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How to Convert Integers to Floats in Pandas DataFrame?
  • Last Updated : 25 Aug, 2020

Pandas Dataframe provides the freedom to change the data type of column values. We can change them from Integers to Float type, Integer to String, String to Integer, etc.

There are 2 methods to convert Integers to Floats:

Method 1: Using DataFrame.astype() method

Syntax : 

DataFrame.astype(dtype, copy=True, errors=’raise’, **kwargs)

Example 1: Converting one column from int to float using DataFrame.astype()



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# importing pandas library
import pandas as pd
  
# Initializing the nested list with Data set
player_list = [['M.S.Dhoni', 36, 75, 5428000, 176], 
               ['A.B.D Villers', 38, 74, 3428000, 175], 
               ['V.Kholi', 31, 70, 8428000, 172],
               ['S.Smith', 34, 80, 4428000, 180], 
               ['C.Gayle', 40, 100, 4528000, 200],
               ['J.Root', 33, 72, 7028000, 170], 
               ['K.Peterson', 42, 85, 2528000, 190]]
  
# creating a pandas dataframe
df = pd.DataFrame(player_list, columns=[
                  'Name', 'Age', 'Weight', 'Salary', 'Strike_rate'])
  
# lets find out the data type 
# of 'Weight' column
print(df.dtypes)

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Output:

Let’s convert weight type to float

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# Now we will convert it from 'int' to 'float' type 
# using DataFrame.astype() function
df['Weight'] = df['Weight'].astype(float)
  
print()
  
# lets find out the data type after changing
print(df.dtypes)
  
# print dataframe. 
df

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Output:

In the above example, we change the data type of column ‘Weight‘ from ‘int64’ to ‘float64’.



Example 2: Converting more than one column from int to float using DataFrame.astype()

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# importing pandas library
import pandas as pd
  
# Initializing the nested list with Data set
player_list = [['M.S.Dhoni', 36, 75, 5428000, 176], 
               ['A.B.D Villers', 38, 74, 3428000, 175],
               ['V.Kholi', 31, 70, 8428000, 172],
               ['S.Smith', 34, 80, 4428000, 180],
               ['C.Gayle', 40, 100, 4528000, 200],
               ['J.Root', 33, 72, 7028000, 170], 
               ['K.Peterson', 42, 85, 2528000, 190]]
  
# creating a pandas dataframe
df = pd.DataFrame(player_list, columns=[
                  'Name', 'Age', 'Weight', 'Salary', 'Strike_rate'])
  
# lets find out the data type of 'Age' 
# and 'Strike_rate' columns
print(df.dtypes)

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Output:

Let’s convert age and strike_rate to float type

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# now Pass a dictionary to astype() function  
# which contains two columns 
# and hence convert them from int to float type
df = df.astype({"Age":'float', "Strike_rate":'float'}) 
print()
  
# lets find out the data type after changing
print(df.dtypes)
  
# print dataframe. 
df 

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Output:

In the above example, we change the data type of columns ‘Age‘ and ‘Strike_rate’ from ‘int64’ to ‘float64’.

Method 2: Using pandas.to_numeric() method



Syntax

pandas.to_numeric(arg, errors=’raise’, downcast=None)

Example 1:  Converting a single column from int to float using pandas.to_numeric()

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# importing pandas library
import pandas as pd
  
# Initializing the nested list with Data set
player_list = [['M.S.Dhoni', 36, 75, 5428000, 176], 4
               ['A.B.D Villers', 38, 74, 3428000, 175], 
               ['V.Kholi', 31, 70, 8428000, 172],
               ['S.Smith', 34, 80, 4428000, 180], 
               ['C.Gayle', 40, 100, 4528000, 200],
               ['J.Root', 33, 72, 7028000, 170], 
               ['K.Peterson', 42, 85, 2528000, 190]]
  
# creating a pandas dataframe
df = pd.DataFrame(player_list, columns=[
                  'Name', 'Age', 'Weight', 'Salary', 'Height'])
  
# lets find out the data type of 
# 'Weight' column
print(df.dtypes)

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Output:

Let’s convert weight from int to float

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# Now we will convert it from 'int' to 'float' type
# using pandas.to_numeric()
df['Weight'] = pd.to_numeric(df['Weight'], downcast='float')
print()
  
# lets find out the data type after changing
print(df.dtypes)
  
# print dataframe. 
df 

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Output:

In the above example, we change the data type of column ‘Weight‘ from ‘int64’ to ‘float32’.

Example 2: 

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# importing pandas library
import pandas as pd
  
# Initializing the nested list with Data set
player_list = [['M.S.Dhoni', 36, 75, 5428000, 176], 
               ['A.B.D Villers', 38, 74, 3428000, 175], 
               ['V.Kholi', 31, 70, 8428000, 172],
               ['S.Smith', 34, 80, 4428000, 180], 
               ['C.Gayle', 40, 100, 4528000, 200],
               ['J.Root', 33, 72, 7028000, 170], 
               ['K.Peterson', 42, 85, 2528000, 190]]
  
# creating a pandas dataframe
df = pd.DataFrame(player_list, columns=[
                  'Name', 'Experience', 'Weight', 'Salary', 'Height'])
  
# lets find out the data type of 
# 'Experience' and 'Height' columns
print(df.dtypes)

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Output:

Let’s convert experience and height from int to float

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# Now we will convert them from 'int' to 'float' type
# using pandas.to_numeric()
df['Experience'] = pd.to_numeric(df['Experience'], downcast='float')
df['Height'] = pd.to_numeric(df['Height'], downcast='float')
  
print()
  
# lets find out the data type after changing
print(df.dtypes)
  
# print dataframe. 
df

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Output:

In the above example, we change the data type of columns ‘Experience’ and ‘Height’  from ‘int64’ to ‘float32’.

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