Why Does Pandas Indexing with Multiple Conditions Exhibit Unexpected Behavior?

Pandas Multiple Conditions Indexing: Unexpected Behavior

With pandas, applying filters to a DataFrame is a common operation. However, when using multiple conditions, especially with logical operators like AND and OR, unexpected results can occur.

Problem:

When filtering rows based on values in two columns, the AND operator appears to behave like OR, and vice versa. For example, the code below should:

• Use the AND operator to exclude rows where either column value is -1.
• Use the OR operator to exclude rows where both column values are -1.

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

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>

Explanation:

The unexpected behavior stems from how the logical operators are interpreted in the context of pandas indexing.

• AND Operator:

  • df[(df.a != -1) & (df.b != -1)] means "keep rows where both df.a is not -1 and df.b is not -1".
  • This filters out rows where at least one value is -1.

• OR Operator:

  • df[(df.a != -1) | (df.b != -1)] means "keep rows where either df.a or df.b is not -1".
  • This filters out rows where both values are -1.

Therefore, the AND operator behaves like OR because it excludes rows based on the absence of -1 in either column. Conversely, the OR operator behaves like AND because it includes rows only when both columns do not contain -1.

Additional Note:

• It's recommended to use .loc and .iloc instead of chained indexing (e.g., df'a' = -1) for cleaner and safer code practices.

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