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df["col"] = df["col"].str.replace({"old1": "new1", "old2": "new2"})In the current pandas 3.0.6 API, a dictionary passed as pat maps each pattern to its own replacement. Use regex=False for literal text or regex=True for regular expressions when you need to specify the matching behavior explicitly.
Replace multiple text patterns in one column
A DataFrame column is a Series, so select the column before using its string accessor. The dictionary form lets you provide several pattern-and-replacement pairs in one call:
df["col"] = df["col"].str.replace({"foo": "bar", "baz": "qux"})Each dictionary key is a pattern and its value is the replacement for that pattern. When pat is a dictionary, leave the separate repl argument as None; the dictionary supplies the replacements. The call returns a transformed Series or Index, so assignment is what stores the result in the DataFrame column. [pandas.Series.str.replace API]
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Use one replacement for several alternatives
If several alternatives should all become the same text, combine them in a regular expression and set regex=True:
df["col"] = df["col"].str.replace(r"foo|baz", "replacement", regex=True)Here, either foo or baz matches, and both are replaced by replacement. Use the dictionary form instead when each pattern needs a different replacement. [pandas.Series.str.replace API]
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Choose literal matching or regular expressions
In the current Series string API, string patterns are treated literally by default. Set regex=False to make that intent explicit, especially when a pattern contains characters that have special meanings in regular expressions:
df["col"] = df["col"].str.replace("a.b", "x", regex=False)With regex=False, the pattern matches the literal text a.b. To use regex syntax, set regex=True, as in the alternatives example above. The pandas text guide notes that since pandas 2.0, even a single-character pattern passed with regex=True is treated as a regular expression. [Series string API] [pandas text-data guide]
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Use DataFrame.replace for whole-cell values
.str.replace() edits matching text within strings in the selected Series. If instead you want to replace cell values as values—for example, change cells equal to "old" to "new"—use DataFrame.replace():
df = df.replace({"old": "new"})DataFrame.replace() supports scalar, list, dictionary, nested-dictionary, and regex forms. It can also express column-specific replacement rules with nested mappings; use its documented argument structure for the column and value relationship you intend. Its arguments and defaults are separate from those of Series.str.replace(), so do not assume the Series method’s literal-matching default applies. [pandas.DataFrame.replace API]
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| Need | Use | What is matched |
|---|---|---|
| Change substrings in text in one column | df["col"].str.replace(...) |
Patterns within strings in the selected Series |
| Change entire cell values or apply DataFrame-level replacement rules | df.replace(...) |
Cell values, with supported mapping and regex forms |
Keep the transformed result
Calling df["col"].str.replace(...) produces a transformed Series; it does not update the DataFrame column just by being called. Assign the result to the column, as in the examples. Missing values are shown as unchanged in the official Series API examples. [pandas.Series.str.replace API]
Apply string replacements to more than one column
The Series string accessor operates on the Series you select, not automatically on every DataFrame column. For a known set of text columns, apply and assign the operation to each one explicitly:
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for col in ["name", "description"]:
df[col] = df[col].str.replace({"old": "new"})Choose the columns deliberately so that non-text columns are not inadvertently included in a string-editing operation.
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