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Single Condition

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Let’s read an Excel file into a DataFrame:

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import pandas as pd\n\nbudget = pd.read_excel("budget.xlsx").fillna(value = 0)\n\nbudget
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Output:

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Let’s say we want to extract the stores that are located in New Delhi.

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We can filter the DataFrame in 2 steps:

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condition = budget["City"] == "New Delhi"\n\ncondition
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The result is a new boolean Series.

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Output:

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Multiple conditions

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Let’s say we want to extract the stores that are flagged as LTL and have July’19 budget numbers greater than 10.

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We can achieve this using the & logical operator in Pandas.

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condition1 = budget["LTL Flag"] == "LTL"\n\ncondition2 = budget["July'19 Budget"] > 10\n\nbudget[condition1 & condition2]
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Output:

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Next, let’s say we want to extract stores that are located either in Mumbai or Bangalore.

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We can achieve this using the | (vertical pipe) logical operator in Pandas.

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condition1 = budget["City"] == "Mumbai"\n\ncondition2 = budget["City"] == "Bangalore"\n\nbudget[condition1 | condition2]
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Output:

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