Working with text data

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

In [1]: s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])
 
In [2]: s.str.lower()
Out[2]: 
0       a
1       b
2       c
3    aaba
4    baca
5     NaN
6    caba
7     dog
8     cat
dtype: object
 
In [3]: s.str.upper()
Out[3]: 
0       A
1       B
2       C
3    AABA
4    BACA
5     NaN
6    CABA
7     DOG
8     CAT
dtype: object
 
In [4]: s.str.len()
Out[4]: 
0    1.0
1    1.0
2    1.0
3    4.0
4    4.0
5    NaN
6    4.0
7    3.0
8    3.0
dtype: float64

In [5]: idx = pd.Index([' jack', 'jill ', ' jesse ', 'frank'])
 
In [6]: idx.str.strip()
Out[6]: Index(['jack', 'jill', 'jesse', 'frank'], dtype='object')
 
In [7]: idx.str.lstrip()
Out[7]: Index(['jack', 'jill ', 'jesse ', 'frank'], dtype='object')
 
In [8]: idx.str.rstrip()
Out[8]: Index([' jack', 'jill', ' jesse', 'frank'], dtype='object')

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

In [9]: df = pd.DataFrame(np.random.randn(3, 2),
   ...:                   columns=[' Column A ', ' Column B '], index=range(3))
   ...: 
 
In [10]: df
Out[10]: 
    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 [11]: df.columns.str.strip()
Out[11]: Index(['Column A', 'Column B'], dtype='object')
 
In [12]: df.columns.str.lower()
Out[12]: 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 [13]: df.columns = df.columns.str.strip().str.lower().str.replace(' ', '_')
 
In [14]: df
Out[14]: 
   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 theSeries), it can be faster to convert the original Series to one of typecategory and then use .str.<method> or .dt.<property> on that.The performance difference comes from the fact that, for Series of type category, thestring operations are done on the .categories and not on each element of theSeries.

Please note that a Series of type category with string .categories hassome limitations in comparison to Series of type string (e.g. you can’t add strings toeach 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 aSeries.

Warning

Before v.0.25.0, the .str-accessor did only the most rudimentary type checks. Starting withv.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 fewexceptions, 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 [15]: s2 = pd.Series(['a_b_c', 'c_d_e', np.nan, 'f_g_h'])
 
In [16]: s2.str.split('_')
Out[16]: 
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 [17]: s2.str.split('_').str.get(1)
Out[17]: 
0      b
1      d
2    NaN
3      g
dtype: object
 
In [18]: s2.str.split('_').str[1]
Out[18]: 
0      b
1      d
2    NaN
3      g
dtype: object

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

In [19]: s2.str.split('_', expand=True)
Out[19]: 
     0    1    2
0    a    b    c
1    c    d    e
2  NaN  NaN  NaN
3    f    g    h

It is also possible to limit the number of splits:

In [20]: s2.str.split('_', expand=True, n=1)
Out[20]: 
     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 [21]: s2.str.rsplit('_', expand=True, n=1)
Out[21]: 
     0    1
0  a_b    c
1  c_d    e
2  NaN  NaN
3  f_g    h

replace by default replaces regular expressions:

In [22]: s3 = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca',
   ....:                '', np.nan, 'CABA', 'dog', 'cat'])
   ....: 
 
In [23]: s3
Out[23]: 
0       A
1       B
2       C
3    Aaba
4    Baca
5        
6     NaN
7    CABA
8     dog
9     cat
dtype: object
 
In [24]: s3.str.replace('^.a|dog', 'XX-XX ', case=False)
Out[24]: 
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: object

Some caution must be taken to keep regular expressions in mind! For example, thefollowing code will cause trouble because of the regular expression meaning of$:

# Consider the following badly formatted financial data
In [25]: dollars = pd.Series(['12', '-$10', '$10,000'])
 
# This does what you'd naively expect:
In [26]: dollars.str.replace('$', '')
Out[26]: 
0        12
1       -10
2    10,000
dtype: object
 
# But this doesn't:
In [27]: dollars.str.replace('-$', '-')
Out[27]: 
0         12
1       -$10
2    $10,000
dtype: object
 
# We need to escape the special character (for >1 len patterns)
In [28]: dollars.str.replace(r'-\$', '-')
Out[28]: 
0         12
1        -10
2    $10,000
dtype: object

New in version 0.23.0.

