.. _text:

{{ header }}

######################
Working with text data
######################

.. versionchanged:: 3.0
   The inference and behavior of strings changed significantly in pandas 3.0.
   See the :ref:`string_migration_guide`.

.. _text.types:

Text data types
===============

There are two ways to store text data in pandas:

1. :class:`StringDtype` extension type.
2. NumPy ``object`` dtype.

We recommend using :class:`StringDtype` to store text data via the alias
``dtype="str"`` (the default when dtype of strings is inferred), see
below for more details.

Prior to pandas 1.0, ``object`` dtype was the only option. This was unfortunate
for many reasons:

1. You can accidentally store a *mixture* of strings and non-strings in an
   ``object`` dtype array. It's better to have a dedicated dtype.
2. ``object`` dtype breaks dtype-specific operations like :meth:`DataFrame.select_dtypes`.
   There isn't a clear way to select *just* text while excluding non-text
   but still object-dtype columns.
3. When reading code, the contents of an ``object`` dtype array is less clear
   than ``'string'``.

When using :class:`StringDtype` with PyArrow as the storage (see below),
users will see large performance improvements in memory as well as time
for certain operations when compared to ``object`` dtype arrays. When
not using PyArrow as the storage, the performance of :class:`StringDtype`
is about the same as that of ``object``. We expect future enhancements
to significantly increase the performance and lower the memory overhead of
:class:`StringDtype` in this case.

.. versionchanged:: 3.0

   The default when pandas infers the dtype of a collection of
   strings is to use ``dtype='str'``. This will use ``np.nan``
   as its NA value and be backed by a PyArrow string array when
   PyArrow is installed, or backed by NumPy ``object`` array
   when PyArrow is not installed.

.. ipython:: python

   pd.Series(["a", "b", "c"])

Specifying :class:`StringDtype` explicitly
==========================================

When it is desired to explicitly specify the dtype, we generally recommend
using the alias ``dtype="str"`` if you desire to have ``np.nan`` as the NA
value or the alias ``dtype="string"`` if you desire to have ``pd.NA`` as
the NA value.

.. ipython:: python

   pd.Series(["a", "b", None], dtype="str")
   pd.Series(["a", "b", None], dtype="string")

Specifying either alias will also convert non-string data to strings:

.. ipython:: python

   s = pd.Series(["a", 2, np.nan], dtype="str")
   s
   type(s[1])

or convert from existing pandas data:

.. ipython:: python

   s1 = pd.Series([1, 2, pd.NA], dtype="Int64")
   s1
   s2 = s1.astype("string")
   s2
   type(s2[0])

However there are four distinct :class:`StringDtype` variants that may be utilized.
See :ref:`text.four_string_variants` section below for details.

.. _text.string_methods:

String methods
==============

Series and Index are equipped with a set of string processing methods
that make it easy to operate on each element of the array. Perhaps most
importantly, these methods exclude missing/NA values automatically. These are
accessed via the ``str`` attribute and generally have names matching
the equivalent (scalar) built-in string methods:

.. ipython:: python

   s = pd.Series(
       ["A", "B", "C", "Aaba", np.nan, "dog", "cat"],
       dtype="str",
   )
   s.str.lower()
   s.str.upper()
   s.str.len()

.. ipython:: python

   idx = pd.Index([" jack", "jill ", " jesse ", "frank"])
   idx.str.strip()
   idx.str.lstrip()
   idx.str.rstrip()

The string methods on Index are especially useful for cleaning up or
transforming DataFrame columns. For instance, you may have columns with
leading or trailing whitespace:

.. ipython:: python

   df = pd.DataFrame(
       np.random.randn(3, 2),
       columns=[" Column A ", " Column B "],
       index=range(3),
   )
   df

Since ``df.columns`` is an Index object, we can use the ``.str`` accessor

.. ipython:: python

   df.columns.str.strip()
   df.columns.str.lower()

These string methods can then be used to clean up the columns as needed.
Here we are removing leading and trailing whitespaces, lower casing all names,
and replacing any remaining whitespaces with underscores:

.. ipython:: python

   df.columns = df.columns.str.strip().str.lower().str.replace(" ", "_")
   df

.. note::

    If you have a ``Series`` where lots of elements are repeated
    (i.e. the number of unique elements in the ``Series`` is a lot smaller than the length of the
    ``Series``), it can be faster to convert the original ``Series`` to one of type
    ``category`` and then use ``.str.<method>`` or ``.dt.<property>`` on that.
    The performance difference comes from the fact that, for ``Series`` of type ``category``, the
    string operations are done on the ``.categories`` and not on each element of the
    ``Series``.

