{{ header }}
.. versionadded:: 1.0.0
There are two ways to store text data in pandas:
object-dtype NumPy array.- :class:`StringDtype` extension type.
We recommend using :class:`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 like :meth:`DataFrame.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
:class:`arrays.StringArray` are about the same. We expect future enhancements
to significantly increase the performance and lower the memory overhead of
:class:`~arrays.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
.. ipython:: python pd.Series(['a', 'b', 'c'])
To explicitly request string dtype, specify the dtype
.. ipython:: python pd.Series(['a', 'b', 'c'], dtype="string") pd.Series(['a', 'b', 'c'], dtype=pd.StringDtype())
Or astype after the Series or DataFrame is created
.. ipython:: python
s = pd.Series(['a', 'b', 'c'])
s
s.astype("string")
.. versionchanged:: 1.1.0
You can also use string dtype on non-string data and it will be converted to
string dtype:
.. ipython:: python s = pd.Series(['a', 2, np.nan], dtype="string") s type(s[1])
or convert from existing pandas data:
s1 = pd.Series([1,2, np.nan], dtype="Int64") s1 s2 = s1.astype("string") s2 type(s2[0])
These are places where the behavior of StringDtype objects differ from
object dtype
For
StringDtype, :ref:`string accessor methods<api.series.str>` 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... ipython:: python s = pd.Series(["a", None, "b"], dtype="string") s s.str.count("a") s.dropna().str.count("a")Both outputs are
Int64dtype. Compare that with object-dtype.. ipython:: python s2 = pd.Series(["a", None, "b"], dtype="object") s2.str.count("a") s2.dropna().str.count("a")When NA values are present, the output dtype is float64. Similarly for methods returning boolean values.
.. ipython:: python s.str.isdigit() s.str.match("a")
- Some string methods, like :meth:`Series.str.decode` are not available
on
StringArraybecauseStringArrayonly holds strings, not bytes. - In comparison operations, :class:`arrays.StringArray` and
Seriesbacked by aStringArraywill return an object with :class:`BooleanDtype`, rather than abooldtype object. Missing values in aStringArraywill propagate in comparison operations, rather than always comparing unequal like :attr:`numpy.nan`.
Everything else that follows in the rest of this document applies equally to
string and object dtype.
Series and Index are equipped with a set of string processing methods
that make it easy to operate on each element of the array. Perhaps most
importantly, these methods exclude missing/NA values automatically. These are
accessed via the str attribute and generally have names matching
the equivalent (scalar) built-in string methods:
.. ipython:: python
s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'],
dtype="string")
s.str.lower()
s.str.upper()
s.str.len()
.. ipython:: python idx = pd.Index([' jack', 'jill ', ' jesse ', 'frank']) idx.str.strip() idx.str.lstrip() idx.str.rstrip()
The string methods on Index are especially useful for cleaning up or transforming DataFrame columns. For instance, you may have columns with leading or trailing whitespace:
.. ipython:: python
df = pd.DataFrame(np.random.randn(3, 2),
columns=[' Column A ', ' Column B '], index=range(3))
df
Since df.columns is an Index object, we can use the .str accessor
.. ipython:: python df.columns.str.strip() df.columns.str.lower()
These string methods can then be used to clean up the columns as needed. Here we are removing leading and trailing whitespaces, lower casing all names, and replacing any remaining whitespaces with underscores:
.. ipython:: python
df.columns = df.columns.str.strip().str.lower().str.replace(' ', '_')
df
Note
If you have a Series where lots of elements are repeated
(i.e. the number of unique elements in the Series is a lot smaller than the length of the
Series), it can be faster to convert the original Series to one of type
category and then use .str.<method> or .dt.<property> on that.
The performance difference comes from the fact that, for Series of type category, the
string operations are done on the .categories and not on each element of the
Series.
