.. currentmodule:: pandas
.. ipython:: python :suppress: import numpy as np import pandas as pd randn = np.random.randn np.set_printoptions(precision=4, suppress=True) from pandas.compat import lrange options.display.max_rows=15
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']) 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(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, lowercasing 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 of 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.
Methods like split return a Series of lists:
.. ipython:: python
s2 = pd.Series(['a_b_c', 'c_d_e', np.nan, 'f_g_h'])
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]
Easy to expand this to return a DataFrame using expand.
.. ipython:: python
s2.str.split('_', expand=True)
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)
Methods like replace and findall take regular expressions, too:
.. ipython:: python
s3 = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca',
'', np.nan, 'CABA', 'dog', 'cat'])
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', '$10,000'])
# This does what you'd naively expect:
dollars.str.replace('$', '')
# But this doesn't:
dollars.str.replace('-$', '-')
# We need to escape the special character (for >1 len patterns)
dollars.str.replace(r'-\$', '-')
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'])
s.str[0]
s.str[1]
.. versionadded:: 0.13.0
Warning
In version 0.18.0, extract gained the expand argument. When
expand=False it returns a Series, Index, or
DataFrame, depending on the subject and regular expression
pattern (same behavior as pre-0.18.0). When expand=True it
always returns a DataFrame, which is more consistent and less
confusing from the perspective of a user.
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']).str.extract('([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']).str.extract('(?P<letter>[ab])(?P<digit>\d)', expand=False)
and optional groups like
.. ipython:: python
pd.Series(['a1', 'b2', '3']).str.extract('([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']).str.extract('[ab](\d)', expand=True)
It returns a Series if expand=False.
.. ipython:: python
pd.Series(['a1', 'b2', 'c3']).str.extract('[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"])
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: This pattern contains no groups to capture.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 |
.. versionadded:: 0.18.0
Unlike extract (which returns only the first match),
.. ipython:: python s = pd.Series(["a1a2", "b1", "c1"], ["A", "B", "C"]) 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']) 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")
You can check whether elements contain a pattern:
.. ipython:: python pattern = r'[a-z][0-9]' pd.Series(['1', '2', '3a', '3b', '03c']).str.contains(pattern)
or match a pattern:
.. ipython:: python pd.Series(['1', '2', '3a', '3b', '03c']).str.match(pattern, as_indexer=True)
The distinction between match and contains is strictness: match
relies on strict re.match, while contains relies on re.search.
Warning
In previous versions, match was for extracting groups,
returning a not-so-convenient Series of tuples. The new method extract
(described in the previous section) is now preferred.
This old, deprecated behavior of match is still the default. As
demonstrated above, use the new behavior by setting as_indexer=True.
In this mode, match is analogous to contains, returning a boolean
Series. The new behavior will become the default behavior in a future
release.
- Methods like
match,contains,startswith, andendswithtake - an extra
naargument so missing values can be considered True or False:
.. ipython:: python
s4 = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])
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'])
s.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.contains` | Return boolean array if each string contains pattern/regex |
| :meth:`~Series.str.replace` | Replace occurrences of pattern/regex with some other string |
| :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.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 |