    Please note that a ``Series`` of type ``category`` with string ``.categories`` has
    some limitations in comparison to ``Series`` of type string (e.g. you can't add strings to
    each other: ``s + " " + s`` won't work if ``s`` is a ``Series`` of type ``category``). Also,
    ``.str`` methods which operate on elements of type ``list`` are not available on such a
    ``Series``.

.. _text.warn_types:

.. warning::

    The type of the Series is inferred and is one among the allowed types (i.e. strings).

    Generally speaking, the ``.str`` accessor is intended to work only on strings. With very few
    exceptions, other uses are not supported, and may be disabled at a later point.

.. _text.split:

Splitting and replacing strings
===============================

Methods like ``split`` return a Series of lists:

.. ipython:: python

   s2 = pd.Series(["a_b_c", "c_d_e", np.nan, "f_g_h"], dtype="str")
   s2.str.split("_")

Elements in the split lists can be accessed using ``get`` or ``[]`` notation:

.. ipython:: python

   s2.str.split("_").str.get(1)
   s2.str.split("_").str[1]

It is easy to expand this to return a DataFrame using ``expand``.

.. ipython:: python

   s2.str.split("_", expand=True)

When original ``Series`` has :class:`StringDtype`, the output columns will all
be :class:`StringDtype` as well.

It is also possible to limit the number of splits:

.. ipython:: python

   s2.str.split("_", expand=True, n=1)

``rsplit`` is similar to ``split`` except it works in the reverse direction,
i.e., from the end of the string to the beginning of the string:

.. ipython:: python

   s2.str.rsplit("_", expand=True, n=1)

``replace`` optionally uses `regular expressions
<https://docs.python.org/3/library/re.html>`__:

.. ipython:: python

   s3 = pd.Series(
       ["A", "B", "C", "Aaba", "Baca", "", np.nan, "CABA", "dog", "cat"],
       dtype="str",
   )
   s3
   s3.str.replace("^.a|dog", "XX-XX ", case=False, regex=True)


.. versionchanged:: 2.0
   Single character pattern with ``regex=True`` will also be treated as regular expressions:

.. ipython:: python

   s4 = pd.Series(["a.b", ".", "b", np.nan, ""], dtype="str")
   s4
   s4.str.replace(".", "a", regex=True)

If you want literal replacement of a string (equivalent to :meth:`str.replace`), you
can set the optional ``regex`` parameter to ``False``, rather than escaping each
character. In this case both ``pat`` and ``repl`` must be strings:

.. ipython:: python

    dollars = pd.Series(["12", "-$10", "$10,000"], dtype="str")

    # These lines are equivalent
    dollars.str.replace(r"-\$", "-", regex=True)
    dollars.str.replace("-$", "-", regex=False)

The ``replace`` method can also take a callable as replacement. It is called
on every ``pat`` using :func:`re.sub`. The callable should expect one
positional argument (a regex object) and return a string.

.. ipython:: python

   # Reverse every lowercase alphabetic word
   pat = r"[a-z]+"

   def repl(m):
       return m.group(0)[::-1]

   pd.Series(["foo 123", "bar baz", np.nan], dtype="str").str.replace(
       pat, repl, regex=True
   )

   # Using regex groups
   pat = r"(?P<one>\w+) (?P<two>\w+) (?P<three>\w+)"

   def repl(m):
       return m.group("two").swapcase()

   pd.Series(["Foo Bar Baz", np.nan], dtype="str").str.replace(
       pat, repl, regex=True
   )

The ``replace`` method also accepts a compiled regular expression object
from :func:`re.compile` as a pattern. All flags should be included in the
compiled regular expression object.

.. ipython:: python

   import re

   regex_pat = re.compile(r"^.a|dog", flags=re.IGNORECASE)
   s3.str.replace(regex_pat, "XX-XX ", regex=True)

Including a ``flags`` argument when calling ``replace`` with a compiled
regular expression object will raise a ``ValueError``.