Please note that a Series of type category with string .categories has
some limitations in comparison to Series of type string (e.g. you can't add strings to
each other: s + " " + s won't work if s is a Series of type category). Also,
.str methods which operate on elements of type list are not available on such a
Series.
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.
Methods like split return a Series of lists:
.. ipython:: python
s2 = pd.Series(['a_b_c', 'c_d_e', np.nan, 'f_g_h'], dtype="string")
s2.str.split('_')
Elements in the split lists can be accessed using get or [] notation:
.. ipython:: python
s2.str.split('_').str.get(1)
s2.str.split('_').str[1]
It is easy to expand this to return a DataFrame using expand.
.. ipython:: python
s2.str.split('_', expand=True)
When original Series has :class:`StringDtype`, the output columns will all
be :class:`StringDtype` as well.
It is also possible to limit the number of splits:
.. ipython:: python
s2.str.split('_', expand=True, n=1)
rsplit is similar to split except it works in the reverse direction,
i.e., from the end of the string to the beginning of the string:
.. ipython:: python
s2.str.rsplit('_', expand=True, n=1)
replace by default replaces regular expressions:
.. ipython:: python
s3 = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca',
'', np.nan, 'CABA', 'dog', 'cat'],
dtype="string")
s3
s3.str.replace('^.a|dog', 'XX-XX ', case=False)
Some caution must be taken to keep regular expressions in mind! For example, the following code will cause trouble because of the regular expression meaning of $:
.. ipython:: python
# Consider the following badly formatted financial data
dollars = pd.Series(['12', '-$10', '$10,000'], dtype="string")
# This does what you'd naively expect:
dollars.str.replace('$', '')
# But this doesn't:
dollars.str.replace('-$', '-')
# We need to escape the special character (for >1 len patterns)
dollars.str.replace(r'-\$', '-')
.. versionadded:: 0.23.0
If you do want literal replacement of a string (equivalent to
:meth:`str.replace`), you can set the optional regex parameter to
False, rather than escaping each character. In this case both pat
and repl must be strings:
.. ipython:: python
# These lines are equivalent
dollars.str.replace(r'-\$', '-')
dollars.str.replace('-$', '-', regex=False)
The replace method can also take a callable as replacement. It is called
on every pat using :func:`re.sub`. The callable should expect one
positional argument (a regex object) and return a string.
.. ipython:: python
# Reverse every lowercase alphabetic word
pat = r'[a-z]+'
def repl(m):
return m.group(0)[::-1]
pd.Series(['foo 123', 'bar baz', np.nan],
dtype="string").str.replace(pat, repl)
# Using regex groups
pat = r"(?P<one>\w+) (?P<two>\w+) (?P<three>\w+)"
def repl(m):
return m.group('two').swapcase()
pd.Series(['Foo Bar Baz', np.nan],
dtype="string").str.replace(pat, repl)
The replace method also accepts a compiled regular expression object
from :func:`re.compile` as a pattern. All flags should be included in the
compiled regular expression object.
.. ipython:: python import re regex_pat = re.compile(r'^.a|dog', flags=re.IGNORECASE) s3.str.replace(regex_pat, 'XX-XX ')
Including a flags argument when calling replace with a compiled
regular expression object will raise a ValueError.
.. ipython::
@verbatim
In [1]: s3.str.replace(regex_pat, 'XX-XX ', flags=re.IGNORECASE)
---------------------------------------------------------------------------
ValueError: case and flags cannot be set when pat is a compiled regex
There are several ways to concatenate a Series or Index, either with itself or others, all based on :meth:`~Series.str.cat`,
resp. Index.str.cat.
The content of a Series (or Index) can be concatenated:
.. ipython:: python
s = pd.Series(['a', 'b', 'c', 'd'], dtype="string")
s.str.cat(sep=',')
If not specified, the keyword sep for the separator defaults to the empty string, sep='':
.. ipython:: python
s.str.cat()
By default, missing values are ignored. Using na_rep, they can be given a representation:
.. ipython:: python
t = pd.Series(['a', 'b', np.nan, 'd'], dtype="string")
t.str.cat(sep=',')
t.str.cat(sep=',', na_rep='-')
The first argument to :meth:`~Series.str.cat` can be a list-like object, provided that it matches the length of the calling Series (or Index).
