How to Select DataFrame Values Based on Multiple Criteria in Pandas?

Selecting with complex criteria from pandas.DataFrame

Selecting specific values from a DataFrame based on multiple criteria

Let's assume we have a simple DataFrame like the following:

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)]})

Our goal is to select values from column 'A' that meet specific criteria for corresponding values in columns 'B' and 'C'.

Approach using Boolean indexing

To achieve this, we can utilize Boolean indexing. First, we create Boolean Series objects for each criterion:

df["B"] > 50
(df["B"] > 50) & (df["C"] != 900)

These Boolean Series represent the rows that satisfy the respective criteria. We can then use these Series as indices to select the desired values:

df["A"][df["B"] > 50]
df["A"][(df["B"] > 50) & (df["C"] != 900)]

Approach using .loc

We can also employ the .loc attribute for more efficient indexing. .loc allows us to specify the rows and columns to retrieve using a single statement:

df.loc[(df["B"] > 50) & (df["C"] != 900), "A"]

Conclusion

Both methods effectively select values from the DataFrame based on complex criteria. The choice between using Boolean indexing or .loc depends on personal preference and code readability.

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