Boolean Indexing in Pandas

In boolean indexing, we will select subsets of data based on the actual values of the data in the DataFrame and not on their row/column labels or integer locations. In boolean indexing, we use a boolean vector to filter the data. 

Boolean indexing is a type of indexing that uses actual values of the data in the DataFrame. In boolean indexing, we can filter a data in four ways:

• Accessing a DataFrame with a boolean index
• Applying a boolean mask to a dataframe
• Masking data based on column value
• Masking data based on an index value

Accessing a DataFrame with a boolean index:

In order to access a dataframe with a boolean index, we have to create a dataframe in which the index of dataframe contains a boolean value that is “True” or “False”.

Example

Python3

# importing pandas as pd
import pandas as pd

# dictionary of lists
dict = {'name':["aparna", "pankaj", "sudhir", "Geeku"],
        'degree': ["MBA", "BCA", "M.Tech", "MBA"],
        'score':[90, 40, 80, 98]}

df = pd.DataFrame(dict, index = [True, False, True, False])

print(df)

Output:

Now we have created a dataframe with the boolean index after that user can access a dataframe with the help of the boolean index. User can access a dataframe using three functions that is .loc[], .iloc[], .ix[] 

Accessing a Dataframe with a boolean index using .loc[]

In order to access a dataframe with a boolean index using .loc[], we simply pass a boolean value (True or False) in a .loc[] function. 

Python3

# importing pandas as pd
import pandas as pd

# dictionary of lists
dict = {'name':["aparna", "pankaj", "sudhir", "Geeku"],
        'degree': ["MBA", "BCA", "M.Tech", "MBA"],
        'score':[90, 40, 80, 98]}

# creating a dataframe with boolean index
df = pd.DataFrame(dict, index = [True, False, True, False])

# accessing a dataframe using .loc[] function
print(df.loc[True])

Output:

Accessing a Dataframe with a boolean index using .iloc[]

In order to access a dataframe using .iloc[], we have to pass a boolean value (True or False)  but iloc[] function accepts only integer as an argument so it will throw an error so we can only access a dataframe when we pass an integer in iloc[] function 

Code #1:

Python3

# importing pandas as pd
import pandas as pd

# dictionary of lists
dict = {'name':["aparna", "pankaj", "sudhir", "Geeku"],
        'degree': ["MBA", "BCA", "M.Tech", "MBA"],
        'score':[90, 40, 80, 98]}

# creating a dataframe with boolean index
df = pd.DataFrame(dict, index = [True, False, True, False])

# accessing a dataframe using .iloc[] function
print(df.iloc[True])

Output:

TypeError

Code #2:

Python3

# importing pandas as pd
import pandas as pd

# dictionary of lists
dict = {'name':["aparna", "pankaj", "sudhir", "Geeku"],
        'degree': ["MBA", "BCA", "M.Tech", "MBA"],
        'score':[90, 40, 80, 98]}

# creating a dataframe with boolean index
df = pd.DataFrame(dict, index = [True, False, True, False])


# accessing a dataframe using .iloc[] function
print(df.iloc[1])

Output:

Accessing a Dataframe with a boolean index using .ix[]

In order to access a dataframe using .ix[], we have to pass boolean value (True or False) and integer value to .ix[] function because as we know that .ix[] function is a hybrid of .loc[] and .iloc[] function. 

Code #1:

Python3

# importing pandas as pd
import pandas as pd

# dictionary of lists
dict = {'name':["aparna", "pankaj", "sudhir", "Geeku"],
        'degree': ["MBA", "BCA", "M.Tech", "MBA"],
        'score':[90, 40, 80, 98]}

# creating a dataframe with boolean index
df = pd.DataFrame(dict, index = [True, False, True, False])


# accessing a dataframe using .ix[] function
print(df.ix[True])

Output:

Code #2:

Python

# importing pandas as pd
import pandas as pd

# dictionary of lists
dict = {'name':["aparna", "pankaj", "sudhir", "Geeku"],
        'degree': ["MBA", "BCA", "M.Tech", "MBA"],
        'score':[90, 40, 80, 98]}

# creating a dataframe with boolean index
df = pd.DataFrame(dict, index = [True, False, True, False])


# accessing a dataframe using .ix[] function
print(df.ix[1])

Output:

Applying a boolean mask to a dataframe :

In a dataframe, we can apply a boolean mask. In order to do that we can use __getitems__ or [] accessor. We can apply a boolean mask by giving a list of True and False of the same length as contain in a dataframe. When we apply a boolean mask it will print only that dataframe in which we pass a boolean value True. To download “nba1.1” CSV file click here.

Code #1:

Python3

# importing pandas as pd
import pandas as pd

# dictionary of lists
dict = {'name':["aparna", "pankaj", "sudhir", "Geeku"],
        'degree': ["MBA", "BCA", "M.Tech", "MBA"],
        'score':[90, 40, 80, 98]}

df = pd.DataFrame(dict, index = [0, 1, 2, 3])



print(df[[True, False, True, False]])

Output:

Code #2:

Python3

# importing pandas package
import pandas as pd

# making data frame from csv file
data = pd.read_csv("nba1.1.csv")

df = pd.DataFrame(data, index = [0, 1, 2, 3, 4, 5, 6,
                                 7, 8, 9, 10, 11, 12])


print(df[[True, False, True, False, True,
    False, True, False, True, False,
                True, False, True]])

Output:

Masking data based on column value:

In a dataframe we can filter a data based on a column value.  In order to filter data, we can apply certain conditions on the dataframe using different operators like ==, >, <, <=, >=. When we apply these operators to the dataframe then it produces a Series of True and False. To download the “nba.csv” CSV, click here.

Code #1:

Python

# importing pandas as pd
import pandas as pd

# dictionary of lists
dict = {'name':["aparna", "pankaj", "sudhir", "Geeku"],
        'degree': ["BCA", "BCA", "M.Tech", "BCA"],
        'score':[90, 40, 80, 98]}

# creating a dataframe
df = pd.DataFrame(dict)

# using a comparison operator for filtering of data
print(df['degree'] == 'BCA')

Output:

Code #2:

Python

# importing pandas package
import pandas as pd

# making data frame from csv file
data = pd.read_csv("nba.csv", index_col ="Name")

# using greater than operator for filtering of data
print(data['Age'] > 25)

Output:

Masking data based on index value :

In a dataframe we can filter a data based on a column value. In order to filter data, we can create a mask based on the index values using different operators like ==, >, <, etc… . To download “nba1.1” CSV file click here.

Code #1:

Python3

# importing pandas as pd
import pandas as pd

# dictionary of lists
dict = {'name':["aparna", "pankaj", "sudhir", "Geeku"],
        'degree': ["BCA", "BCA", "M.Tech", "BCA"],
        'score':[90, 40, 80, 98]}


df = pd.DataFrame(dict, index = [0, 1, 2, 3])

mask = df.index == 0

print(df[mask])

Output:

Code #2:

Python3

# importing pandas package
import pandas as pd

# making data frame from csv file
data = pd.read_csv("nba1.1.csv")

# giving a index to a dataframe
df = pd.DataFrame(data, index = [0, 1, 2, 3, 4, 5, 6,
                                 7, 8, 9, 10, 11, 12])

# filtering data on index value
mask = df.index > 7

print(df[mask])

Output: