Dropping missing values with dropna() in Pandas

Introduction

Sometimes data has empty spots called missing values. We use dropna() to remove those spots so our data is clean and ready to use.

When you have a table with empty cells and want to remove rows or columns that have them.

When preparing data for analysis or machine learning and missing values can cause errors.

When you want to focus only on complete data without any gaps.

When cleaning survey results where some answers are missing.

When you want to quickly remove incomplete records from your dataset.

Syntax

Pandas

DataFrame.dropna(axis=0, how='any', thresh=None, subset=None, inplace=False)

axis=0 means drop rows; axis=1 means drop columns.

how='any' drops if any missing value is found; how='all' drops only if all values are missing.

Examples

Drop all rows that have any missing value.

Pandas

df.dropna()

Drop all columns that have any missing value.

Pandas

df.dropna(axis=1)

Drop rows only if all values in the row are missing.

Pandas

df.dropna(how='all')

Drop rows where Age or Salary columns have missing values.

Pandas

df.dropna(subset=['Age', 'Salary'])

Sample Program

This code creates a table with some missing ages and salaries. Then it removes any row that has a missing value and shows the cleaned table.

Pandas

import pandas as pd

# Create a sample DataFrame with missing values
data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'David'],
    'Age': [25, None, 30, None],
    'Salary': [50000, 60000, None, 45000]
}
df = pd.DataFrame(data)

print('Original DataFrame:')
print(df)

# Drop rows with any missing values
clean_df = df.dropna()

print('\nDataFrame after dropping rows with missing values:')
print(clean_df)

Important Notes

Using inplace=True changes the original DataFrame without needing to assign it again.

Be careful: dropping too many rows can reduce your data size a lot.

You can choose to drop columns instead of rows by setting axis=1.

Summary

dropna() removes rows or columns with missing values.

You can control whether to drop rows or columns using axis.

Use how to decide if any or all missing values trigger dropping.

Practice

1. What does the dropna() function do in pandas?

easy

A. Sorts the DataFrame by index

B. Fills missing values with zeros

C. Removes rows or columns with missing values

D. Calculates the mean of columns

Solution

1. Step 1: Understand the purpose of dropna()

   The dropna() function is designed to remove data that contains missing values (NaN) from a DataFrame.

2. Step 2: Identify what dropna() removes

   It removes either rows or columns that have missing values, depending on the parameters used.

3. Final Answer:

   Removes rows or columns with missing values -> Option C

4. Quick Check:

   dropna() removes missing data [OK]

Hint: Remember: dropna() drops missing data, not fills it [OK]

Common Mistakes:

• Confusing dropna() with fillna()
• Thinking dropna() sorts data
• Assuming dropna() calculates statistics

2. Which of the following is the correct syntax to drop rows with any missing values in a DataFrame df?

easy

A. df.dropna(axis=0, how='any')

B. df.dropna(axis=1, how='any')

C. df.dropna(axis=0, how='all')

D. df.dropna(axis=1, how='all')

Solution

1. Step 1: Identify axis for rows

   In pandas, axis=0 means rows, so to drop rows, use axis=0.

2. Step 2: Use how='any' to drop rows with any missing values

   The how='any' parameter drops rows if any value is missing in that row.

3. Final Answer:

   df.dropna(axis=0, how='any') -> Option A

4. Quick Check:

   Drop rows with any NaN = axis=0, how='any' [OK]

Hint: axis=0 drops rows; how='any' drops if any NaN [OK]

Common Mistakes:

• Using axis=1 to drop rows
• Using how='all' to drop rows with any NaN
• Confusing axis and how parameters

3. Given the DataFrame:

import pandas as pd
data = {'A': [1, 2, None], 'B': [4, None, 6], 'C': [7, 8, 9]}
df = pd.DataFrame(data)

What is the result of df.dropna(axis=0, how='any')?

medium

A. Rows with index 0 and 1 only

B. Rows with index 0 and 2 only

C. All rows dropped

D. Only row with index 0

Solution

1. Step 1: Identify rows with any missing values

   Row 0: A=1, B=4, C=7 (no NaN)
   Row 1: A=2, B=NaN, C=8 (has NaN)
   Row 2: A=NaN, B=6, C=9 (has NaN)

2. Step 2: Drop rows with any NaN

   Only row 0 has no missing values, so only it remains after dropna(axis=0, how='any').

3. Final Answer:

   Only row with index 0 -> Option D

4. Quick Check:

   Drop rows with any NaN leaves only complete rows [OK]

Hint: Check which rows have any NaN to keep them [OK]

Common Mistakes:

• Keeping rows with NaN
• Confusing axis=0 and axis=1
• Misreading how='any' vs how='all'

4. You run the code:

df.dropna(axis=2)

and get an error. What is the problem?

medium

A. axis=2 is invalid; only 0 (rows) or 1 (columns) allowed

B. dropna() cannot be used on DataFrames

C. Missing values must be filled before dropping

D. You must specify how='any' or how='all'

Solution

1. Step 1: Understand valid axis values for dropna()

   In pandas, axis can only be 0 (rows) or 1 (columns) for dropna().

2. Step 2: Identify the error cause

   Using axis=2 is invalid and causes an error because it is not a supported axis.

3. Final Answer:

   axis=2 is invalid; only 0 (rows) or 1 (columns) allowed -> Option A

4. Quick Check:

   Valid axis values = 0 or 1 only [OK]

Hint: axis must be 0 or 1 for dropna() [OK]

Common Mistakes:

• Using axis=2 mistakenly
• Thinking dropna() needs fillna() first
• Assuming how parameter is mandatory

5. You have a DataFrame df with some rows having all values missing and some rows having only some missing values. Which dropna() call will remove only rows where all values are missing, keeping rows with partial data?

hard

A. df.dropna(axis=0, how='any')

B. df.dropna(axis=0, how='all')

C. df.dropna(axis=1, how='any')

D. df.dropna(axis=1, how='all')

Solution

1. Step 1: Understand how='all' parameter

   Using how='all' drops rows only if all values in that row are missing.

2. Step 2: Use axis=0 to drop rows

   Since we want to drop rows, axis=0 is correct.

3. Step 3: Confirm this keeps rows with some data

   Rows with some non-missing values are kept because not all values are missing.

4. Final Answer:

   df.dropna(axis=0, how='all') -> Option B

5. Quick Check:

   Drop rows with all NaN = axis=0, how='all' [OK]

Hint: Use how='all' to drop only fully empty rows [OK]

Common Mistakes:

• Using how='any' which drops partial rows
• Using axis=1 which drops columns
• Confusing how parameter effects