty(S): + return "Underflow" + + else: + top = len(S) - 1 + return S[top] + +def Show(S): + + if isEmpty(S): + print("Sorry, No items in Stack") + + else: + print("(Top)", end=' ') + t = len(S) - 1 + while t >= 0: + print(S[t], "<", end=' ') + t -= 1 + print() +``` + +This code defines a stack data structure along with functions to manipulate it. To provide output, we would need to use these functions to interact with the stack. + +Here's an example: +```python +stack = [] + +Push(stack, 5) +Push(stack, 10) +Push(stack, 15) + +print("Stack after Push operations:") +Show(stack) + +print("Peek operation:", Peek(stack)) + +print("Pop operation:", Pop(stack)) + +print("Stack after Pop operation:") +Show(stack) +``` + +This would output: + +``` +Stack after Push operations: + +(Top) 15 < 10 < 5 < + +Peek operation: 15 + +Pop operation: 15 + +Stack after Pop operation: + +(Top) 10 < 5 < +``` + +## Complexity Analysis + +- **Worst case**: `O(n)` This occurs when the stack is full, it is dominated by the usage of Show operation. +- **Best case**: `O(1)` When the operations like isEmpty, Push, Pop and Peek are used, they have a constant time complexity of O(1). +- **Average case**: `O(n)` The average complexity is likely to be lower than O(n), as the stack is not always full. + + From 79017403f82fe8cd5e1737ff573eab8506a7fffa Mon Sep 17 00:00:00 2001 From: somyasaxena01 <140182178+somyasaxena01@users.noreply.github.com> Date: Thu, 30 May 2024 10:58:36 +0530 Subject: [PATCH 02/76] Update index.md --- contrib/ds-algorithms/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/ds-algorithms/index.md b/contrib/ds-algorithms/index.md index c61ca0b..783f982 100644 --- a/contrib/ds-algorithms/index.md +++ b/contrib/ds-algorithms/index.md @@ -9,3 +9,4 @@ - [Greedy Algorithms](greedy-algorithms.md) - [Dynamic Programming](dynamic-programming.md) - [Linked list](linked-list.md) +- [Stacks in Python](stacks.md) From 4888094a059aad26f1eba062b4765b70b9b19ee8 Mon Sep 17 00:00:00 2001 From: saiumasankar Date: Fri, 31 May 2024 09:26:56 +0530 Subject: [PATCH 03/76] Create Trie.md --- contrib/ds-algorithms/Trie.md | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 contrib/ds-algorithms/Trie.md diff --git a/contrib/ds-algorithms/Trie.md b/contrib/ds-algorithms/Trie.md new file mode 100644 index 0000000..e69de29 From 97f852c99616cab741e8c06469f239bd34b74dd6 Mon Sep 17 00:00:00 2001 From: rohit Date: Fri, 31 May 2024 10:26:09 +0530 Subject: [PATCH 04/76] Added sklearn.md file --- contrib/machine-learning/index.md | 1 + contrib/machine-learning/sklearn.md | 144 ++++++++++++++++++++++++++++ 2 files changed, 145 insertions(+) create mode 100644 contrib/machine-learning/sklearn.md diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index 46100df..68bd24d 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -10,3 +10,4 @@ - [PyTorch.md](pytorch.md) - [Types of optimizers](Types_of_optimizers.md) - [Logistic Regression](logistic-regression.md) +- [sklearn.md](sklearn.md) diff --git a/contrib/machine-learning/sklearn.md b/contrib/machine-learning/sklearn.md new file mode 100644 index 0000000..8174d93 --- /dev/null +++ b/contrib/machine-learning/sklearn.md @@ -0,0 +1,144 @@ +# scikit-learn (sklearn) Python Library + +## Overview + +scikit-learn, also known as sklearn, is a popular open-source Python library that provides simple and efficient tools for data mining and data analysis. It is built on NumPy, SciPy, and matplotlib. The library is designed to interoperate with the Python numerical and scientific libraries. + +## Key Features + +- **Classification**: Identifying which category an object belongs to. Example algorithms include SVM, nearest neighbors, random forest. +- **Regression**: Predicting a continuous-valued attribute associated with an object. Example algorithms include support vector regression (SVR), ridge regression, Lasso. +- **Clustering**: Automatic grouping of similar objects into sets. Example algorithms include k-means, spectral clustering, mean-shift. +- **Dimensionality Reduction**: Reducing the number of random variables to consider. Example algorithms include PCA, feature selection, non-negative matrix factorization. +- **Model Selection**: Comparing, validating, and choosing parameters and models. Example methods include grid search, cross-validation, metrics. +- **Preprocessing**: Feature extraction and normalization. + +## When to Use scikit-learn + +- **Use scikit-learn if**: + - You are working on machine learning tasks such as classification, regression, clustering, dimensionality reduction, model selection, and preprocessing. + - You need an easy-to-use, well-documented library. + - You require tools that are compatible with NumPy and SciPy. + +- **Do not use scikit-learn if**: + - You need to perform deep learning tasks. In such cases, consider using TensorFlow or PyTorch. + - You need out-of-the-box support for large-scale data. scikit-learn is designed to work with in-memory data, so for very large datasets, you might want to consider libraries like Dask-ML. + +## Installation + +You can install scikit-learn using pip: + +```bash +pip install scikit-learn +``` + +Or via conda: + +```bash +conda install scikit-learn +``` + +## Basic Usage with Code Snippets + +### Importing the Library + +```python +import numpy as np +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler +from sklearn.linear_model import LogisticRegression +from sklearn.metrics import accuracy_score +``` + +### Loading Data + +For illustration, let's create a simple synthetic dataset: + +```python +from sklearn.datasets import make_classification + +X, y = make_classification(n_samples=1000, n_features=20, n_classes=2, random_state=42) +``` + +### Splitting Data + +Split the dataset into training and testing sets: + +```python +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) +``` + +### Preprocessing + +Standardizing the features: + +```python +scaler = StandardScaler() +X_train = scaler.fit_transform(X_train) +X_test = scaler.transform(X_test) +``` + +### Training a Model + +Train a Logistic Regression model: + +```python +model = LogisticRegression() +model.fit(X_train, y_train) +``` + +### Making Predictions + +Make predictions on the test set: + +```python +y_pred = model.predict(X_test) +``` + +### Evaluating the Model + +Evaluate the accuracy of the model: + +```python +accuracy = accuracy_score(y_test, y_pred) +print(f"Accuracy: {accuracy * 100:.2f}%") +``` + +### Putting it All Together + +Here is a complete example from data loading to model evaluation: + +```python +import numpy as np +from sklearn.datasets import make_classification +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler +from sklearn.linear_model import LogisticRegression +from sklearn.metrics import accuracy_score + +# Load data +X, y = make_classification(n_samples=1000, n_features=20, n_classes=2, random_state=42) + +# Split data +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) + +# Preprocess data +scaler = StandardScaler() +X_train = scaler.fit_transform(X_train) +X_test = scaler.transform(X_test) + +# Train model +model = LogisticRegression() +model.fit(X_train, y_train) + +# Make predictions +y_pred = model.predict(X_test) + +# Evaluate model +accuracy = accuracy_score(y_test, y_pred) +print(f"Accuracy: {accuracy * 100:.2f}%") +``` + +## Conclusion + +scikit-learn is a powerful and versatile library that can be used for a wide range of machine learning tasks. It is particularly well-suited for beginners due to its easy-to-use interface and extensive documentation. Whether you are working on a simple classification task or a more complex clustering problem, scikit-learn provides the tools you need to build and evaluate your models effectively. \ No newline at end of file From b72cf4ad5a778264701ce634df9d7a6c6f5c5d49 Mon Sep 17 00:00:00 2001 From: saiumasankar Date: Fri, 31 May 2024 11:12:37 +0530 Subject: [PATCH 05/76] adding Trie Data structure --- contrib/ds-algorithms/Trie.md | 152 ++++++++++++++++++++++++++++++++++ 1 file changed, 152 insertions(+) diff --git a/contrib/ds-algorithms/Trie.md b/contrib/ds-algorithms/Trie.md index e69de29..43f0415 100644 --- a/contrib/ds-algorithms/Trie.md +++ b/contrib/ds-algorithms/Trie.md @@ -0,0 +1,152 @@ +# Trie + +A Trie is a tree-like data structure used for storing a dynamic set of strings where the keys are usually strings. It is also known as prefix tree or digital tree. + +>Trie is a type of search tree, where each node represents a single character of a string. + +>Nodes are linked in such a way that they form a tree, where each path from the root to a leaf node represents a unique string stored in the Trie. + +## Characteristics of Trie +- **Prefix Matching**: Tries are particularly useful for prefix matching operations. Any node in the Trie represents a common prefix of all strings below it. +- **Space Efficiency**: Tries can be more space-efficient than other data structures like hash tables for storing large sets of strings with common prefixes. +- **Time Complexity**: Insertion, deletion, and search operations in a Trie have a time complexity of +đť‘‚(đť‘š), where m is the length of the string. This makes Tries very efficient for these operations. + +## Structure of Trie + +Trie mainly consists of three parts: +- **Root**: The root of a Trie is an empty node that does not contain any character. +- **Edges**: Each edge in the Trie represents a character in the alphabet of the stored strings. +- **Nodes**: Each node contains a character and possibly additional information, such as a boolean flag indicating if the node represents the end of a valid string. + +To implement the nodes of trie. We use Classes in Python. Each node is an object of the Node Class. + +Node Class have mainly two components +- *Array of size 26*: It is used to represent the 26 alphabets. Initially all are None. While inserting the words, then array will be filled with object of child nodes. +- *End of word*: It is used to represent the end of word while inserting. + +Code Block of Node Class : + +``` +class Node: + def __init__(self): + self.alphabets = [None] * 26 + self.end_of_word = 0 +``` + +Now we need to implement Trie. We create another class named Trie with some methods like Insertion, Searching and Deletion. + +**Initialization:** In this, we initializes the Trie with a `root` node. + +Code Implementation of Initialization: + +``` +class Trie: + def __init__(self): + self.root = Node() +``` + +## Operations on Trie + +1. **Insertion**: Inserts the word into the Trie. This method takes `word` as parameter. For each character in the word, it checks if there is a corresponding child node. If not, it creates a new `Node`. After processing all the characters in word, it increments the `end_of_word` value of the last node. + +Code Implementation of Insertion: +``` +def insert(self, word): + node = self.root + for char in word: + index = ord(char) - ord('a') + if not node.alphabets[index]: + node.alphabets[index] = Node() + node = node.alphabets[index] + node.end_of_word += 1 +``` + +2. **Searching**: Search the `word` in trie. Searching process starts from the `root` node. Each character of the `word` is processed. After traversing the whole word in trie, it return the count of words. + +There are two cases in Searching: +- *Word Not found*: It happens when the word we search not present in the trie. This case will occur, if the value of `alphabets` array at that character is `None` or if the value of `end_of_word` of the node, reached after traversing the whole word is `0`. +- *Word found*: It happens when the search word is present in the Trie. This case will occur, when the `end_of_word` value is greater than `0` of the node after traversing the whole word. + +Code Implementation of Searching: +``` + def Search(self, word): + node = self.root + for char in word: + index = ord(char) - ord('a') + if not node.alphabets[index]: + return 0 + node = node.alphabets[index] + return node.end_of_word +``` + +3. **Deletion**: To delete a string, follow the path of the string. If the end node is reached and `end_of_word` is greater than `0` then decrement the value. + +Code Implementation of Deletion: + +``` +def delete(self, word): + node = self.root + for char in word: + index = ord(char) - ord('a') + node = node.alphabets[index] + if node.end_of_word: + node.end_of_word-=1 +``` + +Python Code to implement Trie: + +``` +class Node: + def __init__(self): + self.alphabets = [None] * 26 + self.end_of_word = 0 + +class Trie: + def __init__(self): + self.root = Node() + + def insert(self, word): + node = self.root + for char in word: + index = ord(char) - ord('a') + if not node.alphabets[index]: + node.alphabets[index] = Node() + node = node.alphabets[index] + node.end_of_word += 1 + + def Search(self, word): + node = self.root + for char in word: + index = ord(char) - ord('a') + if not node.alphabets[index]: + return 0 + node = node.alphabets[index] + return node.end_of_word + + def delete(self, word): + node = self.root + for char in word: + index = ord(char) - ord('a') + node = node.alphabets[index] + if node.end_of_word: + node.end_of_word-=1 + +if __name__ == "__main__": + trie = Trie() + + word1 = "apple" + word2 = "app" + word3 = "bat" + + trie.insert(word1) + trie.insert(word2) + trie.insert(word3) + + print(trie.Search(word1)) + print(trie.Search(word2)) + print(trie.Search(word3)) + + trie.delete(word2) + print(trie.Search(word2)) +``` From 0a6d9cd1bfedd86cd85a0414e92f43014e9e8c19 Mon Sep 17 00:00:00 2001 From: saiumasankar Date: Fri, 31 May 2024 11:25:29 +0530 Subject: [PATCH 06/76] update index.md --- contrib/ds-algorithms/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/ds-algorithms/index.md b/contrib/ds-algorithms/index.md index 31cff39..653cc98 100644 --- a/contrib/ds-algorithms/index.md +++ b/contrib/ds-algorithms/index.md @@ -10,3 +10,4 @@ - [Greedy Algorithms](greedy-algorithms.md) - [Dynamic Programming](dynamic-programming.md) - [Linked list](linked-list.md) +- [Trie](Trie.md) From d00f146401afba7514f183291d40a62de92866ba Mon Sep 17 00:00:00 2001 From: Yogesh Vishwakarma Date: Fri, 31 May 2024 11:34:39 +0530 Subject: [PATCH 07/76] created Sliding_Window.md and updated index.md --- contrib/ds-algorithms/Sliding_Window.md | 249 ++++++++++++++++++++++++ contrib/ds-algorithms/index.md | 1 + 2 files changed, 250 insertions(+) create mode 100644 contrib/ds-algorithms/Sliding_Window.md diff --git a/contrib/ds-algorithms/Sliding_Window.md b/contrib/ds-algorithms/Sliding_Window.md new file mode 100644 index 0000000..72aa191 --- /dev/null +++ b/contrib/ds-algorithms/Sliding_Window.md @@ -0,0 +1,249 @@ +# Sliding Window Technique + +The sliding window technique is a fundamental approach used to solve problems involving arrays, lists, or sequences. It's particularly useful when you need to calculate something over a subarray or sublist of fixed size that slides over the entire array. + +In easy words, It is the transformation of the nested loops into the single loop +## Concept + +The sliding window technique involves creating a window (a subarray or sublist) that moves or "slides" across the entire array. This window can either be fixed in size or dynamically resized. By maintaining and updating this window as it moves, you can optimize certain computations, reducing time complexity. + +## Types of Sliding Windows + +1. **Fixed Size Window**: The window size remains constant as it slides from the start to the end of the array. +2. **Variable Size Window**: The window size can change based on certain conditions, such as the sum of elements within the window meeting a specified target. + +## Steps to Implement a Sliding Window + +1. **Initialize the Window**: Set the initial position of the window and any required variables (like sum, count, etc.). +2. **Expand the Window**: Add the next element to the window and update the relevant variables. +3. **Shrink the Window**: If needed, remove elements from the start of the window and update the variables. +4. **Slide the Window**: Move the window one position to the right by including the next element and possibly excluding the first element. +5. **Repeat**: Continue expanding, shrinking, and sliding the window until you reach the end of the array. + +## Example Problems + +### 1. Maximum Sum Subarray of Fixed Size K + +Given an array of integers and an integer k, find the maximum sum of a subarray of size k. + +**Steps:** + +1. Initialize the sum of the first k elements. +2. Slide the window from the start of the array to the end, updating the sum by subtracting the element that is left behind and adding the new element. +3. Track the maximum sum encountered. + +**Python Code:** + +```python +def max_sum_subarray(arr, k): + n = len(arr) + if n < k: + return None + + # Compute the sum of the first window + window_sum = sum(arr[:k]) + max_sum = window_sum + + # Slide the window from start to end + for i in range(n - k): + window_sum = window_sum - arr[i] + arr[i + k] + max_sum = max(max_sum, window_sum) + + return max_sum + +# Example usage: +arr = [1, 3, 2, 5, 1, 1, 6, 2, 8, 5] +k = 3 +print(max_sum_subarray(arr, k)) # Output: 16 +``` + +### 2. Longest Substring Without Repeating Characters + +Given a string, find the length of the longest substring without repeating characters. + +**Steps:** + +1. Use two pointers to represent the current window. +2. Use a set to track characters in the current window. +3. Expand the window by moving the right pointer. +4. If a duplicate character is found, shrink