The Tool Desk
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Choose the method that matches your rule
| What you need to do | Use | What happens outside the match |
|---|---|---|
| Replace known values such as old status codes wherever they occur | DataFrame.replace() |
Unmatched values remain unchanged |
| Assign a fixed value to cells selected by a boolean rule | Boolean mask with .loc |
Cells outside the mask remain unchanged |
| Keep values where a condition is true and replace the rest | where() |
Uses the supplied replacement for false positions; without one, fills with a missing value |
| Replace values where a condition is true | mask() |
Uses the supplied replacement at true positions |
| Choose among several condition-based results for a new column | numpy.select() |
Uses the specified default if no condition matches |
| Apply ordered condition/replacement pairs to one Series | Series.case_when() |
Returns a Series; check that the installed pandas version supports it |
Replace several known values with DataFrame.replace()
When the old values are known in advance, pass a dictionary mapping each old value to its replacement. This matches values; it does not select rows based on an arbitrary condition.
out = df.replace({"old": "new", "legacy": "current"})To make different substitutions in particular columns, nest the mappings under the column names:
out = df.replace({
"status": {"N": "new", "C": "closed"},
"priority": {"H": "high", "L": "low"}
})Use regex mode only when you intend to match text patterns rather than exact values. The DataFrame.replace API reference documents the supported forms, including column-scoped mappings and regular-expression replacement.
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Apply a boolean rule to selected cells
For a rule such as “set negative scores to zero,” create a boolean mask and assign through .loc. Naming the target column makes it clear which cells will change.
out = df.copy()
mask = out["score"] < 0
out.loc[mask, "score"] = 0This changes the copy, leaving df available with its original values. If you intend to update the original DataFrame, assign to it directly instead of copying first. The pandas guide covers conditional selection and assignment alongside where and mask: Boolean indexing in the pandas user guide.
Make sure the condition selects the intended rows and that its index aligns with the DataFrame being assigned to. In particular, a mask built from one DataFrame should not be casually reused after changing the rows or index of another.
Choose between where() and mask()
The two methods have opposite condition polarity:
where(condition, other)keeps values where the condition is true and substitutesotherwhere it is false.mask(condition, other)substitutesotherwhere the condition is true and keeps values where it is false.
For example, either of these replaces negative scores with zero:
out["score"] = out["score"].where(out["score"] >= 0, 0)
out["score"] = out["score"].mask(out["score"] < 0, 0)Choose the expression that makes the rule easiest to read. Omitting other from where() means failed positions are filled with a missing value: np.nan for NumPy dtypes and pd.NA for extension dtypes, according to the API documentation. Provide other explicitly if a missing result is not what you want. See the pandas references for DataFrame.where and DataFrame.mask.
Handle several conditions with numpy.select()
When each condition maps to a category and you want a result column, use numpy.select(conditions, choices, default=...). Conditions and choices correspond by position. For example, this classifies scores of 90 or higher as high, scores of 70–89 as medium, and the remaining scores as low:
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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"))Decide on the default deliberately: it determines the value for rows where no condition is true. Also consider overlapping conditions. If more than one rule can match the same row, arrange the rules with the intended priority; for example, test the higher threshold first as in the code above. The pandas guide to boolean indexing and conditional selection includes a multiple-condition example using NumPy selection.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use Series.case_when() for ordered rules on a Series
case_when() applies condition/replacement pairs to a Series and returns a Series; it is not a whole-DataFrame replacement method. It was added in pandas 2.2.0, so check the installed version before using it. The API result for pandas 3.0.3 identifies that version history. See the Series.case_when API reference for the supported syntax and behavior.
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Common mistakes to avoid
- Using
replace()for a threshold: value mappings match specified values; use a boolean mask when the rule is something like “less than zero.” - Reversing
where()andmask():where()replaces false positions, whilemask()replaces true positions. - Leaving unmatched results undefined: select a deliberate default for
numpy.select(), and supplyothertowhere()if missing values are not appropriate. - Overlooking version and scope:
Series.case_when()acts on a Series and requires pandas 2.2.0 or newer. - Changing data you meant to preserve: copy the DataFrame before assignment when you need to retain the original.
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