How to Efficiently Apply Multiple Filters to Pandas DataFrames and Series?

Efficient Filtering of Pandas DataFrames and Series

Filtering data in Pandas DataFrames and Series is essential for data manipulation and analysis. To efficiently apply multiple filters, consider leveraging Pandas' built-in operators and boolean indexing.

For a DataFrame or Series, providing an operation and a list of values in a dictionary format, as shown in the example below:

<code class="python">relops = {'>=': [1], '<=': [1]}

To apply these filters:

<code class="python">import numpy as np

def boolean_filter(x, relops):
    filters = []
    for op, vals in relops.items():
        op_func = getattr(np, op)
        for val in vals:
            filters.append(op_func(x, val))

    return x[(np.logical_and(*filters))]

## Example:

df = pandas.DataFrame({'col1': [0, 1, 2], 'col2': [10, 11, 12]})

result = boolean_filter(df['col1'], {'>=': [1]})
print(result)

## Output:
# col1
# 1       1
# 2       2
# Name: col1</code>

By utilizing boolean indexing, this method avoids unnecessary copying and is highly efficient, especially for large datasets.

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