the window by moving the left pointer until the duplicate is removed. + +**Python Code:** + +```python +def longest_unique_substring(s): + n = len(s) + char_set = set() + left = 0 + max_length = 0 + + for right in range(n): + while s[right] in char_set: + char_set.remove(s[left]) + left += 1 + char_set.add(s[right]) + max_length = max(max_length, right - left + 1) + + return max_length + +# Example usage: +s = "abcabcbb" +print(longest_unique_substring(s)) # Output: 3 +``` +## 3. Minimum Size Subarray Sum + +Given an array of positive integers and a positive integer `s`, find the minimal length of a contiguous subarray of which the sum is at least `s`. If there isn't one, return 0 instead. + +### Steps: +1. Use two pointers, `left` and `right`, to define the current window. +2. Expand the window by moving `right` and adding `arr[right]` to `current_sum`. +3. If `current_sum` is greater than or equal to `s`, update `min_length` and shrink the window from the left by moving `left` and subtracting `arr[left]` from `current_sum`. +4. Repeat until `right` has traversed the array. + +### Python Code: +```python +def min_subarray_len(s, arr): + n = len(arr) + left = 0 + current_sum = 0 + min_length = float('inf') + + for right in range(n): + current_sum += arr[right] + + while current_sum >= s: + min_length = min(min_length, right - left + 1) + current_sum -= arr[left] + left += 1 + + return min_length if min_length != float('inf') else 0 + +# Example usage: +arr = [2, 3, 1, 2, 4, 3] +s = 7 +print(min_subarray_len(s, arr)) # Output: 2 (subarray [4, 3]) +``` + +## 4. Longest Substring with At Most K Distinct Characters + +Given a string `s` and an integer `k`, find the length of the longest substring that contains at most `k` distinct characters. + +### Steps: +1. Use two pointers, `left` and `right`, to define the current window. +2. Use a dictionary `char_count` to count characters in the window. +3. Expand the window by moving `right` and updating `char_count`. +4. If `char_count` has more than `k` distinct characters, shrink the window from the left by moving `left` and updating `char_count`. +5. Keep track of the maximum length of the window with at most `k` distinct characters. + +### Python Code: +```python +def longest_substring_k_distinct(s, k): + n = len(s) + char_count = {} + left = 0 + max_length = 0 + + for right in range(n): + char_count[s[right]] = char_count.get(s[right], 0) + 1 + + while len(char_count) > k: + char_count[s[left]] -= 1 + if char_count[s[left]] == 0: + del char_count[s[left]] + left += 1 + + max_length = max(max_length, right - left + 1) + + return max_length + +# Example usage: +s = "eceba" +k = 2 +print(longest_substring_k_distinct(s, k)) # Output: 3 (substring "ece") +``` + +## 5. Maximum Number of Vowels in a Substring of Given Length + +Given a string `s` and an integer `k`, return the maximum number of vowel letters in any substring of `s` with length `k`. + +### Steps: +1. Use a sliding window of size `k`. +2. Keep track of the number of vowels in the current window. +3. Expand the window by adding the next character and update the count if it's a vowel. +4. If the window size exceeds `k`, remove the leftmost character and update the count if it's a vowel. +5. Track the maximum number of vowels found in any window of size `k`. + +### Python Code: +```python +def max_vowels(s, k): + vowels = set('aeiou') + max_vowel_count = 0 + current_vowel_count = 0 + + for i in range(len(s)): + if s[i] in vowels: + current_vowel_count += 1 + if i >= k: + if s[i - k] in vowels: + current_vowel_count -= 1 + max_vowel_count = max(max_vowel_count, current_vowel_count) + + return max_vowel_count + +# Example usage: +s = "abciiidef" +k = 3 +print(max_vowels(s, k)) # Output: 3 (substring "iii") +``` + +## 6. Subarray Product Less Than K + +Given an array of positive integers `nums` and an integer `k`, return the number of contiguous subarrays where the product of all the elements in the subarray is less than `k`. + +### Steps: +1. Use two pointers, `left` and `right`, to define the current window. +2. Expand the window by moving `right` and multiplying `product` by `nums[right]`. +3. If `product` is greater than or equal to `k`, shrink the window from the left by moving `left` and dividing `product` by `nums[left]`. +4. For each position of `right`, the number of valid subarray ending at `right` is `right - left + 1`. +5. Sum these counts to get the total number of subarray with product less than `k`. + +### Python Code: +```python +def num_subarray_product_less_than_k(nums, k): + if k <= 1: + return 0 + + product = 1 + left = 0 + count = 0 + + for right in range(len(nums)): + product *= nums[right] + + while product >= k: + product /= nums[left] + left += 1 + + count += right - left + 1 + + return count + +# Example usage: +nums = [10, 5, 2, 6] +k = 100 +print(num_subarray_product_less_than_k(nums, k)) # Output: 8 +``` + +## Advantages + +- **Efficiency**: Reduces the time complexity from O(n^2) to O(n) for many problems. +- **Simplicity**: Provides a straightforward way to manage subarrays/substrings with overlapping elements. + +## Applications + +- Finding the maximum or minimum sum of subarrays of fixed size. +- Detecting unique elements in a sequence. +- Solving problems related to dynamic programming with fixed constraints. +- Efficiently managing and processing streaming data or real-time analytics. + +By using the sliding window technique, you can tackle a wide range of problems in a more efficient manner. diff --git a/contrib/ds-algorithms/index.md b/contrib/ds-algorithms/index.md index 31cff39..d3c3c27 100644 --- a/contrib/ds-algorithms/index.md +++ b/contrib/ds-algorithms/index.md @@ -10,3 +10,4 @@ - [Greedy Algorithms](greedy-algorithms.md) - [Dynamic Programming](dynamic-programming.md) - [Linked list](linked-list.md) +- [Sliding Window Technique](Sliding_Window.md) From 56e972133ff33ca0532c6d0637fb465425b41c09 Mon Sep 17 00:00:00 2001 From: manishh12 Date: Fri, 31 May 2024 12:02:09 +0530 Subject: [PATCH 08/76] added types of cost functions issue#625 --- .../Types_of_Cost_Functions.md | 227 ++++++++++++++++++ contrib/machine-learning/index.md | 1 + 2 files changed, 228 insertions(+) create mode 100644 contrib/machine-learning/Types_of_Cost_Functions.md diff --git a/contrib/machine-learning/Types_of_Cost_Functions.md b/contrib/machine-learning/Types_of_Cost_Functions.md new file mode 100644 index 0000000..547a05e --- /dev/null +++ b/contrib/machine-learning/Types_of_Cost_Functions.md @@ -0,0 +1,227 @@ + +# Cost Functions in Machine Learning + +Cost functions, also known as loss functions, play a crucial role in training machine learning models. They measure how well the model performs on the training data by quantifying the difference between predicted and actual values. Different types of cost functions are used depending on the problem domain and the nature of the data. + +## Types of Cost Functions + +### 1. Mean Squared Error (MSE) + +**Explanation:** +MSE is one of the most commonly used cost functions, particularly in regression problems. It calculates the average squared difference between the predicted and actual values. + +**Mathematical Formulation:** +The MSE is defined as: +$$ MSE = \frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2 $$ +Where: +- \( n \) is the number of samples. +- \( y_i \) is the actual value. +- \( y^i\) is the predicted value. + +**Advantages:** +- Sensitive to large errors due to squaring. +- Differentiable and convex, facilitating optimization. + +**Disadvantages:** +- Sensitive to outliers, as the squared term amplifies their impact. + +**Python Implementation:** +```python +import numpy as np + +def mean_squared_error(y_true, y_pred): + n = len(y_true) + return np.mean((y_true - y_pred) ** 2) +``` + +### 2. Mean Absolute Error (MAE) + +**Explanation:** +MAE is another commonly used cost function for regression tasks. It measures the average absolute difference between predicted and actual values. + +**Mathematical Formulation:** +The MAE is defined as: +$$ MAE = \frac{1}{n} \sum_{i=1}^{n} |y_i - \hat{y}_i| $$ +Where: +- \( n \) is the number of samples. +- \( y_i \) is the actual value. +- \( y^i\) is the predicted value. + +**Advantages:** +- Less sensitive to outliers compared to MSE. +- Provides a linear error term, which can be easier to interpret. + + +**Disadvantages:** +- Not differentiable at zero, which can complicate optimization. + +**Python Implementation:** +```python +import numpy as np + +def mean_absolute_error(y_true, y_pred): + n = len(y_true) + return np.mean(np.abs(y_true - y_pred)) +``` + +### 3. Cross-Entropy Loss (Binary) + +**Explanation:** +Cross-entropy loss is commonly used in binary classification problems. It measures the dissimilarity between the true and predicted probability distributions. + +**Mathematical Formulation:** +For binary classification, the cross-entropy loss is defined as: +$$ \text{Cross-Entropy} = -\frac{1}{n} \sum_{i=1}^{n} [y_i \log(\hat{y}_i) + (1 - y_i) \log(1 - \hat{y}_i)] $$ +Where: +- \( n \) is the number of samples. +- \( y_i \) is the actual class label (0 or 1). +- \( y^i\) is the predicted probability of the positive class. + + +**Advantages:** +- Penalizes confident wrong predictions heavily. +- Suitable for probabilistic outputs. + +**Disadvantages:** +- Sensitive to class imbalance. + +**Python Implementation:** +```python +import numpy as np + +def binary_cross_entropy(y_true, y_pred): + n = len(y_true) + return -np.mean(y_true * np.log(y_pred) + (1 - y_true) * np.log(1 - y_pred)) +``` + +### 4. Cross-Entropy Loss (Multiclass) + +**Explanation:** +For multiclass classification problems, the cross-entropy loss is adapted to handle multiple classes. + +**Mathematical Formulation:** +The multiclass cross-entropy loss is defined as: +$$ \text{Cross-Entropy} = -\frac{1}{n} \sum_{i=1}^{n} \sum_{c=1}^{C} y_{i,c} \log(\hat{y}_{i,c}) $$ +Where: +- \( n \) is the number of samples. +- \( C \) is the number of classes. +- \( y_{i,c} \) is the indicator function for the true class of sample \( i \). + +- (y^i,c) is the predicted probability of sample \( i \) belonging to class \( c \). + +**Advantages:** +- Handles multiple classes effectively. +- Encourages the model to assign high probabilities to the correct classes. + +**Disadvantages:** +- Requires one-hot encoding for class labels, which can increase computational complexity. + +**Python Implementation:** +```python +import numpy as np + +def categorical_cross_entropy(y_true, y_pred): + n = len(y_true) + return -np.mean(np.sum(y_true * np.log(y_pred), axis=1)) +``` + +### 5. Hinge Loss (SVM) + +**Explanation:** +Hinge loss is commonly used in support vector machines (SVMs) for binary classification tasks. It penalizes misclassifications by a linear margin. + +**Mathematical Formulation:** +For binary classification, the hinge loss is defined as: +$$ \text{Hinge Loss} = \frac{1}{n} \sum_{i=1}^{n} \max(0, 1 - y_i \cdot \hat{y}_i) $$ +Where: +- \( n \) is the number of samples. +- \( y_i \) is the actual class label (-1 or 1). +- \( \hat{y}_i \) is the predicted score for sample \( i \). + +**Advantages:** +- Encourages margin maximization in SVMs. +- Robust to outliers due to the linear penalty. + +**Disadvantages:** +- Not differentiable at the margin, which can complicate optimization. + +**Python Implementation:** +```python +import numpy as np + +def hinge_loss(y_true, y_pred): + n = len(y_true) + loss = np.maximum(0, 1 - y_true * y_pred) + return np.mean(loss) +``` + +### 6. Huber Loss + +**Explanation:** +Huber loss is a combination of MSE and MAE, providing a compromise between the two. It is less sensitive to outliers than MSE and provides a smooth transition to MAE for large errors. + +**Mathematical Formulation:** + +The Huber loss is defined as: + + +$$ +\text{Huber Loss} = \frac{1}{n} \sum_{i=1}^{n} \left\{ +\begin{array}{ll} +\frac{1}{2} (y_i - \hat{y}_i)^2 & \text{if } |y_i - \hat{y}_i| \leq \delta \\ +\delta(|y_i - \hat{y}_i| - \frac{1}{2} \delta) & \text{otherwise} +\end{array} +\right. +$$ +Where: +- \( n \) is the number of samples. +- \( \delta \) is a threshold parameter. + +**Advantages:** +- Provides a smooth loss function. +- Less sensitive to outliers than MSE. + +**Disadvantages:** +- Requires tuning of the threshold parameter. + +**Python Implementation:** +```python +import numpy as np + +def huber_loss(y_true, y_pred, delta): + error = y_true - y_pred + loss = np.where(np.abs(error) <= delta, 0.5 * error ** 2, delta * (np.abs(error) - 0.5 * delta)) + return np.mean(loss) +``` + +### 7. Log-Cosh Loss + +**Explanation:** +Log-Cosh loss is a smooth approximation of the MAE and is less sensitive to outliers than MSE. It provides a smooth transition from quadratic for small errors to linear for large errors. + +**Mathematical Formulation:** +The Log-Cosh loss is defined as: +$$ \text{Log-Cosh Loss} = \frac{1}{n} \sum_{i=1}^{n} \log(\cosh(y_i - \hat{y}_i)) $$ +Where: +- \( n \) is the number of samples. + +**Advantages:** +- Smooth and differentiable everywhere. +- Less sensitive to outliers. + +**Disadvantages:** +- Computationally more expensive than simple losses like MSE. + +**Python Implementation:** +```python +import numpy as np + +def logcosh_loss(y_true, y_pred): + error = y_true - y_pred + loss = np.log(np.cosh(error)) + return np.mean(loss) +``` + +These implementations provide various options for cost functions suitable for different machine learning tasks. Each function has its advantages and disadvantages, making them suitable for different scenarios and problem domains. + +--- \ No newline at end of file diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index 46100df..cfe9a67 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -10,3 +10,4 @@ - [PyTorch.md](pytorch.md) - [Types of optimizers](Types_of_optimizers.md) - [Logistic Regression](logistic-regression.md) +-[Types_of_Cost_Functions](Types_of_Cost_Functions.md) From d23389a8ea17ca0d16b96b14b710ac41606e8383 Mon Sep 17 00:00:00 2001 From: Manish kumar gupta <97523900+manishh12@users.noreply.github.com> Date: Fri, 31 May 2024 12:07:34 +0530 Subject: [PATCH 09/76] Updated maths formulas --- .../Types_of_Cost_Functions.md | 35 ++++++++++++------- 1 file changed, 23 insertions(+), 12 deletions(-) diff --git a/contrib/machine-learning/Types_of_Cost_Functions.md b/contrib/machine-learning/Types_of_Cost_Functions.md index 547a05e..f650726 100644 --- a/contrib/machine-learning/Types_of_Cost_Functions.md +++ b/contrib/machine-learning/Types_of_Cost_Functions.md @@ -12,7 +12,7 @@ MSE is one of the most commonly used cost functions, particularly in regression **Mathematical Formulation:** The MSE is defined as: -$$ MSE = \frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2 $$ +$$MSE = \frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2$$ Where: - \( n \) is the number of samples. - \( y_i \) is the actual value. @@ -41,7 +41,7 @@ MAE is another commonly used cost function for regression tasks. It measures the **Mathematical Formulation:** The MAE is defined as: -$$ MAE = \frac{1}{n} \sum_{i=1}^{n} |y_i - \hat{y}_i| $$ +$$MAE = \frac{1}{n} \sum_{i=1}^{n} |y_i - \hat{y}_i|$$ Where: - \( n \) is the number of samples. - \( y_i \) is the actual value. @@ -70,8 +70,11 @@ def mean_absolute_error(y_true, y_pred): Cross-entropy loss is commonly used in binary classification problems. It measures the dissimilarity between the true and predicted probability distributions. **Mathematical Formulation:** + For binary classification, the cross-entropy loss is defined as: -$$ \text{Cross-Entropy} = -\frac{1}{n} \sum_{i=1}^{n} [y_i \log(\hat{y}_i) + (1 - y_i) \log(1 - \hat{y}_i)] $$ + +$$\text{Cross-Entropy} = -\frac{1}{n} \sum_{i=1}^{n} [y_i \log(\hat{y}_i) + (1 - y_i) \log(1 - \hat{y}_i)]$$ + Where: - \( n \) is the number of samples. - \( y_i \) is the actual class label (0 or 1). @@ -100,8 +103,11 @@ def binary_cross_entropy(y_true, y_pred): For multiclass classification problems, the cross-entropy loss is adapted to handle multiple classes. **Mathematical Formulation:** + The multiclass cross-entropy loss is defined as: -$$ \text{Cross-Entropy} = -\frac{1}{n} \sum_{i=1}^{n} \sum_{c=1}^{C} y_{i,c} \log(\hat{y}_{i,c}) $$ + +$$\text{Cross-Entropy} = -\frac{1}{n} \sum_{i=1}^{n} \sum_{c=1}^{C} y_{i,c} \log(\hat{y}_{i,c})$$ + Where: - \( n \) is the number of samples. - \( C \) is the number of classes. @@ -131,8 +137,11 @@ def categorical_cross_entropy(y_true, y_pred): Hinge loss is commonly used in support vector machines (SVMs) for binary classification tasks. It penalizes misclassifications by a linear margin. **Mathematical Formulation:** + For binary classification, the hinge loss is defined as: -$$ \text{Hinge Loss} = \frac{1}{n} \sum_{i=1}^{n} \max(0, 1 - y_i \cdot \hat{y}_i) $$ + +$$\text{Hinge Loss} = \frac{1}{n} \sum_{i=1}^{n} \max(0, 1 - y_i \cdot \hat{y}_i)$$ + Where: - \( n \) is the number of samples. - \( y_i \) is the actual class label (-1 or 1). @@ -165,17 +174,16 @@ Huber loss is a combination of MSE and MAE, providing a compromise between the t The Huber loss is defined as: -$$ -\text{Huber Loss} = \frac{1}{n} \sum_{i=1}^{n} \left\{ +$$\text{Huber Loss} = \frac{1}{n} \sum_{i=1}^{n} \left\{ \begin{array}{ll} \frac{1}{2} (y_i - \hat{y}_i)^2 & \text{if } |y_i - \hat{y}_i| \leq \delta \\ \delta(|y_i - \hat{y}_i| - \frac{1}{2} \delta) & \text{otherwise} \end{array} -\right. -$$ +\right.