.. ipython:: python
s.str.cat(['A', 'B', 'C', 'D'])
Missing values on either side will result in missing values in the result as well, unless na_rep is specified:
.. ipython:: python
s.str.cat(t)
s.str.cat(t, na_rep='-')
.. versionadded:: 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).
.. ipython:: python
d = pd.concat([t, s], axis=1)
s
d
s.str.cat(d, na_rep='-')
.. versionadded:: 0.23.0
For concatenation with a Series or DataFrame, it is possible to align the indexes before concatenation by setting
the join-keyword.
.. ipython:: python
:okwarning:
u = pd.Series(['b', 'd', 'a', 'c'], index=[1, 3, 0, 2],
dtype="string")
s
u
s.str.cat(u)
s.str.cat(u, join='left')
Warning
If the join keyword is not passed, the method :meth:`~Series.str.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.
.. ipython:: python
v = pd.Series(['z', 'a', 'b', 'd', 'e'], index=[-1, 0, 1, 3, 4],
dtype="string")
s
v
s.str.cat(v, join='left', na_rep='-')
s.str.cat(v, join='outer', na_rep='-')
The same alignment can be used when others is a DataFrame:
.. ipython:: python
f = d.loc[[3, 2, 1, 0], :]
s
f
s.str.cat(f, join='left', na_rep='-')
Several array-like items (specifically: Series, Index, and 1-dimensional variants of np.ndarray)
can be combined in a list-like container (including iterators, dict-views, etc.).
.. ipython:: python
s
u
s.str.cat([u, u.to_numpy()], join='left')
All elements without an index (e.g. np.ndarray) within the passed list-like must match in length to the calling Series (or Index),
but Series and Index may have arbitrary length (as long as alignment is not disabled with join=None):
.. ipython:: python
v
s.str.cat([v, u, u.to_numpy()], join='outer', na_rep='-')
If using join='right' on a list-like of others that contains different indexes,
the union of these indexes will be used as the basis for the final concatenation:
.. ipython:: python
u.loc[[3]]
v.loc[[-1, 0]]
s.str.cat([u.loc[[3]], v.loc[[-1, 0]]], join='right', na_rep='-')
You can use [] notation to directly index by position locations. If you index past the end
of the string, the result will be a NaN.
.. ipython:: python
s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan,
'CABA', 'dog', 'cat'],
dtype="string")
s.str[0]
s.str[1]
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.
.. ipython:: python
pd.Series(['a1', 'b2', 'c3'],
dtype="string").str.extract(r'([ab])(\d)', expand=False)
Elements that do not match return a row filled with NaN. Thus, a
Series of messy strings can be "converted" into a like-indexed Series
or DataFrame of cleaned-up or more useful strings, without
necessitating get() to access tuples or re.match objects. The
dtype of the result is always object, even if no match is found and
the result only contains NaN.
Named groups like
.. ipython:: python
pd.Series(['a1', 'b2', 'c3'],
dtype="string").str.extract(r'(?P<letter>[ab])(?P<digit>\d)',
expand=False)
and optional groups like
.. ipython:: python
pd.Series(['a1', 'b2', '3'],
dtype="string").str.extract(r'([ab])?(\d)', expand=False)
can also be used. Note that any capture group names in the regular expression will be used for column names; otherwise capture group numbers will be used.
Extracting a regular expression with one group returns a DataFrame
with one column if expand=True.
.. ipython:: python
pd.Series(['a1', 'b2', 'c3'],
dtype="string").str.extract(r'[ab](\d)', expand=True)
It returns a Series if expand=False.
