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# Working with text data

Changed in version 3.0: The inference and behavior of strings changed significantly in pandas 3.0. See the The new string data type.

## Text data types

There are two ways to store text data in pandas:

1. `StringDtype` extension type.

2. NumPy `object` dtype.

We recommend using `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 `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 `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 `StringDtype` is about the same as that of `object`. We expect future enhancements to significantly increase the performance and lower the memory overhead of `StringDtype` in this case.

Changed in version 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.

    In [1]: pd.Series(["a", "b", "c"])
    Out[1]:
    0    a
    1    b
    2    c
    dtype: str

## Specifying `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.

    In [2]: pd.Series(["a", "b", None], dtype="str")
    Out[2]:
    0      a
    1      b
    2    NaN
    dtype: str

    In [3]: pd.Series(["a", "b", None], dtype="string")
    Out[3]:
    0       a
    1       b
    2    <NA>
    dtype: string

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

    In [4]: s = pd.Series(["a", 2, np.nan], dtype="str")

    In [5]: s
    Out[5]:
    0      a
    1      2
    2    NaN
    dtype: str

    In [6]: type(s[1])
    Out[6]: str

or convert from existing pandas data:

    In [7]: s1 = pd.Series([1, 2, pd.NA], dtype="Int64")

    In [8]: s1
    Out[8]:
    0       1
    1       2
    2    <NA>
    dtype: Int64

    In [9]: s2 = s1.astype("string")

    In [10]: s2
    Out[10]:
    0       1
    1       2
    2    <NA>
    dtype: string

    In [11]: type(s2[0])
    Out[11]: str

However there are four distinct `StringDtype` variants that may be utilized. See The four StringDtype variants section below for details.

## 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:

    In [12]: s = pd.Series(
       ....:     ["A", "B", "C", "Aaba", np.nan, "dog", "cat"],
       ....:     dtype="str",
       ....: )
       ....:

    In [13]: s.str.lower()
    Out[13]:
    0       a
    1       b
    2       c
    3    aaba
    4     NaN
    5     dog
    6     cat
    dtype: str

    In [14]: s.str.upper()
    Out[14]:
    0       A
    1       B
    2       C
    3    AABA
    4     NaN
    5     DOG
    6     CAT
    dtype: str

    In [15]: s.str.len()
    Out[15]:
    0    1.0
    1    1.0
    2    1.0
    3    4.0
    4    NaN
    5    3.0
    6    3.0
    dtype: float64

    In [16]: idx = pd.Index([" jack", "jill ", " jesse ", "frank"])

    In [17]: idx.str.strip()
    Out[17]: Index(['jack', 'jill', 'jesse', 'frank'], dtype='str')

    In [18]: idx.str.lstrip()
    Out[18]: Index(['jack', 'jill ', 'jesse ', 'frank'], dtype='str')

    In [19]: idx.str.rstrip()
    Out[19]: Index([' jack', 'jill', ' jesse', 'frank'], dtype='str')

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:

    In [20]: df = pd.DataFrame(
       ....:     np.random.randn(3, 2),
       ....:     columns=[" Column A ", " Column B "],
       ....:     index=range(3),
       ....: )
       ....:

    In [21]: df
    Out[21]:
       Column A   Column B
    0   0.469112  -0.282863
    1  -1.509059  -1.135632
    2   1.212112  -0.173215

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

    In [22]: df.columns.str.strip()
    Out[22]: Index(['Column A', 'Column B'], dtype='str')

    In [23]: df.columns.str.lower()
    Out[23]: Index([' column a ', ' column b '], dtype='str')

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:

    In [24]: df.columns = df.columns.str.strip().str.lower().str.replace(" ", "_")

    In [25]: df
    Out[25]:
       column_a  column_b
    0  0.469112 -0.282863
    1 -1.509059 -1.135632
    2  1.212112 -0.173215

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`.

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.

