How to Use info() in pandas to Inspect DataFrames

Use the info() method on a pandas DataFrame to get a concise summary of its structure, including the number of rows, columns, data types, and non-null counts. This helps you quickly understand your data's shape and missing values.

Syntax

The info() method is called on a pandas DataFrame object. It has optional parameters to customize the output.

• verbose: If True, shows full summary; if False, shows only summary info.
• memory_usage: Shows memory usage; can be True, False, or 'deep' for detailed memory info.
• null_counts: Deprecated, use show_counts instead in newer pandas versions.

python

DataFrame.info(verbose=None, buf=None, max_cols=None, memory_usage=None, show_counts=None)

Example

This example shows how to create a DataFrame and use info() to see its structure, data types, and missing values.

python

import pandas as pd

data = {
    'Name': ['Alice', 'Bob', 'Charlie', None],
    'Age': [25, 30, 35, 40],
    'Salary': [50000, 60000, None, 80000]
}

df = pd.DataFrame(data)

# Use info() to get summary
print(df.info())

Output

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4 entries, 0 to 3
Data columns (total 3 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   Name    3 non-null      object 
 1   Age     4 non-null      int64  
 2   Salary  3 non-null      float64
dtypes: float64(1), int64(1), object(1)
memory usage: 224.0+ bytes
None

Common Pitfalls

Some common mistakes when using info() include:

• Expecting info() to return a value. It prints output and returns None.
• Not noticing that missing values show as fewer non-null counts.
• Using deprecated parameters like null_counts in newer pandas versions.

python

import pandas as pd

data = {'A': [1, 2, None]}
df = pd.DataFrame(data)

# Wrong: expecting info() to return data
result = df.info()
print('Returned:', result)

# Right: just call info() to print summary
# df.info()

Output

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3 entries, 0 to 2
Data columns (total 1 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   A       2 non-null      float64
dtypes: float64(1)
memory usage: 152.0 bytes
Returned: None

Quick Reference

Summary of info() usage:

Parameter	Description	Default
verbose	Show full summary if True, else concise	None
memory_usage	Show memory usage; True, False, or 'deep'	None
show_counts	Show non-null counts (replaces deprecated null_counts)	None
buf	Output destination (default stdout)	None
max_cols	Max columns to display info for	None

Key Takeaways

Use DataFrame.info() to quickly see data types, non-null counts, and memory usage.

info() prints output and returns None; do not assign its result expecting data.

Missing values show as lower non-null counts in the summary.

Avoid deprecated parameters like null_counts; use show_counts instead.

Customize output with parameters like verbose and memory_usage.