Working with text data

Text data types

New in version 1.0.0.

There are two ways to store text data in pandas:

1. object -dtype NumPy array.

2. StringDtype extension type.

We recommend using StringDtype to store text data.

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'.

Currently, the performance of object dtype arrays of strings and arrays.StringArray are about the same. We expect future enhancements to significantly increase the performance and lower the memory overhead of StringArray.

Warning

StringArray is currently considered experimental. The implementation and parts of the API may change without warning.

For backwards-compatibility, object dtype remains the default type we infer a list of strings to

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

To explicitly request string dtype, specify the dtype

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

In [3]: pd.Series(["a", "b", "c"], dtype=pd.StringDtype())
Out[3]: 
0    a
1    b
2    c
dtype: string

Or astype after the Series or DataFrame is created

In [4]: s = pd.Series(["a", "b", "c"])

In [5]: s
Out[5]: 
0    a
1    b
2    c
dtype: object

In [6]: s.astype("string")
Out[6]: 
0    a
1    b
2    c
dtype: string

Changed in version 1.1.0.

You can also use StringDtype/"string" as the dtype on non-string data and it will be converted to string dtype:

In [7]: s = pd.Series(["a", 2, np.nan], dtype="string")

In [8]: s
Out[8]: 
0       a
1       2
2    <NA>
dtype: string

In [9]: type(s[1])
Out[9]: str

or convert from existing pandas data:

In [10]: s1 = pd.Series([1, 2, np.nan], dtype="Int64")

In [11]: s1
Out[11]: 
0       1
1       2
2    <NA>
dtype: Int64

In [12]: s2 = s1.astype("string")

In [13]: s2
Out[13]: 
0       1
1       2
2    <NA>
dtype: string

In [14]: type(s2[0])
Out[14]: str

Behavior differences

These are places where the behavior of StringDtype objects differ from object dtype

12. For StringDtype, string accessor methods that return numeric 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 [15]: s = pd.Series(["a", None, "b"], dtype="string")
    
    In [16]: s
    Out[16]: 
    0       a
    1    <NA>
    2       b
    dtype: string
    
    In [17]: s.str.count("a")
    Out[17]: 
    0       1
    1    <NA>
    2       0
    dtype: Int64
    
    In [18]: s.dropna().str.count("a")
    Out[18]: 
    0    1
    2    0
    dtype: Int64
    

    Both outputs are Int64 dtype. Compare that with object-dtype

    In [19]: s2 = pd.Series(["a", None, "b"], dtype="object")
    
    In [20]: s2.str.count("a")
    Out[20]: 
    0    1.0
    1    NaN
    2    0.0
    dtype: float64
    
    In [21]: s2.dropna().str.count("a")
    Out[21]: 
    0    1
    2    0
    dtype: int64
    

    When NA values are present, the output dtype is float64. Similarly for methods returning boolean values.

    In [22]: s.str.isdigit()
    Out[22]: 
    0    False
    1     <NA>
    2    False
    dtype: boolean
    
    In [23]: s.str.match("a")
    Out[23]: 
    0     True
    1     <NA>
    2    False
    dtype: boolean
    

2. Some string methods, like Series.str.decode() are not available on StringArray because StringArray only holds strings, not bytes.

3. In comparison operations, arrays.StringArray and Series backed by a StringArray will return an object with BooleanDtype, rather than a bool dtype object. Missing values in a StringArray will propagate in comparison operations, rather than always comparing unequal like numpy.nan.

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

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

In [25]: s.str.lower()
Out[25]: 
0       a
1       b
2       c
3    aaba
4    baca
5    <NA>
6    caba
7     dog
8     cat
dtype: string

In [26]: s.str.upper()
Out[26]: 
0       A
1       B
2       C
3    AABA
4    BACA
5    <NA>
6    CABA
7     DOG
8     CAT
dtype: string

In [27]: s.str.len()
Out[27]: 
0       1
1       1
2       1
3       4
4       4
5    <NA>
6       4
7       3
8       3
dtype: Int64

In [28]: idx = pd.Index([" jack", "jill ", " jesse ", "frank"])

In [29]: idx.str.strip()
Out[29]: Index(['jack', 'jill', 'jesse', 'frank'], dtype='object')

In [30]: idx.str.lstrip()
Out[30]: Index(['jack', 'jill ', 'jesse ', 'frank'], dtype='object')

In [31]: idx.str.rstrip()
Out[31]: Index([' jack', 'jill', ' jesse', 'frank'], dtype='object')

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

In [33]: df
Out[33]: 
    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 [34]: df.columns.str.strip()
Out[34]: Index(['Column A', 'Column B'], dtype='object')

In [35]: df.columns.str.lower()
Out[35]: Index([' column a ', ' column b '], dtype='object')

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

In [37]: df
Out[37]: 
   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

Before v.0.25.0, the .str-accessor did only the most rudimentary type checks. Starting with v.0.25.0, the type of the Series is inferred and the allowed types (i.e. strings) are enforced more rigorously.