$$ + Where: - \( n \) is the number of samples. -- \( \delta \) is a threshold parameter. +- \(delta\) is a threshold parameter. **Advantages:** - Provides a smooth loss function. @@ -200,8 +208,11 @@ def huber_loss(y_true, y_pred, delta): Log-Cosh loss is a smooth approximation of the MAE and is less sensitive to outliers than MSE. It provides a smooth transition from quadratic for small errors to linear for large errors. **Mathematical Formulation:** + The Log-Cosh loss is defined as: -$$ \text{Log-Cosh Loss} = \frac{1}{n} \sum_{i=1}^{n} \log(\cosh(y_i - \hat{y}_i)) $$ + +$$\text{Log-Cosh Loss} = \frac{1}{n} \sum_{i=1}^{n} \log(\cosh(y_i - \hat{y}_i))$$ + Where: - \( n \) is the number of samples. @@ -224,4 +235,4 @@ def logcosh_loss(y_true, y_pred): These implementations provide various options for cost functions suitable for different machine learning tasks. 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Matplotlib along with NumPy can be considered as the open source equivalent of MATLAB. + +- Matplotlib has a procedural interface named the Pylab, which is designed to resemble MATLAB. However, it is completely independent of Matlab. + +## Starting with Matplotlib + +### 1.] Install and import the neccasary libraries - mayplotlib.pylplot +```python +pip install matplotlib +``` + +```python +import maptplotlib.pyplot as plt +import numpy as np +``` + + + +### 2.] Scatter plot +Scatter plot is a type of plot that uses the cartesian coordinates between x and y to describe the relation between them. It uses dots to represent relation between the data variables of the data set. + +```python +x = [5,4,5,8,9,8,6,7,3,2] +y = [9,1,7,3,5,7,6,1,2,8] + +plt.scatter(x,y, color = "red") + +plt.title("Scatter plot") +plt.xlabel("X values") +plt.ylabel("Y values") + +plt.tight_layout() +plt.show() +``` + +![img](scatterplot.png) + + + +### 3.] Bar plot +Bar plot is a type of plot that plots the frequency distrubution of the categorical variables. Each entity of the categoric variable is represented as a bar. The size of the bar represents its numeric value. + +```python +x = np.array(['A','B','C','D']) +y = np.array([42,50,15,35]) + +plt.bar(x,y,color = "red") + +plt.title("Bar plot") +plt.xlabel("X values") +plt.ylabel("Y values") + +plt.show() +``` +![img](barplot.png) + + +### 4.] Histogram +Histogram is the representation of frequency distribution of qualitative data. The height of each rectangle defines the amount, or how often that variable appears. + +```python +x = [9,1,7,3,5,7,6,1,2,8] + +plt.hist(x, color = "red", edgecolor= "white", bins =5) + +plt.title("Histogram") +plt.xlabel("X values") +plt.ylabel("Frequency Distribution") + +plt.show() +``` + +![img](histogram.png) + + + + + + diff --git a/contrib/plotting-visualization/scatterplot.png b/contrib/plotting-visualization/scatterplot.png new file mode 100644 index 0000000000000000000000000000000000000000..94c91484f54e9dc6e492862c036bab2a804add3b GIT binary patch literal 13739 zcmdUWXINBO+HGNM1K0*cQLt5z3@Rcy7!go%jtU}BI+Mxh45b{--1}I8+kZ`T1KW*)doQJc#GARaY{zxQl5`D9=hu z%tm}tjE6R!;uyAV+qO%t3*%VVo*pioN|L-lcKe+ukr2b(q=WP0beLGHhzmx~K9`q= zgoGsAe8CvvGCti`zVIYJ9d?l*1jF%LzUw@KOILcWPSzxvw}BP^_jR zCEQnrUkG+LZmh2k*a-c!5jw)akW^cHdw%@8N?>qc;173;jkV?OA&-@jq{4*`b9!dx zj9s*h$@r=6mEpJmS>woy#=a-%ym6CVDN1oSInJE9s-f>}{Iv5f+-4_qa%H;wfMbLB zVzNxoxxM@LwOHm;63X%#Wp+w`FK&-F>(?V_mv@^Uj;g 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zW#!}=kxV0L46GumIQJkZ7~(H`daj|{E`_dEt%Li%eOaqHOC59h@Y{<+fKw|IKJG-z zZRHgfN3fo)yH61v38G5~i#7o%KU-dEmKk3hO? zJcu{(U_X9?u`;9$LlmM*8ldM5IlG5IbOYwB-*!0+HI&DzCQ!Q2NIHcN4rs!%uam$B zE)4k|>r=;xX@X5%ui;L-WF$rl{;yvB*47txM?GRsyKxJeg(M>-e=haRmAn5238{EC From 57c369bd0ec3d02e283224f903e760abb368b51f Mon Sep 17 00:00:00 2001 From: Harshita Jain <122027571+Harshita297@users.noreply.github.com> Date: Fri, 31 May 2024 13:00:40 +0530 Subject: [PATCH 15/76] Update matplotlib.md --- contrib/plotting-visualization/matplotlib.md | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/contrib/plotting-visualization/matplotlib.md b/contrib/plotting-visualization/matplotlib.md index 720dff8..02f17f5 100644 --- a/contrib/plotting-visualization/matplotlib.md +++ b/contrib/plotting-visualization/matplotlib.md @@ -41,9 +41,10 @@ plt.tight_layout() plt.show() ``` -![img](scatterplot.png) +![scatterplot](https://github.com/Harshita297/learn-python/assets/122027571/aa57eb31-4377-4f75-9a97-e4fa7a54a938) + ### 3.] Bar plot Bar plot is a type of plot that plots the frequency distrubution of the categorical variables. Each entity of the categoric variable is represented as a bar. The size of the bar represents its numeric value. @@ -60,7 +61,8 @@ plt.ylabel("Y values") plt.show() ``` -![img](barplot.png) +![barplot](https://github.com/Harshita297/learn-python/assets/122027571/00cea3cd-a962-4bfe-b158-921389ee564c) + ### 4.] Histogram @@ -77,8 +79,11 @@ plt.ylabel("Frequency Distribution") plt.show() ``` - -![img](histogram.png) +![histogram](https://github.com/Harshita297/learn-python/assets/122027571/32470819-bac5-4bb2-9f36-79521fa40bf3) + + + + From 9470b8c1a255f8e36c51ae1db4b263a8ca05297f Mon Sep 17 00:00:00 2001 From: Harshita Jain <122027571+Harshita297@users.noreply.github.com> Date: Fri, 31 May 2024 13:02:02 +0530 Subject: [PATCH 16/76] Update index.md --- contrib/plotting-visualization/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/plotting-visualization/index.md b/contrib/plotting-visualization/index.md index 32261d6..84d07f9 100644 --- a/contrib/plotting-visualization/index.md +++ b/contrib/plotting-visualization/index.md @@ -3,3 +3,4 @@ - [Installing Matplotlib](matplotlib-installation.md) - [Bar Plots in Matplotlib](matplotlib-bar-plots.md) - [Pie Charts in Matplotlib](matplotlib-pie-charts.md) +- [Introducing Matplotlib](matplotlib.md) From 540163c863037f28afa4b9b7463198bf57e41866 Mon Sep 17 00:00:00 2001 From: Dishika Vaishkiyar <152963337+Dishika18@users.noreply.github.com> Date: Fri, 31 May 2024 14:09:16 +0530 Subject: [PATCH 17/76] Added Content: Line Charts in Matplotlib --- .../plotting-visualization/images/dot-line.png | Bin 0 -> 1180 bytes .../images/line-asymptote.png | Bin 0 -> 29209 bytes .../images/line-curve.png | Bin 0 -> 13975 bytes .../images/line-labels.png | Bin 0 -> 16851 bytes .../images/line-ticks.png | Bin 0 -> 22354 bytes .../images/line-with-text-scale.png | Bin 0 -> 19249 bytes .../images/simple_line.png | Bin 0 -> 14451 bytes .../plotting-visualization/images/two-lines.png | Bin 0 -> 18440 bytes 8 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 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.../matplotlib-line-plot.md | 268 ++++++++++++++++++ 1 file changed, 268 insertions(+) create mode 100644 contrib/plotting-visualization/matplotlib-line-plot.md diff --git a/contrib/plotting-visualization/matplotlib-line-plot.md b/contrib/plotting-visualization/matplotlib-line-plot.md new file mode 100644 index 0000000..00ddc90 --- /dev/null +++ b/contrib/plotting-visualization/matplotlib-line-plot.md @@ -0,0 +1,268 @@ +# Line Chart in Matplotlib + +A line chart is a simple way to visualize data where we connect individual data points. It helps us to see trends and patterns over time or across categories. +
This type of chart is particularly useful for:
+* Comparing Data: Comparing multiple datasets on the same axes. +* Highlighting Changes: Illustrating changes and patterns in data. +* Visualizing Trends: Showing trends over time or other continuous variables. + +## Prerequisites +Line plots can be created in Python with Matplotlib's ``pyplot`` library. To build a line plot, first import ``Matplotlib``. It is a standard convention to import Matplotlib's pyplot library as ``plt``. +``` +import matplotlib.pyplot as plt + +``` + +## Creating a simple Line Plot + +First import matplotlib and numpy, these are useful for charting. +
You can use the ``plot(x,y)`` method to create a line chart.
+ +``` +import matplotlib.pyplot as plt +import numpy as np + +x = np.linspace(-1, 1, 50) +print(x) +y = 2*x + 1 + +plt.plot(x, y) +plt.show() +``` +When executed, this will show the following line plot: + +![Basic line Chart](contrib/plotting-visualization/images/simple_line.png) + + +## Curved line + +The ``plot()`` method also works for other types of line charts. It doesn’t need to be a straight line, y can have any type of values. +``` +import matplotlib.pyplot as plt +import numpy as np + +x = np.linspace(-1, 1, 50) +y = 2**x + 1 + +plt.plot(x, y) +plt.show() +``` +When executed, this will show the following Curved line plot: + +![Curved line](contrib/plotting-visualization/images/line-curve.png) + + +## Line with Labels + +To know what you are looking at, you need meta data. Labels are a type of meta data. They show what the chart is about. The chart has an ``x label``, ``y label`` and ``title``. +``` +import matplotlib.pyplot as plt +import numpy as np + +x = np.linspace(-1, 1, 50) +y1 = 2*x + 1 +y2 = 2**x + 1 + +plt.figure() +plt.plot(x, y1) + +plt.xlabel("I am x") +plt.ylabel("I am y") +plt.title("With Labels") + +plt.show() +``` +When executed, this will show the following line with labels plot: + +![line with labels](contrib/plotting-visualization/images/line-labels.png) + +## Multiple lines + +More than one line can be in the plot. To add another line, just call the ``plot(x,y)`` function again. In the example below we have two different values for ``y(y1,y2)`` that are plotted onto the chart. + +``` +import matplotlib.pyplot as plt +import numpy as np + +x = np.linspace(-1, 1, 50) +y1 = 2*x + 1 +y2 = 2**x + 1 + +plt.figure(num = 3, figsize=(8, 5)) +plt.plot(x, y2) +plt.plot(x, y1, + color='red', + linewidth=1.0, + linestyle='--' + ) + +plt.show() +``` +When executed, this will show the following Multiple lines plot: + +![multiple lines](contrib/plotting-visualization/images/two-lines.png) + + +## Dotted line + +Lines can be in the form of dots like the image below. Instead of calling ``plot(x,y)`` call the ``scatter(x,y)`` method. The ``scatter(x,y)`` method can also be used to (randomly) plot points onto the chart. + +``` +import matplotlib.pyplot as plt +import numpy as np + +n = 1024 +X = np.random.normal(0, 1, n) +Y = np.random.normal(0, 1, n) +T = np.arctan2(X, Y) + +plt.scatter(np.arange(5), np.arange(5)) + +plt.xticks(()) +plt.yticks(()) + +plt.show() +``` + +When executed, this will show the following Dotted line plot: + +![dotted lines](contrib/plotting-visualization/images/dot-line.png) + +## Line ticks + +You can change the ticks on the plot. Set them on the ``x-axis``, ``y-axis`` or even change their color. The line can be more thick and have an alpha value. + +``` +import matplotlib.pyplot as plt +import numpy as np + +x = np.linspace(-1, 1, 50) +y = 2*x - 1 + +plt.figure(figsize=(12, 8)) +plt.plot(x, y, color='r', linewidth=10.0, alpha=0.5) + +ax = plt.gca() + +ax.spines['right'].set_color('none') +ax.spines['top'].set_color('none') + +ax.xaxis.set_ticks_position('bottom') +ax.yaxis.set_ticks_position('left') + +ax.spines['bottom'].set_position(('data', 0)) +ax.spines['left'].set_position(('data', 0)) + +for label in ax.get_xticklabels() + ax.get_yticklabels(): + label.set_fontsize(12) + label.set_bbox(dict(facecolor='y', edgecolor='None', alpha=0.7)) + +plt.show() +``` + +When executed, this will show the following line ticks plot: + +![line ticks](contrib/plotting-visualization/images/line-ticks.png) + +## Line with asymptote + +An asymptote can be added to the plot. To do that, use ``plt.annotate()``. There’s lso a dotted line in the plot below. You can play around with the code to see how it works. + +``` +import matplotlib.pyplot as plt +import numpy as np + +x = np.linspace(-1, 1, 50) +y1 = 2*x + 1 +y2 = 2**x + 1 + +plt.figure(figsize=(12, 8)) +plt.plot(x, y2) +plt.plot(x, y1, color='red', linewidth=1.0, linestyle='--') + +ax = plt.gca() + +ax.spines['right'].set_color('none') +ax.spines['top'].set_color('none') + +ax.xaxis.set_ticks_position('bottom') +ax.yaxis.set_ticks_position('left') + +ax.spines['bottom'].set_position(('data', 0)) +ax.spines['left'].set_position(('data', 0)) + + +x0 = 1 +y0 = 2*x0 + 1 + +plt.scatter(x0, y0, s = 66, color = 'b') +plt.plot([x0, x0], [y0, 0], 'k-.', lw= 2.5) + +plt.annotate(r'$2x+1=%s$' % + y0, + xy=(x0, y0), + xycoords='data', + + xytext=(+30, -30), + textcoords='offset points', + fontsize=16, + arrowprops=dict(arrowstyle='->',connectionstyle='arc3,rad=.2') + ) + +plt.text(0, 3, + r'$This\ is\ a\ good\ idea.\ \mu\ \sigma_i\ \alpha_t$', + fontdict={'size':16,'color':'r'}) + +plt.show() +``` + +When executed, this will show the following Line with asymptote plot: + +![Line with asymptote](contrib/plotting-visualization/images/line-asymptote.png) + +## Line with text scale + +It doesn’t have to be a numeric scale. The scale can also contain textual words like the example below. In ``plt.yticks()`` we just pass a list with text values. These values are then show against the ``y axis``. + +``` +import matplotlib.pyplot as plt +import numpy as np + +x = np.linspace(-1, 1, 50) +y1 = 2*x + 1 +y2 = 2**x + 1 + +plt.figure(num = 3, figsize=(8, 5)) +plt.plot(x, y2) + +plt.plot(x, y1, + color='red', + linewidth=1.0, + linestyle='--' + ) + +plt.xlim((-1, 2)) +plt.ylim((1, 3)) + +new_ticks = np.linspace(-1, 2, 5) +plt.xticks(new_ticks) +plt.yticks([-2, -1.8, -1, 1.22, 3], + [r'$really\ bad$', r'$bad$', r'$normal$', r'$good$', r'$readly\ good$']) + +ax = plt.gca() +ax.spines['right'].set_color('none') +ax.spines['top'].set_color('none') + +ax.xaxis.set_ticks_position('bottom') +ax.yaxis.set_ticks_position('left') + +ax.spines['bottom'].set_position(('data', 0)) +ax.spines['left'].set_position(('data', 0)) + +plt.show() +``` +When executed, this will show the following Line with text scale plot: + +![Line with text scale](contrib/plotting-visualization/images/line-with-text-scale.png) + + From 310ff8f340c15e0eef66b8b5acc383c586846662 Mon Sep 17 00:00:00 2001 From: Dishika Vaishkiyar <152963337+Dishika18@users.noreply.github.com> Date: Fri, 31 May 2024 14:29:39 +0530 Subject: [PATCH 19/76] Update index.md --- contrib/plotting-visualization/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/plotting-visualization/index.md b/contrib/plotting-visualization/index.md index 32261d6..61dd537 100644 --- a/contrib/plotting-visualization/index.md +++ b/contrib/plotting-visualization/index.md @@ -3,3 +3,4 @@ - [Installing Matplotlib](matplotlib-installation.md) - [Bar Plots in Matplotlib](matplotlib-bar-plots.md) - [Pie Charts in Matplotlib](matplotlib-pie-charts.md) +- [Line Charts in Matplotlib](matplotlib-line-plot.md) From 34d7bc39c9aa7ed30565cded96c14566ed7e9f80 Mon Sep 17 00:00:00 2001 From: Dishika Vaishkiyar <152963337+Dishika18@users.noreply.github.com> Date: Fri, 31 May 2024 14:32:11 +0530 Subject: [PATCH 20/76] Update matplotlib-line-plot.md --- .../matplotlib-line-plot.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/contrib/plotting-visualization/matplotlib-line-plot.md b/contrib/plotting-visualization/matplotlib-line-plot.md index 00ddc90..566fdbb 100644 --- a/contrib/plotting-visualization/matplotlib-line-plot.md +++ b/contrib/plotting-visualization/matplotlib-line-plot.md @@ -31,7 +31,7 @@ plt.show() ``` When executed, this will show the following line plot: -![Basic line Chart](contrib/plotting-visualization/images/simple_line.png) +![Basic line Chart](images/simple_line.png) ## Curved line @@ -49,7 +49,7 @@ plt.show() ``` When executed, this will show the following Curved line plot: -![Curved line](contrib/plotting-visualization/images/line-curve.png) +![Curved line](images/line-curve.png) ## Line with Labels @@ -74,7 +74,7 @@ plt.show() ``` When executed, this will show the following line with labels plot: -![line with labels](contrib/plotting-visualization/images/line-labels.png) +![line with labels](images/line-labels.png) ## Multiple lines @@ -100,7 +100,7 @@ plt.show() ``` When executed, this will show the following Multiple lines plot: -![multiple lines](contrib/plotting-visualization/images/two-lines.png) +![multiple lines](images/two-lines.png) ## Dotted line @@ -126,7 +126,7 @@ plt.show() When executed, this will show the following Dotted line plot: -![dotted lines](contrib/plotting-visualization/images/dot-line.png) +![dotted lines](images/dot-line.png) ## Line ticks @@ -162,7 +162,7 @@ plt.show() When executed, this will show the following line ticks plot: -![line ticks](contrib/plotting-visualization/images/line-ticks.png) +![line ticks](images/line-ticks.png) ## Line with asymptote @@ -218,7 +218,7 @@ plt.show() When executed, this will show the following Line with asymptote plot: -![Line with asymptote](contrib/plotting-visualization/images/line-asymptote.png) +![Line with asymptote](images/line-asymptote.png) ## Line with text scale @@ -263,6 +263,6 @@ plt.show() ``` When executed, this will show the following Line with text scale plot: -![Line with text scale](contrib/plotting-visualization/images/line-with-text-scale.png) +![Line with text scale](images/line-with-text-scale.png) From 27dcfba508722dc35a96544e259cd49c9e3a7f9a Mon Sep 17 00:00:00 2001 From: Rishi Goswami <92781956+rishig2003@users.noreply.github.com> Date: Fri, 31 May 2024 18:03:35 +0530 Subject: [PATCH 21/76] Update index.md --- contrib/plotting-visualization/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/plotting-visualization/index.md b/contrib/plotting-visualization/index.md index 32261d6..3f27246 100644 --- a/contrib/plotting-visualization/index.md +++ b/contrib/plotting-visualization/index.md @@ -3,3 +3,4 @@ - [Installing Matplotlib](matplotlib-installation.md) - [Bar Plots in Matplotlib](matplotlib-bar-plots.md) - [Pie Charts in Matplotlib](matplotlib-pie-charts.md) +- [Introduction to Seaborn and Installation](seaborn-intro.md) From 273515a8c22a17fc9af4eefcab30f42d6065a31c Mon Sep 17 00:00:00 2001 From: Rishi Goswami <92781956+rishig2003@users.noreply.github.com> Date: Fri, 31 May 2024 18:04:45 +0530 Subject: [PATCH 22/76] Create seaborn-intro.md --- contrib/plotting-visualization/seaborn-intro.