.. ipython::

    @verbatim
    In [1]: s3.str.replace(regex_pat, 'XX-XX ', flags=re.IGNORECASE)
    ---------------------------------------------------------------------------
    ValueError: case and flags cannot be set when pat is a compiled regex

``removeprefix`` and ``removesuffix`` have the same effect as ``str.removeprefix`` and ``str.removesuffix`` added in
`Python 3.9 <https://docs.python.org/3/library/stdtypes.html#str.removeprefix>`__:

.. ipython:: python

   s = pd.Series(["str_foo", "str_bar", "no_prefix"])
   s.str.removeprefix("str_")

   s = pd.Series(["foo_str", "bar_str", "no_suffix"])
   s.str.removesuffix("_str")

.. _text.concatenate:

Concatenation
=============

There are several ways to concatenate a ``Series`` or ``Index``, either with itself or others, all based on :meth:`~Series.str.cat`,
resp. ``Index.str.cat``.

Concatenating a single Series into a string
-------------------------------------------

The content of a ``Series`` (or ``Index``) can be concatenated:

.. ipython:: python

    s = pd.Series(["a", "b", "c", "d"], dtype="str")
    s.str.cat(sep=",")

If not specified, the keyword ``sep`` for the separator defaults to the empty string, ``sep=''``:

.. ipython:: python

    s.str.cat()

By default, missing values are ignored. Using ``na_rep``, they can be given a representation:

.. ipython:: python

    t = pd.Series(["a", "b", np.nan, "d"], dtype="str")
    t.str.cat(sep=",")
    t.str.cat(sep=",", na_rep="-")

Concatenating a Series and something list-like into a Series
------------------------------------------------------------

The first argument to :meth:`~Series.str.cat` can be a list-like object, provided that it matches the length of the calling ``Series`` (or ``Index``).

.. ipython:: python

    s.str.cat(["A", "B", "C", "D"])

Missing values on either side will result in missing values in the result as well, *unless* ``na_rep`` is specified:

.. ipython:: python

    s.str.cat(t)
    s.str.cat(t, na_rep="-")

Concatenating a Series and something array-like into a Series
-------------------------------------------------------------

The parameter ``others`` can also be two-dimensional. In this case, the number of rows must match the lengths of the calling ``Series`` (or ``Index``).

.. ipython:: python

    d = pd.concat([t, s], axis=1)
    s
    d
    s.str.cat(d, na_rep="-")

Concatenating a Series and an indexed object into a Series, with alignment
--------------------------------------------------------------------------

For concatenation with a ``Series`` or ``DataFrame``, it is possible to align the indexes before concatenation by setting
the ``join``-keyword.

.. ipython:: python
   :okwarning:

   u = pd.Series(["b", "d", "a", "c"], index=[1, 3, 0, 2], dtype="str")
   s
   u
   s.str.cat(u)
   s.str.cat(u, join="left")

The usual options are available for ``join`` (one of ``'left', 'outer', 'inner', 'right'``).
In particular, alignment also means that the different lengths do not need to coincide anymore.

.. ipython:: python

    v = pd.Series(["z", "a", "b", "d", "e"], index=[-1, 0, 1, 3, 4], dtype="str")
    s
    v
    s.str.cat(v, join="left", na_rep="-")
    s.str.cat(v, join="outer", na_rep="-")

The same alignment can be used when ``others`` is a ``DataFrame``:

.. ipython:: python

    f = d.loc[[3, 2, 1, 0], :]
    s
    f
    s.str.cat(f, join="left", na_rep="-")

Concatenating a Series and many objects into a Series
-----------------------------------------------------

Several array-like items (specifically: ``Series``, ``Index``, and 1-dimensional variants of ``np.ndarray``)
can be combined in a list-like container (including iterators, ``dict``-views, etc.).

