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B �5�g� � @ sx U d dl mZ d dlZd dlmZ d dlmZ d dlZd dlm Z m Z d dl Z d dl Z d dlm mZ d dlmZmZ d dlmZ d dlmZmZmZmZmZm Z m Z d d l m Z m Z m!Z!m"Z" d d l#m$Z$ d d l%m&Z& e r�d d l'm(Z( i Z)d e*d< dZ+e+d Z,d*dd�Z-dd� Z.G dd� de&�Z/ddd�dd�Z0ddd�d d �Z1d d � Z2d d!d"�d#d$�Z3d d%d"�d&d'�Z4d+d(d)�Z5dS ),� )� annotationsN)�Callable)�wraps)� TYPE_CHECKING�Hashable)�DtypeObj�FrameOrSeriesUnion)�Appender)� ensure_object� is_bool_dtype�is_categorical_dtype� is_integer� is_list_like�is_object_dtype�is_re)� ABCDataFrame�ABCIndex� ABCMultiIndex� ABCSeries)�isna)�NoNewAttributesMixin)�Indexzdict[str, str]� _shared_docs)zutf-8�utf8zlatin-1�latin1z iso-8859-1�mbcs�ascii)zutf-16zutf-32c s8 | dkr g n| } dddddht | � � � �fdd�}|S ) a� Decorator to forbid specific types for a method of StringMethods. For calling `.str.{method}` on a Series or Index, it is necessary to first initialize the :class:`StringMethods` object, and then call the method. However, different methods allow different input types, and so this can not be checked during :meth:`StringMethods.__init__`, but must be done on a per-method basis. This decorator exists to facilitate this process, and make it explicit which (inferred) types are disallowed by the method. :meth:`StringMethods.__init__` allows the *union* of types its different methods allow (after skipping NaNs; see :meth:`StringMethods._validate`), namely: ['string', 'empty', 'bytes', 'mixed', 'mixed-integer']. The default string types ['string', 'empty'] are allowed for all methods. For the additional types ['bytes', 'mixed', 'mixed-integer'], each method then needs to forbid the types it is not intended for. Parameters ---------- forbidden : list-of-str or None List of forbidden non-string types, may be one or more of `['bytes', 'mixed', 'mixed-integer']`. name : str, default None Name of the method to use in the error message. By default, this is None, in which case the name from the method being wrapped will be copied. However, for working with further wrappers (like _pat_wrapper and _noarg_wrapper), it is necessary to specify the name. Returns ------- func : wrapper The method to which the decorator is applied, with an added check that enforces the inferred type to not be in the list of forbidden types. Raises ------ TypeError If the inferred type of the underlying data is in `forbidden`. N�string�empty�bytes�mixedz mixed-integerc s4 �d kr� j n��t� ��� �fdd��}�|_ |S )Nc s6 | j � kr&d�� d| j � d�}t|���| f|�|�S )NzCannot use .str.z with values of inferred dtype 'z'.)�_inferred_dtype� TypeError)�self�args�kwargs�msg)� allowed_types�func� func_name� �R/home/digitalm-up/venv/lib/python3.7/site-packages/pandas/core/strings/accessor.py�wrapperl s zHforbid_nonstring_types.<locals>._forbid_nonstring_types.<locals>.wrapper)�__name__r )r( r, )r' �name)r( r) r+ �_forbid_nonstring_typesi s z7forbid_nonstring_types.<locals>._forbid_nonstring_types)�set)� forbiddenr. r/ r* )r' r. r+ �forbid_nonstring_types9 s *r2 c s$ t dg� d�� fdd��}||_|S )Nr )r. c s t | jjd� � ��� }| �|�S )NZ_str_)�getattr�_data�array� _wrap_result)r# �result)r. r* r+ r, } s z _map_and_wrap.<locals>.wrapper)r2 �__doc__)r. � docstringr, r* )r. r+ � _map_and_wrap| s r: c @ s� e Zd ZU dZdd� Zedd� �Zdd� Zdd � Zd d e j d d fd dd�dd�Z dd� Z e dddg�d�dd��Zded< eed d d d � �e dg�d�d d!���Zeed d"d#d � �e dg�d�d$d%���Zd&ed'< eed' d(d)d*d+� �e dg�d�d-d.���Zeed' d/d0d1d+� �e dg�d�d2d3���Zd4d5� Ze dg�d6d7� �Ze dg�d�d9d:��Ze dg�d�d;d<��Ze dg�d�d=d>��Ze dg�d�d?d@dAd dAd dB�dCdD��Ze dg�dEdF� �Ze dg�d�dGdH��Z dIedJ< eedJ dKdLd � �e dg�d�dMdN���Z eedJ dOdPd � �e dg�d�dQdR���Z eedJ ddSd � �e dg�d�dTdU���Z e dg�dVdW� �Z d�dXdY�Z!e dg�d�dZd[��Z"d�d]d^�Z#e dg�d�d_d`��Z$daedb< eedb dcdddedf� �e dg�d�dgdh���Z%eedb didjdkdf� �e dg�d�dldm���Z&eedb dndodpdf� �e dg�d�dqdr���Z'e dg�dsdt� �Z(e dg�d�dvdw��Z)e dg�dxdy� �Z*e dg�d�dzd{��Z+e dg�d�d|d}��Z,e dg�d�d~d��Z-e dg�d�d�d���Z.e dg�d�d�dAdd�d��d�d���Z/e dg�d�d�d���Z0d�ed�< eed� d�d�d�d�� �e dg�d�d�d����Z1eed� d�d�d�d�� �e dg�d�d�d����Z2e dg�d�d�� �Z3d�ed�< eed� d�d�d�d�d�� �e