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 contrib/plotting-visualization/seaborn-intro.md diff --git a/contrib/plotting-visualization/seaborn-intro.md b/contrib/plotting-visualization/seaborn-intro.md new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/contrib/plotting-visualization/seaborn-intro.md @@ -0,0 +1 @@ + From 01552110695fc0d9b39797d983946483d2620a3c Mon Sep 17 00:00:00 2001 From: Rishi Goswami <92781956+rishig2003@users.noreply.github.com> Date: Fri, 31 May 2024 18:12:32 +0530 Subject: [PATCH 23/76] Update seaborn-intro.md --- contrib/plotting-visualization/seaborn-intro.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/contrib/plotting-visualization/seaborn-intro.md b/contrib/plotting-visualization/seaborn-intro.md index 8b13789..4e28b6a 100644 --- a/contrib/plotting-visualization/seaborn-intro.md +++ b/contrib/plotting-visualization/seaborn-intro.md @@ -1 +1,4 @@ +Seaborn is a Python data visualization library based on Matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. +# Seaborn Installation +Before installing Matplotlib, ensure you have Python installed on your system. You can download and install Python from the [official Python website](https://www.python.org/). From b81223f8e8e19c67078d835d7679e66036356fb4 Mon Sep 17 00:00:00 2001 From: Rishi Goswami <92781956+rishig2003@users.noreply.github.com> Date: Fri, 31 May 2024 18:12:50 +0530 Subject: [PATCH 24/76] Update seaborn-intro.md --- contrib/plotting-visualization/seaborn-intro.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/plotting-visualization/seaborn-intro.md b/contrib/plotting-visualization/seaborn-intro.md index 4e28b6a..8b0d869 100644 --- a/contrib/plotting-visualization/seaborn-intro.md +++ b/contrib/plotting-visualization/seaborn-intro.md @@ -1,4 +1,4 @@ Seaborn is a Python data visualization library based on Matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. -# Seaborn Installation +## Seaborn Installation Before installing Matplotlib, ensure you have Python installed on your system. You can download and install Python from the [official Python website](https://www.python.org/). From 32fcb9484e26f9ae38e5d6b82a338190b8e83358 Mon Sep 17 00:00:00 2001 From: Rishi Goswami <92781956+rishig2003@users.noreply.github.com> Date: Fri, 31 May 2024 18:15:06 +0530 Subject: [PATCH 25/76] Update seaborn-intro.md --- contrib/plotting-visualization/seaborn-intro.md | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/contrib/plotting-visualization/seaborn-intro.md b/contrib/plotting-visualization/seaborn-intro.md index 8b0d869..bbbfec8 100644 --- a/contrib/plotting-visualization/seaborn-intro.md +++ b/contrib/plotting-visualization/seaborn-intro.md @@ -2,3 +2,10 @@ Seaborn is a Python data visualization library based on Matplotlib. It provides ## Seaborn Installation Before installing Matplotlib, ensure you have Python installed on your system. You can download and install Python from the [official Python website](https://www.python.org/). +Below are the steps to install and setup seaborn + +1. **Install Seaborn**: Open your terminal or command prompt and run the following command to install Seaborn using `pip`: + +```bash +pip install seaborn +``` From 56467e57bb3ac0b91bde6d504813e44e983a80ab Mon Sep 17 00:00:00 2001 From: Rishi Goswami <92781956+rishig2003@users.noreply.github.com> Date: Fri, 31 May 2024 18:19:06 +0530 Subject: [PATCH 26/76] Update seaborn-intro.md --- .../plotting-visualization/seaborn-intro.md | 20 +++++++++++++++++-- 1 file changed, 18 insertions(+), 2 deletions(-) diff --git a/contrib/plotting-visualization/seaborn-intro.md b/contrib/plotting-visualization/seaborn-intro.md index bbbfec8..0375db2 100644 --- a/contrib/plotting-visualization/seaborn-intro.md +++ b/contrib/plotting-visualization/seaborn-intro.md @@ -2,10 +2,26 @@ Seaborn is a Python data visualization library based on Matplotlib. It provides ## Seaborn Installation Before installing Matplotlib, ensure you have Python installed on your system. You can download and install Python from the [official Python website](https://www.python.org/). -Below are the steps to install and setup seaborn -1. **Install Seaborn**: Open your terminal or command prompt and run the following command to install Seaborn using `pip`: +Below are the steps to install and setup Seaborn: + +1. Open your terminal or command prompt and run the following command to install Seaborn using `pip`: ```bash pip install seaborn ``` + +2. The basic invocation of `pip` will install seaborn and, if necessary, its mandatory dependencies. It is possible to include optional dependencies that give access to a few advanced features: +```bash +pip install seaborn[stats] +``` + +3. The library is also included as part of the Anaconda distribution, and it can be installed with `conda`: +```bash +conda install seaborn +``` + +4. As the main Anaconda repository can be slow to add new releases, you may prefer using the conda-forge channel: +```bash +conda install seaborn -c conda-forge +``` From bdab7d9bb5154dc23e2b9b350900a5b148b6d3e1 Mon Sep 17 00:00:00 2001 From: Rishi Goswami <92781956+rishig2003@users.noreply.github.com> Date: Fri, 31 May 2024 18:29:50 +0530 Subject: [PATCH 27/76] Update seaborn-intro.md --- contrib/plotting-visualization/seaborn-intro.md | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/contrib/plotting-visualization/seaborn-intro.md b/contrib/plotting-visualization/seaborn-intro.md index 0375db2..6e0a7d8 100644 --- a/contrib/plotting-visualization/seaborn-intro.md +++ b/contrib/plotting-visualization/seaborn-intro.md @@ -25,3 +25,17 @@ conda install seaborn ```bash conda install seaborn -c conda-forge ``` + +## Dependencies +### Supported Python versions +- Python 3.8+ + +### Mandatory Dependencies + - [numpy](https://numpy.org/) + - [pandas](https://pandas.pydata.org/) + - [matplotlib](https://matplotlib.org/) + +### Optional Dependencies + - [statsmodels](https://www.statsmodels.org/stable/index.html) for advanced regression plots + - [scipy](https://scipy.org/) for clustering matrices and some advanced options + - [fastcluster](https://pypi.org/project/fastcluster/) for faster clustering of large matrices From a8e60cf4b1121311363846e4ca06d0967e4652fb Mon Sep 17 00:00:00 2001 From: Rishi Goswami <92781956+rishig2003@users.noreply.github.com> Date: Fri, 31 May 2024 20:29:42 +0530 Subject: [PATCH 28/76] Update index.md --- contrib/plotting-visualization/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/plotting-visualization/index.md b/contrib/plotting-visualization/index.md index 3f27246..96615db 100644 --- a/contrib/plotting-visualization/index.md +++ b/contrib/plotting-visualization/index.md @@ -4,3 +4,4 @@ - [Bar Plots in Matplotlib](matplotlib-bar-plots.md) - [Pie Charts in Matplotlib](matplotlib-pie-charts.md) - [Introduction to Seaborn and Installation](seaborn-intro.md) +- [Getting started with Seaborn](seaborn-basics.md) From 4446a85d1f81f8a18d75f5a29cc9a1733bd800ab Mon Sep 17 00:00:00 2001 From: Rishi Goswami <92781956+rishig2003@users.noreply.github.com> Date: Fri, 31 May 2024 20:30:11 +0530 Subject: [PATCH 29/76] Create seaborn-basics.md --- contrib/plotting-visualization/seaborn-basics.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 contrib/plotting-visualization/seaborn-basics.md diff --git a/contrib/plotting-visualization/seaborn-basics.md b/contrib/plotting-visualization/seaborn-basics.md new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/contrib/plotting-visualization/seaborn-basics.md @@ -0,0 +1 @@ + From 6aecf49fd04b8777365b7173fa47d59c86e5e0aa Mon Sep 17 00:00:00 2001 From: Rishi Goswami <92781956+rishig2003@users.noreply.github.com> Date: Fri, 31 May 2024 20:47:43 +0530 Subject: [PATCH 30/76] Update seaborn-basics.md --- .../plotting-visualization/seaborn-basics.md | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) diff --git a/contrib/plotting-visualization/seaborn-basics.md b/contrib/plotting-visualization/seaborn-basics.md index 8b13789..603f074 100644 --- a/contrib/plotting-visualization/seaborn-basics.md +++ b/contrib/plotting-visualization/seaborn-basics.md @@ -1 +1,19 @@ +Seaborn helps you explore and understand your data. Its plotting functions operate on dataframes and arrays containing whole datasets and internally perform the necessary semantic mapping and statistical aggregation to produce informative plots. Its dataset-oriented, declarative API lets you focus on what the different elements of your plots mean, rather than on the details of how to draw them. +Here’s an example of what seaborn can do: +```Python +# Import seaborn +import seaborn as sns + +# Apply the default theme +sns.set_theme() + +# Load an example dataset +tips = sns.load_dataset("tips") + +# Create a visualization +sns.relplot( + data=tips, + x="total_bill", y="tip", col="time", + hue="smoker", style="smoker", size="size", +) From 03920cd7501a05316e9a143f0dc7d0406e6117c1 Mon Sep 17 00:00:00 2001 From: Rishi Goswami <92781956+rishig2003@users.noreply.github.com> Date: Fri, 31 May 2024 20:48:58 +0530 Subject: [PATCH 31/76] Add files via upload --- .../images/seaborn-basics1.png | Bin 0 -> 53821 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 contrib/plotting-visualization/images/seaborn-basics1.png diff --git a/contrib/plotting-visualization/images/seaborn-basics1.png 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zDmk8}Qj~*(BPl<4Y|CIEh8$?rfXCamM`#dfgzomQtf#N|36N-eUK{#!5Yr_hMD{&# ze4IF^Jy{cbC3O>RUS7yt`wXS6H(#^IsZ!ajRtiq3^6GC7o8GaSVacvWaa3@xgLzk z1?YXXMtf>JR&!mTJ797rcRXkWpE{~QiNFRE%8{!zpRbg;#Q+AT&As~{@!l5*1Bc`8xT|3;0M8lRl^wiL z{Z@wC)p(?*P@UznDwuZO_&npWjIeji0pNX7mw=9v%;ZgfYGi}dRsxK9U#aylRDn-< z_HLyibpNsZfdf48zq+{uxQF8)`$rV-0)iwf(9>=;>S{e-hL5VEaI< zY=j>_eh_1Y9-JOo)}_Y8#Q3Vd2OdKpdN8v<(5TS=?lpw{^9G-u_y5r4cqCE{<~L*P Ss(20qenEt#g-Qi6$ literal 0 HcmV?d00001 From 5ffcbf21346cc0a56b873c5b11d97e349fa379fc Mon Sep 17 00:00:00 2001 From: Rishi Goswami <92781956+rishig2003@users.noreply.github.com> Date: Fri, 31 May 2024 20:57:53 +0530 Subject: [PATCH 32/76] Update seaborn-basics.md --- .../plotting-visualization/seaborn-basics.md | 20 +++++++++++++++++++ 1 file changed, 20 insertions(+) diff --git a/contrib/plotting-visualization/seaborn-basics.md b/contrib/plotting-visualization/seaborn-basics.md index 603f074..41d76a4 100644 --- a/contrib/plotting-visualization/seaborn-basics.md +++ b/contrib/plotting-visualization/seaborn-basics.md @@ -17,3 +17,23 @@ sns.relplot( x="total_bill", y="tip", col="time", hue="smoker", style="smoker", size="size", ) +``` +Below is the output for the above code snippet: + +![Seaborn intro image](images/seaborn-basics.png) + +```Python +# Load an example dataset +tips = sns.load_dataset("tips") +``` +Most code in the docs will use the `load_dataset()` function to get quick access to an example dataset. There’s nothing special about these datasets: they are just pandas data frames, and we could have loaded them with `pandas.read_csv()` or build them by hand. Most of the examples in the documentation will specify data using pandas data frames, but Seaborn is very flexible about the data structures that it accepts. + +```Python +# Create a visualization +sns.relplot( + data=tips, + x="total_bill", y="tip", col="time", + hue="smoker", style="smoker", size="size", +) +``` +This plot shows the relationship between five variables in the tips dataset using a single call to the seaborn function `relplot()`. Notice how we provided only the names of the variables and their roles in the plot. Unlike when using matplotlib directly, it wasn’t necessary to specify attributes of the plot elements in terms of the color values or marker codes. Behind the scenes, seaborn handled the translation from values in the dataframe to arguments that Matplotlib understands. This declarative approach lets you stay focused on the questions that you want to answer, rather than on the details of how to control matplotlib. From 4a7e00b61aa225b133fc702d23d70b3a74b70adf Mon Sep 17 00:00:00 2001 From: Rishi Goswami <92781956+rishig2003@users.noreply.github.com> Date: Fri, 31 May 2024 20:58:54 +0530 Subject: [PATCH 33/76] Update seaborn-basics.md --- contrib/plotting-visualization/seaborn-basics.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/plotting-visualization/seaborn-basics.md b/contrib/plotting-visualization/seaborn-basics.md index 41d76a4..147083b 100644 --- a/contrib/plotting-visualization/seaborn-basics.md +++ b/contrib/plotting-visualization/seaborn-basics.md @@ -20,7 +20,7 @@ sns.relplot( ``` Below is the output for the above code snippet: -![Seaborn intro image](images/seaborn-basics.png) +![Seaborn intro image](images/seaborn-basics1.png) ```Python # Load an example dataset From ccaa3770e7b308fe25410124dedcdea772ebe504 Mon Sep 17 00:00:00 2001 From: Rishi Goswami <92781956+rishig2003@users.noreply.github.com> Date: Fri, 31 May 2024 21:04:10 +0530 Subject: [PATCH 34/76] Update seaborn-basics.md --- contrib/plotting-visualization/seaborn-basics.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/contrib/plotting-visualization/seaborn-basics.md b/contrib/plotting-visualization/seaborn-basics.md index 147083b..42df552 100644 --- a/contrib/plotting-visualization/seaborn-basics.md +++ b/contrib/plotting-visualization/seaborn-basics.md @@ -26,7 +26,7 @@ Below is the output for the above code snippet: # Load an example dataset tips = sns.load_dataset("tips") ``` -Most code in the docs will use the `load_dataset()` function to get quick access to an example dataset. There’s nothing special about these datasets: they are just pandas data frames, and we could have loaded them with `pandas.read_csv()` or build them by hand. Most of the examples in the documentation will specify data using pandas data frames, but Seaborn is very flexible about the data structures that it accepts. +Most code in the docs will use the `load_dataset()` function to get quick access to an example dataset. There’s nothing special about these datasets: they are just pandas data frames, and we could have loaded them with `pandas.read_csv()` or build them by hand. Many users specify data using pandas data frames, but Seaborn is very flexible about the data structures that it accepts. ```Python # Create a visualization @@ -36,4 +36,4 @@ sns.relplot( hue="smoker", style="smoker", size="size", ) ``` -This plot shows the relationship between five variables in the tips dataset using a single call to the seaborn function `relplot()`. Notice how we provided only the names of the variables and their roles in the plot. Unlike when using matplotlib directly, it wasn’t necessary to specify attributes of the plot elements in terms of the color values or marker codes. Behind the scenes, seaborn handled the translation from values in the dataframe to arguments that Matplotlib understands. This declarative approach lets you stay focused on the questions that you want to answer, rather than on the details of how to control matplotlib. +This plot shows the relationship between five variables in the tips dataset using a single call to the seaborn function `relplot()`. Notice how only the names of the variables and their roles in the plot are provided. Unlike when using matplotlib directly, it wasn’t necessary to specify attributes of the plot elements in terms of the color values or marker codes. Behind the scenes, seaborn handled the translation from values in the dataframe to arguments that Matplotlib understands. This declarative approach lets you stay focused on the questions that you want to answer, rather than on the details of how to control matplotlib. From d20b8efca6cb73ef7543c0db41c34aede656e88f Mon Sep 17 00:00:00 2001 From: Jaya-Prakash-17 Date: Sat, 1 Jun 2024 00:41:56 +0530 Subject: [PATCH 35/76] Content Added: Protocols --- contrib/advanced-python/index.md | 1 + contrib/advanced-python/protocols.md | 243 +++++++++++++++++++++++++++ 2 files changed, 244 insertions(+) create mode 100644 contrib/advanced-python/protocols.md diff --git a/contrib/advanced-python/index.md b/contrib/advanced-python/index.md index b95e4b9..3945721 100644 --- a/contrib/advanced-python/index.md +++ b/contrib/advanced-python/index.md @@ -7,3 +7,4 @@ - [Regular Expressions in Python](regular_expressions.md) - [JSON module](json-module.md) - [Map Function](map-function.md) +- [Protocols](protocols.md) diff --git a/contrib/advanced-python/protocols.md b/contrib/advanced-python/protocols.md new file mode 100644 index 0000000..9b5e74a --- /dev/null +++ b/contrib/advanced-python/protocols.md @@ -0,0 +1,243 @@ +# Protocols in Python +Python can establish informal interfaces using protocols In order to improve code structure, reusability, and type checking. Protocols allow for progressive adoption and are more flexible than standard interfaces in other programming languages like JAVA, which are tight contracts that specify the methods and attributes a class must implement. + +>Before going into depth of this topic let's understand another topic which is pre-requisite od this topic \#TypingModule + +## Typing Module +This is a module in python which provides +1. Provides classes, functions, and type aliases. +2. Allows adding type annotations to our