## Splitting and replacing strings

Methods like `split` return a Series of lists:

    In [26]: s2 = pd.Series(["a_b_c", "c_d_e", np.nan, "f_g_h"], dtype="str")

    In [27]: s2.str.split("_")
    Out[27]:
    0    [a, b, c]
    1    [c, d, e]
    2          NaN
    3    [f, g, h]
    dtype: object

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

    In [28]: s2.str.split("_").str.get(1)
    Out[28]:
    0      b
    1      d
    2    NaN
    3      g
    dtype: object

    In [29]: s2.str.split("_").str[1]
    Out[29]:
    0      b
    1      d
    2    NaN
    3      g
    dtype: object

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

    In [30]: s2.str.split("_", expand=True)
    Out[30]:
         0    1    2
    0    a    b    c
    1    c    d    e
    2  NaN  NaN  NaN
    3    f    g    h

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

It is also possible to limit the number of splits:

    In [31]: s2.str.split("_", expand=True, n=1)
    Out[31]:
         0    1
    0    a  b_c
    1    c  d_e
    2  NaN  NaN
    3    f  g_h

`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:

    In [32]: s2.str.rsplit("_", expand=True, n=1)
    Out[32]:
         0    1
    0  a_b    c
    1  c_d    e
    2  NaN  NaN
    3  f_g    h

`replace` optionally uses regular expressions:

    In [33]: s3 = pd.Series(
       ....:     ["A", "B", "C", "Aaba", "Baca", "", np.nan, "CABA", "dog", "cat"],
       ....:     dtype="str",
       ....: )
       ....:

    In [34]: s3
    Out[34]:
    0       A
    1       B
    2       C
    3    Aaba
    4    Baca
    5
    6     NaN
    7    CABA
    8     dog
    9     cat
    dtype: str

    In [35]: s3.str.replace("^.a|dog", "XX-XX ", case=False, regex=True)
    Out[35]:
    0           A
    1           B
    2           C
    3    XX-XX ba
    4    XX-XX ca
    5
    6         NaN
    7    XX-XX BA
    8      XX-XX
    9     XX-XX t
    dtype: str

Changed in version 2.0: Single character pattern with `regex=True` will also be treated as regular expressions:

    In [36]: s4 = pd.Series(["a.b", ".", "b", np.nan, ""], dtype="str")

    In [37]: s4
    Out[37]:
    0    a.b
    1      .
    2      b
    3    NaN
    4
    dtype: str

    In [38]: s4.str.replace(".", "a", regex=True)
    Out[38]:
    0    aaa
    1      a
    2      a
    3    NaN
    4
    dtype: str

If you want literal replacement of a string (equivalent to `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:

    In [39]: dollars = pd.Series(["12", "-$10", "$10,000"], dtype="str")

    # These lines are equivalent
    In [40]: dollars.str.replace(r"-\$", "-", regex=True)
    Out[40]:
    0         12
    1        -10
    2    $10,000
    dtype: str

    In [41]: dollars.str.replace("-$", "-", regex=False)
    Out[41]:
    0         12
    1        -10
    2    $10,000
    dtype: str

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

    # Reverse every lowercase alphabetic word
    In [42]: pat = r"[a-z]+"

    In [43]: def repl(m):
       ....:     return m.group(0)[::-1]
       ....:

    In [44]: pd.Series(["foo 123", "bar baz", np.nan], dtype="str").str.replace(
       ....:     pat, repl, regex=True
       ....: )
       ....:
    Out[44]:
    0    oof 123
    1    rab zab
    2        NaN
    dtype: str

    # Using regex groups
    In [45]: pat = r"(?P<one>\w+) (?P<two>\w+) (?P<three>\w+)"

    In [46]: def repl(m):
       ....:     return m.group("two").swapcase()
       ....:

    In [47]: pd.Series(["Foo Bar Baz", np.nan], dtype="str").str.replace(
       ....:     pat, repl, regex=True
       ....: )
       ....:
    Out[47]:
    0    bAR
    1    NaN
    dtype: str

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

    In [48]: import re

    In [49]: regex_pat = re.compile(r"^.a|dog", flags=re.IGNORECASE)

    In [50]: s3.str.replace(regex_pat, "XX-XX ", regex=True)
    Out[50]:
    0           A
    1           B
    2           C
    3    XX-XX ba
    4    XX-XX ca
    5
    6         NaN
    7    XX-XX BA
    8      XX-XX
    9     XX-XX t
    dtype: str

