Example Code: Obtaining Element-wise Logical NOT of a Pandas Series

Here's an example:

import pandas as pd

# Create a Series with boolean values
series = pd.Series([True, False, True, False])

# Apply the logical NOT operator
result = ~series

print(result)

This code will output:

0    False
1     True
2    False
3     True
dtype: bool

Example Code: Obtaining Element-wise Logical NOT of a Pandas Series

Explanation:

• Import pandas: This line imports the pandas library, which provides data structures and analysis tools for Python.
• Create a Series: A Series is created with boolean values.
• Apply the ~ operator: The tilde (~) operator is used to perform the element-wise logical NOT operation on the Series. This inverts the boolean values of each element.
• Print the result: The resulting Series, which contains the inverted boolean values, is printed.

import pandas as pd

# Create a Series with boolean values
series = pd.Series([True, False, True, False])

# Apply the logical NOT operator
result = ~series

print(result)

Output:

0    False
1     True
2    False
3     True
dtype: bool

Example Code: Using Logical NOT in Pandas Series for Filtering

• Create a DataFrame: A DataFrame is created with a column named 'condition'.
• Filter rows: The ~ operator is combined with a boolean condition to filter rows where the 'condition' is not True.
• Print the filtered DataFrame: The DataFrame containing only the rows where 'condition' is False is printed.

import pandas as pd

# Create a DataFrame
data = {'condition': [True, False, True, False]}
df = pd.DataFrame(data)

# Filter rows where 'condition' is not True
filtered_df = df[~df['condition']]

print(filtered_df)

   condition
1     False
3     False

Alternative Methods for Logical NOT in Pandas Series

While the ~ operator is the most common and direct way to perform element-wise logical NOT on a Pandas Series, there are a few alternative approaches:

Using the map() function:

• You can create a custom mapping function that inverts boolean values and apply it to the Series using the map() function.

import pandas as pd

series = pd.Series([True, False, True, False])

# Custom mapping function
def invert_bool(value):
    return not value

result = series.map(invert_bool)

• Similar to map(), you can create a custom function and apply it to the Series using the apply() function.

import pandas as pd

series = pd.Series([True, False, True, False])

# Custom mapping function
def invert_bool(value):
    return not value

result = series.apply(invert_bool)

Using vectorized operations:

• For larger Series, vectorized operations can be more efficient. You can use NumPy's invert() function:

import pandas as pd
import numpy as np

series = pd.Series([True, False, True, False])

result = pd.Series(np.invert(series))