.. ipython:: python
pd.Series(['a1', 'b2', 'c3'],
dtype="string").str.extract(r'[ab](\d)', expand=False)
Calling on an Index with a regex with exactly one capture group
returns a DataFrame with one column if expand=True.
.. ipython:: python
s = pd.Series(["a1", "b2", "c3"], ["A11", "B22", "C33"],
dtype="string")
s
s.index.str.extract("(?P<letter>[a-zA-Z])", expand=True)
It returns an Index if expand=False.
.. ipython:: python
s.index.str.extract("(?P<letter>[a-zA-Z])", expand=False)
Calling on an Index with a regex with more than one capture group
returns a DataFrame if expand=True.
.. ipython:: python
s.index.str.extract("(?P<letter>[a-zA-Z])([0-9]+)", expand=True)
It raises ValueError if expand=False.
>>> s.index.str.extract("(?P<letter>[a-zA-Z])([0-9]+)", expand=False)
ValueError: only one regex group is supported with IndexThe 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 |
Unlike extract (which returns only the first match),
.. ipython:: python
s = pd.Series(["a1a2", "b1", "c1"], index=["A", "B", "C"],
dtype="string")
s
two_groups = '(?P<letter>[a-z])(?P<digit>[0-9])'
s.str.extract(two_groups, expand=True)
the extractall method returns every match. The result of
extractall is always a DataFrame with a MultiIndex on its
rows. The last level of the MultiIndex is named match and
indicates the order in the subject.
.. ipython:: python s.str.extractall(two_groups)
When each subject string in the Series has exactly one match,
.. ipython:: python s = pd.Series(['a3', 'b3', 'c2'], dtype="string") s
then extractall(pat).xs(0, level='match') gives the same result as
extract(pat).
.. ipython:: python extract_result = s.str.extract(two_groups, expand=True) extract_result extractall_result = s.str.extractall(two_groups) extractall_result extractall_result.xs(0, level="match")
Index also supports .str.extractall. It returns a DataFrame which has the
same result as a Series.str.extractall with a default index (starts from 0).
.. ipython:: python pd.Index(["a1a2", "b1", "c1"]).str.extractall(two_groups) pd.Series(["a1a2", "b1", "c1"], dtype="string").str.extractall(two_groups)
You can check whether elements contain a pattern:
.. ipython:: python
pattern = r'[0-9][a-z]'
pd.Series(['1', '2', '3a', '3b', '03c', '4dx'],
dtype="string").str.contains(pattern)
Or whether elements match a pattern:
.. ipython:: python
pd.Series(['1', '2', '3a', '3b', '03c', '4dx'],
dtype="string").str.match(pattern)
.. versionadded:: 1.1.0
.. ipython:: python
pd.Series(['1', '2', '3a', '3b', '03c', '4dx'],
dtype="string").str.fullmatch(pattern)
Note
The distinction between match, fullmatch, and contains is strictness:
fullmatch tests whether the entire string matches the regular expression;
match tests whether there is a match of the regular expression that begins
at the first character of the string; and contains tests whether there is
a match of the regular expression at any position within the string.
The corresponding functions in the re package for these three match modes are
re.fullmatch,
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:
.. ipython:: python
s4 = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'],
dtype="string")
s4.str.contains('A', na=False)
You can extract dummy variables from string columns.
For example if they are separated by a '|':
.. ipython:: python
s = pd.Series(['a', 'a|b', np.nan, 'a|c'], dtype="string")
s.str.get_dummies(sep='|')
String Index also supports get_dummies which returns a MultiIndex.