code. +3. Enhances code readability. +4. Helps in catching errors early. + +### Type Hints in Python: +Type hints allow you to specify the expected data types of variables, function parameters, and return values. This can improve code readability and help with debugging. + +Here is a simple function that adds two numbers: +```python +def add(a,b): + return a + b +add(10,20) +``` +>Output: 30 + +While this works fine, adding type hints makes the code more understandable and serves as documentation: + +```python +def add(a:int, b:int)->int: + return a + b +print(add(1,10)) +``` +>Output: 11 + +In this version, `a` and `b` are expected to be integers, and the function is expected to return an integer. This makes the function's purpose and usage clearer. + +#### let's see another example + +The function given below takes an iterable (it can be any off list, tuple, dict, set, frozeset, String... etc) and print it's content in a single line along with it's type. + +```python +from typing import Iterable +# type alias + +def print_all(l: Iterable)->None: + print(type(l),end=' ') + for i in l: + print(i,end=' ') + print() + +l = [1,2,3,4,5] # type: List[int] +s = {1,2,3,4,5} # type: Set[int] +t = (1,2,3,4,5) # type: Tuple[int] + +for iter_obj in [l,s,t]: + print_all(iter_obj) + +``` +Output: +> 1 2 3 4 5 +> 1 2 3 4 5 +> 1 2 3 4 5 + +and now lets try calling the function `print_all` using a non-iterable object `int` as argument. + +```python +a = 10 +print_all(a) # This will raise an error +``` +Output: +>TypeError: 'int' object is not iterable + +This error occurs because `a` is an `integer`, and the `integer` class does not have any methods or attributes that make it work like an iterable. In other words, the integer class does not conform to the `Iterable` protocol. + +**Benefits of Type Hints** +Using type hints helps in several ways: + +1. **Error Detection**: Tools like mypy can catch type-related problems during development, decreasing runtime errors. +2. **Code Readability**: Type hints serve as documentation, making it easy to comprehend what data types are anticipated and returned. +3. **Improved Maintenance**: With unambiguous type expectations, maintaining and updating code becomes easier, especially in huge codebases. + +Now that we have understood about type hints and typing module let's dive deep into protocols. + +## Understanding Protocols + +In Python, protocols define interfaces similar to Java interfaces. They let you specify methods and attributes that an object must implement without requiring inheritance from a base class. Protocols are part of the `typing` module and provide a way to enforce certain structures in your classes, enhancing type safety and code clarity. + +### What is a Protocol? + +A protocol specifies one or more method signatures that a class must implement to be considered as conforming to the protocol. + This concept is often referred to as "structural subtyping" or "duck typing," meaning that if an object implements the required methods and attributes, it can be treated as an instance of the protocol. + +Let's write our own protocol: + +```python +from typing import Protocol + +# Define a Printable protocol +class Printable(Protocol): + def print(self) -> None: + """Print the object""" + pass + +# Book class implements the Printable protocol +class Book: + def __init__(self, title: str): + self.title = title + + def print(self) -> None: + print(f"Book Title: {self.title}") + +# print_object function takes a Printable object and calls its print method +def print_object(obj: Printable) -> None: + obj.print() + +book = Book("Python Programming") +print_object(book) +``` +Output: +> Book Title: Python Programming + +In this example: + +1. **Printable Protocol:** Defines an interface with a single method print. +2. **Book Class:** Implements the Printable protocol by providing a print method. +3. **print_object Function:** Accepts any object that conforms to the Printable protocol and calls its print method. + +we got our output because the class `Book` confirms to the protocols `printable`. +similarly When you pass an object to `print_object` that does not conform to the Printable protocol, an error will occur. This is because the object does not implement the required `print` method. +Let's see an example: +```python +class Team: + def huddle(self) -> None: + print("Team Huddle") + +c = Team() +print_object(c) # This will raise an error +``` +Output: +>AttributeError: 'Team' object has no attribute 'print' + +In this case: +- The `Team` class has a `huddle` method but does not have a `print` method. +- When `print_object` tries to call the `print` method on a `Team` instance, it raises an `AttributeError`. + +> This is an important aspect of using protocols: they ensure that objects provide the necessary methods, leading to more predictable and reliable code. + +**Ensuring Protocol Conformance** +To avoid such errors, you need to ensure that any object passed to `print_object` implements the `Printable` protocol. Here's how you can modify the `Team` class to conform to the protocol: +```python +class Team: + def __init__(self, name: str): + self.name = name + + def huddle(self) -> None: + print("Team Huddle") + + def print(self) -> None: + print(f"Team Name: {self.name}") + +c = Team("Dream Team") +print_object(c) +``` +Output: +>Team Name: Dream Team + +The `Team` class now implements the `print` method, conforming to the `Printable` protocol. and hence, no longer raises an error. + +### Protocols and Inheritance: +Protocols can also be used in combination with inheritance to create more complex interfaces. +we can do that by following these steps: +**Step 1 - Base protocol**: Define a base protocol that specifies a common set of methods and attributes. +**Step 2 - Derived Protocols**: Create derives protocols that extends the base protocol with addition requirements +**Step 3 - Polymorphism**: Objects can then conform to multiple protocols, allowing for Polymorphic behavior. + +Let's see an example on this as well: + +```python +from typing import Protocol + +# Base Protocols +class Printable(Protocol): + def print(self) -> None: + """Print the object""" + pass + +# Base Protocols-2 +class Serializable(Protocol): + def serialize(self) -> str: + pass + +# Derived Protocol +class PrintableAndSerializable(Printable, Serializable): + pass + +# class with implementation of both Printable and Serializable +class Book_serialize: + def __init__(self, title: str): + self.title = title + + def print(self) -> None: + print(f"Book Title: {self.title}") + + def serialize(self) -> None: + print(f"serialize: {self.title}") + +# function accepts the object which implements PrintableAndSerializable +def test(obj: PrintableAndSerializable): + obj.print() + obj.serialize() + +book = Book_serialize("lean-in") +test(book) +``` +Output: +> Book Title: lean-in +serialize: lean-in + +In this example: + +**Printable Protocol:** Specifies a `print` method. +**Serializable Protocol:** Specifies a `serialize` method. +**PrintableAndSerializable Protocol:** Combines both `Printable` and `Serializable`. +**Book Class**: Implements both `print` and `serialize` methods, conforming to `PrintableAndSerializable`. +**test Function:** Accepts any object that implements the `PrintableAndSerializable` protocol. + +If you try to pass an object that does not conform to the `PrintableAndSerializable` protocol to the test function, it will raise an `error`. Let's see an example: + +```python +class Team: + def huddle(self) -> None: + print("Team Huddle") + +c = Team() +test(c) # This will raise an error +``` +output: +> AttributeError: 'Team' object has no attribute 'print' + +In this case: +The `Team` class has a `huddle` method but does not implement `print` or `serialize` methods. +When test tries to call `print` and `serialize` on a `Team` instance, it raises an `AttributeError`. + +**In Conclusion:** +>Python protocols offer a versatile and powerful means of defining interfaces, encouraging the decoupling of code, improving readability, and facilitating static type checking. They are particularly handy for scenarios involving file-like objects, bespoke containers, and any case where you wish to enforce certain behaviors without requiring inheritance from a specific base class. Ensuring that classes conform to protocols reduces runtime problems and makes your code more robust and maintainable. \ No newline at end of file From 9a504138c2b4f1ccb412e4e05cd0d2f76f864448 Mon Sep 17 00:00:00 2001 From: somyasaxena01 <140182178+somyasaxena01@users.noreply.github.com> Date: Sat, 1 Jun 2024 10:12:12 +0530 Subject: [PATCH 36/76] Delete contrib/ds-algorithms/stacks.md --- contrib/ds-algorithms/stacks.md | 131 -------------------------------- 1 file changed, 131 deletions(-) delete mode 100644 contrib/ds-algorithms/stacks.md diff --git a/contrib/ds-algorithms/stacks.md b/contrib/ds-algorithms/stacks.md deleted file mode 100644 index f9f662b..0000000 --- a/contrib/ds-algorithms/stacks.md +++ /dev/null @@ -1,131 +0,0 @@ -# STACKS IN PYTHON -In Data Structures and Algorithms, a stack is a linear data structure that complies with the Last In, First Out (LIFO) rule. It works by use of two fundamental techniques: *PUSH* which inserts an element on top of the stack and *POP* which takes out the topmost element.This concept is similar to a stack of plates in a cafeteria. Stacks are usually used for handling function calls, expression evaluation, and parsing in programming. Indeed, they are efficient in managing memory as well as tracking program state. - -**POINTS TO BE REMEMBERED :-** -- A stack is a collection of data items that can be accessed at only one end, called *TOP*. -- Items can be inserted and deleted in a stack only at the *TOP*. -- The last item inserted in a stack is the first one to be deleted. -- Therefore, a stack is called a **Last-In-First-Out (LIFO)** data structure. - -## REAL LIFE EXAMPLES OF STACKS - -**PILE OF BOOKS** - Suppose a set of books are placed one over the other in a pile. When you remove books from the pile, the topmost book will be removed first. Similarly, when you have to add a book to the pile, the book will be placed at the top of the file. - -**PILE OF PLATES** - The first plate begins the pile. The second plate is placed on the top of the first plate and the third plate is placed on the top of the second plate, and so on. In general, if you want to add a plate to the pile, you can keep it on the top of the pile. Similarly, if you want to remove a plate, you can remove the plate from the top of the pile. - -**BANGLES IN A HAND** - When a person wears bangles, the last bangle worn is the first one to be removed. - -## APPLICATIONS OF STACKS - -Stacks are widely used in Computer Science: -- *Function call* management -- Maintaining the *UNDO* list for the application -- Web browser *history management* -- Evaluating expressions -- Checking the nesting of parentheses in an expression -- *Backtracking* algorithms (Recursion) - -Understanding these applications is essential for Software Development. - -## OPERATIONS ON A STACK - -Key operations on a stack include: -- **PUSH** - It is the process of inserting a new element on the top of a stack. -- **OVERFLOW** - A situation when we are pushing an item in a stack that is full. -- **POP** - It is the process of deleting an element from the top of a stack. -- **UNDERFLOW** - A situation when we are popping item from an empty stack. -- **PEEK** - It is the process of getting the most recent value of stack *(i.e. the value at the top of the stack)* -- **ISEMPTY** - It is the function which return true if stack is empty else false. -- **SHOW** -Displaying stack items. - -## IMPLEMENTING STACKS IN PYTHON - -```python -def isEmpty(S): - - if len(S) == 0: - return True - - else: - - return False - -def Push(S, item): - S.append(item) - -def Pop(S): - - if isEmpty(S): - return "Underflow" - - else: - val = S.pop() - return val - -def Peek(S): - - if isEmpty(S): - return "Underflow" - - else: - top = len(S) - 1 - return S[top] - -def Show(S): - - if isEmpty(S): - print("Sorry, No items in Stack") - - else: - print("(Top)", end=' ') - t = len(S) - 1 - while t >= 0: - print(S[t], "<", end=' ') - t -= 1 - print() -``` - -This code defines a stack data structure along with functions to manipulate it. To provide output, we would need to use these functions to interact with the stack. - -Here's an example: -```python -stack = [] - -Push(stack, 5) -Push(stack, 10) -Push(stack, 15) - -print("Stack after Push operations:") -Show(stack) - -print("Peek operation:", Peek(stack)) - -print("Pop operation:", Pop(stack)) - -print("Stack after Pop operation:") -Show(stack) -``` - -This would output: - -``` -Stack after Push operations: - -(Top) 15 < 10 < 5 < - -Peek operation: 15 - -Pop operation: 15 - -Stack after Pop operation: - -(Top) 10 < 5 < -``` - -## Complexity Analysis - -- **Worst case**: `O(n)` This occurs when the stack is full, it is dominated by the usage of Show operation. -- **Best case**: `O(1)` When the operations like isEmpty, Push, Pop and Peek are used, they have a constant time complexity of O(1). -- **Average case**: `O(n)` The average complexity is likely to be lower than O(n), as the stack is not always full. - - From 1f54c5a7ccb5ba49d5f49728837521796f2b4de7 Mon Sep 17 00:00:00 2001 From: somyasaxena01 <140182178+somyasaxena01@users.noreply.github.com> Date: Sat, 1 Jun 2024 10:12:55 +0530 Subject: [PATCH 37/76] Add files via upload --- contrib/ds-algorithms/stacks.md | 116 ++++++++++++++++++++++++++++++++ 1 file changed, 116 insertions(+) create mode 100644 contrib/ds-algorithms/stacks.md diff --git a/contrib/ds-algorithms/stacks.md b/contrib/ds-algorithms/stacks.md new file mode 100644 index 0000000..428a193 --- /dev/null +++ b/contrib/ds-algorithms/stacks.md @@ -0,0 +1,116 @@ +# Stacks in Python + +In Data Structures and Algorithms, a stack is a linear data structure that complies with the Last In, First Out (LIFO) rule. It works by use of two fundamental techniques: **PUSH** which inserts an element on top of the stack and **POP** which takes out the topmost element.This concept is similar to a stack of plates in a cafeteria. Stacks are usually used for handling function calls, expression evaluation, and parsing in programming. Indeed, they are efficient in managing memory as well as tracking program state. + +## Points to be Remebered + +- A stack is a collection of data items that can be accessed at only one end, called **TOP**. +- Items can be inserted and deleted in a stack only at the TOP. +- The last item inserted in a stack is the first one to be deleted. +- Therefore, a stack is called a **Last-In-First-Out (LIFO)** data structure. + +## Real Life Examples of Stacks + +- **PILE OF BOOKS** - Suppose a set of books are placed one over the other in a pile. When you remove books from the pile, the topmost book will be removed first. Similarly, when you have to add a book to the pile, the book will be placed at the top of the file. + +- **PILE OF PLATES** - The first plate begins the pile. The second plate is placed on the top of the first plate and the third plate is placed on the top of the second plate, and so on. In general, if you want to add a plate to the pile, you can keep it on the top of the pile. Similarly, if you want to remove a plate, you can remove the plate from the top of the pile. + +- **BANGLES IN A HAND** - When a person wears bangles, the last bangle worn is the first one to be removed. + +## Applications of Stacks + +Stacks are widely used in Computer Science: + +- Function call management +- Maintaining the UNDO list for the application +- Web browser *history management* +- Evaluating expressions +- Checking the nesting of parentheses in an expression +- Backtracking algorithms (Recursion) + +Understanding these applications is essential for Software Development. + +## Operations on a Stack + +Key operations on a stack include: + +- **PUSH** - It is the process of inserting a new element on the top of a stack. +- **OVERFLOW** - A situation when we are pushing an item in a stack that is full. +- **POP** - It is the process of deleting an element from the top of a stack. +- **UNDERFLOW** - A situation when we are popping item from an empty stack. +- **PEEK** - It is the process of getting the most recent value of stack *(i.e. the value at the top of the stack)* +- **isEMPTY** - It is the function which return true if stack is empty else false. +- **SHOW** -Displaying stack items. + +## Implementing Stacks in Python + +```python +def isEmpty(S): + if len(S) == 0: + return True + else: + return False + +def Push(S, item): + S.append(item) + +def Pop(S): + if isEmpty(S): + return "Underflow" + else: + val = S.pop() + return val + +def Peek(S): + if isEmpty(S): + return "Underflow" + else: + top = len(S) - 1 + return S[top] + +def Show(S): + if isEmpty(S): + print("Sorry, No items in Stack") + else: + print("(Top)", end=' ') + t = len(S) - 1 + while t >= 0: + print(S[t], "<", end=' ') + t -= 1 + print() + +stack = [] # initially stack is empty + +Push(stack, 5) +Push(stack, 10) +Push(stack, 15) + +print("Stack after Push operations:") +Show(stack) +print("Peek operation:", Peek(stack)) +print("Pop operation:", Pop(stack)) +print("Stack after Pop operation:") +Show(stack) +``` + +## Output + +```markdown +Stack after Push operations: + +(Top) 15 < 10 < 5 < + +Peek operation: 15 + +Pop operation: 15 + +Stack after Pop operation: + +(Top) 10 < 5 < +``` + +## Complexity Analysis + +- **Worst case**: `O(n)` This occurs when the stack is full, it is dominated by the usage of Show operation. +- **Best case**: `O(1)` When the operations like isEmpty, Push, Pop and Peek are used, they have a constant time complexity of O(1). +- **Average case**: `O(n)` The average complexity is likely to be lower than O(n), as the stack is not always full. From 56e7774648e3dbccd68dbdc1d8e61521524b83aa Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 03:20:23 +0530 Subject: [PATCH 38/76] 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 d3c3c27..e3fc378 100644 --- a/contrib/ds-algorithms/index.md +++ b/contrib/ds-algorithms/index.md @@ -10,4 +10,4 @@ - [Greedy Algorithms](greedy-algorithms.md) - [Dynamic Programming](dynamic-programming.md) - [Linked list](linked-list.md) -- [Sliding Window Technique](Sliding_Window.md) +- [Sliding Window Technique](sliding-window.md) From 234df0ee0b4b4d29a799eac61b942cea57a785a9 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 03:20:46 +0530 Subject: [PATCH 39/76] Rename Sliding_Window.md to sliding-window.md --- contrib/ds-algorithms/{Sliding_Window.md => sliding-window.