Boolean Indexing in Python

Isn’t it interesting that Boolean values can be used as indexes in dataframes? Boolean indexes represent each row in a DataFrame. Boolean indexing can help us filter unnecessary data from a dataset. Filtering the data can get you some in-depth information that otherwise could not have been found. In this article, we will learn how to use Boolean indexing to filter and segment data. So let’s begin!

Let’s start by creating a DataFrame. We will create a DataFrame using data on the age of a group of candidates taking part in a competition.

import pandas as pd
# Creating a dictionary
data = {'Name':["Tommy","Linda","Justin","Brendon"], 'Age':[31,24,16,22]}
df = pd.DataFrame(data,index=[True,False,True,False])
print(df)

Output

        Name         Age
True     Tommy   31
False    Linda   24
True    Justin   16
False  Brendon   22

1. Using the.loc [] function

This is an excellent and simple function that can help you filter your data according to the Boolean index. Using this function, we can filter out the data with a particular Boolean value. Let’s suppose we pass True to the .loc [] function, we will only get the filtered data having index values as True. We cannot use integers as Boolean values in this method.

For example:

import pandas as pd
# Creating a dictionary
data = {'Name':["Tommy","Linda","Justin","Brendon"], 'Age':[31,24,16,22]}
df = pd.DataFrame(data,index=[True,False,True,False])
print(df.loc[True])

Output:

        Name       Age
True   Tommy   31
True  Justin   16

2. Using .iloc[] function

iloc[] function only accepts integer values and so we need to pass integer values to the function.

For example:

import pandas as pd
# Creating a dictionary
data = {'Name':["Tommy","Linda","Justin","Brendon"], 'Age':[31,24,16,22]}
df = pd.DataFrame(data,index=[1,0,0,1])
print(df.iloc[1])

Output:

Name    Linda
Age        24
Name: 0, dtype: object

3. Using the .ix[] function

This is also a similar kind of method to the above one, but we can use integers as Boolean values in this case. So, for instance, if we assign the index values as 1 and 0, we can filter the rows having index values of 0 or 1.

import pandas as pd
# Creating a dictionary
data = {'Name':["Tommy","Linda","Justin","Brendon"], 'Age':[31,24,16,22]}
df = pd.DataFrame(data,index=[1,1,0,0])
print(df.ix[0])

Output:

           Name       Age
0       Justin          16
0       Brendon     22

Conclusion

In summary, we learned how to use boolean indexing in python and filter the useful data. Hope you found this article helpful.