Pandas DataFrame.info

Pandas DataFrame.info

The DataFrame.info method in pandas provides essential details about a DataFrame, such as the index type, column data types, non-null values, and memory usage. It is a convenient way to get a quick overview of the structure and contents of a DataFrame.

Syntax

The syntax for the DataFrame.info method is:

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

Here, DataFrame refers to the pandas DataFrame whose information is being displayed.

Parameters

ParameterDescription
verboseControls whether to show a summary of the DataFrame. If None, it depends on the number of columns.
bufFile-like object where the output is written. Defaults to sys.stdout.
max_colsLimits the number of columns to display. If None, it defaults to pandas.options.display.max_info_columns.
memory_usageDisplays memory usage. Can be True, False, or 'deep' for detailed memory usage.
show_countsDisplays the count of non-null values per column if True. Defaults to None, depending on verbose.

Examples

Basic Usage

To display basic information about a DataFrame, use the DataFrame.info method without any parameters.

Python Program

import pandas as pd

# Create a DataFrame
data = {
    'Name': ['Arjun', 'Ram', 'Suresh'],
    'Age': [25, 30, 35],
    'Salary': [70000, 80000, 90000]
}
df = pd.DataFrame(data)

# Display DataFrame information
print("Basic Information about the DataFrame:")
df.info()

Output

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3 entries, 0 to 2
Data columns (total 3 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   Name    3 non-null      object 
 1   Age     3 non-null      int64  
 2   Salary  3 non-null      int64  
dtypes: int64(2), object(1)
memory usage: 200.0 bytes

Using memory_usage='deep'

Display detailed memory usage, including object types.

Python Program

import pandas as pd

# Create a DataFrame
data = {
    'Name': ['Arjun', 'Ram', 'Suresh'],
    'Age': [25, 30, 35],
    'Salary': [70000, 80000, 90000]
}
df = pd.DataFrame(data)

# Display detailed memory usage
print("Detailed Memory Usage:")
df.info(memory_usage='deep')

Output

Detailed Memory Usage:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3 entries, 0 to 2
Data columns (total 3 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   Name    3 non-null      object 
 1   Age     3 non-null      int64  
 2   Salary  3 non-null      int64  
dtypes: int64(2), object(1)
memory usage: 544.0 bytes

Using show_counts=True

Enable non-null value counts for all columns.

Python Program

import pandas as pd

# Create a DataFrame
data = {
    'Name': ['Arjun', 'Ram', 'Suresh'],
    'Age': [25, 30, 35],
    'Salary': [70000, 80000, 90000]
}
df = pd.DataFrame(data)

# Display non-null value counts
df.info(show_counts=True)

Output

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3 entries, 0 to 2
Data columns (total 3 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   Name    3 non-null      object 
 1   Age     3 non-null      int64  
 2   Salary  3 non-null      int64  
dtypes: int64(2), object(1)
memory usage: 200.0 bytes

Summary

In this tutorial, we explored the DataFrame.info method in pandas. Key takeaways include:

  • Inspecting DataFrame structure and column types
  • Displaying memory usage and non-null counts
  • Customizing verbosity and output destination

The DataFrame.info method is a powerful tool for data analysis and understanding dataset structure.

Python Libraries