dg�d�d�d����Z4eed� d�d�d�d�d�� �e dg�d�d�d����Z5d�d�� Z6d�ed�< i Z7d�e8d�< d�d�d�d��e7d�< d�d�d�d��e7d�< d�d�d�d��e7d�< d�d�d�d��e7d�< d�d�d�d��e7d�< d�d�d�d��e7d�< eed� e7d� �e dg�d�d�� ��Z9eed� e7d� �e dg�d�d�� ��Z:eed� e7d� �e dg�d�d�� ��Z;eed� e7d� �e dg�d�d�� ��Z<eed� e7d� �e dg�d�d�� ��Z=eed� e7d� �e dg�d�d�� ��Z>d�ed�< d�d�dƜe7d�< d�d�dƜe7d�< d�d�dƜe7d�< d�d�dƜe7d�< d�d�dƜe7d�< d�d�dƜe7d�< d�d�dƜe7d�< d�d�dƜe7d�< d�d�dƜe7d�< e?d�ed� e7d� dԍZ@e?d�ed� e7d� dԍZAe?d�ed� e7d� dԍZBe?d�ed� e7d� dԍZCe?d�ed� e7d� dԍZDe?d�ed� e7d� dԍZEe?d�ed� e7d� dԍZFe?d�ed� e7d� dԍZGe?d�ed� e7d� dԍZHd S )�� StringMethodsa� Vectorized string functions for Series and Index. NAs stay NA unless handled otherwise by a particular method. Patterned after Python's string methods, with some inspiration from R's stringr package. Examples -------- >>> s = pd.Series(["A_Str_Series"]) >>> s 0 A_Str_Series dtype: object >>> s.str.split("_") 0 [A, Str, Series] dtype: object >>> s.str.replace("_", "") 0 AStrSeries dtype: object c C s� ddl m} | �|�| _t|j�| _t|j|�| _|| _ d | _ | _ t|t �r^|j | _ |j| _ | jrl|jjn|| _|| _| �� d S )Nr )� StringDtype)�pandas.core.arrays.string_r< � _validater! r �dtypeZ_is_categorical� isinstance� _is_stringr4 �_index�_namer �indexr. �_values� categories�_parent�_origZ_freeze)r# �datar< r* r* r+ �__init__� s zStringMethods.__init__c C sb t | t�rtd��dddddg}ddlm} || �} t| d | �}tj|d d �}||kr^td ��|S ) a Auxiliary function for StringMethods, infers and checks dtype of data. This is a "first line of defence" at the creation of the StringMethods- object, and just checks that the dtype is in the *union* of the allowed types over all string methods below; this restriction is then refined on a per-method basis using the decorator @forbid_nonstring_types (more info in the corresponding docstring). This really should exclude all series/index with any non-string values, but that isn't practical for performance reasons until we have a str dtype (GH 9343 / 13877) Parameters ---------- data : The content of the Series Returns ------- dtype : inferred dtype of data z5Can only use .str accessor with Index, not MultiIndexr r r r z mixed-integerr )�_extract_arrayrF T)�skipnaz.Can only use .str accessor with string values!)r@ r �AttributeErrorZ pandas.core.internals.managersrK r3 �lib� infer_dtype)rI r' rK �valuesZinferred_dtyper* r* r+ r> � s zStringMethods._validatec C s | j j�|�}| �|�S )N)r4 r5 Z _str_getitemr6 )r# �keyr7 r* r* r+ � __getitem__� s zStringMethods.__getitem__c c sL t jdtdd� d}| �|�}x(|�� �� rF|V |d7 }| �|�}q W d S )NzIColumnar iteration over characters will be deprecated in future releases.� )� stacklevelr � )�warnings�warn� FutureWarning�getZnotna�any)r# �i�gr* r* r+ �__iter__� s zStringMethods.__iter__NTFz bool | None�bool)�expand� returns_boolc s ddl m}m} t|d�r$t|d�sBt|t�r>|j| jdd�}|S |jdk sPt �|d krd|jdk}nd|d kr�t |�r�t| jt �s�d d � � � fd d �|D �}|r�| j s�t dd� |D ����fdd �|D �}t|t�s�td��|dk�r |d kr�t|dd �}|d k�r | jj}t| jt ��rjt|��r&|S |�r\t|�}|j||d�} | jdk�rX| �d�} | S |||d�S n�| jj} t|dd �} | j �r�t| ��r�|j} n|�r�| jj} n| } n| } |�r�| jj} | ||| | d�}n| jj} | ||| | d�}|j| jdd�}|d k �r|jdk�r||_|S d S )Nr )r � MultiIndex�ndimr? �str)r. � rU Tc S s t | �r | S | gS d S )N)r )�xr* r* r+ �cons_row s z,StringMethods._wrap_result.<locals>.cons_rowc s g | ] }� |��qS r* r* )�.0re )rf r* r+ � <listcomp> s z.StringMethods._wrap_result.<locals>.<listcomp>c s s | ]}t |�V qd S )N)�len)rg re r* r* r+ � <genexpr># s z-StringMethods._wrap_result.<locals>.<genexpr>c s2 g | ]*}t |�d ks"|d tjkr*|� n|�qS )r )ri �np�nan)rg re )�max_lenr* r+ rh % s z expand must be True or FalseFr. )�names)�columnsrD r? )r. rD r? )�method)�pandasr ra �hasattrr@ r � __finalize__rH rb �AssertionErrorr r rA �maxr^ � ValueErrorr3 r. r �list� from_tuples�nlevelsZget_level_valuesrD r? �_constructor_expanddimZ _constructor)r# r7 r. r_ � fill_value�returns_stringr` r ra �outrD Zvdtyper? Zconsr* )rf rm r+ r6 � sf zStringMethods._wrap_resultc s& ddl m}m} t| jt�r"| jn| jj}t� t�r:� gS t� t�rX|� j|� j d�gS t� t �rt� fdd�� D �S t� t j �r�� j dkr�|� |d�� � fdd�� D �S t� d d ��rt� �� td d � � D ��� r�g }x � r�|| �� �d�� }q�W |S td d � � D ���r|� |d�gS td��dS )a� Auxiliary function for :meth:`str.cat`. Turn potentially mixed input into a list of Series (elements without an index must match the length of the calling Series/Index). Parameters ---------- others : Series, DataFrame, np.ndarray, list-like or list-like of Objects that are either Series, Index or np.ndarray (1-dim). Returns ------- list of Series Others transformed into list of Series. r )� DataFrame�Series)rD r? c s g | ] }� | �qS r* r* )rg re )�othersr* r+ rh � s z2StringMethods._get_series_list.