Python - Filter Pandas DataFrame with numpy

The numpy.where() method can be used to filter Pandas DataFrame by returning indices that meet specified conditions. This approach is particularly useful when you need to apply multiple filtering conditions simultaneously.

Setup

First, let's import the required libraries and create a sample DataFrame ?

import pandas as pd
import numpy as np

# Create a DataFrame with product records
dataFrame = pd.DataFrame({
    "Product": ["SmartTV", "ChromeCast", "Speaker", "Earphone"],
    "Opening_Stock": [300, 700, 1200, 1500],
    "Closing_Stock": [200, 500, 1000, 900]
})

print("Original DataFrame:")
print(dataFrame)

Original DataFrame:
     Product  Opening_Stock  Closing_Stock
0    SmartTV            300            200
1  ChromeCast            700            500
2    Speaker           1200           1000
3   Earphone           1500            900

Filtering with Two Conditions

Use np.where() to find rows where Opening_Stock >= 700 AND Closing_Stock

import pandas as pd
import numpy as np

dataFrame = pd.DataFrame({
    "Product": ["SmartTV", "ChromeCast", "Speaker", "Earphone"],
    "Opening_Stock": [300, 700, 1200, 1500],
    "Closing_Stock": [200, 500, 1000, 900]
})

# Filter with 2 conditions
indices = np.where((dataFrame['Opening_Stock'] >= 700) & (dataFrame['Closing_Stock'] < 1000))

print("Filtered DataFrame (2 conditions):")
print(dataFrame.loc[indices])

Filtered DataFrame (2 conditions):
     Product  Opening_Stock  Closing_Stock
1  ChromeCast            700            500
3   Earphone           1500            900

Filtering with Three Conditions

Add a third condition to filter products that start with the letter 'C' ?

import pandas as pd
import numpy as np

dataFrame = pd.DataFrame({
    "Product": ["SmartTV", "ChromeCast", "Speaker", "Earphone"],
    "Opening_Stock": [300, 700, 1200, 1500],
    "Closing_Stock": [200, 500, 1000, 900]
})

# Filter with 3 conditions
indices = np.where((dataFrame['Opening_Stock'] >= 500) & 
                   (dataFrame['Closing_Stock'] < 1000) & 
                   (dataFrame['Product'].str.startswith('C')))

print("Filtered DataFrame (3 conditions):")
print(dataFrame.loc[indices])

Filtered DataFrame (3 conditions):
     Product  Opening_Stock  Closing_Stock
1  ChromeCast            700            500

How It Works

The np.where() function returns a tuple of arrays containing the indices where the condition is True. When used with dataFrame.loc[], it selects only the rows that meet all specified conditions.

Key Points

• Use & for AND operations between conditions
• Use | for OR operations between conditions
• Wrap each condition in parentheses when combining multiple conditions
• The result is a tuple of indices that can be used with .loc[]

Conclusion

The numpy.where() method provides an efficient way to filter Pandas DataFrames with complex conditions. It returns indices that meet your criteria, which can then be used to select the filtered rows using .loc[].