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Split a column into separate columns
Call the string accessor on the Series and pass expand=True. Replace the comma below with the delimiter in your data:
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parts = df["column"].str.split(",", expand=True)parts is a DataFrame with one column for each resulting piece. To replace the original column, inspect the output first, then assign meaningful names that match its number of columns:
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parts = df["column"].str.split(",", expand=True)
parts.columns = ["first", "second"]
df[["first", "second"]] = partsThis naming example assumes the split produces exactly two columns; adjust the names to the actual output shape. Without expand=True, str.split returns lists in a Series rather than separate DataFrame columns. If you omit the delimiter, pandas splits on whitespace.
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Choose how many delimiters to use
By default, n=-1 means split at every occurrence. Use a positive n to limit the number of splits, starting from the left:
df["column"].str.split(",", n=1, expand=True)This makes at most one split per value, leaving any later commas in the final piece. n=None and n=0 also mean split at all occurrences.
Make literal and regex separators explicit
With the default regex=None, pandas treats a one-character pattern as a literal separator, but a pattern longer than one character as a regular expression. For a multi-character delimiter that should be matched exactly, set regex=False:
df["column"].str.split("::", regex=False, expand=True)Use regex=True when the pattern is intentionally a regular expression. In that case, escape regex metacharacters that should be matched literally.
Know what happens with uneven or missing values
Expanded results have a rectangular shape. When some rows split into fewer pieces than others, pandas pads the shorter results with missing values. Missing input values remain missing in the expanded output; they are not converted into empty strings.
Use a different method for only the first or last separator
Split around the first separator
Series.str.partition returns three pieces: the text before the separator, the separator itself, and the text after it. It is useful when you want to preserve the remainder as one piece.
Split from the right
Use Series.str.rsplit(delimiter, n=1, expand=True) to split at most once, working from the end of each value.
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- Separate columns: use
str.split(..., expand=True). - Lists in one Series: omit
expand=True. - Rows from list-like values: use
Series.explodeafter splitting; this reshapes the data into long format rather than creating columns.
See the pandas documentation for Series.str.split, the working with text guide, Series.str.partition, Series.str.rsplit, and Series.explode.
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