Pandas DataFrame

A DataFrame is the most important data structure provided by the Pandas library. It is used to store data in a tabular form consisting of rows and columns, similar to an Excel worksheet or a database table.

Almost every data analysis task in Python uses DataFrames because they are easy to create, organize, filter, and analyze.

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What is a DataFrame?

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Why Do We Use DataFrames?

• Store data in tabular form.
• Represent real-world datasets.
• Perform data analysis efficiently.
• Read and write CSV or Excel files.
• Filter, sort, and modify data easily.
• Handle large datasets efficiently.

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Characteristics of a DataFrame

Property	Description
Two-Dimensional	Stores data in rows and columns.
Labeled	Rows and columns have labels.
Mutable	Rows and columns can be added or removed.
Heterogeneous	Different columns can have different data types.
Size	Can store thousands or millions of records.

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Structure of a DataFrame

      Name     Marks    City

0     Amit      85     Jaipur

1     Neha      91     Delhi

2     Rohan     78     Mumbai

Here,

• Rows are identified by an Index.
• Columns have Names (Labels).
• Each cell stores one value.

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Creating a DataFrame

Syntax

import pandas as pd

df = pd.DataFrame(data)

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Creating a DataFrame from a Dictionary of Lists

import pandas as pd

data = {

"Name":["Amit","Neha","Rohan"],

"Marks":[85,92,78],

"City":["Jaipur","Delhi","Mumbai"]

}

df = pd.DataFrame(data)

print(df)

Output

    Name   Marks    City

0   Amit      85   Jaipur

1   Neha      92   Delhi

2  Rohan      78   Mumbai

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Creating a DataFrame from a Dictionary of Series

import pandas as pd

name = pd.Series(["Amit","Neha","Rohan"])

marks = pd.Series([85,92,78])

city = pd.Series(["Jaipur","Delhi","Mumbai"])

data = {

"Name":name,

"Marks":marks,

"City":city

}

df = pd.DataFrame(data)

print(df)

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Creating a DataFrame from a List of Dictionaries

import pandas as pd

students = [

{"Name":"Amit","Marks":85},

{"Name":"Neha","Marks":91},

{"Name":"Rohan","Marks":78}

]

df = pd.DataFrame(students)

print(df)

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Creating a DataFrame from a CSV File

Suppose a file named student.csv already exists.

import pandas as pd

df = pd.read_csv("student.csv")

print(df)

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Displaying a DataFrame

print(df)

The print() function displays the complete DataFrame.

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Shape of a DataFrame

The shape attribute returns the number of rows and columns.

print(df.shape)

Output

(3,3)

Meaning:

• 3 Rows
• 3 Columns

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Size of a DataFrame

Returns the total number of elements.

print(df.size)

Output

9

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Index of a DataFrame

print(df.index)

Output

RangeIndex(start=0, stop=3, step=1)

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Column Names

print(df.columns)

Output

Index(['Name','Marks','City'])

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Data Types

print(df.dtypes)

Output

Name     object

Marks     int64

City      object

dtype: object

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info() Function

Displays complete information about the DataFrame.

df.info()

It shows:

• Number of rows
• Number of columns
• Column names
• Data types
• Memory usage
• Non-null values

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describe() Function

Displays statistical information about numerical columns.

df.describe()

It calculates:

• Count
• Mean
• Standard Deviation
• Minimum
• Maximum
• 25%
• 50% (Median)
• 75%

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Difference Between Series and DataFrame

Series	DataFrame
One-dimensional	Two-dimensional
Single column	Multiple columns
Stores one type of data	Stores multiple columns of different data types
Can be a part of a DataFrame	Collection of multiple Series

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Real-Life Applications

Field	Example
School	Student Result Sheet
Hospital	Patient Database
Bank	Customer Records
Business	Sales Report
Sports	Player Statistics

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Common Errors

Error	Reason
ValueError	Columns have unequal lengths.
FileNotFoundError	CSV file does not exist.
KeyError	Incorrect column name.

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Quick Revision

Concept	Remember
DataFrame	2-D Data Structure
Shape	Rows × Columns
Size	Total Elements
Columns	Column Labels
Index	Row Labels
info()	Complete Information
describe()	Statistical Summary

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CBSE Exam Tips

• Remember all four methods of creating a DataFrame.
• Know the difference between shape and size.
• Practice the outputs of info() and describe().
• Remember that a DataFrame is a collection of Series.
• Understand how CSV files are imported using read_csv().

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Summary

A DataFrame is a two-dimensional labeled data structure in Pandas used for storing and analyzing tabular data. It can be created from dictionaries, Series, lists of dictionaries, and CSV files. DataFrames provide powerful features such as viewing data, checking its structure, and performing statistical analysis, making them the most widely used data structure in data science and analytics.