How to Check Data Types in Pandas DataFrame in Python

To check data types of columns in a pandas DataFrame, use df.dtypes to see each column's type or df.info() for a summary including data types. These commands help you understand what kind of data each column holds.

Syntax

df.dtypes returns a Series showing the data type of each column in the DataFrame df.

df.info() prints a summary of the DataFrame including the number of non-null values and data types of each column.

python

df.dtypes

df.info()

Example

This example creates a simple pandas DataFrame and shows how to check the data types of its columns using dtypes and info().

python

import pandas as pd

# Create a sample DataFrame
data = {'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': [25, 30, 35],
        'Height': [5.5, 6.0, 5.8],
        'Member': [True, False, True]}
df = pd.DataFrame(data)

# Check data types of each column
print(df.dtypes)

# Get summary info including data types
print('\nDataFrame info:')
df.info()

Output

Name       object
Age         int64
Height    float64
Member       bool
dtype: object

DataFrame info:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3 entries, 0 to 2
Data columns (total 4 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   Name    3 non-null      object 
 1   Age     3 non-null      int64  
 2   Height  3 non-null      float64
 3   Member  3 non-null      bool   
 dtypes: bool(1), float64(1), int64(1), object(1)
memory usage: 311.0 bytes

Common Pitfalls

• Using type(df) only shows the type of the whole object (DataFrame), not the columns.
• Confusing df.dtypes output with Python built-in types; pandas uses its own types like int64, float64, and object.
• For mixed types in a column, pandas shows object, which means it can hold any Python object.

python

import pandas as pd

df = pd.DataFrame({'A': [1, 2], 'B': ['x', 'y']})

# Wrong: This shows DataFrame type, not column types
print(type(df))  # Output: <class 'pandas.core.frame.DataFrame'>

# Right: Shows data types of each column
print(df.dtypes)

Output

<class 'pandas.core.frame.DataFrame'>
A     int64
B    object
dtype: object

Quick Reference

Use these commands to check data types in pandas:

• df.dtypes: Returns a Series with data types of each column.
• df.info(): Prints a summary including data types and non-null counts.
• df.select_dtypes(include=[type]): Select columns of a specific data type.

Command	Description
df.dtypes	Shows data type of each column
df.info()	Prints summary with data types and non-null counts
df.select_dtypes(include=[type])	Selects columns of a specific data type

Key Takeaways

Use df.dtypes to see the data type of each column in a pandas DataFrame.

Use df.info() for a detailed summary including data types and non-null counts.

Remember df.dtypes returns pandas-specific types like int64, float64, and object.

Avoid using type(df) to check column data types; it only shows the DataFrame type.

Use df.select_dtypes() to filter columns by their data type.