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/ds-algorithms/{Sliding_Window.md => sliding-window.md} (100%) diff --git a/contrib/ds-algorithms/Sliding_Window.md b/contrib/ds-algorithms/sliding-window.md similarity index 100% rename from contrib/ds-algorithms/Sliding_Window.md rename to contrib/ds-algorithms/sliding-window.md From be0b5b69324ee8e4cb997d327e4ac753e1499093 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 03:22:13 +0530 Subject: [PATCH 40/76] 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 653cc98..a07dc23 100644 --- a/contrib/ds-algorithms/index.md +++ b/contrib/ds-algorithms/index.md @@ -10,4 +10,4 @@ - [Greedy Algorithms](greedy-algorithms.md) - [Dynamic Programming](dynamic-programming.md) - [Linked list](linked-list.md) -- [Trie](Trie.md) +- [Trie](trie.md) From 1b0bd26cbe9aebbdfbf9135384f4928371a61a19 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 03:22:32 +0530 Subject: [PATCH 41/76] Rename Trie.md to trie.md --- contrib/ds-algorithms/{Trie.md => trie.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/ds-algorithms/{Trie.md => trie.md} (100%) diff --git a/contrib/ds-algorithms/Trie.md b/contrib/ds-algorithms/trie.md similarity index 100% rename from contrib/ds-algorithms/Trie.md rename to contrib/ds-algorithms/trie.md From 04bc0893a2c596c042dd6dae1fe401714310b582 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 03:23:47 +0530 Subject: [PATCH 42/76] Update trie.md --- contrib/ds-algorithms/trie.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/contrib/ds-algorithms/trie.md b/contrib/ds-algorithms/trie.md index 43f0415..0ccfbaa 100644 --- a/contrib/ds-algorithms/trie.md +++ b/contrib/ds-algorithms/trie.md @@ -27,7 +27,7 @@ Node Class have mainly two components Code Block of Node Class : -``` +```python class Node: def __init__(self): self.alphabets = [None] * 26 @@ -40,7 +40,7 @@ Now we need to implement Trie. We create another class named Trie with some meth Code Implementation of Initialization: -``` +```python class Trie: def __init__(self): self.root = Node() @@ -51,7 +51,7 @@ class Trie: 1. **Insertion**: Inserts the word into the Trie. This method takes `word` as parameter. For each character in the word, it checks if there is a corresponding child node. If not, it creates a new `Node`. After processing all the characters in word, it increments the `end_of_word` value of the last node. Code Implementation of Insertion: -``` +```python def insert(self, word): node = self.root for char in word: @@ -69,7 +69,7 @@ There are two cases in Searching: - *Word found*: It happens when the search word is present in the Trie. This case will occur, when the `end_of_word` value is greater than `0` of the node after traversing the whole word. Code Implementation of Searching: -``` +```python def Search(self, word): node = self.root for char in word: @@ -84,7 +84,7 @@ Code Implementation of Searching: Code Implementation of Deletion: -``` +```python def delete(self, word): node = self.root for char in word: @@ -96,7 +96,7 @@ def delete(self, word): Python Code to implement Trie: -``` +```python class Node: def __init__(self): self.alphabets = [None] * 26 From f20baa60d2614be595098b5a2d0fe4497dbfdf7e Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 03:28:14 +0530 Subject: [PATCH 43/76] Update and rename matplotlib.md to matplotlib-introduction.md --- .../{matplotlib.md => matplotlib-introduction.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/plotting-visualization/{matplotlib.md => matplotlib-introduction.md} (100%) diff --git a/contrib/plotting-visualization/matplotlib.md b/contrib/plotting-visualization/matplotlib-introduction.md similarity index 100% rename from contrib/plotting-visualization/matplotlib.md rename to contrib/plotting-visualization/matplotlib-introduction.md From 0d420ef4a6914d35a986e65db90b6329d0c30d88 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 03:28:42 +0530 Subject: [PATCH 44/76] Update index.md --- contrib/plotting-visualization/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/plotting-visualization/index.md b/contrib/plotting-visualization/index.md index 84d07f9..d20340b 100644 --- a/contrib/plotting-visualization/index.md +++ b/contrib/plotting-visualization/index.md @@ -1,6 +1,6 @@ # List of sections - [Installing Matplotlib](matplotlib-installation.md) +- [Introducing Matplotlib](matplotlib-introduction.md) - [Bar Plots in Matplotlib](matplotlib-bar-plots.md) - [Pie Charts in Matplotlib](matplotlib-pie-charts.md) -- [Introducing Matplotlib](matplotlib.md) From 31eaf9948dc9f4f2417b0b91b69a852bc5618fea Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 03:29:56 +0530 Subject: [PATCH 45/76] Rename bar-plots.md to matplotlib-bar-plots.md --- .../{bar-plots.md => matplotlib-bar-plots.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/plotting-visualization/{bar-plots.md => matplotlib-bar-plots.md} (100%) diff --git a/contrib/plotting-visualization/bar-plots.md b/contrib/plotting-visualization/matplotlib-bar-plots.md similarity index 100% rename from contrib/plotting-visualization/bar-plots.md rename to contrib/plotting-visualization/matplotlib-bar-plots.md From 358d8b05e4792ff9e819c36e14df3a3d2459c41f Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 03:32:56 +0530 Subject: [PATCH 46/76] Update matplotlib-introduction.md --- .../matplotlib-introduction.md | 32 ++++++------------- 1 file changed, 10 insertions(+), 22 deletions(-) diff --git a/contrib/plotting-visualization/matplotlib-introduction.md b/contrib/plotting-visualization/matplotlib-introduction.md index 02f17f5..691d1f8 100644 --- a/contrib/plotting-visualization/matplotlib-introduction.md +++ b/contrib/plotting-visualization/matplotlib-introduction.md @@ -12,8 +12,9 @@ To make this process of data visualization easier and clearer, matplotlib librar ## Starting with Matplotlib -### 1.] Install and import the neccasary libraries - mayplotlib.pylplot -```python +### 1. Install and import the neccasary libraries - mayplotlib.pylplot + +```bash pip install matplotlib ``` @@ -22,9 +23,7 @@ import maptplotlib.pyplot as plt import numpy as np ``` - - -### 2.] Scatter plot +### 2. Scatter plot Scatter plot is a type of plot that uses the cartesian coordinates between x and y to describe the relation between them. It uses dots to represent relation between the data variables of the data set. ```python @@ -41,12 +40,9 @@ plt.tight_layout() plt.show() ``` +![scatterplot](images/scatterplot.png) - -![scatterplot](https://github.com/Harshita297/learn-python/assets/122027571/aa57eb31-4377-4f75-9a97-e4fa7a54a938) - - -### 3.] Bar plot +### 3. Bar plot Bar plot is a type of plot that plots the frequency distrubution of the categorical variables. Each entity of the categoric variable is represented as a bar. The size of the bar represents its numeric value. ```python @@ -61,11 +57,10 @@ plt.ylabel("Y values") plt.show() ``` -![barplot](https://github.com/Harshita297/learn-python/assets/122027571/00cea3cd-a962-4bfe-b158-921389ee564c) +![barplot](images/barplot.png) - -### 4.] Histogram +### 4. Histogram Histogram is the representation of frequency distribution of qualitative data. The height of each rectangle defines the amount, or how often that variable appears. ```python @@ -79,14 +74,7 @@ plt.ylabel("Frequency Distribution") plt.show() ``` -![histogram](https://github.com/Harshita297/learn-python/assets/122027571/32470819-bac5-4bb2-9f36-79521fa40bf3) - - - - - - - - + +![histogram](images/histogram.png) From 4468dd6e91b4481fd15249bed93988ea82acfe89 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 03:35:40 +0530 Subject: [PATCH 47/76] Rename Descriptive_Statistics.md to descriptive-statistics.md --- .../{Descriptive_Statistics.md => descriptive-statistics.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/pandas/{Descriptive_Statistics.md => descriptive-statistics.md} (100%) diff --git a/contrib/pandas/Descriptive_Statistics.md b/contrib/pandas/descriptive-statistics.md similarity index 100% rename from contrib/pandas/Descriptive_Statistics.md rename to contrib/pandas/descriptive-statistics.md From 06935da991e2412c60f3b750b3dfe2955e2c4f5e Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 03:36:05 +0530 Subject: [PATCH 48/76] Rename GroupBy_Functions_Pandas.md to groupby-functions.md --- .../pandas/{GroupBy_Functions_Pandas.md => groupby-functions.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/pandas/{GroupBy_Functions_Pandas.md => groupby-functions.md} (100%) diff --git a/contrib/pandas/GroupBy_Functions_Pandas.md b/contrib/pandas/groupby-functions.md similarity index 100% rename from contrib/pandas/GroupBy_Functions_Pandas.md rename to contrib/pandas/groupby-functions.md From df9ed4453f022c338fdfc4b588ac6fee861ece0b Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 03:36:22 +0530 Subject: [PATCH 49/76] Rename excel_with_pandas.md to excel-with-pandas.md --- contrib/pandas/{excel_with_pandas.md => excel-with-pandas.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/pandas/{excel_with_pandas.md => excel-with-pandas.md} (100%) diff --git a/contrib/pandas/excel_with_pandas.md b/contrib/pandas/excel-with-pandas.md similarity index 100% rename from contrib/pandas/excel_with_pandas.md rename to contrib/pandas/excel-with-pandas.md From 6df31671efe29800d369ec7c9d74dcb297a18b6a Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 03:36:42 +0530 Subject: [PATCH 50/76] Rename pandas_series_vs_numpy_ndarray.md to pandas-series-vs-numpy-ndarray.md --- ...ries_vs_numpy_ndarray.md => pandas-series-vs-numpy-ndarray.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/pandas/{pandas_series_vs_numpy_ndarray.md => pandas-series-vs-numpy-ndarray.md} (100%) diff --git a/contrib/pandas/pandas_series_vs_numpy_ndarray.md b/contrib/pandas/pandas-series-vs-numpy-ndarray.md similarity index 100% rename from contrib/pandas/pandas_series_vs_numpy_ndarray.md rename to contrib/pandas/pandas-series-vs-numpy-ndarray.md From ccd5dea51ebf9da139f85762c98f42ec3f26f9cc Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 03:37:36 +0530 Subject: [PATCH 51/76] Update index.md --- contrib/pandas/index.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/contrib/pandas/index.md b/contrib/pandas/index.md index c17d839..33c7347 100644 --- a/contrib/pandas/index.md +++ b/contrib/pandas/index.md @@ -1,10 +1,10 @@ # List of sections - [Pandas Introduction and Dataframes in Pandas](introduction.md) -- [Pandas Series Vs NumPy ndarray](pandas_series_vs_numpy_ndarray.md) -- [Pandas Descriptive Statistics](Descriptive_Statistics.md) -- [Group By Functions with Pandas](GroupBy_Functions_Pandas.md) -- [Excel using Pandas DataFrame](excel_with_pandas.md) +- [Pandas Series Vs NumPy ndarray](pandas-series-vs-numpy-ndarray.md) +- [Pandas Descriptive Statistics](descriptive-statistics.md) +- [Group By Functions with Pandas](groupby-functions.md) +- [Excel using Pandas DataFrame](excel-with-pandas.md) - [Working with Date & Time in Pandas](datetime.md) - [Importing and Exporting Data in Pandas](import-export.md) - [Handling Missing Values in Pandas](handling-missing-values.md) From 59773b20eed7cff5a85b5785f96edfe4e90a7139 Mon Sep 17 00:00:00 2001 From: "Kaza.Sunitha" <133480054+Kazasunitha@users.noreply.github.com> Date: Sun, 2 Jun 2024 03:49:19 +0530 Subject: [PATCH 52/76] Added viewing_data_in_pandas.md file (#660) * Add files via upload * Update classes.md * Update classes.md * Update classes.md * Update index.md * Update classes.md * Update index.md * Add files via upload * Update viewing_data_in_pandas.md * Delete contrib/advanced-python/classes.md * Delete contrib/advanced-python/index.md * Revert "Delete contrib/advanced-python/index.md" This reverts commit 55fe958c1c57daf431b3dce1d5d103bb8361612a. * Update index.md * Update viewing_data_in_pandas.md Updated the heading format * Update index.md * Update and rename viewing_data_in_pandas.md to viewing-data.md * Update viewing-data.md --------- Co-authored-by: Ankit Mahato --- contrib/pandas/index.md | 1 + contrib/pandas/viewing-data.md | 67 ++++++++++++++++++++++++++++++++++ 2 files changed, 68 insertions(+) create mode 100644 contrib/pandas/viewing-data.md diff --git a/contrib/pandas/index.md b/contrib/pandas/index.md index 33c7347..e5a8353 100644 --- a/contrib/pandas/index.md +++ b/contrib/pandas/index.md @@ -1,6 +1,7 @@ # List of sections - [Pandas Introduction and Dataframes in Pandas](introduction.md) +- [Viewing data in pandas](viewing-data.md) - [Pandas Series Vs NumPy ndarray](pandas-series-vs-numpy-ndarray.md) - [Pandas Descriptive Statistics](descriptive-statistics.md) - [Group By Functions with Pandas](groupby-functions.md) diff --git a/contrib/pandas/viewing-data.md b/contrib/pandas/viewing-data.md new file mode 100644 index 0000000..8aaa4ae --- /dev/null +++ b/contrib/pandas/viewing-data.md @@ -0,0 +1,67 @@ +# Viewing rows of the frame + +## `head()` method + +The pandas library in Python provides a convenient method called `head()` that allows you to view the first few rows of a DataFrame. Let me explain how it works: +- The `head()` function returns the first n rows of a DataFrame or Series. +- By default, it displays the first 5 rows, but you can specify a different number of rows using the n parameter. + +### Syntax + +```python +dataframe.head(n) +``` + +`n` is the Optional value. The number of rows to return. Default value is `5`. + +### Example + +```python +import pandas as pd +df = pd.DataFrame({'animal': ['alligator', 'bee', 'falcon', 'lion','tiger','rabit','dog','fox','monkey','elephant']}) +df.head(n=5) +``` + +#### Output + +``` + animal +0 alligator +1 bee +2 falcon +3 lion +4 tiger +``` + +## `tail()` method + +The `tail()` function in Python displays the last five rows of the dataframe by default. It takes in a single parameter: the number of rows. We can use this parameter to display the number of rows of our choice. +- The `tail()` function returns the last n rows of a DataFrame or Series. +- By default, it displays the last 5 rows, but you can specify a different number of rows using the n parameter. + +### Syntax + +```python +dataframe.tail(n) +``` + +`n` is the Optional value. The number of rows to return. Default value is `5`. + +### Example + +```python +import pandas as pd +df = pd.DataFrame({'fruits': ['mongo', 'orange', 'apple', 'lemon','banana','water melon','papaya','grapes','cherry','coconut']}) +df.tail(n=5) +``` + +#### Output + +``` + fruits +5 water melon +6 papaya +7 grapes +8 cherry +9 coconut +``` From 839211767809e4c7d53f38dd21dafd90ccd9bd5a Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 03:56:26 +0530 Subject: [PATCH 53/76] Update matplotlib-line-plot.md --- .../matplotlib-line-plot.md | 56 +++++++++++-------- 1 file changed, 33 insertions(+), 23 deletions(-) diff --git a/contrib/plotting-visualization/matplotlib-line-plot.md b/contrib/plotting-visualization/matplotlib-line-plot.md index 566fdbb..b7488e6 100644 --- a/contrib/plotting-visualization/matplotlib-line-plot.md +++ b/contrib/plotting-visualization/matplotlib-line-plot.md @@ -1,24 +1,27 @@ # Line Chart in Matplotlib A line chart is a simple way to visualize data where we connect individual data points. It helps us to see trends and patterns over time or across categories. -
This type of chart is particularly useful for:
-* Comparing Data: Comparing multiple datasets on the same axes. -* Highlighting Changes: Illustrating changes and patterns in data. -* Visualizing Trends: Showing trends over time or other continuous variables. + +This type of chart is particularly useful for: +- Comparing Data: Comparing multiple datasets on the same axes. +- Highlighting Changes: Illustrating changes and patterns in data. +- Visualizing Trends: Showing trends over time or other continuous variables. ## Prerequisites -Line plots can be created in Python with Matplotlib's ``pyplot`` library. To build a line plot, first import ``Matplotlib``. It is a standard convention to import Matplotlib's pyplot library as ``plt``. -``` -import matplotlib.pyplot as plt +Line plots can be created in Python with Matplotlib's `pyplot` library. To build a line plot, first import `matplotlib`. It is a standard convention to import Matplotlib's pyplot library as `plt`. + +```python +import matplotlib.pyplot as plt ``` ## Creating a simple Line Plot First import matplotlib and numpy, these are useful for charting. -
You can use the ``plot(x,y)`` method to create a line chart.
