Pandas Change Datatype Last Updated : 13 Jan, 2025 Comments Improve Suggest changes Like Article Like Report 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 Python 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) OutputName object Age float64 Gender object Salary int64 dtype: object Converting a Column to a DateTime TypeSometimes, 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. Python # 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) OutputName object Age int64 Gender object Salary int64 Join Date datetime64[ns] dtype: object Changing Multiple Columns' Data TypesIf 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. Python # Convert 'Age' to float and 'Salary' to string df = df.astype({'Age': 'float64', 'Salary': 'str'}) print(df.dtypes) OutputName object Age float64 Gender object Salary object dtype: object Comment More infoAdvertise with us Next Article Pandas Change Datatype A abhirajksingh Follow Improve Article Tags : Pandas AI-ML-DS Python-pandas Python pandas-basics Python pandas-io +1 More Similar Reads Change String To Date In Pandas Dataframe Working with date and time data in a Pandas DataFrame is common, but sometimes dates are stored as strings and need to be converted into proper date formats for analysis and visualization. In this article, we will explore multiple methods to convert string data to date format in a Pandas DataFrame.U 5 min read Create empty dataframe in Pandas The Pandas Dataframe is a structure that has data in the 2D format and labels with it. DataFrames are widely used in data science, machine learning, and other such places. DataFrames are the same as SQL tables or Excel sheets but these are faster in use.Empty DataFrame could be created with the help 1 min read Pandas Drop Column When working with large datasets, there are often columns that are irrelevant or redundant. Pandas provides an efficient way to remove these unnecessary columns using the `drop()` function. In this article, we will cover various methods to drop columns from a DataFrame.Pythonimport pandas as pd data 4 min read Convert JSON to Pandas DataFrame When working with data, it's common to encounter JSON (JavaScript Object Notation) files, which are widely used for storing and exchanging data. Pandas, a powerful data manipulation library in Python, provides a convenient way to convert JSON data into a Pandas data frame. In this article, we'll exp 4 min read Python | Pandas DataFrame.astype() DataFrame.astype() function in pandas cast a pandas object such as a DataFrame or Series to a specified data type. This is especially useful when you need to ensure that columns have the correct type, such as converting strings to integers or floats to strings. For example:Pythonimport pandas as pd 3 min read Like