.. ipython:: python
idx = pd.Index(['a', 'a|b', np.nan, 'a|c'])
idx.str.get_dummies(sep='|')
See also :func:`~pandas.get_dummies`.
| Method | Description |
|---|---|
| :meth:`~Series.str.cat` | Concatenate strings |
| :meth:`~Series.str.split` | Split strings on delimiter |
| :meth:`~Series.str.rsplit` | Split strings on delimiter working from the end of the string |
| :meth:`~Series.str.get` | Index into each element (retrieve i-th element) |
| :meth:`~Series.str.join` | Join strings in each element of the Series with passed separator |
| :meth:`~Series.str.get_dummies` | Split strings on the delimiter returning DataFrame of dummy variables |
| :meth:`~Series.str.contains` | Return boolean array if each string contains pattern/regex |
| :meth:`~Series.str.replace` | Replace occurrences of pattern/regex/string with some other string or the return value of a callable given the occurrence |
| :meth:`~Series.str.repeat` | Duplicate values (s.str.repeat(3) equivalent to x * 3) |
| :meth:`~Series.str.pad` | Add whitespace to left, right, or both sides of strings |
| :meth:`~Series.str.center` | Equivalent to str.center |
| :meth:`~Series.str.ljust` | Equivalent to str.ljust |
| :meth:`~Series.str.rjust` | Equivalent to str.rjust |
| :meth:`~Series.str.zfill` | Equivalent to str.zfill |
| :meth:`~Series.str.wrap` | Split long strings into lines with length less than a given width |
| :meth:`~Series.str.slice` | Slice each string in the Series |
| :meth:`~Series.str.slice_replace` | Replace slice in each string with passed value |
| :meth:`~Series.str.count` | Count occurrences of pattern |
| :meth:`~Series.str.startswith` | Equivalent to str.startswith(pat) for each element |
| :meth:`~Series.str.endswith` | Equivalent to str.endswith(pat) for each element |
| :meth:`~Series.str.findall` | Compute list of all occurrences of pattern/regex for each string |
| :meth:`~Series.str.match` | Call re.match on each element, returning matched groups as list |
| :meth:`~Series.str.extract` | Call re.search on each element, returning DataFrame with one row for each element and one column for each regex capture group |
| :meth:`~Series.str.extractall` | Call re.findall on each element, returning DataFrame with one row for each match and one column for each regex capture group |
| :meth:`~Series.str.len` | Compute string lengths |
| :meth:`~Series.str.strip` | Equivalent to str.strip |
| :meth:`~Series.str.rstrip` | Equivalent to str.rstrip |
| :meth:`~Series.str.lstrip` | Equivalent to str.lstrip |
| :meth:`~Series.str.partition` | Equivalent to str.partition |
| :meth:`~Series.str.rpartition` | Equivalent to str.rpartition |
| :meth:`~Series.str.lower` | Equivalent to str.lower |
| :meth:`~Series.str.casefold` | Equivalent to str.casefold |
| :meth:`~Series.str.upper` | Equivalent to str.upper |
| :meth:`~Series.str.find` | Equivalent to str.find |
| :meth:`~Series.str.rfind` | Equivalent to str.rfind |
| :meth:`~Series.str.index` | Equivalent to str.index |
| :meth:`~Series.str.rindex` | Equivalent to str.rindex |
| :meth:`~Series.str.capitalize` | Equivalent to str.capitalize |
| :meth:`~Series.str.swapcase` | Equivalent to str.swapcase |
| :meth:`~Series.str.normalize` | Return Unicode normal form. Equivalent to unicodedata.normalize |
| :meth:`~Series.str.translate` | Equivalent to str.translate |
| :meth:`~Series.str.isalnum` | Equivalent to str.isalnum |
| :meth:`~Series.str.isalpha` | Equivalent to str.isalpha |
| :meth:`~Series.str.isdigit` | Equivalent to str.isdigit |
| :meth:`~Series.str.isspace` | Equivalent to str.isspace |
| :meth:`~Series.str.islower` | Equivalent to str.islower |
| :meth:`~Series.str.isupper` | Equivalent to str.isupper |
| :meth:`~Series.str.istitle` | Equivalent to str.istitle |
| :meth:`~Series.str.isnumeric` | Equivalent to str.isnumeric |
| :meth:`~Series.str.isdecimal` | Equivalent to str.isdecimal |