Python | Pandas Series.astype() to convert Data type of series

Last Updated : 01 Oct, 2018

Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Pandas is one of those packages and makes importing and analyzing data much easier.

Pandas astype() is the one of the most important methods. It is used to change data type of a series. When data frame is made from a csv file, the columns are imported and data type is set automatically which many times is not what it actually should have. For example, a salary column could be imported as string but to do operations we have to convert it into float.
astype() is used to do such data type conversions.

Syntax: DataFrame.astype(dtype, copy=True, errors=’raise’)

Parameters:
dtype: Data type to convert the series into. (for example str, float, int)
copy: Makes a copy of dataframe/series.
errors: Error raising on conversion to invalid data type. For example dict to string. ‘raise’ will raise the error and ‘ignore’ will pass without raising error.

Return type: Series with changed data types

To download the data set used in following example, click here.
In the following examples, the data frame used contains data of some NBA players. The image of data frame before any operations is attached below.

Example:
In this example, the data frame is imported and .dtypes is called on the data frame to view the data types of series. After that some columns are converted using .astype() method and the dtypes are viewed again to see the changes.

# importing pandas module  
import pandas as pd 
  
# reading csv file from url  
data = pd.read_csv("https://media.geeksforgeeks.org/wp-content/uploads/nba.csv") 
   
# dropping null value columns to avoid errors 
data.dropna(inplace = True) 
  
# storing dtype before converting 
before = data.dtypes 
  
# converting dtypes using astype 
data["Salary"]= data["Salary"].astype(int) 
data["Number"]= data["Number"].astype(str) 
  
# storing dtype after converting 
after = data.dtypes 
  
# printing to compare 
print("BEFORE CONVERSION\n", before, "\n") 
print("AFTER CONVERSION\n", after, "\n") 