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

    In [51]: 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:

    In [52]: s = pd.Series(["str_foo", "str_bar", "no_prefix"])

    In [53]: s.str.removeprefix("str_")
    Out[53]:
    0          foo
    1          bar
    2    no_prefix
    dtype: str

    In [54]: s = pd.Series(["foo_str", "bar_str", "no_suffix"])

    In [55]: s.str.removesuffix("_str")
    Out[55]:
    0          foo
    1          bar
    2    no_suffix
    dtype: str

## Concatenation

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

### Concatenating a single Series into a string

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

    In [56]: s = pd.Series(["a", "b", "c", "d"], dtype="str")

    In [57]: s.str.cat(sep=",")
    Out[57]: 'a,b,c,d'

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

    In [58]: s.str.cat()
    Out[58]: 'abcd'

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

    In [59]: t = pd.Series(["a", "b", np.nan, "d"], dtype="str")

    In [60]: t.str.cat(sep=",")
    Out[60]: 'a,b,d'

    In [61]: t.str.cat(sep=",", na_rep="-")
    Out[61]: 'a,b,-,d'

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

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

    In [62]: s.str.cat(["A", "B", "C", "D"])
    Out[62]:
    0    aA
    1    bB
    2    cC
    3    dD
    dtype: str

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

    In [63]: s.str.cat(t)
    Out[63]:
    0     aa
    1     bb
    2    NaN
    3     dd
    dtype: str

    In [64]: s.str.cat(t, na_rep="-")
    Out[64]:
    0    aa
    1    bb
    2    c-
    3    dd
    dtype: str

### 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`).

    In [65]: d = pd.concat([t, s], axis=1)

    In [66]: s
    Out[66]:
    0    a
    1    b
    2    c
    3    d
    dtype: str

    In [67]: d
    Out[67]:
         0  1
    0    a  a
    1    b  b
    2  NaN  c
    3    d  d

    In [68]: s.str.cat(d, na_rep="-")
    Out[68]:
    0    aaa
    1    bbb
    2    c-c
    3    ddd
    dtype: str

### 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.

    In [69]: u = pd.Series(["b", "d", "a", "c"], index=[1, 3, 0, 2], dtype="str")

    In [70]: s
    Out[70]:
    0    a
    1    b
    2    c
    3    d
    dtype: str

    In [71]: u
    Out[71]:
    1    b
    3    d
    0    a
    2    c
    dtype: str

    In [72]: s.str.cat(u)
    Out[72]:
    0    aa
    1    bb
    2    cc
    3    dd
    dtype: str

    In [73]: s.str.cat(u, join="left")
    Out[73]:
    0    aa
    1    bb
    2    cc
    3    dd
    dtype: str

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.

    In [74]: v = pd.Series(["z", "a", "b", "d", "e"], index=[-1, 0, 1, 3, 4], dtype="str")

    In [75]: s
    Out[75]:
    0    a
    1    b
    2    c
    3    d
    dtype: str

    In [76]: v
    Out[76]:
    -1    z
     0    a
     1    b
     3    d
     4    e
    dtype: str

    In [77]: s.str.cat(v, join="left", na_rep="-")
    Out[77]:
    0    aa
    1    bb
    2    c-
    3    dd
    dtype: str

    In [78]: s.str.cat(v, join="outer", na_rep="-")
    Out[78]:
    -1    -z
     0    aa
     1    bb
     2    c-
     3    dd
     4    -e
    dtype: str

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

    In [79]: f = d.loc[[3, 2, 1, 0], :]

    In [80]: s
    Out[80]:
    0    a
    1    b
    2    c
    3    d
    dtype: str

    In [81]: f
    Out[81]:
         0  1
    3    d  d
    2  NaN  c
    1    b  b
    0    a  a

    In [82]: s.str.cat(f, join="left", na_rep="-")
    Out[82]:
    0    aaa
    1    bbb
    2    c-c
    3    ddd
    dtype: str

### 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.).

    In [83]: s
    Out[83]:
    0    a
    1    b
    2    c
    3    d
    dtype: str

    In [84]: u
    Out[84]:
    1    b
    3    d
    0    a
    2    c
    dtype: str

