Pandas convert multiple columns to float

Pandas-type-Conversion

Sometimes we have string numeric float values in the dataframe. In this post, we are going to learn in Pandas convert multiple columns to float with example by using the built-in method

Methods to convert mutiple column to float

  • astype(float)
  • to_numeric()


Pandas astype() method

The astype(float) method is very convenient when we have to convert any column values of the dataframe to another data type, even we can use python dictionary to change multiple columns datatypes at a time, Where keys specify the column and values specify the new datatype.

1. Pandas Convert multiple columns to float

In this example, we are converting multiple columns that have a numeric string to float by using the astype(float) method of the panda’s library.

We are python dictionary to change multiple columns datatype Where keys specify the column and values specify a new datatype

Program Example

import pandas as pd
 
Student_dict = {
    'Name': ['Jack', 'Rack', 'Max'],
    'Marks':['100','100', '100'],
    'Fee':['100','200','300'],
    'Subject': ['Math', 'Math', 'Music']
}
 


dfobj = pd.DataFrame(Student_dict)

dict_columns_type = {'Marks': float,
                'Fee': float
               }
  
dfobj = dfobj.astype(dict_columns_type)
print('dataframe str to float:\n',dfobj)

print(f'\n {dfobj.dtypes}')

Output

dataframe str to float:
    Name  Marks    Fee Subject
0  Jack  100.0  100.0    Math
1  Rack  100.0  200.0    Math
2   Max  100.0  300.0   Music

 Name        object
Marks      float64
Fee        float64
Subject     object
dtype: object

2. Convert multiple columns type to numeric

In this example, we are using apply() method and passing datatype to_numeric as an argument to change columns numeric string value to an integer.

Program Example

import pandas as pd
 
Student_dict = {
    'Name': ['Jack', 'Rack', 'Max'],
    'Marks':['100','100', '100'],
    'Fee':['100','200','300'],
    'Subject': ['Math', 'Math', 'Music']
}
 


dfobj = pd.DataFrame(Student_dict)

dfobj[['Marks','Fee']]= dfobj[['Marks','Fee']].apply(pd.to_numeric)
  

print('dataframe str to float:\n',dfobj)

print(f'\n {dfobj.dtypes}')

Output

dataframe str to float:
    Name  Marks  Fee Subject
0  Jack    100  100    Math
1  Rack    100  200    Math
2   Max    100  300   Music

 Name       object
Marks       int64
Fee         int64
Subject    object
dtype: object

3. Convert entire dataframe to float

To convert an entire dataframe column to float we just need to call the astype(float) method using the dataframe object.

Program Example

import pandas as pd
 
Student_dict = {
    'StudID': ['12', '13', '14'],    
    'Marks':['100','100', '100'],
    'Fee':['100','200','300']
    
}
 


dfobj = pd.DataFrame(Student_dict)

dfobj= dfobj.astype(float)
  

print('dataframe str to float:\n',dfobj)

print(f'\n {dfobj.dtypes}')

Output

dataframe str to int:
    StudID  Marks    Fee
0    12.0  100.0  100.0
1    13.0  100.0  200.0
2    14.0  100.0  300.0

 StudID    float64
Marks     float64
Fee       float64
dtype: object

4. Dataframe.Inter_objects()

 “Inter_objects() is a soft conversion of object-dtyped columns, leaving non-object and unconvertible columns unchanged”.

Program Example

import pandas as pd
import numpy as np
 
Student_dict = {
    'Name': ['Jack', 'Rack', 'Max'],
    'Marks':[100,100,100],
    'Subject': ['Math', 'Math', 'Music']
}
 


dfobj = pd.DataFrame(Student_dict,dtype ='object')


dfobj = dfobj.infer_objects()
print(dfobj.dtypes)

Output

Name       object
Marks       int64
Subject    object
dtype: object

Summary

In this post, we have understood different multiple ways in Pandas how to convert multiple columns to float.

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