If you do want literal replacement of a string (equivalent tostr.replace()), you can set the optional regex parameter toFalse, rather than escaping each character. In this case both patand repl must be strings:

# These lines are equivalent
In [29]: dollars.str.replace(r'-\$', '-')
Out[29]: 
0         12
1        -10
2    $10,000
dtype: object
 
In [30]: dollars.str.replace('-$', '-', regex=False)
Out[30]: 
0         12
1        -10
2    $10,000
dtype: object

New in version 0.20.0.

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

# Reverse every lowercase alphabetic word
In [31]: pat = r'[a-z]+'
 
In [32]: def repl(m):
   ....:     return m.group(0)[::-1]
   ....: 
 
In [33]: pd.Series(['foo 123', 'bar baz', np.nan]).str.replace(pat, repl)
Out[33]: 
0    oof 123
1    rab zab
2        NaN
dtype: object
 
# Using regex groups
In [34]: pat = r"(?P<one>\w+) (?P<two>\w+) (?P<three>\w+)"
 
In [35]: def repl(m):
   ....:     return m.group('two').swapcase()
   ....: 
 
In [36]: pd.Series(['Foo Bar Baz', np.nan]).str.replace(pat, repl)
Out[36]: 
0    bAR
1    NaN
dtype: object

New in version 0.20.0.

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

In [37]: import re
 
In [38]: regex_pat = re.compile(r'^.a|dog', flags=re.IGNORECASE)
 
In [39]: s3.str.replace(regex_pat, 'XX-XX ')
Out[39]: 
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: object

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

In [40]: 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 [41]: s = pd.Series(['a', 'b', 'c', 'd'])
 
In [42]: s.str.cat(sep=',')
Out[42]: 'a,b,c,d'

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

In [43]: s.str.cat()
Out[43]: 'abcd'

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

In [44]: t = pd.Series(['a', 'b', np.nan, 'd'])
 
In [45]: t.str.cat(sep=',')
Out[45]: 'a,b,d'
 
In [46]: t.str.cat(sep=',', na_rep='-')
Out[46]: '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 [47]: s.str.cat(['A', 'B', 'C', 'D'])
Out[47]: 
0    aA
1    bB
2    cC
3    dD
dtype: object

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

In [48]: s.str.cat(t)
Out[48]: 
0     aa
1     bb
2    NaN
3     dd
dtype: object
 
In [49]: s.str.cat(t, na_rep='-')
Out[49]: 
0    aa
1    bb
2    c-
3    dd
dtype: object

Concatenating a Series and something array-like into a Series

New in version 0.23.0.

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 [50]: d = pd.concat([t, s], axis=1)
 
In [51]: s
Out[51]: 
0    a
1    b
2    c
3    d
dtype: object
 
In [52]: d
Out[52]: 
     0  1
0    a  a
1    b  b
2  NaN  c
3    d  d
 
In [53]: s.str.cat(d, na_rep='-')
Out[53]: 
0    aaa
1    bbb
2    c-c
3    ddd
dtype: object

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

New in version 0.23.0.

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

In [54]: u = pd.Series(['b', 'd', 'a', 'c'], index=[1, 3, 0, 2])
 
In [55]: s
Out[55]: 
0    a
1    b
2    c
3    d
dtype: object
 
In [56]: u
Out[56]: 
1    b
3    d
0    a
2    c
dtype: object
 
In [57]: s.str.cat(u)
Out[57]: 
0    ab
1    bd
2    ca
3    dc
dtype: object
 
In [58]: s.str.cat(u, join='left')
Out[58]: 
0    aa
1    bb
2    cc
3    dd
dtype: object

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 [59]: v = pd.Series(['z', 'a', 'b', 'd', 'e'], index=[-1, 0, 1, 3, 4])
 
In [60]: s
Out[60]: 
0    a
1    b
2    c
3    d
dtype: object
 
In [61]: v
Out[61]: 
-1    z
 0    a
 1    b
 3    d
 4    e
dtype: object
 
In [62]: s.str.cat(v, join='left', na_rep='-')
Out[62]: 
0    aa
1    bb
2    c-
3    dd
dtype: object
 
In [63]: s.str.cat(v, join='outer', na_rep='-')
Out[63]: 
-1    -z
 0    aa
 1    bb
 2    c-
 3    dd
 4    -e
dtype: object

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

In [64]: f = d.loc[[3, 2, 1, 0], :]
 
In [65]: s
Out[65]: 
0    a
1    b
2    c
3    d
dtype: object
 
In [66]: f
Out[66]: 
     0  1
3    d  d
2  NaN  c
1    b  b
0    a  a
 
In [67]: s.str.cat(f, join='left', na_rep='-')
Out[67]: 
0    aaa
1    bbb
2    c-c
3    ddd
dtype: object

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 [68]: s
Out[68]: 
0    a
1    b
2    c
3    d
dtype: object
 
In [69]: u
Out[69]: 
1    b
3    d
0    a
2    c
dtype: object
 
In [70]: s.str.cat([u, u.to_numpy()], join='left')
Out[70]: 
0    aab
1    bbd
2    cca
3    ddc
dtype: object

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 [71]: v
Out[71]: 
-1    z
 0    a
 1    b
 3    d
 4    e
dtype: object
 
In [72]: s.str.cat([v, u, u.to_numpy()], join='outer', na_rep='-')
Out[72]: 
-1    -z--
 0    aaab
 1    bbbd
 2    c-ca
 3    dddc
 4    -e--
dtype: object

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 [73]: u.loc[[3]]
Out[73]: 
3    d
dtype: object
 
In [74]: v.loc[[-1, 0]]
Out[74]: 
-1    z
 0    a
dtype: object
 
In [75]: s.str.cat([u.loc[[3]], v.loc[[-1, 0]]], join='right', na_rep='-')
Out[75]: 
-1    --z
 0    a-a
 3    dd-
dtype: object

Indexing with .str

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

In [76]: s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan,
   ....:                'CABA', 'dog', 'cat'])
   ....: 
 
In [77]: s.str[0]
Out[77]: 
0      A
1      B
2      C
3      A
4      B
5    NaN
6      C
7      d
8      c
dtype: object
 
In [78]: s.str[1]
Out[78]: 
0    NaN
1    NaN
2    NaN
3      a
4      a
5    NaN
6      A
7      o
8      a
dtype: object

Extracting substrings

Extract first match in each subject (extract)

Warning

In version 0.18.0, extract gained the expand argument. Whenexpand=False it returns a Series, Index, orDataFrame, depending on the subject and regular expressionpattern (same behavior as pre-0.18.0). When expand=True italways returns a DataFrame, which is more consistent and lessconfusing from the perspective of a user. expand=True is thedefault since version 0.23.0.

The extract method accepts a regular expression with at least onecapture group.

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

In [79]: pd.Series(['a1', 'b2', 'c3']).str.extract(r'([ab])(\d)', expand=False)
Out[79]: 
     0    1
0    a    1
1    b    2
2  NaN  NaN

Elements that do not match return a row filled with NaN. Thus, aSeries of messy strings can be “converted” into a like-indexed Seriesor DataFrame of cleaned-up or more useful strings, withoutnecessitating get() to access tuples or re.match objects. Thedtype of the result is always object, even if no match is found andthe result only contains NaN.

Named groups like

In [80]: pd.Series(['a1', 'b2', 'c3']).str.extract(r'(?P<letter>[ab])(?P<digit>\d)',
   ....:                                           expand=False)
   ....: 
Out[80]: 
  letter digit
0      a     1
1      b     2
2    NaN   NaN

and optional groups like

In [81]: pd.Series(['a1', 'b2', '3']).str.extract(r'([ab])?(\d)', expand=False)
Out[81]: 
     0  1
0    a  1
1    b  2
2  NaN  3

can also be used. Note that any capture group names in the regularexpression will be used for column names; otherwise capture groupnumbers will be used.

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

In [82]: pd.Series(['a1', 'b2', 'c3']).str.extract(r'[ab](\d)', expand=True)
Out[82]: 
     0
0    1
1    2
2  NaN

It returns a Series if expand=False.