    In [85]: s.str.cat([u, u.to_numpy()], join="left")
    Out[85]:
    0    aab
    1    bbd
    2    cca
    3    ddc
    dtype: str

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`):

    In [86]: v
    Out[86]:
    -1    z
     0    a
     1    b
     3    d
     4    e
    dtype: str

    In [87]: s.str.cat([v, u, u.to_numpy()], join="outer", na_rep="-")
    Out[87]:
    -1    -z--
    0     aaab
    1     bbbd
    2     c-ca
    3     dddc
    4     -e--
    dtype: str

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:

    In [88]: u.loc[[3]]
    Out[88]:
    3    d
    dtype: str

    In [89]: v.loc[[-1, 0]]
    Out[89]:
    -1    z
     0    a
    dtype: str

    In [90]: s.str.cat([u.loc[[3]], v.loc[[-1, 0]]], join="right", na_rep="-")
    Out[90]:
     3    dd-
    -1    --z
     0    a-a
    dtype: str

## Indexing with `.str`

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`.

    In [91]: s = pd.Series(
       ....:     ["A", "B", "C", "Aaba", "Baca", np.nan, "CABA", "dog", "cat"], dtype="str"
       ....: )
       ....:

    In [92]: s.str[0]
    Out[92]:
    0      A
    1      B
    2      C
    3      A
    4      B
    5    NaN
    6      C
    7      d
    8      c
    dtype: str

    In [93]: s.str[1]
    Out[93]:
    0    NaN
    1    NaN
    2    NaN
    3      a
    4      a
    5    NaN
    6      A
    7      o
    8      a
    dtype: str

## Extracting substrings

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

The `extract` method accepts a regular expression with at least one capture group.

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

    In [94]: pd.Series(
       ....:     ["a1", "b2", "c3"],
       ....:     dtype="str",
       ....: ).str.extract(r"([ab])(\d)", expand=False)
       ....:
    Out[94]:
         0    1
    0    a    1
    1    b    2
    2  NaN  NaN

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

    In [95]: pd.Series(["a1", "b2", "c3"], dtype="str").str.extract(
       ....:     r"(?P<letter>[ab])(?P<digit>\d)", expand=False
       ....: )
       ....:
    Out[95]:
      letter digit
    0      a     1
    1      b     2
    2    NaN   NaN

and optional groups like

    In [96]: pd.Series(
       ....:     ["a1", "b2", "3"],
       ....:     dtype="str",
       ....: ).str.extract(r"([ab])?(\d)", expand=False)
       ....:
    Out[96]:
         0  1
    0    a  1
    1    b  2
    2  NaN  3

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`.

    In [97]: pd.Series(["a1", "b2", "c3"], dtype="str").str.extract(r"[ab](\d)", expand=True)
    Out[97]:
         0
    0    1
    1    2
    2  NaN

It returns a Series if `expand=False`.

    In [98]: pd.Series(["a1", "b2", "c3"], dtype="str").str.extract(r"[ab](\d)", expand=False)
    Out[98]:
    0      1
    1      2
    2    NaN
    dtype: str

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

    In [99]: s = pd.Series(["a1", "b2", "c3"], ["A11", "B22", "C33"], dtype="str")

    In [100]: s
    Out[100]:
    A11    a1
    B22    b2
    C33    c3
    dtype: str

    In [101]: s.index.str.extract("(?P<letter>[a-zA-Z])", expand=True)
    Out[101]:
      letter
    0      A
    1      B
    2      C

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

    In [102]: s.index.str.extract("(?P<letter>[a-zA-Z])", expand=False)
    Out[102]: Index(['A', 'B', 'C'], dtype='str', name='letter')

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

    In [103]: s.index.str.extract("(?P<letter>[a-zA-Z])([0-9]+)", expand=True)
    Out[103]:
      letter   1
    0      A  11
    1      B  22
    2      C  33

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

    In [104]: s.index.str.extract("(?P<letter>[a-zA-Z])([0-9]+)", expand=False)
    ---------------------------------------------------------------------------
    ValueError                                Traceback (most recent call last)
    Cell In[104], line 1
    ----> 1 s.index.str.extract("(?P<letter>[a-zA-Z])([0-9]+)", expand=False)

