Pandas : Python Data analysis tool.

Python Pandas
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
DataFrame Style
Pandas Date & Time

Exercise1Basic data handling , DataFrame
Exercise1-1Using cut(), groupby and plotting graphs
Exercise-AdvUsing groupby and merge of DataFrame
Exercise2Using str.contains(), max(), min(),len() of DataFrame
Exercise3Using date and time functions with groupby of DataFrame
Exercise3-2Using date and time functions of DataFrame
Exercise3-3Using date and time functions with groupby
Exercise3-4Using 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
locValues at different position using column label
rowsFiltering rows based on data

handling string using str methods

str.containsstring matching against data columns
str.contains.sumMax Min Sum of any column
Convert CaseLower 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'
Managing Date
Pandas Date and time Managing Date and time in Pandas DataFrame

Excel to MySQL

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 

MySQL to Excel

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 )


Columns and rows

columnsList of column headers of a DataFrame
renamerename columns of DataFrame
add_suffixadding suffix to column names of a DataFrame
add_prefixadding prefix to column names of a DataFrame
dropDelete 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):
Pandas DataFrame

Parameters of functions

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

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