<locals>.<listcomp>rS )rD c s g | ] }� | �qS r* r* )rg re )r� r* r+ rh � s F)Z allow_setsc s s2 | ]*}t |ttf�p(t |tj�o(|jd kV qdS )rU N)r@ r r rk �ndarrayrb )rg re r* r* r+ rj � s z1StringMethods._get_series_list.<locals>.<genexpr>c s s | ]}t |� V qd S )N)r )rg re r* r* r+ rj � s z�others must be Series, Index, DataFrame, np.ndarray or list-like (either containing only strings or containing only objects of type Series/Index/np.ndarray[1-dim])N)rq r~ r r@ rH r rD r rE r? r rk r� rb r rw �all�_get_series_list�popr" )r# r� r~ r �idxZlosr* )r� r+ r� d s0 z StringMethods._get_series_listr r z mixed-integer�leftc s� ddl m}m}m} t�t�r&td��|dkr2d}t| jt�rV|| j| j| jj d�� n| j� �dkr�t � �� t � �}�dkr�|� � r�� | � n �dk r�|� � r�t �|�� �� |�� �S y| ����W n, tk r� } ztd�| �W dd} ~ X Y nX t � fdd ��D ���rZ|�d |d k�r |nd tt���d d d��� j�|d�\� ��fdd��D ��dd� � g� D �} t �dd� | D ��} t jj| dd�} �dk�r�| � � �r�t jt� �td�} t �| | t j� | �t�fdd�| D �|�| �< nB�dk �r$| � � �r$�fdd�t | | �D �} t| |�} n t| |�} t| jt��rP|| t| jj d�} nBt | jj ��rdd}n| jj }|| |� j | jj d�} | j | jdd�} | S ) a� Concatenate strings in the Series/Index with given separator. If `others` is specified, this function concatenates the Series/Index and elements of `others` element-wise. If `others` is not passed, then all values in the Series/Index are concatenated into a single string with a given `sep`. Parameters ---------- others : Series, Index, DataFrame, np.ndarray or list-like Series, Index, DataFrame, np.ndarray (one- or two-dimensional) and other list-likes of strings must have the same length as the calling Series/Index, with the exception of indexed objects (i.e. Series/Index/DataFrame) if `join` is not None. If others is a list-like that contains a combination of Series, Index or np.ndarray (1-dim), then all elements will be unpacked and must satisfy the above criteria individually. If others is None, the method returns the concatenation of all strings in the calling Series/Index. sep : str, default '' The separator between the different elements/columns. By default the empty string `''` is used. na_rep : str or None, default None Representation that is inserted for all missing values: - If `na_rep` is None, and `others` is None, missing values in the Series/Index are omitted from the result. - If `na_rep` is None, and `others` is not None, a row containing a missing value in any of the columns (before concatenation) will have a missing value in the result. join : {'left', 'right', 'outer', 'inner'}, default 'left' Determines the join-style between the calling Series/Index and any Series/Index/DataFrame in `others` (objects without an index need to match the length of the calling Series/Index). To disable alignment, use `.values` on any Series/Index/DataFrame in `others`. .. versionadded:: 0.23.0 .. versionchanged:: 1.0.0 Changed default of `join` from None to `'left'`. Returns ------- str, Series or Index If `others` is None, `str` is returned, otherwise a `Series/Index` (same type as caller) of objects is returned. See Also -------- split : Split each string in the Series/Index. join : Join lists contained as elements in the Series/Index. Examples -------- When not passing `others`, all values are concatenated into a single string: >>> s = pd.Series(['a', 'b', np.nan, 'd']) >>> s.str.cat(sep=' ') 'a b d' By default, NA values in the Series are ignored. Using `na_rep`, they can be given a representation: >>> s.str.cat(sep=' ', na_rep='?') 