Series is a one-dimensional labeled array capable of holding data of the type integer, string, float, python objects, etc. The axis labels are collectively called index.
Let’s see the program to change the data type of column or a Series in Pandas Dataframe.
Method 1: Using DataFrame.astype() method.
We can pass any Python, Numpy or Pandas datatype to change all columns of a dataframe to that type, or we can pass a dictionary having column names as keys and datatype as values to change type of selected columns.
Syntax: DataFrame.astype(dtype, copy = True, errors = ’raise’, **kwargs)
Return: casted : type of caller
Let’s see the examples:
Example 1: The Data type of the column is changed to “str” object.
Python3
# importing the pandas libraryimport pandas as pd # creating a DataFramedf = pd.DataFrame({'srNo': [1, 2, 3], 'Name': ['Geeks', 'for', 'Geeks'], 'id': [111, 222, 333]})# show the dataframeprint(df) # show the datatypesprint(df.dtypes) |
Output:
Now, changing the dataframe data types to string.
Python3
# changing the dataframe # data types to stringdf = df.astype(str) # show the data types # of dataframedf.dtypes |
Output:
Example 2: Now, let us change the data type of the “id” column from “int” to “str”. We create a dictionary and specify the column name with the desired data type.
Python3
# importing the pandas libraryimport pandas as pd # creating a DataFramedf = pd.DataFrame({'No': [1, 2, 3], 'Name': ['Geeks', 'for', 'Geeks'], 'id': [111, 222, 333]})# show the dataframeprint(df) # show the datatypesprint(df.dtypes) |
Output:
Now, change the data type of ‘id’ column to string.
Python3
# creating a dictionary # with column name and data typedata_types_dict = {'id': str} # we will change the data type # of id column to str by giving# the dict to the astype methoddf = df.astype(data_types_dict) # checking the data types# using df.dtypes methoddf.dtypes |
Output:
Example 3: Convert the data type of “grade” column from “float” to “int”.
Python3
# import pandas libraryimport pandas as pd # dictionaryresult_data = {'name': ['Alia', 'Rima', 'Kate', 'John', 'Emma', 'Misa', 'Matt'], 'grade': [13.5, 7.1, 11.5, 3.77, 8.21, 21.22, 17.5], 'qualify': ['yes', 'no', 'yes', 'no', 'no', 'yes', 'yes']} # create a dataframedf = pd.DataFrame(result_data) # show the dataframeprint(df) #show the datatypesprint(df.dtypes) |
Output:
Now, we convert the data type of “grade” column from “float” to “int”.
Python3
# convert data type of grade column # into integerdf.grade = df.grade.astype(int) # show the dataframeprint(df) # show the datatypesprint(df.dtypes) |
Output:
Method 2: Using Dataframe.apply() method.
We can pass pandas.to_numeric, pandas.to_datetime and pandas.to_timedelta as argument to apply() function to change the datatype of one or more columns to numeric, datetime and timedelta respectively.
Syntax: Dataframe/Series.apply(func, convert_dtype=True, args=())
Return: Dataframe/Series after applied function/operation.
Let’s see the example:
Example: Convert the data type of “B” column from “string” to “int”.
Python3
# importing pandas as pd import pandas as pd # sample dataframe df = pd.DataFrame({ 'A': ['a', 'b', 'c', 'd', 'e'], 'B': [12, 22, 35, '47', '55'], 'C': [1.1, '2.1', 3.0, '4.1', '5.1'] }) # show the dataframeprint(df) # show the data types# of all columnsdf.dtypes |
Output:
Now, we convert the datatype of column “B” into an “int” type.
Python3
# using apply method df[['B']] = df[['B']].apply(pd.to_numeric) # show the data types# of all columnsdf.dtypes |
Output:
