Why Does Pandas\' AND (&) and OR (|) Operators Seem Reversed When Indexing a DataFrame with Multiple Conditions?

pandas: Unexpected Behavior with Multiple Conditions while Indexing Data Frame

When filtering rows in a DataFrame by values in multiple columns, it's essential to understand the behavior of the AND (&) and OR (|) operators.

In a recent observation, it was noted that the behavior of these operators seemed reversed. The OR operator appeared to behave like the AND operator, and vice versa.

To illustrate, consider the following DataFrame:

<code class="python">df = pd.DataFrame({'a': range(5), 'b': range(5) })

# Insert -1 values
df['a'][1] = -1
df['b'][1] = -1
df['a'][3] = -1
df['b'][4] = -1

df1 = df[(df.a != -1) & (df.b != -1)]
df2 = df[(df.a != -1) | (df.b != -1)]

print(pd.concat([df, df1, df2], axis=1, keys=['Original df', 'Using AND (&)', 'Using OR (|)']))</code>

The result is:

<code class="python">      Original df      Using AND (&)      Using OR (|)    
             a  b              a   b             a   b
0            0  0              0   0             0   0
1           -1 -1            NaN NaN           NaN NaN
2            2  2              2   2             2   2
3           -1  3            NaN NaN            -1   3
4            4 -1            NaN NaN             4  -1

[5 rows x 6 columns]</code>

As seen in the output, the AND operator drops rows where at least one value is -1, while the OR operator retains rows where both values are not -1.

This behavior may seem counterintuitive, but it makes sense if we remember that we're specifying the conditions for rows we want to keep, not drop.

• For df1, we're specifying that we want to keep rows where both df.a and df.b are not -1.
• For df2, we're specifying that we want to keep rows where either df.a or df.b is not -1.

Therefore, the behavior observed is correct.

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