Pandas data analysis tool for Python

Pandas is a popular open-source Python library used for data manipulation and analysis. It provides powerful tools for working with structured data, including the ability to read and write data from various file formats such as CSV, Excel, SQL databases, and more.

What it can do

Pandas allows users to perform data wrangling tasks, such as filtering, cleaning, transforming, merging, and reshaping data. It provides two main data structures:

1. Series, which is a one-dimensional labeled array that can hold any data type,
Pandas Series
2. DataFrame, which is a two-dimensional labeled data structure with columns of potentially different data types.
Pandas DataFrame Atributes Methods

Where Pandas is used

Pandas is widely used in data science, finance, economics, social sciences, and many other fields where data analysis and manipulation are required. It is also integrated with other Python libraries such as NumPy, Matplotlib, and Scikit-learn to provide a complete data analysis ecosystem in Python.

How to install Python Pandas library

pip install pandas

Getting the version of Pandas

import pandas as pd 
print("Pandas Version : ",pd.__version__)

Upgrade Pandas

pip install --upgrade pandas

Data Exploration and Analysis

Data statistics, filtering, Aggregation and grouping, data merging and joining etc.

Data Analysis

Data Cleaning

Handling Null, NaN data, finding duplicate data, replacing data and more .

Data Cleaning

Five important basic Pandas DataFrame functions info(), head(), tail(), shape,size,ndim, columns

Creating Pandas DataFrame by using Numpy ndarray
validate_email
DataFrame Style
Pandas Date & Time

Exercises

Exercise1	Basic data handling , DataFrame
Exercise1-1	Using cut(), groupby and plotting graphs
Exercise-Adv	Using groupby and merge of DataFrame
Exercise2	Using str.contains(), max(), min(),len() of DataFrame
Exercise3	Using date and time functions with groupby of DataFrame
Exercise3-2	Using date and time functions of DataFrame
Exercise3-3	Using date and time functions with groupby
Exercise3-4	Using date and time with where timedelta64

I/O : Input and output Data from Pandas ( Excel , MySQL , JSon)

We can’t store data in Pandas DataFrame. We can process the data by using Pandas DataFrame after reading data from different sources. Similarly after processing we can save data in different files or database by using available tools.
Data input and output from Pandas DataFrame
Filtering records

loc	Values at different position using column label
rows	Filtering rows based on data

handling string using str methods

str.contains	string matching against data columns
str.contains.sum	Max Min Sum of any column
Convert Case	Lower to Upper and vice versa
split()	Breaking string using delimiter
slice()	Substring by breaking string
cat()	Concatenate strings
count()	Number of occurrences of pattern
replace()	Replace part of string by regex
len()	Length of the data in our DataFrame
zfill()	Prepending string with '0'

Data Visualization:

Graphs using MySQL data
Exercise on Pandas Plot
Dataframe Plot

Managing Date

Pandas Date and time	Managing Date and time in Pandas DataFrame

Excel to MySQL

import pandas as pd 
my_data = pd.read_excel('D:\emp.xlsx')
# reading data from root of D drive. 
from sqlalchemy import create_engine
my_conn = create_engine("mysql+mysqldb://userid:password@localhost/database_name")

### Creating new table emp or appending existing table 
my_data.to_sql(con=my_conn,name='emp',if_exists='append')

MySQL to Excel

import pandas as pd 

from sqlalchemy import create_engine
my_conn = create_engine("mysql+mysqldb://userid:password@localhost/database_name")

sql="SELECT * FROM emp "
my_data = pd.read_sql(sql,my_conn )

my_data.to_excel('D:\emp2.xlsx')

Columns and rows

columns	List of column headers of a DataFrame
rename	rename columns of DataFrame
add_suffix	adding suffix to column names of a DataFrame
add_prefix	adding prefix to column names of a DataFrame
drop	Delete columns or rows

Get the list of all functions of Pandas. ( Used dir() )

import pandas as pd
print(len(dir(pd))) # 139

for i in dir(pd):
    print(i)

Pandas DataFrame

Parameters of functions

There are some common parameters used in Pandas functions. Understanding them will help in quick learning of functionality.

inplace

Boolean ( True / False ), Result is written back to same dataframe ( True ). The source dataframe is changed. False otherwise.

Upload Excel or CSV file in google drive and connect from Colab Python platform using mount drive

Pandas and Tkinter

Integrating Pandas with Tkinter involves using Pandas to read and manipulate data, and then displaying that data using Tkinter widgets such as labels, tables, and plots.
Projects using Tkinter

Download files of our Video Tutorials

Section	Details	Download
A	Introduction	Download

Sample student DataFrame Pandas and Search Engine Optimization Pandas Projects

Numpy arrays Python & MySQL Python- Tutorials

Subhendu Mohapatra

Author

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Passionate about coding and teaching, I publish practical tutorials on PHP, Python, JavaScript, SQL, and web development. My goal is to make learning simple, engaging, and project‑oriented with real examples and source code.