'a b ? d' If `others` is specified, corresponding values are concatenated with the separator. Result will be a Series of strings. >>> s.str.cat(['A', 'B', 'C', 'D'], sep=',') 0 a,A 1 b,B 2 NaN 3 d,D dtype: object Missing values will remain missing in the result, but can again be represented using `na_rep` >>> s.str.cat(['A', 'B', 'C', 'D'], sep=',', na_rep='-') 0 a,A 1 b,B 2 -,C 3 d,D dtype: object If `sep` is not specified, the values are concatenated without separation. >>> s.str.cat(['A', 'B', 'C', 'D'], na_rep='-') 0 aA 1 bB 2 -C 3 dD dtype: object Series with different indexes can be aligned before concatenation. The `join`-keyword works as in other methods. >>> t = pd.Series(['d', 'a', 'e', 'c'], index=[3, 0, 4, 2]) >>> s.str.cat(t, join='left', na_rep='-') 0 aa 1 b- 2 -c 3 dd dtype: object >>> >>> s.str.cat(t, join='outer', na_rep='-') 0 aa 1 b- 2 -c 3 dd 4 -e dtype: object >>> >>> s.str.cat(t, join='inner', na_rep='-') 0 aa 2 -c 3 dd dtype: object >>> >>> s.str.cat(t, join='right', na_rep='-') 3 dd 0 aa 4 -e 2 -c dtype: object For more examples, see :ref:`here <text.concatenate>`. r )r r �concatz'Did you mean to supply a `sep` keyword?N� )rD r? z�If `others` contains arrays or lists (or other list-likes without an index), these must all be of the same length as the calling Series/Index.c 3 s | ]}� j �|j � V qd S )N)rD �equals)rg re )rI r* r+ rj O s z$StringMethods.cat.<locals>.<genexpr>rU �inner�outerF)�axis�join�keys�sort�copy)r� c s g | ] }� | �qS r* r* )rg re )r� r* r+ rh Z s z%StringMethods.cat.<locals>.<listcomp>c S s g | ] }t |��qS r* )r )rg re r* r* r+ rh \ s c S s g | ] }t |��qS r* )r )rg re r* r* r+ rh ] s )r� )r? c s g | ] }|� �qS r* r* )rg re )� not_maskedr* r+ rh g s c s g | ]\}}t �|� |��qS r* )rk �where)rg �nm�col)�na_repr* r+ rh k s )r? r. )r? rD r. Zstr_cat)rp )!rq r r r� r@ rc rv rH r r? r r rZ rk r� r� r� �rangeri �alignr5 � logical_or�reducer �objectZputmaskrl �cat_safe�zipr. r rD rs )r# r� �sepr� r� r r r� Zna_mask�errZall_colsZna_masksZ union_maskr7 r? r* )rI r� r� r� r+ �cat� sp zStringMethods.cata� Split strings around given separator/delimiter. Splits the string in the Series/Index from the %(side)s, at the specified delimiter string. Equivalent to :meth:`str.%(method)s`. Parameters ---------- pat : str, optional String or regular expression to split on. If not specified, split on whitespace. n : int, default -1 (all) Limit number of splits in output. ``None``, 0 and -1 will be interpreted as return all splits. expand : bool, default False Expand the split strings into separate columns. * If ``True``, return DataFrame/MultiIndex expanding dimensionality. * If ``False``, return Series/Index, containing lists of strings. Returns ------- Series, Index, DataFrame or MultiIndex Type matches caller unless ``expand=True`` (see Notes). See Also -------- Series.str.split : Split strings around given separator/delimiter. Series.str.rsplit : Splits string around given separator/delimiter, starting from the right. Series.str.join : Join lists contained as elements in the Series/Index with passed delimiter. str.split : Standard library version for split. str.rsplit : Standard library version for rsplit. Notes ----- The handling of the `n` keyword depends on the number of found splits: - If found splits > `n`, make first `n` splits only - If found splits <= `n`, make all splits - If for a certain row the number of found splits < `n`, append `None` for padding up to `n` if ``expand=True`` If using ``expand=True``, Series and Index callers return DataFrame and MultiIndex objects, respectively. Examples -------- >>> s = pd.Series( ... [ ... "this is a regular sentence", ... "https://docs.python.org/3/tutorial/index.html", ... np.nan ... ] ... ) >>> s 0 this is a regular sentence 1 https://docs.python.org/3/tutorial/index.html 2 NaN dtype: object In the default setting, the string is split by whitespace. >>> s.str.split() 0 [this, is, a, regular, sentence] 1 [https://docs.python.org/3/tutorial/index.html] 2 NaN dtype: object Without the `n` parameter, the outputs of `rsplit` and `split` are identical. >>> s.str.rsplit() 0 [this, is, a, regular, sentence] 