.. ipython:: python

    s
    u
    s.str.cat([u, u.to_numpy()], join="left")

All elements without an index (e.g. ``np.ndarray``) within the passed list-like must match in length to the calling ``Series`` (or ``Index``),
but ``Series`` and ``Index`` may have arbitrary length (as long as alignment is not disabled with ``join=None``):

.. ipython:: python

    v
    s.str.cat([v, u, u.to_numpy()], join="outer", na_rep="-")

If using ``join='right'`` on a list-like of ``others`` that contains different indexes,
the union of these indexes will be used as the basis for the final concatenation:

.. ipython:: python

    u.loc[[3]]
    v.loc[[-1, 0]]
    s.str.cat([u.loc[[3]], v.loc[[-1, 0]]], join="right", na_rep="-")

Indexing with ``.str``
======================

.. _text.indexing:

You can use ``[]`` notation to directly index by position locations. If you index past the end
of the string, the result will be a ``NaN``.


.. ipython:: python

   s = pd.Series(
       ["A", "B", "C", "Aaba", "Baca", np.nan, "CABA", "dog", "cat"], dtype="str"
   )

   s.str[0]
   s.str[1]

Extracting substrings
=====================

.. _text.extract:

Extract first match in each subject (extract)
---------------------------------------------

The ``extract`` method accepts a `regular expression
<https://docs.python.org/3/library/re.html>`__ with at least one
capture group.

Extracting a regular expression with more than one group returns a
DataFrame with one column per group.

.. ipython:: python

   pd.Series(
       ["a1", "b2", "c3"],
       dtype="str",
   ).str.extract(r"([ab])(\d)", expand=False)

Elements that do not match return a row filled with ``NaN``. Thus, a
Series of messy strings can be "converted" into a like-indexed Series
or DataFrame of cleaned-up or more useful strings, without
necessitating ``get()`` to access tuples or ``re.match`` objects. The
dtype of the result is always object, even if no match is found and
the result only contains ``NaN``.

Named groups like

.. ipython:: python

   pd.Series(["a1", "b2", "c3"], dtype="str").str.extract(
       r"(?P<letter>[ab])(?P<digit>\d)", expand=False
   )

and optional groups like

.. ipython:: python

   pd.Series(
       ["a1", "b2", "3"],
       dtype="str",
   ).str.extract(r"([ab])?(\d)", expand=False)

can also be used. Note that any capture group names in the regular
expression will be used for column names; otherwise capture group
numbers will be used.

Extracting a regular expression with one group returns a ``DataFrame``
with one column if ``expand=True``.

.. ipython:: python

   pd.Series(["a1", "b2", "c3"], dtype="str").str.extract(r"[ab](\d)", expand=True)

It returns a Series if ``expand=False``.

.. ipython:: python

   pd.Series(["a1", "b2", "c3"], dtype="str").str.extract(r"[ab](\d)", expand=False)

Calling on an ``Index`` with a regex with exactly one capture group
returns a ``DataFrame`` with one column if ``expand=True``.

.. ipython:: python

   s = pd.Series(["a1", "b2", "c3"], ["A11", "B22", "C33"], dtype="str")
   s
   s.index.str.extract("(?P<letter>[a-zA-Z])", expand=True)

It returns an ``Index`` if ``expand=False``.

.. ipython:: python

   s.index.str.extract("(?P<letter>[a-zA-Z])", expand=False)

Calling on an ``Index`` with a regex with more than one capture group
returns a ``DataFrame`` if ``expand=True``.

.. ipython:: python

   s.index.str.extract("(?P<letter>[a-zA-Z])([0-9]+)", expand=True)

It raises ``ValueError`` if ``expand=False``.

.. ipython:: python
   :okexcept:

    s.index.str.extract("(?P<letter>[a-zA-Z])([0-9]+)", expand=False)

The table below summarizes the behavior of ``extract(expand=False)``
(input subject in first column, number of groups in regex in
first row)

+--------+---------+------------+
|        | 1 group | >1 group   |
+--------+---------+------------+
| Index  | Index   | ValueError |
+--------+---------+------------+
| Series | Series  | DataFrame  |
+--------+---------+------------+

Extract all matches in each subject (extractall)
------------------------------------------------

.. _text.extractall:

Unlike ``extract`` (which returns only the first match),

.. ipython:: python

   s = pd.Series(["a1a2", "b1", "c1"], index=["A", "B", "C"], dtype="str")
   s
   two_groups = "(?P<letter>[a-z])(?P<digit>[0-9])"
   s.str.extract(two_groups, expand=True)

the ``extractall`` method returns every match. The result of
``extractall`` is always a ``DataFrame`` with a ``MultiIndex`` on its
rows. The last level of the ``MultiIndex`` is named ``match`` and
indicates the order in the subject.

