How can a 2-dimensional array be filtered based on multiple columns from another 2-dimensional array that does not have all the columns present in the first array?

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One way to filter a 2-dimensional array based on multiple columns from another array is to use the pandas library in Python.

Here is an example code snippet:

import pandas as pd

# create the first array with columns 'name', 'age', 'gender', 'city'
array1 = pd.DataFrame({
    'name': ['Alice', 'Bob', 'Charlie', 'Dave'],
    'age': [25, 30, 35, 40],
    'gender': ['F', 'M', 'M', 'M'],
    'city': ['NYC', 'LA', 'Chicago', 'Boston']
})

# create the second array with columns 'name', 'city', 'income'
array2 = pd.DataFrame({
    'name': ['Alice', 'Bob', 'Charlie', 'Dave'],
    'city': ['NYC', 'LA', 'Chicago', 'Boston'],
    'income': [50000, 70000, 60000, 80000]
})

# merge the two arrays on 'name' and 'city' columns
merged_array = pd.merge(array1, array2, on=['name', 'city'])

# filter the merged array based on age and income
filtered_array = merged_array[(merged_array['age'] > 30) & (merged_array['income'] > 60000)]

# print the filtered array
print(filtered_array)

In this example, we first create two separate pandas DataFrames (array1 and array2) and then merge them based on the common columns 'name' and 'city' using the pd.merge() function. We then filter the merged DataFrame by age and income using pandas DataFrame indexing, which returns only the rows that meet the specified conditions. Finally, we print the resulting filtered array.

Note that if the two arrays have different column names, you can rename the columns to match using the pd.DataFrame.rename() function before merging them.