Working with text data#
Text data types#
New in version 1.0.0.
There are two ways to store text data in pandas:
object-dtype NumPy array.StringDtypeextension 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:
You can accidentally store a mixture of strings and non-strings in an
objectdtype array. It’s better to have a dedicated dtype.objectdtype breaks dtype-specific operations likeDataFrame.select_dtypes(). There isn’t a clear way to select just text while excluding non-text but still object-dtype columns.When reading code, the contents of an
objectdtype 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
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
Int64dtype. Compare that with object-dtypeIn [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
Some string methods, like
Series.str.decode()are not available onStringArraybecauseStringArrayonly holds strings, not bytes.In comparison operations,
arrays.StringArrayandSeriesbacked by aStringArraywill return an object withBooleanDtype, rather than abooldtype object. Missing values in aStringArraywill propagate in comparison operations, rather than always comparing unequal likenumpy.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
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>`__:
New in version 1.4.0.
In [61]: s = pd.Series(["str_foo", "str_bar", "no_prefix"])
In [62]: s.str.removeprefix("str_")
Out[62]:
0 foo
1 bar
2 no_prefix
dtype: object
In [63]: s = pd.Series(["foo_str", "bar_str", "no_suffix"])
In [64]: s.str.removesuffix("_str")
Out[64]:
0 foo
1 bar
2 no_suffix
dtype: object
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 [65]: s = pd.Series(["a", "b", "c", "d"], dtype="string")
In [66]: s.str.cat(sep=",")
Out[66]: 'a,b,c,d'
If not specified, the keyword sep for the separator defaults to the empty string, sep='':
In [67]: s.str.cat()
Out[67]: 'abcd'
By default, missing values are ignored. Using na_rep, they can be given a representation:
In [68]: t = pd.Series(["a", "b", np.nan, "d"], dtype="string")
In [69]: t.str.cat(sep=",")
Out[69]: 'a,b,d'
In [70]: t.str.cat(sep=",", na_rep="-")
Out[70]: '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 [71]: s.str.cat(["A", "B", "C", "D"])
Out[71]:
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 [72]: s.str.cat(t)
Out[72]:
0 aa
1 bb
2 <NA>
3 dd
dtype: string
In [73]: s.str.cat(t, na_rep="-")
Out[73]:
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 [74]: d = pd.concat([t, s], axis=1)
In [75]: s
Out[75]:
0 a
1 b
2 c
3 d
dtype: string
In [76]: d
Out[76]:
0 1
0 a a
1 b b
2 <NA> c
3 d d
In [77]: s.str.cat(d, na_rep="-")
Out[77]:
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 [78]: u = pd.Series(["b", "d", "a", "c"], index=[1, 3, 0, 2], dtype="string")
In [79]: s
Out[79]:
0 a
1 b
2 c
3 d
dtype: string
In [80]: u
Out[80]:
1 b
3 d
0 a
2 c
dtype: string
In [81]: s.str.cat(u)
Out[81]:
0 aa
1 bb
2 cc
3 dd
dtype: string
In [82]: s.str.cat(u, join="left")
Out[82]:
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 [83]: v = pd.Series(["z", "a", "b", "d", "e"], index=[-1, 0, 1, 3, 4], dtype="string")
In [84]: s
Out[84]:
0 a
1 b
2 c
3 d
dtype: string
In [85]: v
Out[85]:
-1 z
0 a
1 b
3 d
4 e
dtype: string
In [86]: s.str.cat(v, join="left", na_rep="-")
Out[86]:
0 aa
1 bb
2 c-
3 dd
dtype: string