In [83]: pd.Series(['a1', 'b2', 'c3']).str.extract(r'[ab](\d)', expand=False)
Out[83]: 
0      1
1      2
2    NaN
dtype: object

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

In [84]: s = pd.Series(["a1", "b2", "c3"], ["A11", "B22", "C33"])
 
In [85]: s
Out[85]: 
A11    a1
B22    b2
C33    c3
dtype: object
 
In [86]: s.index.str.extract("(?P<letter>[a-zA-Z])", expand=True)
Out[86]: 
  letter
0      A
1      B
2      C

It returns an Index if expand=False.

In [87]: s.index.str.extract("(?P<letter>[a-zA-Z])", expand=False)
Out[87]: Index(['A', 'B', 'C'], dtype='object', name='letter')

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

In [88]: s.index.str.extract("(?P<letter>[a-zA-Z])([0-9]+)", expand=True)
Out[88]: 
  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 infirst row)

Extract all matches in each subject (extractall)

New in version 0.18.0.

Unlike extract (which returns only the first match),

In [89]: s = pd.Series(["a1a2", "b1", "c1"], index=["A", "B", "C"])
 
In [90]: s
Out[90]: 
A    a1a2
B      b1
C      c1
dtype: object
 
In [91]: two_groups = '(?P<letter>[a-z])(?P<digit>[0-9])'
 
In [92]: s.str.extract(two_groups, expand=True)
Out[92]: 
  letter digit
A      a     1
B      b     1
C      c     1

the extractall method returns every match. The result ofextractall is always a DataFrame with a MultiIndex on itsrows. The last level of the MultiIndex is named match andindicates the order in the subject.

In [93]: s.str.extractall(two_groups)
Out[93]: 
        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 [94]: s = pd.Series(['a3', 'b3', 'c2'])
 
In [95]: s
Out[95]: 
0    a3
1    b3
2    c2
dtype: object

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

In [96]: extract_result = s.str.extract(two_groups, expand=True)
 
In [97]: extract_result
Out[97]: 
  letter digit
0      a     3
1      b     3
2      c     2
 
In [98]: extractall_result = s.str.extractall(two_groups)
 
In [99]: extractall_result
Out[99]: 
        letter digit
  match             
0 0          a     3
1 0          b     3
2 0          c     2
 
In [100]: extractall_result.xs(0, level="match")
Out[100]: 
  letter digit
0      a     3
1      b     3
2      c     2

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

New in version 0.19.0.

In [101]: pd.Index(["a1a2", "b1", "c1"]).str.extractall(two_groups)
Out[101]: 
        letter digit
  match             
0 0          a     1
  1          a     2
1 0          b     1
2 0          c     1
 
In [102]: pd.Series(["a1a2", "b1", "c1"]).str.extractall(two_groups)
Out[102]: 
        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 [103]: pattern = r'[0-9][a-z]'
 
In [104]: pd.Series(['1', '2', '3a', '3b', '03c']).str.contains(pattern)
Out[104]: 
0    False
1    False
2     True
3     True
4     True
dtype: bool

Or whether elements match a pattern:

In [105]: pd.Series(['1', '2', '3a', '3b', '03c']).str.match(pattern)
Out[105]: 
0    False
1    False
2     True
3     True
4    False
dtype: bool

The distinction between match and contains is strictness: matchrelies on strict re.match, while contains relies on re.search.

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

In [106]: s4 = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])
 
In [107]: s4.str.contains('A', na=False)
Out[107]: 
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 [108]: s = pd.Series(['a', 'a|b', np.nan, 'a|c'])
 
In [109]: s.str.get_dummies(sep='|')
Out[109]: 
   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.

New in version 0.18.1.

In [110]: idx = pd.Index(['a', 'a|b', np.nan, 'a|c'])
 
In [111]: idx.str.get_dummies(sep='|')
Out[111]: 
MultiIndex([(1, 0, 0),
            (1, 1, 0),
            (0, 0, 0),
            (1, 0, 1)],
           names=['a', 'b', 'c'])

See also get_dummies().

Method summary