Why Does Using AND (`&`) and OR (`|`) Operators in Pandas Filtering Operations Produce Unexpected Results?

pandas: Multiple Conditions While Indexing Data Frame - Unexpected Behavior

In data analysis, pandas is a crucial library for manipulating and processing data frames. While performing filtering operations, it's essential to understand the behavior of operators when using multiple conditions.

Let's consider a scenario where we want to filter rows in a data frame based on values in two columns, 'a' and 'b'. Using the AND '&' operator and OR '|' operator, we expect AND to drop rows where at least one value equals -1 while OR should retain rows where both 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>

Unexpectedly, the AND operator drops every row where at least one value is -1, while the OR operator requires both values to be -1 to drop them.

The key to understanding this behavior lies in remembering that we're writing the condition in terms of what we want to keep, not what we want to drop.

• For df1: (df.a != -1) & (df.b != -1) means "keep rows where df.a isn't -1 and df.b isn't -1", which is equivalent to dropping rows where at least one value is -1.
• For df2: (df.a != -1) | (df.b != -1) means "keep rows where either df.a or df.b is not -1", which is equivalent to dropping rows where both values are -1.

It's crucial to use chained access like df.loc and df.iloc instead of df['a'][1] = -1 to avoid potential issues.

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