Change the data type of a column or a Pandas Series

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 library
import pandas as pd

# creating a DataFrame
df = pd.DataFrame({'srNo': [1, 2, 3],
                   'Name': ['Geeks', 'for',
                            'Geeks'],
                   'id': [111, 222,
                          333]})
# show the dataframe
print(df)

# show the datatypes
print(df.dtypes)

Output:

Now, changing the dataframe data types to string.

Python3

# changing the dataframe
# data types to string
df = df.astype(str)

# show the data types
# of dataframe
df.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 library
import pandas as pd

# creating a DataFrame
df = pd.DataFrame({'No': [1, 2, 3],
                   'Name': ['Geeks', 'for',
                            'Geeks'],
                   'id': [111, 222,
                          333]})
# show the dataframe
print(df)

# show the datatypes
print(df.dtypes)

Output:

Now, change the data type of ‘id’ column to string. 

Python3

# creating a dictionary
# with column name and data type
data_types_dict = {'id': str}

# we will change the data type
# of id column to str by giving
# the dict to the astype method
df = df.astype(data_types_dict)

# checking the data types
# using df.dtypes method
df.dtypes

Output:

Example 3: Convert the data type of “grade” column from “float” to “int”.

Python3

# import pandas library
import pandas as pd

# dictionary
result_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 dataframe
df = pd.DataFrame(result_data)

# show the dataframe
print(df)

#show the datatypes
print(df.dtypes)

Output:

Now, we convert the data type of “grade” column from “float” to “int”.

Python3

# convert data type of grade column
# into integer
df.grade = df.grade.astype(int)

# show the dataframe
print(df)

# show the datatypes
print(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 dataframe
print(df)

# show the data types
# of all columns
df.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 columns
df.dtypes

Output: