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Boost DataFrame Filtering Speed with Python's isin()

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Filtering large DataFrames in Python is a common data manipulation task, often leading to performance challenges. In this description, we'll discuss how to speed up DataFrame filtering using the `isin()` function. This approach is particularly effective when dealing with large datasets and Boolean indexing.

First, let's briefly touch upon DataFrame filtering in Python. This process involves selecting a subset of rows based on specific conditions. For instance, applying a filter using a condition like `df[df['column_name'] 10]` can be time-consuming for extensive datasets.

To overcome this issue, consider using the `isin()` function instead. This function checks if an entire DataFrame series (a column) belongs to a specified list of values. It creates an array of Boolean masks, which is efficient when filtering multiple conditions:

```python
import pandas as pd
import numpy as np

Create a mock dataset
data = {'column_1': list(range(1, 101)), 'column_2': np.random.rand(100, 1)}
df = pd.DataFrame(data)

Let's assume we want to filter rows based on values in 'column_1'
filter_values = [5, 8, 12]

Filtering using regular condition
time_start = time.time()
filtered_df_cond = df[df['column_1'].isin(filter_values)]
time_end = time.time()
print(f'Filtering time with condition: {time_end - time_start} seconds.')

Filtering using isin()
time_start = time.time()
filtered_df_isin = df.isin(filter_values).all(1).astype(bool).sum(axis=1).to_frame().reset_index(name='isin')
filtered_df_isin = df[filtered_df_isin['isin']].drop(columns=['isin'])
time_end = time.time()
print(f'Filtering time with isin(): {time_end - time_start} seconds

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