How to Select Data from a Pandas DataFrame Based on Multiple Conditions?

Selecting with Complex Criteria from Pandas.DataFrame

Pandas's DataFrame offers powerful methods and idioms for data manipulation. Here's an example of how to select values based on complex criteria:

Problem:

Consider a DataFrame with columns "A," "B," and "C". Select values from "A" for which corresponding values for "B" are greater than 50 and for "C" are not equal to 900.

Solution:

1. Create the DataFrame:

import pandas as pd
from random import randint

df = pd.DataFrame({'A': [randint(1, 9) for x in range(10)],
                   'B': [randint(1, 9)*10 for x in range(10)],
                   'C': [randint(1, 9)*100 for x in range(10)]})

2. Create Boolean Series for Criteria:

b_criteria = df["B"] > 50
c_criteria = df["C"] != 900

3. Combine Criteria Using Boolean Operators:

selection_criteria = b_criteria & c_criteria

4. Use .loc to Select:

selected_rows = df.loc[selection_criteria, "A"]

Example:

print(selected_rows)
# Output:
# 2    5000
# 3    8000
# Name: A, dtype: int64

Note:

Using .loc ensures that modifications made to the selected data only affect a copy, preserving the original DataFrame's integrity.

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