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Filter rows by a column condition
Boolean indexing is the usual way to remove rows conditionally. A comparison on a column produces a True/False mask; selecting with that mask keeps the True rows. To drop rows matching a condition, select its opposite.
# Keep rows whose status is not inactive
active = df[df["status"] != "inactive"]This creates a filtered DataFrame in active; it does not change df. Use df.loc[mask] instead of df[mask] if you prefer the explicit row-selection form. The pandas indexing guide describes boolean selection.
Exclude one value
Use != to retain rows whose value differs from the value you want to remove:
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not_inactive = df[df["status"] != "inactive"]Keep rows in a numeric range
Ordinary comparison operators work for numeric conditions too. This example keeps adults:
adults = df[df["age"] >= 18]Exclude several exact values with isin
Use Series.isin to test whether each value belongs to a set, then invert the resulting mask with ~ to retain values outside that set:
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active = df[~df["status"].isin(["inactive", "archived"])]This is clearer than chaining many equality checks when you have a list of values to exclude. isin returns a boolean vector indicating membership; see the pandas Series.isin reference.
Combine multiple conditions
Combine boolean masks with & for AND and | for OR. Put parentheses around each comparison before combining them:
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# Keep rows with a score of at least 70 and a status other than withdrawn
kept = df[(df["score"] >= 70) & (df["status"] != "withdrawn")]For example, if either of two conditions is enough to keep a row, use |. Use ~ to invert a mask. Do not use Python’s and or or with Series masks; use the bitwise operators and parenthesize each comparison so the expression is evaluated as intended.
Use query for compact expressions
DataFrame.query evaluates an expression against DataFrame columns and returns the matching rows by default. This is an alternative when the expression-string style is more readable:
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adults = df.query("age >= 18")
not_archived = df.query('status not in ["archived"]')The pandas API describes query as a way to “Query the columns of a DataFrame with a boolean expression.” It supports membership expressions such as in and not in; see the DataFrame.query reference. Avoid building a query expression from untrusted user input: the API warns that query expressions can run arbitrary code. For dynamic or externally supplied criteria, use an explicitly constructed boolean mask instead.
Choose the right operation
Conditional values, index labels, and missing values are different cases. Use the method that matches what you are trying to remove:
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| What you want to remove | Use | Example |
|---|---|---|
| Rows matching a column condition | Boolean mask or query |
df[df["status"] != "inactive"] |
| Rows whose index labels are known | DataFrame.drop |
df.drop(index=[2, 5]) |
| Rows missing values in selected columns | DataFrame.dropna |
df.dropna(subset=["status"]) |
Known index labels: drop
DataFrame.drop removes axis labels; it does not inspect a column for a value predicate. Rows are the default axis, so df.drop(index=labels) makes the intent explicit. It returns a new DataFrame unless inplace=True, and raises a KeyError for missing labels by default. See the DataFrame.drop reference.
Missing values: dropna
Use dropna when the condition is missingness rather than a particular value. The subset argument limits the check to named columns; how and thresh provide missing-value-specific controls. It is not a general-purpose filter for arbitrary values. See the DataFrame.dropna reference.
What happens to the index?
Boolean filtering keeps the original index labels of selected rows. That is useful when labels carry meaning, but the result may have gaps in its index. If you need a fresh consecutive index for presentation or downstream work, reset it separately, for example with result.reset_index(drop=True).
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