Dealing with Rows and Columns in Pandas DataFrame

Last Updated : 11 Nov, 2025

A Pandas DataFrame is a two-dimensional data structure made up of rows and columns, similar to a spreadsheet or SQL table. In Pandas, you can easily select, add, delete or rename rows and columns to manage and analyze your data efficiently.

Below is the Sample DataFrame used in this article:

1
sample_dataframe

To Download the Sample DataFrame used in this article, click here

Dealing with Columns

Below image shows a sample Pandas DataFrame with rows and columns labeled. The rows represent individual records (like players), while the columns represent attributes such as Name, Team, Number, Position and Age.

Selecting Columns

You can access one or more columns using their column names.

Python 
import pandas as pd
df = pd.read_csv('your_file.csv') 

print(df[['Name', 'Age']])

Output

4

Explanation: df[['Name', 'Age']]: selects the specified columns and returns a new DataFrame with only those columns.

Adding Columns

New columns can be added by assigning a list or series to a column name.

Python 
df['Experience'] = 1
print(df.head())

Output

3

Explanation: df['Experience'] = [...]: adds a new column Experience with the given values.

Deleting Columns

You can delete a column from the DataFrame using the drop() method.

Python 
df.drop('Experience', axis=1, inplace=True)
print(df.head())

Output

1

Explanation: df.drop('Experience', axis=1, inplace=True): removes the 'Experience' column from the DataFrame permanently.

Dealing with Rows

Selecting Rows

Rows can be selected using .loc[] by row label or index.

Python 
data = pd.read_csv("your_file.csv", index_col="Name")

first = data.loc["Avery Bradley"]
second = data.loc["R.J. Hunter"]

print(first, "\n\n", second)

Output

5

Explanation: .loc[row_label]: returns a Series corresponding to the row. You can select multiple rows similarly by passing a list.

Adding Rows

New rows can be added using pd.concat().

Python 
import pandas as pd
df = pd.read_csv('your_file.csv')

new_row = {
    'Name': 'Geeks',
    'Team': 'Boston',
    'Number': 3,
    'Position': 'PG',
    'Age': 33,
    'Height': '6-2',
    'Weight': 189,
    'College': 'MIT',
    'Salary': 99999
}
df = pd.concat([df, pd.DataFrame([new_row])], ignore_index=True)
df.tail(2)


Output

6

Explanation:

  • pd.concat([new_row, df]): combines the new row with the existing DataFrame.
  • .reset_index(drop=True): resets the index to maintain sequential row numbering.

Deleting Rows

Rows can be removed using drop() with row labels.

Python 
data = pd.read_csv("your_file.csv", index_col="Name")
data.drop(["Avery Bradley", "John Holland", "R.J. Hunter"], inplace=True)

Output

7

Explanation: data.drop(["Avery Bradley", "John Holland", "R.J. Hunter"], inplace=True): deletes the specified rows from the DataFrame permanently.

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Article Tags:
Pandas
AI-ML-DS

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