Pandas DataFrame.dtypes

Pandas DataFrame.dtypes

The DataFrame.dtypes property in pandas is used to inspect the data types of all columns in a DataFrame. This property is particularly useful for understanding the structure of a dataset and ensuring that the data types are correct for operations.

Syntax

The syntax to access the data types of columns in a DataFrame is:

DataFrame.dtypes

Here, DataFrame refers to the pandas DataFrame whose column data types are being inspected.

Examples

Accessing Data Types

To inspect the data types of all columns, use the DataFrame.dtypes property.

Python Program

import pandas as pd

# Create a DataFrame
data = {
    'Name': ['Arjun', 'Ram', 'Krishna'],
    'Age': [25, 32, 40],
    'Salary': [50000.5, 60000.0, 70000.0],
    'JoiningDate': pd.to_datetime(['2022-01-01', '2021-05-12', '2020-08-15'])
}
df = pd.DataFrame(data)

# Display the data types of all columns
print("Data Types of Columns:")
print(df.dtypes)

Output

Data Types of Columns:
Name                  object
Age                    int64
Salary               float64
JoiningDate    datetime64[ns]
dtype: object

Checking the Data Type of a Specific Column

You can check the data type of a specific column using the dtypes property.

Python Program

import pandas as pd

# Create a DataFrame
data = {
    'Name': ['Arjun', 'Ram', 'Krishna'],
    'Age': [25, 32, 40],
    'Salary': [50000.5, 60000.0, 70000.0],
    'JoiningDate': pd.to_datetime(['2022-01-01', '2021-05-12', '2020-08-15'])
}
df = pd.DataFrame(data)

# Check the data type of a specific column
print("Data type of 'Name':", df.dtypes['Name'])
print("Data type of 'Salary':", df.dtypes['Salary'])

Output

Data type of 'Name': object
Data type of 'Salary': float64

Filtering Columns by Data Type

Use the dtypes property to filter columns based on their data type.

Python Program

import pandas as pd

# Create a DataFrame
data = {
    'Name': ['Arjun', 'Ram', 'Krishna'],
    'Age': [25, 32, 40],
    'Salary': [50000.5, 60000.0, 70000.0],
    'JoiningDate': pd.to_datetime(['2022-01-01', '2021-05-12', '2020-08-15'])
}
df = pd.DataFrame(data)

# Filter numeric columns
numeric_columns = df.dtypes[df.dtypes == 'float64'].index
print("Numeric Columns:")
print(df[numeric_columns])

Output

Numeric Columns:
    Salary
0  50000.5
1  60000.0
2  70000.0

Iterating Over Column Data Types

Iterate through the columns and their data types using the dtypes property.

Python Program

import pandas as pd

# Create a DataFrame
data = {
    'Name': ['Arjun', 'Ram', 'Krishna'],
    'Age': [25, 32, 40],
    'Salary': [50000.5, 60000.0, 70000.0],
    'JoiningDate': pd.to_datetime(['2022-01-01', '2021-05-12', '2020-08-15'])
}
df = pd.DataFrame(data)

# Iterate over columns and their data types
print("Columns and Their Data Types:")
for column, dtype in df.dtypes.items():
    print(f"Column: {column}, Data Type: {dtype}")

Output

Columns and Their Data Types:
Column: Name, Data Type: object
Column: Age, Data Type: int64
Column: Salary, Data Type: float64
Column: JoiningDate, Data Type: datetime64[ns]

Summary

In this tutorial, we explored the DataFrame.dtypes property in pandas. We covered:

  • Accessing data types for all columns
  • Checking data types of specific columns
  • Filtering columns by data type
  • Iterating over columns and their data types

Understanding column data types is essential for efficient and error-free data analysis in pandas.

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