    File ~/work/pandas/pandas/pandas/core/strings/accessor.py:142, in forbid_nonstring_types.<locals>._forbid_nonstring_types.<locals>.wrapper(self, *args, **kwargs)
        137     msg = (
        138         f"Cannot use .str.{func_name} with values of "
        139         f"inferred dtype '{self._inferred_dtype}'."
        140     )
        141     raise TypeError(msg)
    --> 142 return func(self, *args, **kwargs)

    File ~/work/pandas/pandas/pandas/core/strings/accessor.py:3425, in StringMethods.extract(self, pat, flags, expand)
       3422     raise ValueError("pattern contains no capture groups")
       3424 if not expand and regex.groups > 1 and isinstance(self._data, ABCIndex):
    -> 3425     raise ValueError("only one regex group is supported with Index")
       3427 obj = self._data
       3428 result_dtype = _result_dtype(obj)

    ValueError: only one regex group is supported with Index

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)

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

    In [105]: s = pd.Series(["a1a2", "b1", "c1"], index=["A", "B", "C"], dtype="str")

    In [106]: s
    Out[106]:
    A    a1a2
    B      b1
    C      c1
    dtype: str

    In [107]: two_groups = "(?P<letter>[a-z])(?P<digit>[0-9])"

    In [108]: s.str.extract(two_groups, expand=True)
    Out[108]:
      letter digit
    A      a     1
    B      b     1
    C      c     1

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.

    In [109]: s.str.extractall(two_groups)
    Out[109]:
            letter digit
      match
    A 0          a     1
      1          a     2
    B 0          b     1
    C 0          c     1

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

    In [110]: s = pd.Series(["a3", "b3", "c2"], dtype="str")

    In [111]: s
    Out[111]:
    0    a3
    1    b3
    2    c2
    dtype: str

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

    In [112]: extract_result = s.str.extract(two_groups, expand=True)

    In [113]: extract_result
    Out[113]:
      letter digit
    0      a     3
    1      b     3
    2      c     2

    In [114]: extractall_result = s.str.extractall(two_groups)

    In [115]: extractall_result
    Out[115]:
            letter digit
      match
    0 0          a     3
    1 0          b     3
    2 0          c     2

    In [116]: extractall_result.xs(0, level="match")
    Out[116]:
      letter digit
    0      a     3
    1      b     3
    2      c     2

`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).

    In [117]: pd.Index(["a1a2", "b1", "c1"]).str.extractall(two_groups)
    Out[117]:
            letter digit
      match
    0 0          a     1
      1          a     2
    1 0          b     1
    2 0          c     1

    In [118]: pd.Series(["a1a2", "b1", "c1"], dtype="str").str.extractall(two_groups)
    Out[118]:
            letter digit
      match
    0 0          a     1
      1          a     2
    1 0          b     1
    2 0          c     1

## Testing for strings that match or contain a pattern

You can check whether elements contain a pattern:

    In [119]: pattern = r"[0-9][a-z]"

    In [120]: pd.Series(
       .....:     ["1", "2", "3a", "3b", "03c", "4dx"],
       .....:     dtype="str",
       .....: ).str.contains(pattern)
       .....:
    Out[120]:
    0    False
    1    False
    2     True
    3     True
    4     True
    5     True
    dtype: bool

Or whether elements match a pattern:

    In [121]: pd.Series(
       .....:     ["1", "2", "3a", "3b", "03c", "4dx"],
       .....:     dtype="str",
       .....: ).str.match(pattern)
       .....:
    Out[121]:
    0    False
    1    False
    2     True
    3     True
    4    False
    5     True
    dtype: bool

    In [122]: pd.Series(
       .....:     ["1", "2", "3a", "3b", "03c", "4dx"],
       .....:     dtype="str",
       .....: ).str.fullmatch(pattern)
       .....:
    Out[122]:
    0    False
    1    False
    2     True
    3     True
    4    False
    5    False
    dtype: bool

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, re.match, and 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:

    In [123]: s4 = pd.Series(
       .....:     ["A", "B", "C", "Aaba", "Baca", np.nan, "CABA", "dog", "cat"], dtype="str"
       .....: )
       .....:

