Get the data type of column in Pandas - Python

Pandas is a popular and powerful Python library commonly used for data analysis and manipulation. It offers data structures like Series and DataFrame for working with tabular data.

Each column in a Pandas DataFrame can contain a different data type. This article covers various methods for determining column data types in a DataFrame ?

Sample DataFrame

Let's create a sample DataFrame to demonstrate different approaches ?

import pandas as pd

# Create a sample dataframe
df = pd.DataFrame({
    'Vehicle name': ['Supra', 'Honda', 'Lamborghini'],
    'price': [5000000, 600000, 7000000],
    'mileage': [12.5, 18.2, 9.8],
    'year': [2020, 2019, 2021]
})

print(df)

  Vehicle name    price  mileage  year
0        Supra  5000000     12.5  2020
1        Honda   600000     18.2  2019
2  Lamborghini  7000000      9.8  2021

Using the dtypes Attribute

The dtypes attribute returns the data type of each column in the DataFrame ?

Getting All Column Data Types

import pandas as pd

df = pd.DataFrame({
    'Vehicle name': ['Supra', 'Honda', 'Lamborghini'],
    'price': [5000000, 600000, 7000000],
    'mileage': [12.5, 18.2, 9.8],
    'year': [2020, 2019, 2021]
})

# Get data types of all columns
print("Data types of each column:")
print(df.dtypes)

Data types of each column:
Vehicle name     object
price             int64
mileage         float64
year              int64
dtype: object

Getting Single Column Data Type

import pandas as pd

df = pd.DataFrame({
    'Vehicle name': ['Supra', 'Honda', 'Lamborghini'],
    'price': [5000000, 600000, 7000000],
    'mileage': [12.5, 18.2, 9.8]
})

# Get data type of a specific column
print("Data type of 'price' column:", df.dtypes['price'])
print("Data type of 'mileage' column:", df.dtypes['mileage'])

Data type of 'price' column: int64
Data type of 'mileage' column: float64

Using select_dtypes() Method

The select_dtypes() method filters columns based on their data types ?

import pandas as pd

df = pd.DataFrame({
    'Vehicle name': ['Supra', 'Honda', 'Lamborghini'],
    'price': [5000000, 600000, 7000000],
    'mileage': [12.5, 18.2, 9.8],
    'year': [2020, 2019, 2021]
})

# Select numeric columns
numeric_cols = df.select_dtypes(include=['int64', 'float64']).columns
print("Numeric columns:", list(numeric_cols))

# Select object columns
object_cols = df.select_dtypes(include=['object']).columns
print("Object columns:", list(object_cols))

# Print data types of numeric columns
for col in numeric_cols:
    print(f"Data type of '{col}': {df[col].dtype}")

Numeric columns: ['price', 'mileage', 'year']
Object columns: ['Vehicle name']
Data type of 'price': int64
Data type of 'mileage': float64
Data type of 'year': int64

Using the info() Method

The info() method provides a comprehensive summary including data types, non-null counts, and memory usage ?

import pandas as pd

df = pd.DataFrame({
    'Vehicle name': ['Supra', 'Honda', 'Lamborghini'],
    'price': [5000000, 600000, 7000000],
    'mileage': [12.5, 18.2, 9.8],
    'year': [2020, 2019, 2021]
})

print("DataFrame Info:")
df.info()

DataFrame Info:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3 entries, 0 to 2
Data columns (total 4 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   Vehicle name  3 non-null      object 
 1   price         3 non-null      int64  
 2   mileage       3 non-null      float64
 3   year          3 non-null      int64  
dtypes: float64(1), int64(2), object(1)
memory usage: 224.0+ bytes

Comparison of Methods

Conclusion

Use dtypes for quick data type checks, select_dtypes() for filtering columns by type, and info() for comprehensive DataFrame information. Each method serves different purposes in data analysis workflows.