-``` +You can use the `plot(x,y)` method to create a line chart. + +```python import matplotlib.pyplot as plt import numpy as np @@ -29,6 +32,7 @@ y = 2*x + 1 plt.plot(x, y) plt.show() ``` + When executed, this will show the following line plot: ![Basic line Chart](images/simple_line.png) @@ -36,8 +40,9 @@ When executed, this will show the following line plot: ## Curved line -The ``plot()`` method also works for other types of line charts. It doesn’t need to be a straight line, y can have any type of values. -``` +The `plot()` method also works for other types of line charts. It doesn’t need to be a straight line, y can have any type of values. + +```python import matplotlib.pyplot as plt import numpy as np @@ -47,6 +52,7 @@ y = 2**x + 1 plt.plot(x, y) plt.show() ``` + When executed, this will show the following Curved line plot: ![Curved line](images/line-curve.png) @@ -54,8 +60,9 @@ When executed, this will show the following Curved line plot: ## Line with Labels -To know what you are looking at, you need meta data. Labels are a type of meta data. They show what the chart is about. The chart has an ``x label``, ``y label`` and ``title``. -``` +To know what you are looking at, you need meta data. Labels are a type of meta data. They show what the chart is about. The chart has an `x label`, `y label` and `title`. + +```python import matplotlib.pyplot as plt import numpy as np @@ -72,15 +79,16 @@ plt.title("With Labels") plt.show() ``` + When executed, this will show the following line with labels plot: ![line with labels](images/line-labels.png) ## Multiple lines -More than one line can be in the plot. To add another line, just call the ``plot(x,y)`` function again. In the example below we have two different values for ``y(y1,y2)`` that are plotted onto the chart. +More than one line can be in the plot. To add another line, just call the `plot(x,y)` function again. In the example below we have two different values for `y(y1,y2)` that are plotted onto the chart. -``` +```python import matplotlib.pyplot as plt import numpy as np @@ -98,6 +106,7 @@ plt.plot(x, y1, plt.show() ``` + When executed, this will show the following Multiple lines plot: ![multiple lines](images/two-lines.png) @@ -105,9 +114,9 @@ When executed, this will show the following Multiple lines plot: ## Dotted line -Lines can be in the form of dots like the image below. Instead of calling ``plot(x,y)`` call the ``scatter(x,y)`` method. The ``scatter(x,y)`` method can also be used to (randomly) plot points onto the chart. +Lines can be in the form of dots like the image below. Instead of calling `plot(x,y)` call the `scatter(x,y)` method. The `scatter(x,y)` method can also be used to (randomly) plot points onto the chart. -``` +```python import matplotlib.pyplot as plt import numpy as np @@ -130,9 +139,9 @@ When executed, this will show the following Dotted line plot: ## Line ticks -You can change the ticks on the plot. Set them on the ``x-axis``, ``y-axis`` or even change their color. The line can be more thick and have an alpha value. +You can change the ticks on the plot. Set them on the `x-axis`, `y-axis` or even change their color. The line can be more thick and have an alpha value. -``` +```python import matplotlib.pyplot as plt import numpy as np @@ -166,9 +175,9 @@ When executed, this will show the following line ticks plot: ## Line with asymptote -An asymptote can be added to the plot. To do that, use ``plt.annotate()``. There’s lso a dotted line in the plot below. You can play around with the code to see how it works. +An asymptote can be added to the plot. To do that, use `plt.annotate()`. There’s lso a dotted line in the plot below. You can play around with the code to see how it works. -``` +```python import matplotlib.pyplot as plt import numpy as np @@ -222,9 +231,9 @@ When executed, this will show the following Line with asymptote plot: ## Line with text scale -It doesn’t have to be a numeric scale. The scale can also contain textual words like the example below. In ``plt.yticks()`` we just pass a list with text values. These values are then show against the ``y axis``. +It doesn’t have to be a numeric scale. The scale can also contain textual words like the example below. In `plt.yticks()` we just pass a list with text values. These values are then show against the `y axis`. -``` +```python import matplotlib.pyplot as plt import numpy as np @@ -261,6 +270,7 @@ ax.spines['left'].set_position(('data', 0)) plt.show() ``` + When executed, this will show the following Line with text scale plot: ![Line with text scale](images/line-with-text-scale.png) From aeb947bb1a0c3a44ff3b2ece398649bae6a9e5df Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 03:59:12 +0530 Subject: [PATCH 54/76] Rename ArtificialNeuralNetwork.md to ann.md --- contrib/machine-learning/{ArtificialNeuralNetwork.md => ann.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/machine-learning/{ArtificialNeuralNetwork.md => ann.md} (100%) diff --git a/contrib/machine-learning/ArtificialNeuralNetwork.md b/contrib/machine-learning/ann.md similarity index 100% rename from contrib/machine-learning/ArtificialNeuralNetwork.md rename to contrib/machine-learning/ann.md From e3c2799e6f8a4e58ba00778d7d0cedc76b1495b3 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 03:59:31 +0530 Subject: [PATCH 55/76] Rename Decision-Tree.md to decision-tree.md --- contrib/machine-learning/{Decision-Tree.md => decision-tree.md} | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename contrib/machine-learning/{Decision-Tree.md => decision-tree.md} (99%) diff --git a/contrib/machine-learning/Decision-Tree.md b/contrib/machine-learning/decision-tree.md similarity index 99% rename from contrib/machine-learning/Decision-Tree.md rename to contrib/machine-learning/decision-tree.md index 6563a22..8159bcf 100644 --- a/contrib/machine-learning/Decision-Tree.md +++ b/contrib/machine-learning/decision-tree.md @@ -254,4 +254,4 @@ The final decision tree classifies instances based on the following rules: - If Outlook is Rain and Wind is Weak, PlayTennis is Yes - If Outlook is Rain and Wind is Strong, PlayTennis is No -> Note that the calculated entropies and information gains may vary slightly depending on the specific implementation and rounding methods used. \ No newline at end of file +> Note that the calculated entropies and information gains may vary slightly depending on the specific implementation and rounding methods used. From 038bc20a2805baf4165129ebdda4dc059cdcb223 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 03:59:44 +0530 Subject: [PATCH 56/76] Rename Regression.md to regression.md --- contrib/machine-learning/{Regression.md => regression.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/machine-learning/{Regression.md => regression.md} (100%) diff --git a/contrib/machine-learning/Regression.md b/contrib/machine-learning/regression.md similarity index 100% rename from contrib/machine-learning/Regression.md rename to contrib/machine-learning/regression.md From b19af0241afc2e239dfe31039d743ff888ec59ec Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 04:00:07 +0530 Subject: [PATCH 57/76] Rename tensorFlow.md to tensorflow.md --- contrib/machine-learning/{tensorFlow.md => tensorflow.md} | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename contrib/machine-learning/{tensorFlow.md => tensorflow.md} (99%) diff --git a/contrib/machine-learning/tensorFlow.md b/contrib/machine-learning/tensorflow.md similarity index 99% rename from contrib/machine-learning/tensorFlow.md rename to contrib/machine-learning/tensorflow.md index 1d9357d..b2c847c 100644 --- a/contrib/machine-learning/tensorFlow.md +++ b/contrib/machine-learning/tensorflow.md @@ -61,4 +61,4 @@ TensorFlow is a great choice if you: ## Example Use Cases - Building and deploying complex neural networks for image recognition, natural language processing, or recommendation systems. -- Developing models that need to be run on mobile or embedded devices. \ No newline at end of file +- Developing models that need to be run on mobile or embedded devices. From f909c3a2b92d6e1379b9ee276edf78b06592c6a5 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 04:00:31 +0530 Subject: [PATCH 58/76] Rename binomial_distribution.md to binomial-distribution.md --- .../{binomial_distribution.md => binomial-distribution.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/machine-learning/{binomial_distribution.md => binomial-distribution.md} (100%) diff --git a/contrib/machine-learning/binomial_distribution.md b/contrib/machine-learning/binomial-distribution.md similarity index 100% rename from contrib/machine-learning/binomial_distribution.md rename to contrib/machine-learning/binomial-distribution.md From 3b161f57fabb3c1665ccec773578dfcd3377dbe5 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 04:00:53 +0530 Subject: [PATCH 59/76] Rename Types_of_optimizers.md to types-of-optimizers.md --- .../{Types_of_optimizers.md => types-of-optimizers.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/machine-learning/{Types_of_optimizers.md => types-of-optimizers.md} (100%) diff --git a/contrib/machine-learning/Types_of_optimizers.md b/contrib/machine-learning/types-of-optimizers.md similarity index 100% rename from contrib/machine-learning/Types_of_optimizers.md rename to contrib/machine-learning/types-of-optimizers.md From 87b2d056531182f69dea33798807b895214c731c Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 04:01:46 +0530 Subject: [PATCH 60/76] Update index.md --- contrib/machine-learning/index.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index e56691d..a0dd999 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -1,16 +1,16 @@ # List of sections -- [Binomial Distribution](binomial_distribution.md) -- [Regression in Machine Learning](Regression.md) +- [Binomial Distribution](binomial-distribution.md) +- [Regression in Machine Learning](regression.md) - [Confusion Matrix](confusion-matrix.md) -- [Decision Tree Learning](Decision-Tree.md) +- [Decision Tree Learning](decision-tree.md) - [Random Forest](random-forest.md) - [Support Vector Machine Algorithm](support-vector-machine.md) -- [Artificial Neural Network from the Ground Up](ArtificialNeuralNetwork.md) +- [Artificial Neural Network from the Ground Up](ann.md) - [Introduction To Convolutional Neural Networks (CNNs)](intro-to-cnn.md) -- [TensorFlow.md](tensorFlow.md) +- [TensorFlow.md](tensorflow.md) - [PyTorch.md](pytorch.md) -- [Types of optimizers](Types_of_optimizers.md) +- [Types of optimizers](types-of-optimizers.md) - [Logistic Regression](logistic-regression.md) - [Clustering](clustering.md) - [Grid Search](grid-search.md) From d6e610d8cc3505c806687db1d4dfb9ad0f4f5e66 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 04:03:36 +0530 Subject: [PATCH 61/76] Rename sklearn.md to sklearn-introduction.md --- .../machine-learning/{sklearn.md => sklearn-introduction.md} | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename contrib/machine-learning/{sklearn.md => sklearn-introduction.md} (99%) diff --git a/contrib/machine-learning/sklearn.md b/contrib/machine-learning/sklearn-introduction.md similarity index 99% rename from contrib/machine-learning/sklearn.md rename to contrib/machine-learning/sklearn-introduction.md index 8174d93..7bb5aa8 100644 --- a/contrib/machine-learning/sklearn.md +++ b/contrib/machine-learning/sklearn-introduction.md @@ -141,4 +141,4 @@ print(f"Accuracy: {accuracy * 100:.2f}%") ## Conclusion -scikit-learn is a powerful and versatile library that can be used for a wide range of machine learning tasks. It is particularly well-suited for beginners due to its easy-to-use interface and extensive documentation. Whether you are working on a simple classification task or a more complex clustering problem, scikit-learn provides the tools you need to build and evaluate your models effectively. \ No newline at end of file +scikit-learn is a powerful and versatile library that can be used for a wide range of machine learning tasks. It is particularly well-suited for beginners due to its easy-to-use interface and extensive documentation. Whether you are working on a simple classification task or a more complex clustering problem, scikit-learn provides the tools you need to build and evaluate your models effectively. From f671f4d3e9229ba6c622bad87a3972d4e74fea39 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 04:10:58 +0530 Subject: [PATCH 62/76] Rename matplotlib-line-plot.md to matplotlib-line-plots.md --- .../{matplotlib-line-plot.md => matplotlib-line-plots.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/plotting-visualization/{matplotlib-line-plot.md => matplotlib-line-plots.md} (100%) diff --git a/contrib/plotting-visualization/matplotlib-line-plot.md b/contrib/plotting-visualization/matplotlib-line-plots.md similarity index 100% rename from contrib/plotting-visualization/matplotlib-line-plot.md rename to contrib/plotting-visualization/matplotlib-line-plots.md From c7746086b91d44369aa6f8e7f3c4a863cd655da2 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 04:18:14 +0530 Subject: [PATCH 63/76] Rename Types_of_Cost_Functions.md to cost-functions.md --- .../{Types_of_Cost_Functions.md => cost-functions.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/machine-learning/{Types_of_Cost_Functions.md => cost-functions.md} (100%) diff --git a/contrib/machine-learning/Types_of_Cost_Functions.md b/contrib/machine-learning/cost-functions.md similarity index 100% rename from contrib/machine-learning/Types_of_Cost_Functions.md rename to contrib/machine-learning/cost-functions.md From f125cf4a33de46d7f8fee1066a347551f71ea13a Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 04:26:45 +0530 Subject: [PATCH 64/76] Update cost-functions.md --- contrib/machine-learning/cost-functions.md | 41 ++++++++++------------ 1 file changed, 19 insertions(+), 22 deletions(-) diff --git a/contrib/machine-learning/cost-functions.md b/contrib/machine-learning/cost-functions.md index f650726..c1fe217 100644 --- a/contrib/machine-learning/cost-functions.md +++ b/contrib/machine-learning/cost-functions.md @@ -14,9 +14,9 @@ MSE is one of the most commonly used cost functions, particularly in regression The MSE is defined as: $$MSE = \frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2$$ Where: -- \( n \) is the number of samples. -- \( y_i \) is the actual value. -- \( y^i\) is the predicted value. +- `n` is the number of samples. +- $y_i$ is the actual value. +- $\hat{y}_i$ is the predicted value. **Advantages:** - Sensitive to large errors due to squaring. @@ -43,9 +43,9 @@ MAE is another commonly used cost function for regression tasks. It measures the The MAE is defined as: $$MAE = \frac{1}{n} \sum_{i=1}^{n} |y_i - \hat{y}_i|$$ Where: -- \( n \) is the number of samples. -- \( y_i \) is the actual value. -- \( y^i\) is the predicted value. +- `n` is the number of samples. +- $y_i$ is the actual value. +- $\hat{y}_i$ is the predicted value. **Advantages:** - Less sensitive to outliers compared to MSE. @@ -76,9 +76,9 @@ For binary classification, the cross-entropy loss is defined as: $$\text{Cross-Entropy} = -\frac{1}{n} \sum_{i=1}^{n} [y_i \log(\hat{y}_i) + (1 - y_i) \log(1 - \hat{y}_i)]$$ Where: -- \( n \) is the number of samples. -- \( y_i \) is the actual class label (0 or 1). -- \( y^i\) is the predicted probability of the positive class. +- `n` is the number of samples. +- $y_i$ is the actual class label (0 or 1). +- $\hat{y}_i$ is the predicted probability of the positive class. **Advantages:** @@ -109,11 +109,10 @@ The multiclass cross-entropy loss is defined as: $$\text{Cross-Entropy} = -\frac{1}{n} \sum_{i=1}^{n} \sum_{c=1}^{C} y_{i,c} \log(\hat{y}_{i,c})$$ Where: -- \( n \) is the number of samples. -- \( C \) is the number of classes. -- \( y_{i,c} \) is the indicator function for the true class of sample \( i \). - -- (y^i,c) is the predicted probability of sample \( i \) belonging to class \( c \). +- `n` is the number of samples. +- `C` is the number of classes. +- $y_{i,c}$ is the indicator function for the true class of sample `i`. +- $\hat{y}_{i,c}$ is the predicted probability of sample `i` belonging to class `c`. **Advantages:** - Handles multiple classes effectively. @@ -143,9 +142,9 @@ For binary classification, the hinge loss is defined as: $$\text{Hinge Loss} = \frac{1}{n} \sum_{i=1}^{n} \max(0, 1 - y_i \cdot \hat{y}_i)$$ Where: -- \( n \) is the number of samples. -- \( y_i \) is the actual class label (-1 or 1). -- \( \hat{y}_i \) is the predicted score for sample \( i \). +- `n` is the number of samples. +- $y_i$ is the actual class label (-1 or 1). +- $\hat{y}_i$ is the predicted score for sample \( i \). **Advantages:** - Encourages margin maximization in SVMs. @@ -182,8 +181,8 @@ $$\text{Huber Loss} = \frac{1}{n} \sum_{i=1}^{n} \left\{ \right.$$ Where: -- \( n \) is the number of samples. -- \(delta\) is a threshold parameter. +- `n` is the number of samples. +- $\delta$ is a threshold parameter. **Advantages:** - Provides a smooth loss function. @@ -214,7 +213,7 @@ The Log-Cosh loss is defined as: $$\text{Log-Cosh Loss} = \frac{1}{n} \sum_{i=1}^{n} \log(\cosh(y_i - \hat{y}_i))$$ Where: -- \( n \) is the number of samples. +- `n` is the number of samples. **Advantages:** - Smooth and differentiable everywhere. @@ -234,5 +233,3 @@ def logcosh_loss(y_true, y_pred): ``` These implementations provide various options for cost functions suitable for different machine learning tasks. Each function has its advantages and disadvantages, making them suitable for different scenarios and problem domains. - ---- From 15fed7ee6e448637b27ecdc23ef5157ed2ef5ce4 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 04:31:21 +0530 Subject: [PATCH 65/76] Update index.md --- contrib/api-development/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/api-development/index.md b/contrib/api-development/index.md index 7278907..8d4dc59 100644 --- a/contrib/api-development/index.md +++ b/contrib/api-development/index.md @@ -1,4 +1,4 @@ # List of sections - [API Methods](api-methods.md) -- [FastAPI](fast-api.md) \ No newline at end of file +- [FastAPI](fast-api.md) From f65c949564f003591c746088bf8c25be44a65853 Mon Sep 17 00:00:00 2001 From: Ritesh Date: Sun, 2 Jun 2024 09:17:22 +0530 Subject: [PATCH 66/76] Create hierarchical-clustering.md --- .../hierarchical-clustering.md | 119 ++++++++++++++++++ 1 file changed, 119 insertions(+) create mode 100644 contrib/machine-learning/hierarchical-clustering.md diff --git a/contrib/machine-learning/hierarchical-clustering.md b/contrib/machine-learning/hierarchical-clustering.md new file mode 100644 index 0000000..3f9e606 --- /dev/null +++ b/contrib/machine-learning/hierarchical-clustering.md @@ -0,0 +1,119 @@ +# Hierarchical Clustering + +Hierarchical Clustering is a method of cluster analysis that seeks to build a hierarchy of clusters. This README provides an overview of the hierarchical clustering algorithm, including its fundamental concepts, types, steps, and how to implement it using Python. + +## Table of Contents + +1. [Introduction](#introduction) +2. [Concepts](#concepts) +3. [Types of Hierarchical Clustering](#types-of-hierarchical-clustering) +4. [Steps in Hierarchical Clustering](#steps-in-hierarchical-clustering) +5. [Linkage Criteria](#linkage-criteria) +6. [Implementation](#implementation) + - [Using Scikit-learn](#using-scikit-learn) + - [Code Example](#code-example) +7. [Evaluation Metrics](#evaluation-metrics) +8. [Conclusion](#conclusion) +9. [References](#references) + +## Introduction + +Hierarchical Clustering is an unsupervised learning method used to group similar objects into clusters. Unlike other clustering techniques, hierarchical clustering does not require the number of clusters to be specified beforehand. It produces a tree-like structure called a dendrogram, which displays the arrangement of the clusters and their sub-clusters. + +## Concepts + +### Dendrogram + +A dendrogram is a tree-like diagram that records the sequences of merges or splits. It is a useful tool for visualizing the process of hierarchical clustering. + +### Distance Measure + +Distance measures are used to quantify the similarity or dissimilarity between data points. Common distance measures include Euclidean distance, Manhattan distance, and cosine similarity. + +### Linkage Criteria + +Linkage criteria determine how the distance between clusters is calculated. Different linkage criteria include single linkage, complete linkage, average linkage, and Ward's linkage. + +## Types of Hierarchical Clustering + +1. **Agglomerative Clustering (Bottom-Up Approach)**: + - Starts with each data point as a separate cluster. + - Repeatedly merges the closest pairs of clusters until only one cluster remains or a stopping criterion is met. + +2. **Divisive Clustering (Top-Down Approach)**: + - Starts with all data points in a single cluster. + - Repeatedly splits clusters into smaller clusters until each data point is its own cluster or a stopping criterion is met. + +## Steps in Hierarchical Clustering + +1. **Calculate Distance Matrix**: Compute the distance between each pair of data points. +2. **Create Clusters**: Treat each data point as a single cluster. +3. **Merge Closest Clusters**: Find the two clusters that are closest to each other and merge them into a single cluster. +4. **Update Distance Matrix**: Update the distance matrix to reflect the distance between the new cluster and the remaining clusters. +5. **Repeat**: Repeat steps 3 and 4 until all data points are merged into a single cluster or the desired number of clusters is achieved. + +## Linkage Criteria + +1. **Single Linkage (Minimum Linkage)**: The distance between two clusters is defined as the minimum distance between any single data point in the first cluster and any single data point in the second cluster. +2. **Complete Linkage (Maximum Linkage)**: The distance between two clusters is defined as the maximum distance between any single data point in the first cluster and any single data point in the second cluster. +3. **Average Linkage**: The distance between two clusters is defined as the average distance between all pairs of data points, one from each cluster. +4. **Ward's Linkage**: The distance between two clusters is defined as the increase in the sum of squared deviations from the mean when the two clusters are merged. + +## Implementation + +### Using Scikit-learn + +Scikit-learn is a popular machine learning library in Python that provides tools for hierarchical clustering. + +### Code Example + +```python +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +from scipy.cluster.hierarchy