Use DataFrame.replace() when you are matching known existing values; use a boolean mask with .loc, where(), or mask() when the change depends on a condition. For several rules that produce a new column, use numpy.select(). The right choice depends on whether your rule is an exact match or a boolean test, and what should happen when no condition matches.
Choose the method that matches your rule
| What you need | Use | Key behavior |
|---|---|---|
| Substitute known values such as old codes | DataFrame.replace() |
Matches values, optionally within specified columns. |
| Set cells selected by a boolean condition | Boolean mask with .loc |
Assigns a value to the rows and columns you select. |
| Keep values where a condition is true; replace the rest | where() |
Replaces positions where the condition is false. |
| Replace values where a condition is true | mask() |
Replaces positions where the condition is true. |
| Apply several rules to create a categorized column | numpy.select() |
Pairs conditions with choices and a fallback. |
| Apply condition/replacement pairs to one Series | Series.case_when() |
Returns a Series; available starting in pandas 2.2.0. |
Replace several known values with DataFrame.replace()
Use replace() when you know the exact old values, rather than when you need to evaluate an arbitrary rule such as “less than zero.” A dictionary maps each old value to its replacement:
out = df.replace({"old": "new", "legacy": "current"})To apply different mappings to particular columns, nest a mapping under each column name:
out = df.replace({"status": {"N": "new", "C": "closed"}})DataFrame.replace() also supports regex matching when configured to interpret strings as regular expressions. Use that mode only when you intend pattern matching, not exact-value substitution. See the pandas DataFrame.replace API documentation.
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Set cells that meet a boolean condition with .loc
For a rule such as changing negative scores to zero, build a boolean mask and assign to the intended column:
out = df.copy()
mask = out["score"] < 0
out.loc[mask, "score"] = 0The row condition selects which entries to change; the column label makes the target explicit. Copy first if you need to preserve the original DataFrame. Check that the mask corresponds to the DataFrame’s row index so the intended rows are selected. The pandas indexing guide documents boolean selection and assignment.
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Use where() or mask() when their polarity fits
These methods express conditional replacement on a Series or DataFrame. Their condition polarity is opposite:
where(condition, other)keeps entries where the condition is true and substitutesotherwhere it is false.mask(condition, other)substitutesotherwhere the condition is true and keeps entries where it is false.
For example, keep nonnegative scores and replace negative ones:
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out["score"] = out["score"].where(out["score"] >= 0, 0)The equivalent using mask() is:
out["score"] = out["score"].mask(out["score"] < 0, 0)If where() is called without an explicit other, failing positions are filled with a missing value: np.nan for NumPy dtypes and pd.NA for extension dtypes, according to the API documentation. Supply other when you want a specific replacement instead. Consult the pandas API references for DataFrame.where and DataFrame.mask.
Apply multiple conditions with numpy.select()
When several rules assign categories to a result column, pair each condition with its choice and provide a default for rows that match none of the conditions:
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import numpy as np
conditions = [df["score"] >= 90, df["score"] >= 70]
choices = ["high", "medium"]
out = df.assign(band=np.select(conditions, choices, default="low"))The conditions and choices correspond by position. Decide deliberately what should happen when no condition matches, and make the conditions mutually exclusive or establish their intended priority if they overlap. The pandas advanced user guide demonstrates selecting values from criteria.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use Series.case_when() for rules on one Series
Series.case_when() accepts condition/replacement pairs and returns a new Series; it is not a whole-DataFrame replacement method. It was added in pandas 2.2.0. Check your installed pandas version before relying on it, and see the Series.case_when API documentation.
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Common mistakes to avoid
- Using value replacement for a threshold rule:
replace()matches existing values; use a mask or conditional method for rules such asscore < 0. - Reversing
where()andmask():where()replaces false positions, whilemask()replaces true positions. - Leaving unmatched cases undefined: choose an explicit fallback for
numpy.select(), and provideothertowhere()if missing values are not appropriate. - Overlooking scope: make the target column explicit with
.loc;case_when()operates on a Series. - Changing the original unintentionally: assignments to
dfmodify that object; work on a copy when you need to retain it.
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