1 [https://docs.python.org/3/tutorial/index.html] 2 NaN dtype: object The `n` parameter can be used to limit the number of splits on the delimiter. The outputs of `split` and `rsplit` are different. >>> s.str.split(n=2) 0 [this, is, a regular sentence] 1 [https://docs.python.org/3/tutorial/index.html] 2 NaN dtype: object >>> s.str.rsplit(n=2) 0 [this is a, regular, sentence] 1 [https://docs.python.org/3/tutorial/index.html] 2 NaN dtype: object The `pat` parameter can be used to split by other characters. >>> s.str.split(pat="/") 0 [this is a regular sentence] 1 [https:, , docs.python.org, 3, tutorial, index... 2 NaN dtype: object When using ``expand=True``, the split elements will expand out into separate columns. If NaN is present, it is propagated throughout the columns during the split. >>> s.str.split(expand=True) 0 1 2 3 4 0 this is a regular sentence 1 https://docs.python.org/3/tutorial/index.html None None None None 2 NaN NaN NaN NaN NaN For slightly more complex use cases like splitting the html document name from a url, a combination of parameter settings can be used. >>> s.str.rsplit("/", n=1, expand=True) 0 1 0 this is a regular sentence None 1 https://docs.python.org/3/tutorial index.html 2 NaN NaN Remember to escape special characters when explicitly using regular expressions. >>> s = pd.Series(["1+1=2"]) >>> s 0 1+1=2 dtype: object >>> s.str.split(r"\+|=", expand=True) 0 1 2 0 1 1 2 Z str_splitZ beginning�split)�siderp ���c C s" | j j�|||�}| j|||d�S )N)r| r_ )r4 r5 Z _str_splitr6 )r# �pat�nr_ r7 r* r* r+ r� s zStringMethods.split�end�rsplitc C s" | j jj||d�}| j|||d�S )N)r� )r_ r| )r4 r5 Z _str_rsplitr6 )r# r� r� r_ r7 r* r* r+ r� s zStringMethods.rsplita; Split the string at the %(side)s occurrence of `sep`. This method splits the string at the %(side)s occurrence of `sep`, and returns 3 elements containing the part before the separator, the separator itself, and the part after the separator. If the separator is not found, return %(return)s. Parameters ---------- sep : str, default whitespace String to split on. expand : bool, default True If True, return DataFrame/MultiIndex expanding dimensionality. If False, return Series/Index. Returns ------- DataFrame/MultiIndex or Series/Index of objects See Also -------- %(also)s Series.str.split : Split strings around given separators. str.partition : Standard library version. Examples -------- >>> s = pd.Series(['Linda van der Berg', 'George Pitt-Rivers']) >>> s 0 Linda van der Berg 1 George Pitt-Rivers dtype: object >>> s.str.partition() 0 1 2 0 Linda van der Berg 1 George Pitt-Rivers To partition by the last space instead of the first one: >>> s.str.rpartition() 0 1 2 0 Linda van der Berg 1 George Pitt-Rivers To partition by something different than a space: >>> s.str.partition('-') 0 1 2 0 Linda van der Berg 1 George Pitt - Rivers To return a Series containing tuples instead of a DataFrame: >>> s.str.partition('-', expand=False) 0 (Linda van der Berg, , ) 1 (George Pitt, -, Rivers) dtype: object Also available on indices: >>> idx = pd.Index(['X 123', 'Y 999']) >>> idx Index(['X 123', 'Y 999'], dtype='object') Which will create a MultiIndex: >>> idx.str.partition() MultiIndex([('X', ' ', '123'), ('Y', ' ', '999')], ) Or an index with tuples with ``expand=False``: >>> idx.str.partition(expand=False) Index([('X', ' ', '123'), ('Y', ' ', '999')], dtype='object') Z str_partition�firstzF3 elements containing the string itself, followed by two empty stringsz>rpartition : Split the string at the last occurrence of `sep`.)r� �return�also� c C s | j j�||�}| j|||d�S )N)r_ r| )r4 r5 Z_str_partitionr6 )r# r� r_ r7 r* r* r+ � partitionp s zStringMethods.partition�lastzF3 elements containing two empty strings, followed by the string itselfz>partition : Split the string at the first occurrence of `sep`.c C s | j j�||�}| j|||d�S )N)r_ r| )r4 r5 Z_str_rpartitionr6 )r# r� r_ r7 r* r* r+ � rpartition~ s zStringMethods.rpartitionc C s | j j�|�}| �|�S )a� Extract element from each component at specified position. Extract element from lists, tuples, or strings in each element in the Series/Index. Parameters ---------- i : int Position of element to extract. Returns ------- Series or Index Examples -------- >>> s = pd.Series(["String", ... (1, 2, 3), ... ["a", "b", "c"], ... 