DataFrame.astype() to change dtype of the object to specific dtype Pandas

Cast dtype to a particular dtype with options.
Let us change one type to other. Here column MATH is changed to string and '2' ( '2' dtpye is string not integer ) is added.

import pandas as pd 
my_dict={'NAME':['Ravi','Raju','Alex','Ron','King','Jack'],
         'ID':[1,2,3,4,5,6],
         'MATH':[80,40,70,70,70,30],
         'ENGLISH':[80,70,40,50,60,30]}
my_data = pd.DataFrame(data=my_dict)
print(my_data['MATH'].astype(str)+'2')

Output

Let us change the last line only in above code.

print(my_data['MATH']+2)

Output

0    82
1    42
2    72
3    72
4    72
5    32
Name: MATH, dtype: int64

Let us change the MATH column to int32

print(my_data['MATH'].astype('int32'))

Output

0    80
1    40
2    70
3    70
4    70
5    30
Name: MATH, dtype: int32

We can use dtypes to get the data type of columns.

import pandas as pd 
my_dict={'NAME':['Ravi','Raju','Alex','Ron','King','Jack'],
         'ID':[1,2,3,4,5,6],
         'MATH':[80,40,70,70,70,30],
         'ENGLISH':[80,70,40,50,60,30]}
my_data = pd.DataFrame(data=my_dict)

print(my_data.dtypes) #  dtype of all columns
print('----')
print(my_data['MATH'].dtypes) # dtype of MATH column
print(my_data['NAME'].dtypes) # dtype of NAME column

Output

NAME       object
ID          int64
MATH        int64
ENGLISH     int64
dtype: object
----
int64
object

We can successfully convert the data types if data matches to new data type. Otherwise we have to clean the data before using astype()
Data Cleaning
Pandas to_timedelta() dtypes() select_dtypes() timedelta64()

Numpy arrays Python & MySQL Python- Tutorials

Subhendu Mohapatra

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