.. ipython:: python

   s.str.extractall(two_groups)

When each subject string in the Series has exactly one match,

.. ipython:: python

   s = pd.Series(["a3", "b3", "c2"], dtype="str")
   s

then ``extractall(pat).xs(0, level='match')`` gives the same result as
``extract(pat)``.

.. ipython:: python

   extract_result = s.str.extract(two_groups, expand=True)
   extract_result
   extractall_result = s.str.extractall(two_groups)
   extractall_result
   extractall_result.xs(0, level="match")

``Index`` also supports ``.str.extractall``. It returns a ``DataFrame`` which has the
same result as a ``Series.str.extractall`` with a default index (starts from 0).

.. ipython:: python

   pd.Index(["a1a2", "b1", "c1"]).str.extractall(two_groups)

   pd.Series(["a1a2", "b1", "c1"], dtype="str").str.extractall(two_groups)


Testing for strings that match or contain a pattern
===================================================

You can check whether elements contain a pattern:

.. ipython:: python

   pattern = r"[0-9][a-z]"
   pd.Series(
       ["1", "2", "3a", "3b", "03c", "4dx"],
       dtype="str",
   ).str.contains(pattern)

Or whether elements match a pattern:

.. ipython:: python

   pd.Series(
       ["1", "2", "3a", "3b", "03c", "4dx"],
       dtype="str",
   ).str.match(pattern)

.. ipython:: python

   pd.Series(
       ["1", "2", "3a", "3b", "03c", "4dx"],
       dtype="str",
   ).str.fullmatch(pattern)

.. note::

    The distinction between ``match``, ``fullmatch``, and ``contains`` is strictness:
    ``fullmatch`` tests whether the entire string matches the regular expression;
    ``match`` tests whether there is a match of the regular expression that begins
    at the first character of the string; and ``contains`` tests whether there is
    a match of the regular expression at any position within the string.

    The corresponding functions in the ``re`` package for these three match modes are
    `re.fullmatch <https://docs.python.org/3/library/re.html#re.fullmatch>`_,
    `re.match <https://docs.python.org/3/library/re.html#re.match>`_, and
    `re.search <https://docs.python.org/3/library/re.html#re.search>`_,
    respectively.

Methods like ``match``, ``fullmatch``, ``contains``, ``startswith``, and
``endswith`` take an extra ``na`` argument so missing values can be considered
True or False:

.. ipython:: python

   s4 = pd.Series(
       ["A", "B", "C", "Aaba", "Baca", np.nan, "CABA", "dog", "cat"], dtype="str"
   )
   s4.str.contains("A", na=False)

.. _text.indicator:

Creating indicator variables
============================

You can extract dummy variables from string columns.
For example if they are separated by a ``'|'``:

.. ipython:: python

    s = pd.Series(["a", "a|b", np.nan, "a|c"], dtype="str")
    s.str.get_dummies(sep="|")

String ``Index`` also supports ``get_dummies`` which returns a ``MultiIndex``.

.. ipython:: python

    idx = pd.Index(["a", "a|b", np.nan, "a|c"])
    idx.str.get_dummies(sep="|")

See also :func:`~pandas.get_dummies`.

.. _text.differences:

Behavior differences
====================

Differences in behavior will be primarily due to the kind of NA value.

``StringDtype`` with ``np.nan`` NA values
-----------------------------------------

1. Like ``dtype="object"``, :ref:`string accessor methods<api.series.str>`
   that return **integer** output will return a NumPy array that is
   either dtype int or float depending on the presence of NA values.
   Methods returning **boolean** output will return a NumPy array that is
   dtype bool, with the value ``False`` when an NA value is encountered.

   .. ipython:: python

      s = pd.Series(["a", None, "b"], dtype="str")
      s
      s.str.count("a")
      s.dropna().str.count("a")

   When NA values are present, the output dtype is float64. However
   **boolean** output results in ``False`` for the NA values.

