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Filter rows by a column condition
Pandas Boolean indexing selects rows wherever a condition evaluates to True. To drop rows that match a condition, write the opposite condition and select the rows that remain.
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# Keep rows whose status is not inactive
active = df[df["status"] != "inactive"]
# Keep rows where age is at least 18
adults = df[df["age"] >= 18]These expressions return a filtered DataFrame; they do not change df itself. If you want to replace the original variable, assign the result back to it: df = df[df["status"] != "inactive"].
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Use Series.isin when the rows to remove have one of several exact values. It returns a Boolean Series, and ~ inverts that mask so the selection keeps values outside the list.
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# Exclude both inactive and archived rows
active = df[~df["status"].isin(["inactive", "archived"])]This is clearer than chaining many equality checks. The values are matched against the column as supplied; use values with the appropriate types and spelling for your data.
Combine multiple conditions
Use & for AND, | for OR, and ~ for NOT when combining Boolean Series. Put parentheses around each comparison:
# Keep rows meeting both conditions
kept = df[(df["score"] >= 70) & (df["status"] != "withdrawn")]
# Keep rows meeting either condition
kept = df[(df["score"] >= 70) | (df["status"] == "review")]
# Exclude rows matching a condition
kept = df[~(df["status"] == "withdrawn")]Do not use Python’s and or or between Series conditions; use the bitwise operators shown above. Parentheses make each comparison explicit and avoid operator-precedence surprises.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesUse query for a compact expression
DataFrame.query offers a string-based alternative for conditions that read naturally as expressions over column names.
adults = df.query("age >= 18")
active = df.query('status not in ["inactive", "archived"]')Like Boolean indexing, query returns the selected rows by default; its API also supports an inplace option. The pandas documentation describes it as a way to “Query the columns of a DataFrame with a boolean expression.” Do not construct query strings from untrusted user input: query expressions can run arbitrary code. For dynamically supplied criteria, prefer a Boolean mask.
Choose the operation that matches what you want to remove
| Goal | Use | Example |
|---|---|---|
| Remove rows because a column value meets a condition | Boolean indexing or query |
df[df["age"] >= 18] keeps qualifying rows |
| Remove rows with one of several exact values | isin with an inverted mask |
df[~df["status"].isin(["inactive", "archived"])] |
| Remove rows with known index labels | DataFrame.drop |
df.drop(index=[2, 5]) |
| Remove rows with missing values in selected columns | DataFrame.dropna |
df.dropna(subset=["status"]) |
Column values: filter with a mask
This is the usual choice for a predicate such as “age is under 18” or “status is archived.” The condition examines column values and determines which rows to retain.
Index labels: use drop
DataFrame.drop removes axis labels, not rows selected by a value test. It defaults to rows and returns a new DataFrame unless inplace=True. If a requested label is missing, it raises KeyError by default.
Missing values: use dropna
DataFrame.dropna is specifically for missing data. Use subset to limit the check to particular columns; its how and thresh options control how missingness is assessed. It is not a general-purpose condition filter.
What happens to the index?
Boolean selection keeps the original index labels for the rows that remain. Filtering does not automatically renumber them. If you need a fresh consecutive index for display or export, reset it as a separate step, for example active = active.reset_index(drop=True).
Documentation and version notes
The official pandas documentation covers Boolean indexing and selection, Series.isin, DataFrame.query, DataFrame.drop, and DataFrame.dropna. Documentation behavior and available options can vary by pandas release; consult the version installed in your environment if a version-specific detail matters.
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