Pandas Change Datatype

In data analysis, ensuring that each column in a Pandas DataFrame has the correct data type is crucial for accurate computations and analyses. The most common way to change the data type of a column in a Pandas DataFrame is by using the astype() method. This method allows you to convert a specific column to a desired data type. Here's the example:

Using astype() method

import pandas as pd

data = {'Name': ['John', 'Alice', 'Bob', 'Eve', 'Charlie'], 
        'Age': [25, 30, 22, 35, 28], 
        'Gender': ['Male', 'Female', 'Male', 'Female', 'Male'], 
        'Salary': [50000, 55000, 40000, 70000, 48000]}

df = pd.DataFrame(data)

# Convert 'Age' column to float type
df['Age'] = df['Age'].astype(float)
print(df.dtypes)

Name       object
Age       float64
Gender     object
Salary      int64
dtype: object

Converting a Column to a DateTime Type

Sometimes, a column that contains date information may be stored as a string. You can convert it to the datetime type using the pd.to_datetime() function.

# Example: Create a 'Join Date' column as a string
df['Join Date'] = ['2021-01-01', '2020-05-22', '2022-03-15', '2021-07-30', '2020-11-11']

# Convert 'Join Date' to datetime type
df['Join Date'] = pd.to_datetime(df['Join Date'])
print(df.dtypes)

Name                 object
Age                   int64
Gender               object
Salary                int64
Join Date    datetime64[ns]
dtype: object

Changing Multiple Columns' Data Types

If you need to change the data types of multiple columns at once, you can pass a dictionary to the astype() method, where keys are column names and values are the desired data types.

# Convert 'Age' to float and 'Salary' to string
df = df.astype({'Age': 'float64', 'Salary': 'str'})
print(df.dtypes)

Name       object
Age       float64
Gender     object
Salary     object
dtype: object