   .. ipython:: python

      s.str.isdigit()
      s.str.match("a")

2. Some string methods, like :meth:`Series.str.decode`, are not
   available because the underlying array can only contain
   strings, not bytes.
3. Comparison operations will return a NumPy array with dtype bool. Missing
   values will always compare as unequal just as :attr:`np.nan` does.

``StringDtype`` with ``pd.NA`` NA values
----------------------------------------

1. :ref:`String accessor methods<api.series.str>`
   that return **integer** output will always return a nullable integer dtype,
   rather than either int or float dtype (depending on the presence of NA values).
   Methods returning **boolean** output will return a nullable boolean dtype.

   .. ipython:: python

      s = pd.Series(["a", None, "b"], dtype="string")
      s
      s.str.count("a")
      s.dropna().str.count("a")

   Both outputs are ``Int64`` dtype. Similarly for methods returning boolean values.

   .. ipython:: python

      s.str.isdigit()
      s.str.match("a")

2. Some string methods, like :meth:`Series.str.decode` because the underlying
   array can only contain strings, not bytes.
3. Comparison operations will return an object with :class:`BooleanDtype`,
   rather than a ``bool`` dtype object. Missing values will propagate
   in comparison operations, rather than always comparing
   unequal like :attr:`numpy.nan`.


.. important::
   Everything else that follows in the rest of this document applies equally to
   ``'str'``, ``'string'``, and ``object`` dtype.

.. _text.four_string_variants:

The four :class:`StringDtype` variants
======================================

There are four :class:`StringDtype` variants that are available to users,
controlled by the ``storage`` and ``na_value`` parameters of :class:`StringDtype`.
At runtime, these can be checked via the :attr:`StringDtype.storage`
and :attr:`StringDtype.na_value` attributes.

Python storage with ``np.nan`` values
-------------------------------------

.. note::
   This is the same as ``dtype='str'`` *when PyArrow is not installed*.

The implementation uses a NumPy object array, which directly stores the
Python string objects, hence why the storage here is called ``'python'``.
NA values in this array are represented and behave as ``np.nan``.

.. ipython:: python

   pd.Series(
       ["a", "b", None, np.nan, pd.NA],
       dtype=pd.StringDtype(storage="python", na_value=np.nan)
   )

Notice that the last three values are all inferred by pandas as being
NA values, and hence stored as ``np.nan``.

PyArrow storage with ``np.nan`` values
--------------------------------------

.. note::
   This is the same as ``dtype='str'`` *when PyArrow is installed*.

The implementation uses a PyArrow array, however NA values in this array
are represented and behave as ``np.nan``.

.. ipython:: python

   pd.Series(
       ["a", "b", None, np.nan, pd.NA],
       dtype=pd.StringDtype(storage="pyarrow", na_value=np.nan)
   )

Notice that the last three values are all inferred by pandas as being
NA values, and hence stored as ``np.nan``.

Python storage with ``pd.NA`` values
------------------------------------

.. note::
   This is the same as ``dtype='string'`` *when PyArrow is not installed*.

The implementation uses a NumPy object array, which directly stores the
Python string objects, hence why the storage here is called ``'python'``.
NA values in this array are represented and behave as ``np.nan``.

.. ipython:: python

   pd.Series(
       ["a", "b", None, np.nan, pd.NA],
       dtype=pd.StringDtype(storage="python", na_value=pd.NA)
   )

Notice that the last three values are all inferred by pandas as
being NA values, and hence stored as ``pd.NA``.

PyArrow storage with ``pd.NA`` values
-------------------------------------

.. note::
   This is the same as ``dtype='string'`` *when PyArrow is installed*.

The implementation uses a PyArrow array. NA values in this array are
represented and behave as ``pd.NA``.

.. ipython:: python

   pd.Series(
       ["a", "b", None, np.nan, pd.NA],
       dtype=pd.StringDtype(storage="pyarrow", na_value=pd.NA)
   )

Notice that the last three values are all inferred by pandas as being NA
values, and hence stored as ``pd.NA``.

