Pandas: Intro to DataFrames

Understanding basics of Pandas DataFrames, a core data structure for data analysis in Python.

DataFrames are the core data structure in pandas for tabular data. This notebook covers creating DataFrames from various sources and basic operations.

You will learn how to: - Import essential libraries (Pandas, Seaborn, Matplotlib, NumPy) - Create DataFrames from dictionaries, lists, and NumPy arrays - View and manipulate DataFrames - Export DataFrames to CSV and Excel files

Importing Libraries

Start by importing pandas and other useful libraries for data analysis and visualization.

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

Creating DataFrames from Dictionaries

DataFrames can be created from Python dictionaries where keys become column names.

data = {'Name': ['Adil', 'Aman', 'Ziya', 'Zahra'],
        'Age': [23,19,15,9],
        'City': ['Matannur','Vellore', 'Tly', 'Knr' ]
        }
data

{'Name': ['Adil', 'Aman', 'Ziya', 'Zahra'],
 'Age': [23, 19, 15, 9],
 'City': ['Matannur', 'Vellore', 'Tly', 'Knr']}

df = pd.DataFrame(data)
df

	Name	Age	City
0	Adil	23	Matannur
1	Aman	19	Vellore
2	Ziya	15	Tly
3	Zahra	9	Knr

Creating DataFrames from Lists

You can also create DataFrames from lists of lists, specifying column names.

data_list = [ 
    ['Adil', 23, 'Mattanur'],
    ['Aman', 19, 'Vellore'],
    ['Siraj', 55, 'Tly'],
    ['Faritha', 40, 'Chokli']
    ]

data_list

[['Adil', 23, 'Mattanur'],
 ['Aman', 19, 'Vellore'],
 ['Siraj', 55, 'Tly'],
 ['Faritha', 40, 'Chokli']]

df_list = pd.DataFrame(data_list, columns=['Name', 'Age', 'City'])
df_list

	Name	Age	City
0	Adil	23	Mattanur
1	Aman	19	Vellore
2	Siraj	55	Tly
3	Faritha	40	Chokli

Creating DataFrames from NumPy Arrays

Pandas integrates with NumPy; create DataFrames from arrays with column names.

import numpy as np

data_array = np.array([[1,2,3],
                       [4,5,6],
                       [7,8,9]])

data_array

array([[1, 2, 3],
       [4, 5, 6],
       [7, 8, 9]])

df_array = pd.DataFrame(data_array, columns=['A', 'B', 'C'])
df_array

	A	B	C
0	1	2	3
1	4	5	6
2	7	8	9

Exporting DataFrames

Save DataFrames to files like CSV or Excel for sharing or further analysis.

df.to_csv('example.csv', index=False)

df.to_excel('example.xlsx', index=False)

Best Practices

• Use descriptive column names.
• Check data types with df.dtypes after creation.
• Handle missing data appropriately.

Summary

This notebook introduced creating and exporting DataFrames. DataFrames are versatile for data manipulation—explore more operations next!