info() for column types and nulls in Pandas

Introduction

The info() function helps you quickly see the types of data in each column and how many missing values there are. This helps you understand your data better before analysis.

When you want to check what kind of data each column holds, like numbers or text.

When you want to find out if any columns have missing or empty values.

When you want a quick summary of your dataset's size and structure.

Before cleaning data to know which columns need fixing.

When exploring a new dataset to get a quick overview.

Syntax

Pandas

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

DataFrame is your data table.

You usually just call info() without extra arguments for a quick summary.

Examples

Shows a summary of the DataFrame including column names, non-null counts, and data types.

Pandas

df.info()

Shows counts of non-null values for each column explicitly (useful in newer pandas versions).

Pandas

df.info(show_counts=True)

Sample Program

This code creates a small table with some missing values. Then it uses info() to show the data types and how many values are missing in each column.

Pandas

import pandas as pd

data = {
    'Name': ['Alice', 'Bob', 'Charlie', None],
    'Age': [25, 30, None, 22],
    'Salary': [50000, 60000, 55000, None]
}

df = pd.DataFrame(data)

# Use info() to see column types and null counts
df.info()

Important Notes

The info() output shows non-null counts, which tell you how many values are NOT missing.

Data types like object usually mean text data.

Missing values appear as fewer non-null counts than total rows.

Summary

info() gives a quick look at your data's columns, types, and missing values.

It helps you decide what cleaning or processing your data needs.

Use it early when exploring any new dataset.