Method summary
==============

.. _text.summary:

.. csv-table::
    :header: "Method", "Description"
    :widths: 20, 80

    :meth:`~Series.str.cat`,Concatenate strings
    :meth:`~Series.str.split`,Split strings on delimiter
    :meth:`~Series.str.rsplit`,Split strings on delimiter working from the end of the string
    :meth:`~Series.str.get`,Index into each element (retrieve i-th element)
    :meth:`~Series.str.join`,Join strings in each element of the Series with passed separator
    :meth:`~Series.str.get_dummies`,Split strings on the delimiter returning DataFrame of dummy variables
    :meth:`~Series.str.contains`,Return boolean array if each string contains pattern/regex
    :meth:`~Series.str.replace`,Replace occurrences of pattern/regex/string with some other string or the return value of a callable given the occurrence
    :meth:`~Series.str.removeprefix`,Remove prefix from string i.e. only remove if string starts with prefix.
    :meth:`~Series.str.removesuffix`,Remove suffix from string i.e. only remove if string ends with suffix.
    :meth:`~Series.str.repeat`,Duplicate values (``s.str.repeat(3)`` equivalent to ``x * 3``)
    :meth:`~Series.str.pad`,Add whitespace to the sides of strings
    :meth:`~Series.str.center`,Equivalent to ``str.center``
    :meth:`~Series.str.ljust`,Equivalent to ``str.ljust``
    :meth:`~Series.str.rjust`,Equivalent to ``str.rjust``
    :meth:`~Series.str.zfill`,Equivalent to ``str.zfill``
    :meth:`~Series.str.wrap`,Split long strings into lines with length less than a given width
    :meth:`~Series.str.slice`,Slice each string in the Series
    :meth:`~Series.str.slice_replace`,Replace slice in each string with passed value
    :meth:`~Series.str.count`,Count occurrences of pattern
    :meth:`~Series.str.startswith`,Equivalent to ``str.startswith(pat)`` for each element
    :meth:`~Series.str.endswith`,Equivalent to ``str.endswith(pat)`` for each element
    :meth:`~Series.str.findall`,Compute list of all occurrences of pattern/regex for each string
    :meth:`~Series.str.match`,Call ``re.match`` on each element returning matched groups as list
    :meth:`~Series.str.extract`,Call ``re.search`` on each element returning DataFrame with one row for each element and one column for each regex capture group
    :meth:`~Series.str.extractall`,Call ``re.findall`` on each element returning DataFrame with one row for each match and one column for each regex capture group
    :meth:`~Series.str.len`,Compute string lengths
    :meth:`~Series.str.strip`,Equivalent to ``str.strip``
    :meth:`~Series.str.rstrip`,Equivalent to ``str.rstrip``
    :meth:`~Series.str.lstrip`,Equivalent to ``str.lstrip``
    :meth:`~Series.str.partition`,Equivalent to ``str.partition``
    :meth:`~Series.str.rpartition`,Equivalent to ``str.rpartition``
    :meth:`~Series.str.lower`,Equivalent to ``str.lower``
    :meth:`~Series.str.casefold`,Equivalent to ``str.casefold``
    :meth:`~Series.str.upper`,Equivalent to ``str.upper``
    :meth:`~Series.str.find`,Equivalent to ``str.find``
    :meth:`~Series.str.rfind`,Equivalent to ``str.rfind``
    :meth:`~Series.str.index`,Equivalent to ``str.index``
    :meth:`~Series.str.rindex`,Equivalent to ``str.rindex``
    :meth:`~Series.str.capitalize`,Equivalent to ``str.capitalize``
    :meth:`~Series.str.swapcase`,Equivalent to ``str.swapcase``
    :meth:`~Series.str.normalize`,Return Unicode normal form. Equivalent to ``unicodedata.normalize``
    :meth:`~Series.str.translate`,Equivalent to ``str.translate``
    :meth:`~Series.str.isalnum`,Equivalent to ``str.isalnum``
    :meth:`~Series.str.isalpha`,Equivalent to ``str.isalpha``
    :meth:`~Series.str.isdigit`,Equivalent to ``str.isdigit``
    :meth:`~Series.str.isspace`,Equivalent to ``str.isspace``
    :meth:`~Series.str.islower`,Equivalent to ``str.islower``
    :meth:`~Series.str.isupper`,Equivalent to ``str.isupper``
    :meth:`~Series.str.istitle`,Equivalent to ``str.istitle``
    :meth:`~Series.str.isnumeric`,Equivalent to ``str.isnumeric``
    :meth:`~Series.str.isdecimal`,Equivalent to ``str.isdecimal``
