Pandas DataFrame.astype() Function
Minahil Noor
Jan 30, 2023
Pandas
Pandas DataFrame
-
Syntax of
pandas.DataFrame.astype(): -
Example Codes:
DataFrame.astype()Method to Change Data Type of One Column -
Example Codes:
DataFrame.astype()Method to Change the Data Type of All Columns of Data Frame -
Example Codes:
DataFrame.astype()Method to Change the Data Type With Exception
Python Pandas DataFrame.astype() function changes the data type of the objects to a specified data type.
Syntax of pandas.DataFrame.astype():
DataFrame.astype(dtype, copy=True, errors="raise")
Parameters
dtype |
Data type that we want to assign to our object. |
copy |
A Boolean parameter. It returns a copy when True. |
errors |
It controls the raising of exceptions on invalid data for the provided data type. It has two options. raise: allows exceptions to be raised. ignore: suppresses exceptions. If an error exists, then it returns the original object. |
Return
It returns the DataFrame with the casted data types.
Example Codes: DataFrame.astype() Method to Change Data Type of One Column
import pandas as pd
dataframe=pd.DataFrame({'Attendance': {0: 60, 1: 100, 2: 80,3: 78,4: 95},
'Name': {0: 'Olivia', 1: 'John', 2: 'Laura',3: 'Ben',4: 'Kevin'},
'Obtained Marks': {0: 90, 1: 75, 2: 82, 3: 64, 4: 45}})
print("The Original Data Types of the Data frame are: \n")
print(dataframe.dtypes)
dataframe1 = dataframe.astype({'Attendance': 'int32'}).dtypes
print("The Modified Data Types of the Data frame are: \n")
print(dataframe1)
Output:
The Original Data Types of the Data frame are:
Attendance int64
Name object
Obtained Marks int64
dtype: object
The Modified Data Types of the Data frame are:
Attendance int32
Name object
Obtained Marks int64
dtype: object
The function has returned the casted data type. We have used dtypes() function to show the data types of the columns of the DataFrame.
Example Codes: DataFrame.astype() Method to Change the Data Type of All Columns of Data Frame
We will try to change the data type of the given DataFrame.
import pandas as pd
dataframe=pd.DataFrame({'Attendance': {0: 60, 1: 100, 2: 80,3: 78,4: 95},
'Name': {0: 'Olivia', 1: 'John', 2: 'Laura',3: 'Ben',4: 'Kevin'},
'Obtained Marks': {0: 90, 1: 75, 2: 82, 3: 64, 4: 45}})
print("The Original Data Types of the Data frame are: \n")
print(dataframe.dtypes)
dataframe1 = dataframe.astype('object').dtypes
print("The Modified Data Types of the Data frame are: \n")
print(dataframe1)
Output:
The Original Data Types of the Data frame are:
Attendance int64
Name object
Obtained Marks int64
dtype: object
The Modified Data Types of the Data frame are:
Attendance object
Name object
Obtained Marks object
dtype: object
The function has returned the modified DataFrame. It has changed the data type of all columns to object.
Example Codes: DataFrame.astype() Method to Change the Data Type With Exception
Now we will set the data type object to int32. The function will ignore the exception as we will pass the parameter errors= 'ignore'.
import pandas as pd
dataframe=pd.DataFrame({'Attendance': {0: 60, 1: 100, 2: 80,3: 78,4: 95},
'Name': {0: 'Olivia', 1: 'John', 2: 'Laura',3: 'Ben',4: 'Kevin'},
'Obtained Marks': {0: 90, 1: 75, 2: 82, 3: 64, 4: 45}})
print("The Original Data Types of the Data frame are: \n")
print(dataframe.dtypes)
dataframe1 = dataframe.astype('int32', errors='ignore').dtypes
print("The Modified Data Types of the Data frame are: \n")
print(dataframe1)
Output:
The Original Data Types of the Data frame are:
Attendance int64
Name object
Obtained Marks int64
dtype: object
The Modified Data Types of the Data frame are:
Attendance int32
Name object
Obtained Marks int32
dtype: object
Note that the function has not raised any exceptions. It has ignored the error as we were casting the object to int32. It merely has not changed the data type of the Name column.
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