import dendrogram, linkage +from sklearn.cluster import AgglomerativeClustering +from sklearn.preprocessing import StandardScaler + +# Load dataset +data = pd.read_csv('path/to/your/dataset.csv') + +# Preprocess the data +scaler = StandardScaler() +data_scaled = scaler.fit_transform(data) + +# Perform hierarchical clustering +Z = linkage(data_scaled, method='ward') + +# Plot the dendrogram +plt.figure(figsize=(10, 7)) +dendrogram(Z) +plt.title('Dendrogram') +plt.xlabel('Data Points') +plt.ylabel('Distance') +plt.show() + +# Perform Agglomerative Clustering +agg_clustering = AgglomerativeClustering(n_clusters=3, affinity='euclidean', linkage='ward') +labels = agg_clustering.fit_predict(data_scaled) + +# Add cluster labels to the original data +data['Cluster'] = labels +print(data.head()) +``` + +## Evaluation Metrics + +- **Silhouette Score**: Measures how similar a data point is to its own cluster compared to other clusters. +- **Cophenetic Correlation Coefficient**: Measures how faithfully a dendrogram preserves the pairwise distances between the original data points. +- **Dunn Index**: Ratio of the minimum inter-cluster distance to the maximum intra-cluster distance. + +## Conclusion + +Hierarchical clustering is a versatile and intuitive method for clustering data. It is particularly useful when the number of clusters is not known beforehand. By understanding the different linkage criteria and evaluation metrics, one can effectively apply hierarchical clustering to various types of data. + +## References + +- [Scikit-learn Documentation](https://scikit-learn.org/stable/modules/clustering.html#hierarchical-clustering) +- [Wikipedia: Hierarchical Clustering](https://en.wikipedia.org/wiki/Hierarchical_clustering) +- [Towards Data Science: Hierarchical Clustering Explained](https://towardsdatascience.com/hierarchical-clustering-explained-925d9e1600c1) From 5b6cd66f79e2cc0341f94bd0ec3ee935ccf8a20c Mon Sep 17 00:00:00 2001 From: Ritesh Date: Sun, 2 Jun 2024 09:21:45 +0530 Subject: [PATCH 67/76] Update index.md --- contrib/machine-learning/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index b6945cd..20305dc 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -15,4 +15,5 @@ - [Logistic Regression](logistic-regression.md) - [Types_of_Cost_Functions](cost-functions.md) - [Clustering](clustering.md) +- [Hierarchical Clustering](hierarchical-clustering.md) - [Grid Search](grid-search.md) From 643cadfa330d4fa5443ba1f4044ecc5942e2b7f3 Mon Sep 17 00:00:00 2001 From: manishh12 Date: Sun, 2 Jun 2024 11:54:06 +0530 Subject: [PATCH 68/76] Added Generators under Advance python --- contrib/advanced-python/Generators.md | 87 +++++++++++++++++++++++++++ contrib/advanced-python/index.md | 1 + 2 files changed, 88 insertions(+) create mode 100644 contrib/advanced-python/Generators.md diff --git a/contrib/advanced-python/Generators.md b/contrib/advanced-python/Generators.md new file mode 100644 index 0000000..ff5226e --- /dev/null +++ b/contrib/advanced-python/Generators.md @@ -0,0 +1,87 @@ +# Generators Under Advanced Python + +## Introduction + +Generators in Python are a sophisticated feature that enables the creation of iterators without the need to construct a full list in memory. They allow you to generate values on-the-fly, which is particularly beneficial for working with large datasets or infinite sequences. In this README, we'll explore generators in depth, covering their types, mathematical formulation, advantages, disadvantages, and implementation examples. + +## Function Generators + +Function generators are created using the `yield` keyword within a function. When invoked, a function generator returns a generator iterator, allowing you to iterate over the values generated by the function. + +### Mathematical Formulation + +Function generators can be represented mathematically using set-builder notation. The general form is: + +``` +{expression | variable in iterable, condition} +``` + +Where: +- `expression` is the expression to generate values. +- `variable` is the variable used in the expression. +- `iterable` is the sequence of values to iterate over. +- `condition` is an optional condition that filters the values. + +### Advantages of Function Generators + +1. **Memory Efficiency**: Function generators produce values lazily, meaning they generate values only when needed, saving memory compared to constructing an entire sequence upfront. + +2. **Lazy Evaluation**: Values are generated on-the-fly as they are consumed, leading to improved performance and reduced overhead, especially when dealing with large datasets. + +3. **Infinite Sequences**: Function generators can represent infinite sequences, such as the Fibonacci sequence, allowing you to work with data streams of arbitrary length without consuming excessive memory. + +### Disadvantages of Function Generators + +1. **Single Iteration**: Once a function generator is exhausted, it cannot be reused. If you need to iterate over the sequence again, you'll have to create a new generator. + +2. **Limited Random Access**: Function generators do not support random access like lists. They only allow sequential access, which might be a limitation depending on the use case. + +### Implementation Example + +```python +def fibonacci(): + a, b = 0, 1 + while True: + yield a + a, b = b, a + b + +# Usage +fib_gen = fibonacci() +for _ in range(10): + print(next(fib_gen)) +``` + +## Generator Expressions + +Generator expressions are similar to list comprehensions but return a generator object instead of a list. They offer a concise way to create generators without the need for a separate function. + +### Mathematical Formulation + +Generator expressions can also be represented mathematically using set-builder notation. The general form is the same as for function generators. + +### Advantages of Generator Expressions + +1. **Memory Efficiency**: Generator expressions produce values lazily, similar to function generators, resulting in memory savings. + +2. **Lazy Evaluation**: Values are generated on-the-fly as they are consumed, providing improved performance and reduced overhead. + +### Disadvantages of Generator Expressions + +1. **Single Iteration**: Like function generators, once a generator expression is exhausted, it cannot be reused. + +2. **Limited Random Access**: Generator expressions, similar to function generators, do not support random access. + +### Implementation Example + +```python +# Generate squares of numbers from 0 to 9 +square_gen = (x**2 for x in range(10)) + +# Usage +for num in square_gen: + print(num) +``` + +## Conclusion + +Generators offer a powerful mechanism for creating iterators efficiently in Python. By understanding the differences between function generators and generator expressions, along with their mathematical formulation, advantages, and disadvantages, you can leverage them effectively in various scenarios. Whether you're dealing with large datasets or need to work with infinite sequences, generators provide a memory-efficient solution with lazy evaluation capabilities, contributing to more elegant and scalable code. \ No newline at end of file diff --git a/contrib/advanced-python/index.md b/contrib/advanced-python/index.md index fa2fd7b..df4b545 100644 --- a/contrib/advanced-python/index.md +++ b/contrib/advanced-python/index.md @@ -9,3 +9,4 @@ - [Map Function](map-function.md) - [Protocols](protocols.md) - [Exception Handling in Python](exception-handling.md) +- [Generators](Generators.md) From f84da4252f1a80405a98451389d70c0ec2d1d3e0 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 21:39:17 +0530 Subject: [PATCH 69/76] Rename OOPs.md to oops.md --- contrib/advanced-python/{OOPs.md => oops.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/advanced-python/{OOPs.md => oops.md} (100%) diff --git a/contrib/advanced-python/OOPs.md b/contrib/advanced-python/oops.md similarity index 100% rename from contrib/advanced-python/OOPs.md rename to contrib/advanced-python/oops.md From 18003e05e2d9c9f019601c0bddd89e7eab456d90 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 21:39:35 +0530 Subject: [PATCH 70/76] Update index.md --- contrib/advanced-python/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/advanced-python/index.md b/contrib/advanced-python/index.md index fa2fd7b..16210c2 100644 --- a/contrib/advanced-python/index.md +++ b/contrib/advanced-python/index.md @@ -1,6 +1,6 @@ # List of sections -- [OOPs](OOPs.md) +- [OOPs](oops.md) - [Decorators/\*args/**kwargs](decorator-kwargs-args.md) - [Lambda Function](lambda-function.md) - [Working with Dates & Times in Python](dates_and_times.md) From 20494f222f48f91209a112475961cde7d1f01175 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 21:47:03 +0530 Subject: [PATCH 71/76] Update index.md --- contrib/advanced-python/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/advanced-python/index.md b/contrib/advanced-python/index.md index df4b545..0ce294f 100644 --- a/contrib/advanced-python/index.md +++ b/contrib/advanced-python/index.md @@ -9,4 +9,4 @@ - [Map Function](map-function.md) - [Protocols](protocols.md) - [Exception Handling in Python](exception-handling.md) -- [Generators](Generators.md) +- [Generators](generators.md) From bb245f09792e9853a7545503d56bcb027754ac19 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 21:48:10 +0530 Subject: [PATCH 72/76] Update and rename Generators.md to generators.md --- contrib/advanced-python/{Generators.md => generators.md} | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) rename contrib/advanced-python/{Generators.md => generators.md} (94%) diff --git a/contrib/advanced-python/Generators.md b/contrib/advanced-python/generators.md similarity index 94% rename from contrib/advanced-python/Generators.md rename to contrib/advanced-python/generators.md index ff5226e..96287ef 100644 --- a/contrib/advanced-python/Generators.md +++ b/contrib/advanced-python/generators.md @@ -1,8 +1,8 @@ -# Generators Under Advanced Python +# Generators ## Introduction -Generators in Python are a sophisticated feature that enables the creation of iterators without the need to construct a full list in memory. They allow you to generate values on-the-fly, which is particularly beneficial for working with large datasets or infinite sequences. In this README, we'll explore generators in depth, covering their types, mathematical formulation, advantages, disadvantages, and implementation examples. +Generators in Python are a sophisticated feature that enables the creation of iterators without the need to construct a full list in memory. They allow you to generate values on-the-fly, which is particularly beneficial for working with large datasets or infinite sequences. We will explore generators in depth, covering their types, mathematical formulation, advantages, disadvantages, and implementation examples. ## Function Generators @@ -84,4 +84,4 @@ for num in square_gen: ## Conclusion -Generators offer a powerful mechanism for creating iterators efficiently in Python. By understanding the differences between function generators and generator expressions, along with their mathematical formulation, advantages, and disadvantages, you can leverage them effectively in various scenarios. Whether you're dealing with large datasets or need to work with infinite sequences, generators provide a memory-efficient solution with lazy evaluation capabilities, contributing to more elegant and scalable code. \ No newline at end of file +Generators offer a powerful mechanism for creating iterators efficiently in Python. By understanding the differences between function generators and generator expressions, along with their mathematical formulation, advantages, and disadvantages, you can leverage them effectively in various scenarios. Whether you're dealing with large datasets or need to work with infinite sequences, generators provide a memory-efficient solution with lazy evaluation capabilities, contributing to more elegant and scalable code. From edd8aec3a2b1f9855e7456d0065520d9a43243e2 Mon Sep 17 00:00:00 2001 From: rohit Date: Tue, 4 Jun 2024 00:28:41 +0530 Subject: [PATCH 73/76] Added K-Nearest Neighbors (KNN).md file --- .../K-nearest neighbor (KNN).md | 122 ++++++++++++++++++ contrib/machine-learning/index.md | 1 + 2 files changed, 123 insertions(+) create mode 100644 contrib/machine-learning/K-nearest neighbor (KNN).md diff --git a/contrib/machine-learning/K-nearest neighbor (KNN).md b/contrib/machine-learning/K-nearest neighbor (KNN).md new file mode 100644 index 0000000..748f808 --- /dev/null +++ b/contrib/machine-learning/K-nearest neighbor (KNN).md @@ -0,0 +1,122 @@ +# K-Nearest Neighbors (KNN) Machine Learning Algorithm in Python + +## Introduction +K-Nearest Neighbors (KNN) is a simple, yet powerful, supervised machine learning algorithm used for both classification and regression tasks. It assumes that similar things exist in close proximity. In other words, similar data points are near to each other. + +## How KNN Works +KNN works by finding the distances between a query and all the examples in the data, selecting the specified number of examples (K) closest to the query, then voting for the most frequent label (in classification) or averaging the labels (in regression). + +### Steps: +1. **Choose the number K of neighbors** +2. **Calculate the distance** between the query-instance and all the training samples +3. **Sort the distances** and determine the nearest neighbors based on the K-th minimum distance +4. **Gather the labels** of the nearest neighbors +5. **Vote for the most frequent label** (in case of classification) or **average the labels** (in case of regression) + +## When to Use KNN +### Advantages: +- **Simple and easy to understand:** KNN is intuitive and easy to implement. +- **No training phase:** KNN is a lazy learner, meaning there is no explicit training phase. +- **Effective with a small dataset:** KNN performs well with a small number of input variables. + +### Disadvantages: +- **Computationally expensive:** The algorithm becomes significantly slower as the number of examples and/or predictors/independent variables increase. +- **Sensitive to irrelevant features:** All features contribute to the distance equally. +- **Memory-intensive:** Storing all the training data can be costly. + +### Use Cases: +- **Recommender Systems:** Suggest items based on similarity to user preferences. +- **Image Recognition:** Classify images by comparing new images to the training set. +- **Finance:** Predict credit risk or fraud detection based on historical data. + +## KNN in Python + +### Required Libraries +To implement KNN, we need the following Python libraries: +- `numpy` +- `pandas` +- `scikit-learn` +- `matplotlib` (for visualization) + +### Installation +```bash +pip install numpy pandas scikit-learn matplotlib +``` + +### Example Code +Let's implement a simple KNN classifier using the Iris dataset. + +#### Step 1: Import Libraries +```python +import numpy as np +import pandas as pd +from sklearn.model_selection import train_test_split +from sklearn.neighbors import KNeighborsClassifier +from sklearn.metrics import accuracy_score +import matplotlib.pyplot as plt +``` + +#### Step 2: Load Dataset +```python +from sklearn.datasets import load_iris +iris = load_iris() +X = iris.data +y = iris.target +``` + +#### Step 3: Split Dataset +```python +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) +``` + +#### Step 4: Train KNN Model +```python +knn = KNeighborsClassifier(n_neighbors=3) +knn.fit(X_train, y_train) +``` + +#### Step 5: Make Predictions +```python +y_pred = knn.predict(X_test) +``` + +#### Step 6: Evaluate the Model +```python +accuracy = accuracy_score(y_test, y_pred) +print(f'Accuracy: {accuracy}') +``` + +### Visualization (Optional) +```python +# Plotting the decision boundary for visualization (for 2D data) +h = .02 # step size in the mesh +# Create color maps +cmap_light = plt.cm.RdYlBu +cmap_bold = plt.cm.RdYlBu + +# For simplicity, we take only the first two features of the dataset +X_plot = X[:, :2] +x_min, x_max = X_plot[:, 0].min() - 1, X_plot[:, 0].max() + 1 +y_min, y_max = X_plot[:, 1].min() - 1, y_plot[:, 1].max() + 1 +xx, yy = np.meshgrid(np.arange(x_min, x_max, h), + np.arange(y_min, y_max, h)) + +Z = knn.predict(np.c_[xx.ravel(), yy.ravel()]) +Z = Z.reshape(xx.shape) +plt.figure() +plt.pcolormesh(xx, yy, Z, cmap=cmap_light) + +# Plot also the training points +plt.scatter(X_plot[:, 0], X_plot[:, 1], c=y, edgecolor='k', cmap=cmap_bold) +plt.xlim(xx.min(), xx.max()) +plt.ylim(yy.min(), yy.max()) +plt.title("3-Class classification (k = 3)") +plt.show() +``` + +## Generalization and Considerations +- **Choosing K:** The choice of K is critical. Smaller values of K can lead to noisy models, while larger values make the algorithm computationally expensive and might oversimplify the model. +- **Feature Scaling:** Since KNN relies on distance calculations, features should be scaled (standardized or normalized) to ensure that all features contribute equally to the distance computation. +- **Distance Metrics:** The choice of distance metric (Euclidean, Manhattan, etc.) can affect the performance of the algorithm. + +In conclusion, KNN is a versatile and easy-to-implement algorithm suitable for various classification and regression tasks, particularly when working with small datasets and well-defined features. However, careful consideration should be given to the choice of K, feature scaling, and distance metrics to optimize its performance. \ No newline at end of file diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index b6945cd..e5f5371 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -16,3 +16,4 @@ - [Types_of_Cost_Functions](cost-functions.md) - [Clustering](clustering.md) - [Grid Search](grid-search.md) +- [K-nearest neighbor (KNN)](K-nearest neighbor (KNN).md) \ No newline at end of file From bccf4eacf8e1364ac1a4229f08c7c9d8d0327f1f Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Thu, 6 Jun 2024 05:55:26 +0530 Subject: [PATCH 74/76] Update hierarchical-clustering.md --- .../hierarchical-clustering.md | 20 ------------------- 1 file changed, 20 deletions(-) diff --git a/contrib/machine-learning/hierarchical-clustering.md b/contrib/machine-learning/hierarchical-clustering.md index 3f9e606..9382270 100644 --- a/contrib/machine-learning/hierarchical-clustering.md +++ b/contrib/machine-learning/hierarchical-clustering.md @@ -2,20 +2,6 @@ Hierarchical Clustering is a method of cluster analysis that seeks to build a hierarchy of clusters. This README provides an overview of the hierarchical clustering algorithm, including its fundamental concepts, types, steps, and how to implement it using Python. -## Table of Contents - -1. [Introduction](#introduction) -2. [Concepts](#concepts) -3. [Types of Hierarchical Clustering](#types-of-hierarchical-clustering) -4. [Steps in Hierarchical Clustering](#steps-in-hierarchical-clustering) -5. [Linkage Criteria](#linkage-criteria) -6. [Implementation](#implementation) - - [Using Scikit-learn](#using-scikit-learn) - - [Code Example](#code-example) -7. [Evaluation Metrics](#evaluation-metrics) -8. [Conclusion](#conclusion) -9. [References](#references) - ## Introduction Hierarchical Clustering is an unsupervised learning method used to group similar objects into clusters. Unlike other clustering techniques, hierarchical clustering does not require the number of clusters to be specified beforehand. It produces a tree-like structure called a dendrogram, which displays the arrangement of the clusters and their sub-clusters. @@ -111,9 +97,3 @@ print(data.head()) ## Conclusion Hierarchical clustering is a versatile and intuitive method for clustering data. It is particularly useful when the number of clusters is not known beforehand. By understanding the different linkage criteria and evaluation metrics, one can effectively apply hierarchical clustering to various types of data. - -## References - -- [Scikit-learn Documentation](https://scikit-learn.org/stable/modules/clustering.html#hierarchical-clustering) -- [Wikipedia: Hierarchical Clustering](https://en.wikipedia.org/wiki/Hierarchical_clustering) -- [Towards Data Science: Hierarchical Clustering Explained](https://towardsdatascience.com/hierarchical-clustering-explained-925d9e1600c1) From 8bafaaa091b06d8cbcd49b607633276ea70d3b01 Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Sat, 8 Jun 2024 10:20:05 +0530 Subject: [PATCH 75/76] Rename K-nearest neighbor (KNN).md to knn.md --- .../machine-learning/{K-nearest neighbor (KNN).md => knn.md} | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename contrib/machine-learning/{K-nearest neighbor (KNN).md => knn.md} (99%) diff --git a/contrib/machine-learning/K-nearest neighbor (KNN).md b/contrib/machine-learning/knn.md similarity index 99% rename from contrib/machine-learning/K-nearest neighbor (KNN).md rename to contrib/machine-learning/knn.md index 748f808..85578f3 100644 --- a/contrib/machine-learning/K-nearest neighbor (KNN).md +++ b/contrib/machine-learning/knn.md @@ -119,4 +119,4 @@ plt.show() - **Feature Scaling:** Since KNN relies on distance calculations, features should be scaled (standardized or normalized) to ensure that all features contribute equally to the distance computation. - **Distance Metrics:** The choice of distance metric (Euclidean, Manhattan, etc.) can affect the performance of the algorithm. -In conclusion, KNN is a versatile and easy-to-implement algorithm suitable for various classification and regression tasks, particularly when working with small datasets and well-defined features. However, careful consideration should be given to the choice of K, feature scaling, and distance metrics to optimize its performance. \ No newline at end of file +In conclusion, KNN is a versatile and easy-to-implement algorithm suitable for various classification and regression tasks, particularly when working with small datasets and well-defined features. However, careful consideration should be given to the choice of K, feature scaling, and distance metrics to optimize its performance. From 906333954390d915e4c85b92e6ccc95919a878d8 Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Sat, 8 Jun 2024 10:20:27 +0530 Subject: [PATCH 76/76] 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 e5f5371..deafdda 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -16,4 +16,4 @@ - [Types_of_Cost_Functions](cost-functions.md) - [Clustering](clustering.md) - [Grid Search](grid-search.md) -- [K-nearest neighbor (KNN)](K-nearest neighbor (KNN).md) \ No newline at end of file +- [K-nearest neighbor (KNN)](knn.md)