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 [38]: s2 = pd.Series(["a_b_c", "c_d_e", np.nan, "f_g_h"], dtype="string")

In [39]: s2.str.split("_")
Out[39]: 
0    [a, b, c]
1    [c, d, e]
2         <NA>
3    [f, g, h]
dtype: object

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

In [40]: s2.str.split("_").str.get(1)
Out[40]: 
0       b
1       d
2    <NA>
3       g
dtype: object

In [41]: s2.str.split("_").str[1]
Out[41]: 
0       b
1       d
2    <NA>
3       g
dtype: object

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

In [42]: s2.str.split("_", expand=True)
Out[42]: 
      0     1     2
0     a     b     c
1     c     d     e
2  <NA>  <NA>  <NA>
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 [43]: s2.str.split("_", expand=True, n=1)
Out[43]: 
      0     1
0     a   b_c
1     c   d_e
2  <NA>  <NA>
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 [44]: s2.str.rsplit("_", expand=True, n=1)
Out[44]: 
      0     1
0   a_b     c
1   c_d     e
2  <NA>  <NA>
3   f_g     h

replace optionally uses regular expressions:

In [45]: s3 = pd.Series(
   ....:     ["A", "B", "C", "Aaba", "Baca", "", np.nan, "CABA", "dog", "cat"],
   ....:     dtype="string",
   ....: )
   ....: 

In [46]: s3
Out[46]: 
0       A
1       B
2       C
3    Aaba
4    Baca
5        
6    <NA>
7    CABA
8     dog
9     cat
dtype: string

In [47]: s3.str.replace("^.a|dog", "XX-XX ", case=False, regex=True)
Out[47]: 
0           A
1           B
2           C
3    XX-XX ba
4    XX-XX ca
5            
6        <NA>
7    XX-XX BA
8      XX-XX 
9     XX-XX t
dtype: string

Warning

Some caution must be taken when dealing with regular expressions! The current behavior is to treat single character patterns as literal strings, even when regex is set to True. This behavior is deprecated and will be removed in a future version so that the regex keyword is always respected.

Changed in version 1.2.0.

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

# These lines are equivalent
In [49]: dollars.str.replace(r"-\$", "-", regex=True)
Out[49]: 
0         12
1        -10
2    $10,000
dtype: string

In [50]: dollars.str.replace("-$", "-", regex=False)
Out[50]: 
0         12
1        -10
2    $10,000
dtype: string

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 [51]: pat = r"[a-z]+"

In [52]: def repl(m):
   ....:     return m.group(0)[::-1]
   ....: 

In [53]: pd.Series(["foo 123", "bar baz", np.nan], dtype="string").str.replace(
   ....:     pat, repl, regex=True
   ....: )
   ....: 
Out[53]: 
0    oof 123
1    rab zab
2       <NA>
dtype: string

# Using regex groups
In [54]: pat = r"(?P<one>\w+) (?P<two>\w+) (?P<three>\w+)"

In [55]: def repl(m):
   ....:     return m.group("two").swapcase()
   ....: 

In [56]: pd.Series(["Foo Bar Baz", np.nan], dtype="string").str.replace(
   ....:     pat, repl, regex=True
   ....: )
   ....: 
Out[56]: 
0     bAR
1    <NA>
dtype: string

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 [57]: import re

In [58]: regex_pat = re.compile(r"^.a|dog", flags=re.IGNORECASE)

In [59]: s3.str.replace(regex_pat, "XX-XX ", regex=True)
Out[59]: 
0           A
1           B
2           C
3    XX-XX ba
4    XX-XX ca
5            
6        <NA>
7    XX-XX BA
8      XX-XX 
9     XX-XX t
dtype: string

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

In [60]: s3.str.replace(regex_pat, 'XX-XX ', flags=re.IGNORECASE)
---------------------------------------------------------------------------
ValueError: case and flags cannot be set when pat is a compiled regex

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 [61]: s = pd.Series(["a", "b", "c", "d"], dtype="string")

In [62]: s.str.cat(sep=",")
Out[62]: 'a,b,c,d'

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

In [63]: s.str.cat()
Out[63]: 'abcd'

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

In [64]: t = pd.Series(["a", "b", np.nan, "d"], dtype="string")

In [65]: t.str.cat(sep=",")
Out[65]: 'a,b,d'

In [66]: t.str.cat(sep=",", na_rep="-")
Out[66]: '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 [67]: s.str.cat(["A", "B", "C", "D"])
Out[67]: 
0    aA
1    bB
2    cC
3    dD
dtype: string

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

In [68]: s.str.cat(t)
Out[68]: 
0      aa
1      bb
2    <NA>
3      dd
dtype: string

