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()
Python3
# 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) |
Output:
Let’s convert weight type to float
Python3
# 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 |
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()
Python3
# 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) |
Output:
Let’s convert age and strike_rate to float type
Python3
# 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 |
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()
Python3
# 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) |
Output:
Let’s convert weight from int to float
Python3
# 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 |
Output:
In the above example, we change the data type of column ‘Weight‘ from ‘int64’ to ‘float32’.
Example 2:
Python3
# 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) |
Output:
Let’s convert experience and height from int to float
Python3
# 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 |
Output:
In the above example, we change the data type of columns ‘Experience’ and ‘Height’ from ‘int64’ to ‘float32’.
