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To split a pandas column of strings into separate columns, use .str.split() with expand=True:

parts = df["column"].str.split(",", expand=True)

Replace the comma with the delimiter in your data. The result is a DataFrame with each piece in its own column. Choose a split limit and literal-versus-regex behavior based on the values you expect.

Split a column into separate columns

For example, to split a column named full_name at a comma:

parts = df["full_name"].str.split(",", expand=True)

expand=True makes pandas return the pieces as columns. Without it, .str.split() returns a Series of lists instead. If you want to replace the original column with the split results, inspect the output first, then assign names that match the number of resulting columns:

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parts = df["full_name"].str.split(",", expand=True)
parts.columns = ["last_name", "first_name"]
df[["last_name", "first_name"]] = parts

The column names in this example assume each row produces two pieces.

Control how many times pandas splits

By default, .str.split() splits at every occurrence of the delimiter. Use n to limit splits from the left:

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df["path"].str.split("/", n=1, expand=True)

This splits at the first slash only. The default n=-1 means split at all occurrences; n=None and n=0 also mean all splits. [pandas Series.str.split API]

Choose literal delimiters or regular expressions

The default regex=None has behavior that depends on the pattern length: a one-character pattern is treated literally, while a pattern longer than one character is treated as a regular expression. [pandas Series.str.split API]

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For a multi-character delimiter that should be matched exactly, set regex=False:

df["code"].str.split("::", regex=False, expand=True)

Set regex=True when you intend a regular-expression pattern. Regex metacharacters such as ., |, and * have special meanings, so escape them if you intend to match those characters literally under regex mode.

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Understand uneven rows and missing values

Expanded results must have a rectangular shape. If some rows contain fewer delimiters than others, pandas pads the shorter results with missing values. A missing source value also remains missing in the expanded output. [pandas text guide]

For example, when one row splits into three pieces and another into two, the second row’s third output cell is missing. Account for this before assigning fixed column names: the names list must match the number of output columns.

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Use another method when the split direction matters

Split only at the first delimiter

Series.str.partition() separates each value into the text before the first separator, the separator itself, and the text after it. Use it when you need to preserve the delimiter as its own piece or want an explicit before/separator/after result. [pandas Series.str.partition API]

Split from the right

Use Series.str.rsplit() to split from the end. For example, df["file"].str.rsplit(".", n=1, expand=True) separates a filename from its final extension when the final dot is the relevant delimiter. [pandas Series.str.rsplit API]

When the pieces should become rows

If the split result should be a long list of rows rather than multiple columns, first leave the split values as lists, then use Series.explode() to turn list-like values into separate rows. This changes the output shape; it is not a substitute for expand=True when you need columns. [pandas Series.explode API]

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