In [87]: s.str.cat(v, join="outer", na_rep="-")
Out[87]:
-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 [88]: f = d.loc[[3, 2, 1, 0], :]
In [89]: s
Out[89]:
0 a
1 b
2 c
3 d
dtype: string
In [90]: f
Out[90]:
0 1
3 d d
2 <NA> c
1 b b
0 a a
In [91]: s.str.cat(f, join="left", na_rep="-")
Out[91]:
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 [92]: s
Out[92]:
0 a
1 b
2 c
3 d
dtype: string
In [93]: u
Out[93]:
1 b
3 d
0 a
2 c
dtype: string
In [94]: s.str.cat([u, u.to_numpy()], join="left")
Out[94]:
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 [95]: v
Out[95]:
-1 z
0 a
1 b
3 d
4 e
dtype: string
In [96]: s.str.cat([v, u, u.to_numpy()], join="outer", na_rep="-")
Out[96]:
-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 [97]: u.loc[[3]]
Out[97]:
3 d
dtype: string
In [98]: v.loc[[-1, 0]]
Out[98]:
-1 z
0 a
dtype: string
In [99]: s.str.cat([u.loc[[3]], v.loc[[-1, 0]]], join="right", na_rep="-")
Out[99]:
3 dd-
-1 --z
0 a-a
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 [100]: s = pd.Series(
.....: ["A", "B", "C", "Aaba", "Baca", np.nan, "CABA", "dog", "cat"], dtype="string"
.....: )
.....:
In [101]: s.str[0]
Out[101]:
0 A
1 B
2 C
3 A
4 B
5 <NA>
6 C
7 d
8 c
dtype: string
In [102]: s.str[1]
Out[102]:
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 [103]: pd.Series(
.....: ["a1", "b2", "c3"],
.....: dtype="string",
.....: ).str.extract(r"([ab])(\d)", expand=False)
.....:
Out[103]:
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 [104]: pd.Series(["a1", "b2", "c3"], dtype="string").str.extract(
.....: r"(?P<letter>[ab])(?P<digit>\d)", expand=False
.....: )
.....:
Out[104]:
letter digit
0 a 1
1 b 2
2 <NA> <NA>
and optional groups like
In [105]: pd.Series(
.....: ["a1", "b2", "3"],
.....: dtype="string",
.....: ).str.extract(r"([ab])?(\d)", expand=False)
.....:
Out[105]:
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 [106]: pd.Series(["a1", "b2", "c3"], dtype="string").str.extract(r"[ab](\d)", expand=True)
Out[106]:
0
0 1
1 2
2 <NA>
It returns a Series if expand=False.
In [107]: pd.Series(["a1", "b2", "c3"], dtype="string").str.extract(r"[ab](\d)", expand=False)
Out[107]:
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 [108]: s = pd.Series(["a1", "b2", "c3"], ["A11", "B22", "C33"], dtype="string")
In [109]: s
Out[109]:
A11 a1
B22 b2
C33 c3
dtype: string
In [110]: s.index.str.extract("(?P<letter>[a-zA-Z])", expand=True)
Out[110]:
letter
0 A
1 B
2 C
It returns an Index if expand=False.
In [111]: s.index.str.extract("(?P<letter>[a-zA-Z])", expand=False)
Out[111]: 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 [112]: s.index.str.extract("(?P<letter>[a-zA-Z])([0-9]+)", expand=True)
Out[112]:
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 [113]: s = pd.Series(["a1a2", "b1", "c1"], index=["A", "B", "C"], dtype="string")
In [114]: s
Out[114]:
A a1a2
B b1
C c1
dtype: string
In [115]: two_groups = "(?P<letter>[a-z])(?P<digit>[0-9])"
In [116]: s.str.extract(two_groups, expand=True)
Out[116]:
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 [117]: s.str.extractall(two_groups)
Out[117]:
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 [118]: s = pd.Series(["a3", "b3", "c2"], dtype="string")
In [119]: s
Out[119]:
0 a3
1 b3
2 c2
dtype: string
then extractall(pat).xs(0, level='match') gives the same result as
extract(pat).
