How to Remove Rows and Columns from DataFrame Using Pandas drop() function

Table of Content

Introduction
Syntax and Parameters
How to use .drop() func?
Error Handling
How to use .drop() based on conditions
Common Mistakes and Fixes
Sorting
Handling Missing Data
Aggregations & Statistics
Grouping & Pivoting
String Methods (.str)
Datetime Handling (.dt)
Reshaping
Merging & Joining
Apply & Lambda

1. Introduction

When working with DataFrames in Pandas, you often need to remove unnecessary rows or columns — maybe a feature you don’t need, a column of missing data, or an incorrect row.

Python gives you a simple yet powerful function for this: drop()

⭐ What is drop() Function?

The .drop() function is used to remove rows or columns from a DataFrame.

By using axis='columns' or columns=, the drop() function deletes the column you mention.

By using axis='index' or index=, the drop() function deletes the row you mention.

2. Syntax and Parameters

# Syntax for drop() function

DataFrame.drop(labels=None, *, axis="0," index="None," columns="None," level="None," inplace="False," errors='raise')

These parameters — labels, axis, index, columns, level, inplace, and errors — are provided as Keyword Arguments.

Parameter	Description
labels	Row index or column name(s) to drop (depends on axis).
axis	Choose what to drop: 0 = rows, 1 = columns.
index	Directly specify row index(es) to remove.
columns	Directly specify column name(s) to remove.
level	Used for MultiIndex; defines which index level to target.
inplace	True:change original DataFrame. False:return new one.
errors	'raise': show error if label not found. 'ignore': skip missing labels.

3. How to use .drop() function?

Let's consider an example with a simple dataframe to use .drop() function — We’ll keep using this same example for the following actions.

#import pandas library

import pandas as pd

df = pd.DataFrame({

'Name': ['Amit', 'Riya', 'Sam'],

'Age': [21, 22, 20],

'Marks': [85, 90, 88]

})

print(df)

⚡️ How to Drop a Column from a DataFrame?

# Drop a column from a DataFrame

df_new = df.drop(columns=['Marks'])

Code Explanation:

df.drop() - tell pandas to delete the column named Marks.

df_new - stores the updated DataFrame without that column.

⛳ Output:

# Updated DataFrame without 'Marks' column

Name Age

0 Amit 21

1 Riya 22

2 Sam 20

⚡️ How to Drop multiple Columns from a DataFrame?

# Drop multiple columns from a DataFrame

df_new = df.drop(columns=['Age', 'Marks'])

Code Explanation:

df.drop() - tell pandas to delete the columns named Age and Marks

df_new - stores the updated DataFrame without that column.

# Updated DataFrame without 'Age' and 'Marks' column

Name

0 Amit

1 Riya

2 Sam

⚡️ How to Drop a Row from a DataFrame?

# Drop a row from a DataFrame

df_new = df.drop(index=[1])

Code Explanation:

df.drop() - tell pandas to delete the row with index=1

df_new - stores the updated DataFrame without that row.

# Updated DataFrame without index 1

Name Age Marks

0 Amit 21 85

2 Sam 20 88

⚡️ How to Drop multiple Rows from a DataFrame?

# Drop multiple rows from a DataFrame

df_new = df.drop(index=[0, 2])

Code Explanation:

df.drop() - tell pandas to delete the row with index 1 and 3

df_new - stores the updated DataFrame without that row.

# Updated DataFrame without 'Age' and 'Marks' column

Name Age Marks

1 Riya 22 90

4. Error Handling

Sometimes you may try to drop a row or column that does not exist in the DataFrame.
By default, Pandas gives an error - KeyError: "['SomeLabel'] not found in axis"

To avoid the error, you can use: errors='ignore'

This tells Pandas:

👉 “If the label is missing, don’t stop the program — just continue.”

# Try dropping a column that does NOT exist

df_new = df.drop(columns=['City'], errors='ignore')

Code Explanation:

The DataFrame has only Name, Age and Marks columns.
We attempt to drop “City”, which does not exist.
Instead of giving an error, errors='ignore' allows the code to run smoothly.
Since the column is not found, the DataFrame stays the same.

# Updated DataFrame

Name Age Marks

0 Amit 21 85

1 Riya 22 90

2 Sam 20 88

5. How to drop rows and columns based on Conditions?

In real data cleaning, you often delete rows and columns based on conditions — for example:

Drop rows where Age < 21
Drop rows where Marks < 40
Drop rows where Name is missing
Drop rows containing outliers

Pandas allows you to do this easily using Boolean Filtering (True/False condition) - a method which keeps only the rows where the condition is True and removes the rows where the condition is False.