In [69]: s.str.cat(t, na_rep="-")
Out[69]: 
0    aa
1    bb
2    c-
3    dd
dtype: string

Concatenating a Series and something array-like into a Series

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

In [70]: d = pd.concat([t, s], axis=1)

In [71]: s
Out[71]: 
0    a
1    b
2    c
3    d
dtype: string

In [72]: d
Out[72]: 
      0  1
0     a  a
1     b  b
2  <NA>  c
3     d  d

In [73]: s.str.cat(d, na_rep="-")
Out[73]: 
0    aaa
1    bbb
2    c-c
3    ddd
dtype: string

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

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

In [76]: u
Out[76]: 
1    b
3    d
0    a
2    c
dtype: string

In [77]: s.str.cat(u)
Out[77]: 
0    aa
1    bb
2    cc
3    dd
dtype: string

In [78]: s.str.cat(u, join="left")
Out[78]: 
0    aa
1    bb
2    cc
3    dd
dtype: string

Warning

If the join keyword is not passed, the method cat() will currently fall back to the behavior before version 0.23.0 (i.e. no alignment), but a FutureWarning will be raised if any of the involved indexes differ, since this default will change to join='left' in a future version.

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

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

In [81]: v
Out[81]: 
-1    z
 0    a
 1    b
 3    d
 4    e
dtype: string

In [82]: s.str.cat(v, join="left", na_rep="-")
Out[82]: 
0    aa
1    bb
2    c-
3    dd
dtype: string

In [83]: s.str.cat(v, join="outer", na_rep="-")
Out[83]: 
-1    -z
 0    aa
 1    bb
 2    c-
 3    dd
 4    -e
dtype: string

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

In [84]: f = d.loc[[3, 2, 1, 0], :]

In [85]: s
Out[85]: 
0    a
1    b
2    c
3    d
dtype: string

In [86]: f
Out[86]: 
      0  1
3     d  d
2  <NA>  c
1     b  b
0     a  a

In [87]: s.str.cat(f, join="left", na_rep="-")
Out[87]: 
0    aaa
1    bbb
2    c-c
3    ddd
dtype: string

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 [88]: s
Out[88]: 
0    a
1    b
2    c
3    d
dtype: string

In [89]: u
Out[89]: 
1    b
3    d
0    a
2    c
dtype: string

In [90]: s.str.cat([u, u.to_numpy()], join="left")
Out[90]: 
0    aab
1    bbd
2    cca
3    ddc
dtype: string

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 [91]: v
Out[91]: 
-1    z
 0    a
 1    b
 3    d
 4    e
dtype: string

In [92]: s.str.cat([v, u, u.to_numpy()], join="outer", na_rep="-")
Out[92]: 
-1    -z--
 0    aaab
 1    bbbd
 2    c-ca
 3    dddc
 4    -e--
dtype: string

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 [93]: u.loc[[3]]
Out[93]: 
3    d
dtype: string

In [94]: v.loc[[-1, 0]]
Out[94]: 
-1    z
 0    a
dtype: string

In [95]: s.str.cat([u.loc[[3]], v.loc[[-1, 0]]], join="right", na_rep="-")
Out[95]: 
-1    --z
 0    a-a
 3    dd-
dtype: string

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

In [97]: s.str[0]
Out[97]: 
0       A
1       B
2       C
3       A
4       B
5    <NA>
6       C
7       d
8       c
dtype: string

In [98]: s.str[1]
Out[98]: 
0    <NA>
1    <NA>
2    <NA>
3       a
4       a
5    <NA>
6       A
7       o
8       a
dtype: string

Extracting substrings

Extract first match in each subject (extract)

Warning

Before version 0.23, argument expand of the extract method defaulted to False. When expand=False, expand returns a Series, Index, or DataFrame, depending on the subject and regular expression pattern. When expand=True, it always returns a DataFrame, which is more consistent and less confusing from the perspective of a user. expand=True has been the default since version 0.23.0.

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 [99]: pd.Series(
   ....:     ["a1", "b2", "c3"],
   ....:     dtype="string",
   ....: ).str.extract(r"([ab])(\d)", expand=False)
   ....: 
Out[99]: 
      0     1
0     a     1
1     b     2
2  <NA>  <NA>

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 [100]: pd.Series(["a1", "b2", "c3"], dtype="string").str.extract(
   .....:     r"(?P<letter>[ab])(?P<digit>\d)", expand=False
   .....: )
   .....: 
Out[100]: 
  letter digit
0      a     1
1      b     2
2   <NA>  <NA>

and optional groups like

In [101]: pd.Series(
   .....:     ["a1", "b2", "3"],
   .....:     dtype="string",
   .....: ).str.extract(r"([ab])?(\d)", expand=False)
   .....: 
Out[101]: 
      0  1
0     a  1
1     b  2
2  <NA>  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 [102]: pd.Series(["a1", "b2", "c3"], dtype="string").str.extract(r"[ab](\d)", expand=True)
Out[102]: 
      0
0     1
1     2
2  <NA>

It returns a Series if expand=False.