123, ... -456, ... {1: "Hello", "2": "World"}]) >>> s 0 String 1 (1, 2, 3) 2 [a, b, c] 3 123 4 -456 5 {1: 'Hello', '2': 'World'} dtype: object >>> s.str.get(1) 0 t 1 2 2 b 3 NaN 4 NaN 5 Hello dtype: object >>> s.str.get(-1) 0 g 1 3 2 c 3 NaN 4 NaN 5 None dtype: object )r4 r5 Z_str_getr6 )r# r[ r7 r* r* r+ rY � s 3zStringMethods.getc C s | j j�|�}| �|�S )a� Join lists contained as elements in the Series/Index with passed delimiter. If the elements of a Series are lists themselves, join the content of these lists using the delimiter passed to the function. This function is an equivalent to :meth:`str.join`. Parameters ---------- sep : str Delimiter to use between list entries. Returns ------- Series/Index: object The list entries concatenated by intervening occurrences of the delimiter. Raises ------ AttributeError If the supplied Series contains neither strings nor lists. See Also -------- str.join : Standard library version of this method. Series.str.split : Split strings around given separator/delimiter. Notes ----- If any of the list items is not a string object, the result of the join will be `NaN`. Examples -------- Example with a list that contains non-string elements. >>> s = pd.Series([['lion', 'elephant', 'zebra'], ... [1.1, 2.2, 3.3], ... ['cat', np.nan, 'dog'], ... ['cow', 4.5, 'goat'], ... ['duck', ['swan', 'fish'], 'guppy']]) >>> s 0 [lion, elephant, zebra] 1 [1.1, 2.2, 3.3] 2 [cat, nan, dog] 3 [cow, 4.5, goat] 4 [duck, [swan, fish], guppy] dtype: object Join all lists using a '-'. The lists containing object(s) of types other than str will produce a NaN. >>> s.str.join('-') 0 lion-elephant-zebra 1 NaN 2 NaN 3 NaN 4 NaN dtype: object )r4 r5 Z _str_joinr6 )r# r� r7 r* r* r+ r� � s ?zStringMethods.joinr c C sF |r t �|�jr tjdtdd� | jj�|||||�}| j ||dd�S )aO Test if pattern or regex is contained within a string of a Series or Index. Return boolean Series or Index based on whether a given pattern or regex is contained within a string of a Series or Index. Parameters ---------- pat : str Character sequence or regular expression. case : bool, default True If True, case sensitive. flags : int, default 0 (no flags) Flags to pass through to the re module, e.g. re.IGNORECASE. na : scalar, optional Fill value for missing values. The default depends on dtype of the array. For object-dtype, ``numpy.nan`` is used. For ``StringDtype``, ``pandas.NA`` is used. regex : bool, default True If True, assumes the pat is a regular expression. If False, treats the pat as a literal string. Returns ------- Series or Index of boolean values A Series or Index of boolean values indicating whether the given pattern is contained within the string of each element of the Series or Index. See Also -------- match : Analogous, but stricter, relying on re.match instead of re.search. Series.str.startswith : Test if the start of each string element matches a pattern. Series.str.endswith : Same as startswith, but tests the end of string. Examples -------- Returning a Series of booleans using only a literal pattern. >>> s1 = pd.Series(['Mouse', 'dog', 'house and parrot', '23', np.NaN]) >>> s1.str.contains('og', regex=False) 0 False 1 True 2 False 3 False 4 NaN dtype: object Returning an Index of booleans using only a literal pattern. >>> ind = pd.Index(['Mouse', 'dog', 'house and parrot', '23.0', np.NaN]) >>> ind.str.contains('23', regex=False) Index([False, False, False, True, nan], dtype='object') Specifying case sensitivity using `case`. >>> s1.str.contains('oG', case=True, regex=True) 0 False 1 False 2 False 3 False 4 NaN dtype: object Specifying `na` to be `False` instead of `NaN` replaces NaN values with `False`. If Series or Index does not contain NaN values the resultant dtype will be `bool`, otherwise, an `object` dtype. >>> s1.str.contains('og', na=False, regex=True) 0 False 1 True 2 False 3 False 4 False dtype: bool Returning 