In [120]: extract_result = s.str.extract(two_groups, expand=True)
In [121]: extract_result
Out[121]:
letter digit
0 a 3
1 b 3
2 c 2
In [122]: extractall_result = s.str.extractall(two_groups)
In [123]: extractall_result
Out[123]:
letter digit
match
0 0 a 3
1 0 b 3
2 0 c 2
In [124]: extractall_result.xs(0, level="match")
Out[124]:
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 [125]: pd.Index(["a1a2", "b1", "c1"]).str.extractall(two_groups)
Out[125]:
letter digit
match
0 0 a 1
1 a 2
1 0 b 1
2 0 c 1
In [126]: pd.Series(["a1a2", "b1", "c1"], dtype="string").str.extractall(two_groups)
Out[126]:
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 [127]: pattern = r"[0-9][a-z]"
In [128]: pd.Series(
.....: ["1", "2", "3a", "3b", "03c", "4dx"],
.....: dtype="string",
.....: ).str.contains(pattern)
.....:
Out[128]:
0 False
1 False
2 True
3 True
4 True
5 True
dtype: boolean
Or whether elements match a pattern:
In [129]: pd.Series(
.....: ["1", "2", "3a", "3b", "03c", "4dx"],
.....: dtype="string",
.....: ).str.match(pattern)
.....:
Out[129]:
0 False
1 False
2 True
3 True
4 False
5 True
dtype: boolean
New in version 1.1.0.
In [130]: pd.Series(
.....: ["1", "2", "3a", "3b", "03c", "4dx"],
.....: dtype="string",
.....: ).str.fullmatch(pattern)
.....:
Out[130]:
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 [131]: s4 = pd.Series(
.....: ["A", "B", "C", "Aaba", "Baca", np.nan, "CABA", "dog", "cat"], dtype="string"
.....: )
.....:
In [132]: s4.str.contains("A", na=False)
Out[132]:
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 [133]: s = pd.Series(["a", "a|b", np.nan, "a|c"], dtype="string")
In [134]: s.str.get_dummies(sep="|")
Out[134]:
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 [135]: idx = pd.Index(["a", "a|b", np.nan, "a|c"])
In [136]: idx.str.get_dummies(sep="|")
Out[136]:
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 |
|---|---|
Concatenate strings |
|
Split strings on delimiter |
|
Split strings on delimiter working from the end of the string |
|
Index into each element (retrieve i-th element) |
|
Join strings in each element of the Series with passed separator |
|
Split strings on the delimiter returning DataFrame of dummy variables |
|
Return boolean array if each string contains pattern/regex |
|
Replace occurrences of pattern/regex/string with some other string or the return value of a callable given the occurrence |
|
Remove prefix from string, i.e. only remove if string starts with prefix. |
|
Remove suffix from string, i.e. only remove if string ends with suffix. |
|
Duplicate values ( |
|
Add whitespace to left, right, or both sides of strings |
|
Equivalent to |
|
Equivalent to |
|
Equivalent to |
|
Equivalent to |
|
Split long strings into lines with length less than a given width |
|
Slice each string in the Series |
|
Replace slice in each string with passed value |
|
Count occurrences of pattern |
|
Equivalent to |
|
Equivalent to |
|
Compute list of all occurrences of pattern/regex for each string |
|
Call |
|
Call |
|
Call |
|
Compute string lengths |
|
Equivalent to |
|
Equivalent to |
|
Equivalent to |
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Equivalent to |
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Equivalent to |
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Equivalent to |
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Equivalent to |
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Equivalent to |
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Equivalent to |
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Equivalent to |
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Equivalent to |
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Equivalent to |
|
Equivalent to |
|
Equivalent to |
|
Return Unicode normal form. Equivalent to |
|
Equivalent to |
|
Equivalent to |
|
Equivalent to |
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Equivalent to |
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Equivalent to |
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Equivalent to |
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Equivalent to |
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Equivalent to |
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Equivalent to |
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Equivalent to |