Boolean Indexing in Pandas

Boolean indexing in Pandas allows you to select data from DataFrames using boolean vectors. This powerful feature lets you filter rows based on True/False values, either through boolean indices or by passing boolean arrays directly to the DataFrame.

Creating a DataFrame with Boolean Index

First, let's create a DataFrame with a boolean index vector ?

import pandas as pd

# data
data = {
    'Name': ['Hafeez', 'Srikanth', 'Rakesh'],
    'Age': [19, 20, 19]
}

# creating a DataFrame with boolean index vector
data_frame = pd.DataFrame(data, index=[True, False, True])
print(data_frame)

        Name  Age
True   Hafeez   19
False  Srikanth   20
True    Rakesh   19

Accessing Data Using Boolean Indices

Using .loc[] Method

The loc[] method allows label-based selection using boolean indices ?

import pandas as pd

data = {
    'Name': ['Hafeez', 'Srikanth', 'Rakesh'],
    'Age': [19, 20, 19]
}

data_frame = pd.DataFrame(data, index=[True, False, True])

# accessing using .loc()
print(data_frame.loc[True])

       Name  Age
True  Hafeez   19
True   Rakesh   19

Using .iloc[] Method

The iloc[] method uses integer positions for selection ?

import pandas as pd

data = {
    'Name': ['Hafeez', 'Srikanth', 'Rakesh'],
    'Age': [19, 20, 19]
}

data_frame = pd.DataFrame(data, index=[True, False, True])

# accessing using .iloc() - takes integer position
print(data_frame.iloc[1])  # Position 1 corresponds to False index

Name    Srikanth
Age           20
Name: False, dtype: object

Boolean Vector Filtering

You can directly pass a boolean vector to filter DataFrame rows ?

import pandas as pd

# data with regular integer index
data = {
    'Name': ['Hafeez', 'Srikanth', 'Rakesh'],
    'Age': [19, 20, 19]
}

data_frame = pd.DataFrame(data)
print("Original DataFrame:")
print(data_frame)
print()

# filtering using boolean vector
print("Filtered DataFrame:")
print(data_frame[[True, True, False]])

Original DataFrame:
       Name  Age
0    Hafeez   19
1  Srikanth   20
2    Rakesh   19

Filtered DataFrame:
       Name  Age
0    Hafeez   19
1  Srikanth   20

Practical Boolean Indexing with Conditions

Boolean indexing is most useful when combined with conditional expressions ?

import pandas as pd

data = {
    'Name': ['Hafeez', 'Srikanth', 'Rakesh', 'Priya'],
    'Age': [19, 20, 19, 22],
    'Score': [85, 92, 78, 95]
}

df = pd.DataFrame(data)

# Filter students with age greater than 19
print("Students older than 19:")
print(df[df['Age'] > 19])
print()

# Filter students with score above 80
print("Students with score > 80:")
print(df[df['Score'] > 80])

Students older than 19:
       Name  Age  Score
1  Srikanth   20     92
3     Priya   22     95

Students with score > 80:
       Name  Age  Score
0    Hafeez   19     85
1  Srikanth   20     92
3     Priya   22     95

Comparison of Boolean Indexing Methods

Conclusion

Boolean indexing in Pandas provides flexible data filtering capabilities. Use boolean vectors for simple row selection and conditional expressions for dynamic filtering based on DataFrame values.