'house' or 'dog' when either expression occurs in a string. >>> s1.str.contains('house|dog', regex=True) 0 False 1 True 2 True 3 False 4 NaN dtype: object Ignoring case sensitivity using `flags` with regex. >>> import re >>> s1.str.contains('PARROT', flags=re.IGNORECASE, regex=True) 0 False 1 False 2 True 3 False 4 NaN dtype: object Returning any digit using regular expression. >>> s1.str.contains('\\d', regex=True) 0 False 1 False 2 False 3 True 4 NaN dtype: object Ensure `pat` is a not a literal pattern when `regex` is set to True. Note in the following example one might expect only `s2[1]` and `s2[3]` to return `True`. However, '.0' as a regex matches any character followed by a 0. >>> s2 = pd.Series(['40', '40.0', '41', '41.0', '35']) >>> s2.str.contains('.0', regex=True) 0 True 1 True 2 False 3 True 4 False dtype: bool zKThis pattern has match groups. To actually get the groups, use str.extract.rd )rT F)r{ r| ) �re�compile�groupsrV rW � UserWarningr4 r5 Z _str_containsr6 )r# r� �case�flags�na�regexr7 r* r* r+ �contains s }zStringMethods.containsc C s& | j jj||||d�}| j||dd�S )a� Determine if each string starts with a match of a regular expression. Parameters ---------- pat : str Character sequence or regular expression. case : bool, default True If True, case sensitive. flags : int, default 0 (no flags) Regex module flags, e.g. re.IGNORECASE. na : scalar, optional Fill value for missing values. The default depends on dtype of the array. For object-dtype, ``numpy.nan`` is used. For ``StringDtype``, ``pandas.NA`` is used. Returns ------- Series/Index/array of boolean values See Also -------- fullmatch : Stricter matching that requires the entire string to match. contains : Analogous, but less strict, relying on re.search instead of re.match. extract : Extract matched groups. )r� r� r� F)r{ r| )r4 r5 Z _str_matchr6 )r# r� r� r� r� r7 r* r* r+ �match� s zStringMethods.matchc C s& | j jj||||d�}| j||dd�S )a� Determine if each string entirely matches a regular expression. .. versionadded:: 1.1.0 Parameters ---------- pat : str Character sequence or regular expression. case : bool, default True If True, case sensitive. flags : int, default 0 (no flags) Regex module flags, e.g. re.IGNORECASE. na : scalar, optional Fill value for missing values. The default depends on dtype of the array. For object-dtype, ``numpy.nan`` is used. For ``StringDtype``, ``pandas.NA`` is used. Returns ------- Series/Index/array of boolean values See Also -------- match : Similar, but also returns `True` when only a *prefix* of the string matches the regular expression. extract : Extract matched groups. )r� r� r� F)r{ r| )r4 r5 Z_str_fullmatchr6 )r# r� r� r� r� r7 r* r* r+ � fullmatch� s zStringMethods.fullmatchzstr | re.Patternzstr | Callable�int)r� �replr� r� r� r� c s |dkrPt � t�rPt� fdd�dD ��rPd}t� �dkr@|d7 }tj|tdd � t |t�sjt|�sjtd ��t � �}|s~|dkr�|r�|dk s�|d kr�t d ��n |r�t d ��nt|�r�t d��t � t�r�t� �dkr�d}|dkr�d}|dkr�d}| j j j � |||||d�} | �| �S )a� Replace each occurrence of pattern/regex in the Series/Index. Equivalent to :meth:`str.replace` or :func:`re.sub`, depending on the regex value. Parameters ---------- pat : str or compiled regex String can be a character sequence or regular expression. repl : str or callable Replacement string or a callable. The callable is passed the regex match object and must return a replacement string to be used. See :func:`re.sub`. n : int, default -1 (all) Number of replacements to make from start. case : bool, default None Determines if replace is case sensitive: - If True, case sensitive (the default if `pat` is a string) - Set to False for case insensitive - Cannot be set if `pat` is a compiled regex. flags : int, default 0 (no flags) Regex module flags, e.g. re.IGNORECASE. Cannot be set if `pat` is a compiled regex. regex : bool, default True Determines if the passed-in pattern is a regular expression: - If True, assumes the passed-in pattern is a regular expression. - If False, treats the pattern as a literal string - Cannot be set to False if `pat` is a compiled regex or `repl` is a callable. .. versionadded:: 