Example: Drop Rows Where Marks < 90

# Drop rows where Marks are less than 90

df_new = df[df['Marks'] >= 90]

Code Explanation:

df['Marks'] >= 90 creates a condition
Only rows where this condition is True are kept
Rows where Marks < 90 (Amit and Sam) are removed

# Updated DataFrame where Marks are less than equal to 90

Name Age Marks

1 Riya 22 90

⭐ Drop Rows Using Multiple Conditions in Pandas

You can combine multiple conditions using:

& → AND
| → OR
~ → NOT

Example: Keep rows where Age > 20 AND Marks > 85

# Drop rows where Marks are less than 90

df_new = df[(df['Age'] > 20) & (df['Marks'] > 85)]

Code Explanation:

(df['Age'] > 20) — Condition 1: Age > 20 → True for Amit and Riya

(df['Marks'] > 85) — Condition 2: Marks > 85 → True for Riya and Sam

Using &, only rows where both conditions are True are kept - Only Riya satisfies both

# Updated DataFrame where Age > 20 AND Marks > 85

Name Age Marks

1 Riya 22 90

6. Common Mistakes and Fixes

1️⃣ Dropping non-existing column → KeyError

❌ Mistake:
df.drop(columns=['City'])

✅ Fix:
df.drop(columns=['City'], errors='ignore')

2️⃣ Wrong axis when dropping columns

❌ Mistake:

df.drop('Marks')

✅ Fix:
df.drop(columns=['Marks']) or df.drop('Marks', axis="1)

3️⃣ Missing parentheses in multiple conditions

❌ Mistake:
df[df['Age'] > 20 & df['Marks'] > 85]

✅ Fix:
df[(df['Age'] > 20) & (df['Marks'] > 85)]

4️⃣ Using `and` / `or` instead of `&` / `|`

❌ Mistake:
df[(df['Age'] > 20) and (df['Marks'] > 85)]

✅ Fix:
df[(df['Age'] > 20) & (df['Marks'] > 85)]

5️⃣ Expecting drop() to modify original DataFrame

❌ Mistake:
df[(df['Age'] > 20) and (df['Marks'] > 85)]

✅ Fix:
df[(df['Age'] > 20) & (df['Marks'] > 85)]

Summary

1. Import & Setup

import pandas as pd
pd.__version__ → check version
pd.set_option('display.max_rows', n) → control display

1. what is .drop() function? - It removes the specified row or column

axis='columns' or columns=... → Removes columns
axis='index' or index=... → Removes rows

3. Input/Output (I/O)

pd.read_csv('file.csv')
pd.read_excel('file.xlsx')
pd.read_json('file.json')
pd.read_sql(query, connection)
df.to_csv('file.csv')
df.to_excel('file.xlsx')

4. Inspecting Data

df.head(n) / df.tail(n)

df.info()

df.shape

df.dtypes

df.columns

df.index

df.describe()

5. Selection & Indexing

df['col'] → select column

df[['col1','col2']] → multiple columns

df.loc[row_index, col_name] → label-based

df.iloc[row_index, col_index] → position-based

df.at[row, col] / df.iat[row, col] → fast scalar access

6. Filtering & Boolean Indexing

df[df['col'] > value]

df.query('col > value')

df[(df['A'] > 0) & (df['B'] < 5)]

7. Sorting

df.sort_values('col')

df.sort_values(['col1','col2'], ascending="[True," False])

df.sort_index()

8. Handling Missing Data

df.isnull() / df.notnull()

df.dropna()

df.fillna(value)

df.interpolate()

9. Aggregations & Statistics

df.sum() / df.mean() / df.median()

df.min() / df.max()

df.std() / df.var()

df.count()

df.value_counts()

df.corr()

10. Grouping & Pivoting

df.groupby('col').mean()

df.groupby(['A','B']).agg({'C':'sum'})

df.pivot(index='col1', columns='col2', values='col3')

df.pivot_table(values='col', index='A', columns='B', aggfunc='mean')

11. String Methods (.str)

df['col'].str.lower() / .upper()

df['col'].str.contains('text')

df['col'].str.replace('a','b')

df['col'].str.len()

12. Datetime Handling (.dt)

pd.to_datetime(df['date'])

df['date'].dt.year / .month / .day

df['date'].dt.weekday

df['date'].dt.strftime('%Y-%m-%d')

13. Reshaping

df.melt(id_vars, value_vars)

df.stack() / df.unstack()

df.pivot_table()

14. Merging & Joining

pd.concat([df1, df2])

pd.merge(df1, df2, on='key')

pd.merge(df1, df2, how='left')

df1.join(df2)

15. Apply & Lambda

df['col'].apply(func)

df.applymap(func) → elementwise on DataFrame

df.transform(lambda x: x+1)

16. Attributes (quick access)

df.shape → (rows, cols)

df.index → row index

df.columns → column names

df.dtypes → data types

df.size → total elements

df.values → underlying NumPy array

17. Export & Save

df.to_csv('data.csv')

df.to_excel('data.xlsx')

df.to_json('data.json')

df.to_sql(table, connection)

18. Visualization (basic)

df.plot()

df['col'].hist()

df.plot.scatter(x='col1', y='col2')

⬅️ Previous

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How to Remove Rows and Columns from DataFrame Using Pandas drop() function

Table of Content

​​1. Introduction

⭐ What is drop() Function?

​​2. Syntax and Parameters

​​​​3. How to use .drop() function?

4. Error Handling

5. How to drop rows and columns based on Conditions?

⭐ Drop Rows Using Multiple Conditions in Pandas

6. Common Mistakes and Fixes

1️⃣ Dropping non-existing column → KeyError

2️⃣ Wrong axis when dropping columns

3️⃣ Missing parentheses in multiple conditions

4️⃣ Using and / or instead of & / |

Summary

1. Introduction

2. Syntax and Parameters

3. How to use .drop() function?

4️⃣ Using `and` / `or` instead of `&` / `|`