5.3 KiB

Czysty Wina Historia

Introduction_to_Pandas_Library_and_DataFrames

As you have learnt Python Programming , now it's time for some applications.

Machine Learning and Data Science is the emerging field of today's time , to work in this this field your first step should be Data Science as Machine Learning is all about data.
To begin with Data Science your first tool will be Pandas Library.

Introduction of Pandas Library

Pandas is a data analysis and manipulation tool, built on top of the python programming language. Pandas got its name from the term Panel data (‘Pa’ from Panel and ‘da’ from data). Panel data is a data which have rows and columns in it like excel spreadsheets, csv files etc.

To use Pandas, first we’ve to import it.

Why pandas?

Pandas provides a simple-to-use but very capable set of functions that you can use on your data.
It is also associate with other machine learning libraries , so it is important to learn it.
For example - It is highly used to transform tha data which will be use by machine learning model during the training.

# Importing the pandas
import pandas as pd

To import any module in Python use “import 'module name' ” command, I used “pd” as pandas abbreviation because we don’t need to type pandas every time only type “pd” to use pandas.

# To check available pandas version
print(f"Pandas Version is : {pd.__version__}")

Pandas Version is : 2.1.4

Understanding Pandas data types

Pandas has two main data types : Series and DataFrames

pandas.Series is a 1-dimensional column of data.
pandas.DataFrames is 2 -dimensional data table having rows and columns.

1. Series datatype

To creeate a series you can use pd.Series() and passing a python list inside().

Note: S in Series is capital if you use small s it will give you an error.

Let's create a series

# Creating a series of car companies
cars = pd.Series(["Honda","Audi","Thar","BMW"])
cars

0    Honda
1     Audi
2     Thar
3      BMW
dtype: object

The above code creates a Series of cars companies the name of series is “cars” the code “pd.Series([“Honda” , “Audi” , “Thar”, "BMW"])” means Hey! pandas (pd) create a Series of cars named "Honda" , "Audi" , "Thar" and "BMW".

The default index of a series is 0,1,2….(Remember it starts from 0)

To change the index of any series set the “index” parameter accordingly. It takes the list of index values:

cars = pd.Series(["Honda","Audi","Thar","BMW"],index = ["A" , "B" , "C" ,"D"])
cars

A    Honda
B     Audi
C     Thar
D      BMW
dtype: object

You can see that the index has been changed from numbers to A, B ,C and D.

And the mentioned ‘dtype’ tells us about the type of data we have in the series.

2. DataFrames datatype

DataFrame contains rows and columns like a csv file have.

You can also create a DataFrame by using pd.DataFrame() and passing it a Python dictionary.

# Let's create
cars_with_colours = pd.DataFrame({"Cars" : ["BMW","Audi","Thar","Honda"],
                                "Colour" : ["Black","White","Red","Green"]})
print(cars_with_colours)

    Cars Colour
0    BMW  Black
1   Audi  White
2   Thar    Red
3  Honda  Green

The dictionary key is the column name and value are the column data.

You can also create a DataFrame with the help of series.

# Let's create two series
students = pd.Series(["Ram","Mohan","Krishna","Shivam"])
age = pd.Series([19,20,21,24])

students

0        Ram
1      Mohan
2    Krishna
3     Shivam
dtype: object

age

0    19
1    20
2    21
3    24
dtype: int64

# Now let's create a dataframe with the help of above series
# pass the series name to the dictionary value

record = pd.DataFrame({"Student_Name":students , 
                      "Age" :age})
print(record)

  Student_Name  Age
0          Ram   19
1        Mohan   20
2      Krishna   21
3       Shivam   24

# To print the list of columns names
record.columns

Index(['Student_Name', 'Age'], dtype='object')

Describe Data

The good news is that pandas has many built-in functions which allow you to quickly get information about a DataFrame. Let's explore the record dataframe

1. Use `.dtypes` to find what datatype a column contains

record.dtypes

Student_Name    object
Age              int64
dtype: object

2. use `.describe()` for statistical overview.

print(record.describe())  # It only display the results for numeric data

             Age
count   4.000000
mean   21.000000
std     2.160247
min    19.000000
25%    19.750000
50%    20.500000
75%    21.750000
max    24.000000

3. Use `.info()` to find information about the dataframe

record.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4 entries, 0 to 3
Data columns (total 2 columns):
 #   Column        Non-Null Count  Dtype 
---  ------        --------------  ----- 
 0   Student_Name  4 non-null      object
 1   Age           4 non-null      int64 
dtypes: int64(1), object(1)
memory usage: 196.0+ bytes

5.3 KiB Czysty Wina Historia Unescape Escape