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Python - Filter Pandas DataFrame with numpy
The numpy where() method can be used to filter Pandas DataFrame. Mention the conditions in the where() method. At first, let us import the required libraries with their respective alias
import pandas as pd import numpy as np
We will now create a Pandas DataFrame with Product records
dataFrame = pd.DataFrame({"Product": ["SmartTV", "ChromeCast", "Speaker", "Earphone"],"Opening_Stock": [300, 700, 1200, 1500],"Closing_Stock": [200, 500, 1000, 900]})Use numpy where() to filter DataFrame with 2 Conditions
resValues1 = np.where((dataFrame['Opening_Stock']>=700) & (dataFrame['Closing_Stock']< 1000)) print"\nFiltered DataFrame Value = \n",dataFrame.loc[resValues1]
Let us use numpy where() again to filter DataFrame with 3 conditions
resValues2 = np.where((dataFrame['Opening_Stock']>=500) & (dataFrame['Closing_Stock']< 1000) & (dataFrame['Product'].str.startswith('C')))Example
Following is the complete code
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]})
print"DataFrame...\n",dataFrame
# using numpy where() to filter DataFrame with 2 Conditions
resValues1 = np.where((dataFrame['Opening_Stock']>=700) & (dataFrame['Closing_Stock']< 1000))
print"\nFiltered DataFrame Value = \n",dataFrame.loc[resValues1]
# using numpy where() to filter DataFrame with 3 conditions
resValues2 = np.where((dataFrame['Opening_Stock']>=500) & (dataFrame['Closing_Stock']< 1000) & (dataFrame['Product'].str.startswith('C')))
print"\nFiltered DataFrame Value = \n",dataFrame.loc[resValues2]
Output
This will produce the following output
DataFrame... Closing_Stock Opening_Stock Product 0 200 300 SmartTV 1 500 700 ChromeCast 2 1000 1200 Speaker 3 900 1500 Earphone Filtered DataFrame Value = Closing_Stock Opening_Stock Product 1 500 700 ChromeCast 3 900 1500 Earphone Filtered DataFrame Value = Closing_Stock Opening_Stock Product 1 500 700 ChromeCast
Updated on: 14-Sep-2021
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