Convert Float to Int in Python using Pandas

Introduction

To play with huge amounts of data, in python we require a tool. The tool which is available in Python is pandas. A panda is an open-source library. It is used to analyze huge datasets without any difficulty. This is the biggest advantage of pandas.

By using pandas a DataFrame can be created. A DataFrame is a table which contains all the contents of a dataset. This DataFrame helps us to visualize the ugly looking dataset into a good-looking table. So, now let us learn about how to convert the data types of the contents present in the dataset.

Creation of DataFrames

To create a DataFrame, there are few steps. They are

• Install pandas (ignore if available)
  
  The command to install pandas is:

pip install pandas 
or 
python –m pip install pandas

• Open Python idle window or any Python compiler

• The first thing we have to do is to import pandas

       We can import pandas into the program with a command import pandas as pd

• Create a dictionary or list or tuple for creating a DataFrame. If we have a dataset, we export it into the program with a command like:

 ds=pd.read_csv(‘File-Name’)

• Now with a command like: 

df=pd.DataFrame(dict_name/tuple_name/list_name,columns=’Col_1’,’Col_2’….)

Example:

# step-3 importing pandas
import pandas as pd


#step-4 creating a dictionary for my convenience
DataSet={
    'players' : ['ROOT','PUJARA','ROHIT'],
    'performance':[564,227,368],
    'average':[94.00,32.43,52.57]
}
#step-5 creating a data frame
df=pd.DataFrame(DataSet);
print(df)
print(df.dtypes)

Output:

  players            performance  average
0    ROOT                 564           94.00
1  PUJARA              227            32.43
2   ROHIT                368             52.57
players         object
performance      int64
average        float64
dtype: object

Now we have learnt how to create a DataFrame in Python using pandas. Now we should convert the data types of the given dataset.

Here, we have already mentioned the column names in the dictionary itself. So, there is no need for creating the names for columns in the DataFrame block.

Our first conversion will be conversion of float values to integer values.

• Explicit Type Conversion:

This is the first way of using the explicit type conversion method. We are going to take a loop and perform explicit type conversion method. As this is the beginner’s way of changing the data_type of given data.

Let us understand this method with the help of an example.

Example:

# step-3 importing pandas
import pandas as pd


#step-4 creating a dictionary for my convenience
DataSet={
    'players' : ['ROOT','PUJARA','ROHIT'],
    'performance':[564,227,368],
    'average':[94.00,32.43,52.57]
}
#step-5 creating a data frame


df=pd.DataFrame(DataSet);


print(df)


print('average datatype is',df['average'].dtypes)




for i in range(len(df['average'])):
    df['average'][i]=int(df['average'][i])


print(df['average'].values)


print('average datatype is',df['average'].dtypes)

Output:

  players  performance  average
0    ROOT          564    94.00
1  PUJARA          227    32.43
2   ROHIT          368    52.57
average datatype is float64


Warning (from warnings module):
SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame
 [94. 32. 52.]
average datatype is float64

But this is method is not suitable and does not yield good results. See the values [94.00,32.43,52.57] have been changed into [94. 32. 52.]. Here only the value after the given number is erased but the data type is remained same as before.

So do not prefer this method of changing datatypes in a DataFrame. A warning is occurred in the compiler because the DataFrame is read only type. So if we change the values of the DataFrame, it is against the rules of DataFrame. So, avoid this method.

• astype() inbuilt Method:

This method helps us in changing the datatype of float to int directly in the DataFrame. astype() is inbuilt method which is present in the pandas library. This inbuilt method helps us to convert float to int, int to float etc.

Syntax:

This is the syntax for astype() method:

DataFrame_name[‘column_name’]= DataFrame_name[‘column_name’].astype(data_type)

Example:

print(df)


print('average datatype is',df['average'].dtypes)


df['average']=df['average'].astype(int);


print(df.dtypes)


print(df)

Output:

  players  performance  average
0    ROOT          564    94.00
1  PUJARA          227    32.43
2   ROHIT          368    52.57
average datatype is float64
players        object
performance     int64
average         int32
dtype: object
  players  performance  average
0    ROOT          564       94
1  PUJARA          227       32
2   ROHIT          368       52

The astype() inbuilt method can be applied for multiple columns too.

Syntax (for multiple columns):

DataFrame_name=DataFrame_name.astype(
{‘column1’:datatype1 , ‘column2’: datatype2,…}
)

Example:

print(df)


df=df.astype({'average':int,'performance':float});


print(df.dtypes)


print(df)

Output:

    players  performance  average
0    ROOT          564    94.00
1  PUJARA          227    32.43
2   ROHIT          368    52.57
average datatype is float64
players         object
performance    float64
average          int32
dtype: object
   players  performance  average
0    ROOT        564.0       94
1  PUJARA        227.0       32
2   ROHIT        368.0       52

Conclusion:

This is the way how a datatype can be changed from float to int inside a DataFrame only using pandas.