    In [124]: s4.str.contains("A", na=False)
    Out[124]:
    0     True
    1    False
    2    False
    3     True
    4    False
    5    False
    6     True
    7    False
    8    False
    dtype: bool

## Creating indicator variables

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

    In [125]: s = pd.Series(["a", "a|b", np.nan, "a|c"], dtype="str")

    In [126]: s.str.get_dummies(sep="|")
    Out[126]:
       a  b  c
    0  1  0  0
    1  1  1  0
    2  0  0  0
    3  1  0  1

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

    In [127]: idx = pd.Index(["a", "a|b", np.nan, "a|c"])

    In [128]: idx.str.get_dummies(sep="|")
    Out[128]:
    MultiIndex([(1, 0, 0),
                (1, 1, 0),
                (0, 0, 0),
                (1, 0, 1)],
               names=['a', 'b', 'c'])

See also `get_dummies()`.

## 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"`, string accessor methods 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.

       In [129]: s = pd.Series(["a", None, "b"], dtype="str")

       In [130]: s
       Out[130]:
       0      a
       1    NaN
       2      b
       dtype: str

       In [131]: s.str.count("a")
       Out[131]:
       0    1.0
       1    NaN
       2    0.0
       dtype: float64

       In [132]: s.dropna().str.count("a")
       Out[132]:
       0    1
       2    0
       dtype: int64

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

       In [133]: s.str.isdigit()
       Out[133]:
       0    False
       1    False
       2    False
       dtype: bool

       In [134]: s.str.match("a")
       Out[134]:
       0     True
       1    False
       2    False
       dtype: bool

2. Some string methods, like `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 `np.nan` does.

### `StringDtype` with `pd.NA` NA values

1. String accessor methods 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.

       In [135]: s = pd.Series(["a", None, "b"], dtype="string")

       In [136]: s
       Out[136]:
       0       a
       1    <NA>
       2       b
       dtype: string

       In [137]: s.str.count("a")
       Out[137]:
       0       1
       1    <NA>
       2       0
       dtype: Int64

       In [138]: s.dropna().str.count("a")
       Out[138]:
       0    1
       2    0
       dtype: Int64

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

       In [139]: s.str.isdigit()
       Out[139]:
       0    False
       1     <NA>
       2    False
       dtype: boolean

       In [140]: s.str.match("a")
       Out[140]:
       0     True
       1     <NA>
       2    False
       dtype: boolean

2. Some string methods, like `Series.str.decode()` because the underlying array can only contain strings, not bytes.

3. Comparison operations will return an object with `BooleanDtype`, rather than a `bool` dtype object. Missing values will propagate in comparison operations, rather than always comparing unequal like `numpy.nan`.

Important

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

## The four `StringDtype` variants

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

    In [141]: pd.Series(
       .....:     ["a", "b", None, np.nan, pd.NA],
       .....:     dtype=pd.StringDtype(storage="python", na_value=np.nan)
       .....: )
       .....:
    Out[141]:
    0      a
    1      b
    2    NaN
    3    NaN
    4    NaN
    dtype: str

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`.

    In [142]: pd.Series(
       .....:     ["a", "b", None, np.nan, pd.NA],
       .....:     dtype=pd.StringDtype(storage="pyarrow", na_value=np.nan)
       .....: )
       .....:
    Out[142]:
    0      a
    1      b
    2    NaN
    3    NaN
    4    NaN
    dtype: str

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`.

    In [143]: pd.Series(
       .....:     ["a", "b", None, np.nan, pd.NA],
       .....:     dtype=pd.StringDtype(storage="python", na_value=pd.NA)
       .....: )
       .....:
    Out[143]:
    0       a
    1       b
    2    <NA>
    3    <NA>
    4    <NA>
    dtype: string

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`.

    In [144]: pd.Series(
       .....:     ["a", "b", None, np.nan, pd.NA],
       .....:     dtype=pd.StringDtype(storage="python", na_value=pd.NA)
       .....: )
       .....:
    Out[144]:
    0       a
    1       b
    2    <NA>
    3    <NA>
    4    <NA>
    dtype: string

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

## Method summary

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