Use DataFrame.drop() to remove rows or columns by their labels. The clearest forms are df.drop(index=...) for index labels and df.drop(columns=...) for column labels. By default, pandas returns a new DataFrame and raises a KeyError if any requested label is missing.
How to drop rows from a pandas DataFrame
Pass the row index labels to index. For example, this removes rows with index labels 0 and 2:
without_rows = df.drop(index=[0, 2])This selects labels, not row positions. If your DataFrame has a custom index, 0 means the index label 0; it does not mean “the first row.” To remove rows by a condition, select rows using that condition instead of treating drop() as a positional or criteria-based filter.
How to drop columns in pandas
Pass column names to columns to remove one or more columns:
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without_columns = df.drop(columns=["temporary", "unused"])The equivalent axis-based form is df.drop(["temporary", "unused"], axis=1). The columns= form makes the target explicit and is often easier to read.
What labels and axes does drop() use?
The pandas API reference describes DataFrame.drop as dropping specified labels from rows or columns. By default, axis=0 targets the index (rows); axis=1 targets columns. You can specify the target with index= or columns=, or use labels= with the appropriate axis.
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The documented signature is DataFrame.drop(labels=None, *, axis=0, index=None, columns=None, level=None, inplace=False, errors='raise') in the stable API reference. A tuple passed as labels is treated as one label, not as a list of labels.
Why does DataFrame.drop() raise a KeyError?
By default, pandas raises KeyError when a requested label is not present on the selected axis. That default is useful when a misspelling or unexpected DataFrame schema should be caught. If missing labels are expected—for example, when applying a cleanup list to similar DataFrames that do not all have the same columns—use errors="ignore":
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without_columns = df.drop(
columns=["temporary", "possibly_absent"],
errors="ignore"
)With this option, pandas removes labels that are present and does not fail just because another requested label is absent.
What does drop() return?
With the default inplace=False, drop() returns a DataFrame with the requested labels removed; assign that result if you want to keep using the modified data:
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df = df.drop(columns=["temporary"])The stable API reference documents inplace=True as modifying the object and returning None. Avoid assigning that call back to the same variable: df = df.drop(columns=["temporary"], inplace=True) makes df equal to None.
Version note: pandas 3.1.0 development documentation marks inplace as deprecated since 3.1.0 and says it is planned for removal in pandas 4.0. This is a development-reference statement, not confirmation of the behavior in every released version. Check the documentation for the pandas version installed in your environment; the development reference points to PDEP-8 for details.
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How to use drop() with a MultiIndex
For a MultiIndex, use level= to identify the index or column level in which pandas should match labels to remove. This removes matching labels from the axis; it does not remove a level from the axis structure itself. To remove a level from that structure, use droplevel().
Quick Recap
When to use a different pandas method
| Goal | Method | How it differs |
|---|---|---|
| Remove known row or column labels | drop() |
Targets specified labels on an axis. |
| Remove rows or columns based on missing values | dropna() |
Selects according to NA presence, with options such as how, thresh, and subset. |
| Remove duplicate rows | drop_duplicates() |
Selects duplicates, optionally using a subset of columns and choosing which copy to keep. |
| Change axis labels | rename() |
Renames labels rather than removing them. |
| Remove an index or column level | droplevel() |
Removes level structure; drop(level=...) instead removes matching labels from a level. |
| Replace the index with a default integer index | reset_index() |
Resets the index and can optionally discard the prior index values. |
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