In [103]: pd.Series(["a1", "b2", "c3"], dtype="string").str.extract(r"[ab](\d)", expand=False)
Out[103]: 
0       1
1       2
2    <NA>
dtype: string

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

In [104]: s = pd.Series(["a1", "b2", "c3"], ["A11", "B22", "C33"], dtype="string")

In [105]: s
Out[105]: 
A11    a1
B22    b2
C33    c3
dtype: string

In [106]: s.index.str.extract("(?P<letter>[a-zA-Z])", expand=True)
Out[106]: 
  letter
0      A
1      B
2      C

It returns an Index if expand=False.

In [107]: s.index.str.extract("(?P<letter>[a-zA-Z])", expand=False)
Out[107]: Index(['A', 'B', 'C'], dtype='object', name='letter')

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

In [108]: s.index.str.extract("(?P<letter>[a-zA-Z])([0-9]+)", expand=True)
Out[108]: 
  letter   1
0      A  11
1      B  22
2      C  33

It raises ValueError if expand=False.

>>> s.index.str.extract("(?P<letter>[a-zA-Z])([0-9]+)", expand=False)
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 [109]: s = pd.Series(["a1a2", "b1", "c1"], index=["A", "B", "C"], dtype="string")

In [110]: s
Out[110]: 
A    a1a2
B      b1
C      c1
dtype: string

In [111]: two_groups = "(?P<letter>[a-z])(?P<digit>[0-9])"

In [112]: s.str.extract(two_groups, expand=True)
Out[112]: 
  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 [113]: s.str.extractall(two_groups)
Out[113]: 
        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 [114]: s = pd.Series(["a3", "b3", "c2"], dtype="string")

In [115]: s
Out[115]: 
0    a3
1    b3
2    c2
dtype: string

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

In [116]: extract_result = s.str.extract(two_groups, expand=True)

In [117]: extract_result
Out[117]: 
  letter digit
0      a     3
1      b     3
2      c     2

In [118]: extractall_result = s.str.extractall(two_groups)

In [119]: extractall_result
Out[119]: 
        letter digit
  match             
0 0          a     3
1 0          b     3
2 0          c     2

In [120]: extractall_result.xs(0, level="match")
Out[120]: 
  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 [121]: pd.Index(["a1a2", "b1", "c1"]).str.extractall(two_groups)
Out[121]: 
        letter digit
  match             
0 0          a     1
  1          a     2
1 0          b     1
2 0          c     1

In [122]: pd.Series(["a1a2", "b1", "c1"], dtype="string").str.extractall(two_groups)
Out[122]: 
        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 [123]: pattern = r"[0-9][a-z]"

In [124]: pd.Series(
   .....:     ["1", "2", "3a", "3b", "03c", "4dx"],
   .....:     dtype="string",
   .....: ).str.contains(pattern)
   .....: 
Out[124]: 
0    False
1    False
2     True
3     True
4     True
5     True
dtype: boolean

Or whether elements match a pattern:

In [125]: pd.Series(
   .....:     ["1", "2", "3a", "3b", "03c", "4dx"],
   .....:     dtype="string",
   .....: ).str.match(pattern)
   .....: 
Out[125]: 
0    False
1    False
2     True
3     True
4    False
5     True
dtype: boolean

New in version 1.1.0.

In [126]: pd.Series(
   .....:     ["1", "2", "3a", "3b", "03c", "4dx"],
   .....:     dtype="string",
   .....: ).str.fullmatch(pattern)
   .....: 
Out[126]: 
0    False
1    False
2     True
3     True
4    False
5    False
dtype: boolean

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

In [128]: s4.str.contains("A", na=False)
Out[128]: 
0     True
1    False
2    False
3     True
4    False
5    False
6     True
7    False
8    False
dtype: boolean

Creating indicator variables

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

In [129]: s = pd.Series(["a", "a|b", np.nan, "a|c"], dtype="string")

In [130]: s.str.get_dummies(sep="|")
Out[130]: 
   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 [131]: idx = pd.Index(["a", "a|b", np.nan, "a|c"])

In [132]: idx.str.get_dummies(sep="|")
Out[132]: 
MultiIndex([(1, 0, 0),
            (1, 1, 0),
            (0, 0, 0),
            (1, 0, 1)],
           names=['a', 'b', 'c'])

See also get_dummies().

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
repeat()	Duplicate values (s.str.repeat(3) equivalent to x * 3)
pad()	Add whitespace to left, right, or both 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

Reshaping and pivot tables Working with missing data