0.23.0 Returns ------- Series or Index of object A copy of the object with all matching occurrences of `pat` replaced by `repl`. Raises ------ ValueError * if `regex` is False and `repl` is a callable or `pat` is a compiled regex * if `pat` is a compiled regex and `case` or `flags` is set Notes ----- When `pat` is a compiled regex, all flags should be included in the compiled regex. Use of `case`, `flags`, or `regex=False` with a compiled regex will raise an error. Examples -------- When `pat` is a string and `regex` is True (the default), the given `pat` is compiled as a regex. When `repl` is a string, it replaces matching regex patterns as with :meth:`re.sub`. NaN value(s) in the Series are left as is: >>> pd.Series(['foo', 'fuz', np.nan]).str.replace('f.', 'ba', regex=True) 0 bao 1 baz 2 NaN dtype: object When `pat` is a string and `regex` is False, every `pat` is replaced with `repl` as with :meth:`str.replace`: >>> pd.Series(['f.o', 'fuz', np.nan]).str.replace('f.', 'ba', regex=False) 0 bao 1 fuz 2 NaN dtype: object When `repl` is a callable, it is called on every `pat` using :func:`re.sub`. The callable should expect one positional argument (a regex object) and return a string. To get the idea: >>> pd.Series(['foo', 'fuz', np.nan]).str.replace('f', repr, regex=True) 0 <re.Match object; span=(0, 1), match='f'>oo 1 <re.Match object; span=(0, 1), match='f'>uz 2 NaN dtype: object Reverse every lowercase alphabetic word: >>> repl = lambda m: m.group(0)[::-1] >>> ser = pd.Series(['foo 123', 'bar baz', np.nan]) >>> ser.str.replace(r'[a-z]+', repl, regex=True) 0 oof 123 1 rab zab 2 NaN dtype: object Using regex groups (extract second group and swap case): >>> pat = r"(?P<one>\w+) (?P<two>\w+) (?P<three>\w+)" >>> repl = lambda m: m.group('two').swapcase() >>> ser = pd.Series(['One Two Three', 'Foo Bar Baz']) >>> ser.str.replace(pat, repl, regex=True) 0 tWO 1 bAR dtype: object Using a compiled regex with flags >>> import re >>> regex_pat = re.compile(r'FUZ', flags=re.IGNORECASE) >>> pd.Series(['foo', 'fuz', np.nan]).str.replace(regex_pat, 'bar', regex=True) 0 foo 1 bar 2 NaN dtype: object Nc 3 s | ]}|� kV qd S )Nr* )rg �c)r� r* r+ rj O s z(StringMethods.replace.<locals>.<genexpr>z.+*|^$?[](){}\zNThe default value of regex will change from True to False in a future version.rU zl In addition, single character regular expressions will *not* be treated as literal strings when regex=True.rd )rT z!repl must be a string or callabler z9case and flags cannot be set when pat is a compiled regexzCCannot use a compiled regex as replacement pattern with regex=Falsez2Cannot use a callable replacement when regex=FalseFT)r� r� r� r� )r@ rc rZ ri rV rW rX �callabler" r rv r4 r5 Z _str_replacer6 ) r# r� r� r� r� r� r� r& Zis_compiled_rer7 r* )r� r+ �replace� s: zStringMethods.replacec C s | j j�|�}| �|�S )ap Duplicate each string in the Series or Index. Parameters ---------- repeats : int or sequence of int Same value for all (int) or different value per (sequence). Returns ------- Series or Index of object Series or Index of repeated string objects specified by input parameter repeats. Examples -------- >>> s = pd.Series(['a', 'b', 'c']) >>> s 0 a 1 b 2 c dtype: object Single int repeats string in Series >>> s.str.repeat(repeats=2) 0 aa 1 bb 2 cc dtype: object Sequence of int repeats corresponding string in Series >>> s.str.repeat(repeats=[1, 2, 3]) 0 a 1 bb 2 ccc dtype: object )r4 r5 Z _str_repeatr6 )r# Zrepeatsr7 r* r* r+ �repeat~ s )zStringMethods.repeatc C st t |t�s"dt|�j� �}t|��t|�dkr6td��t|�sVdt|�j� �}t|��| jjj |||d�}| � |�S )a� Pad strings in the Series/Index up to width. Parameters ---------- width : int Minimum width of resulting string; additional characters will be filled with character defined in `fillchar`. side : {'left', 'right', 'both'}, default 'left' Side from which to fill resulting string. fillchar : str, default ' ' Additional character for filling, default is whitespace. Returns ------- Series or Index of object Returns Series or Index with minimum number of char in object. See Also -------- Series.str.rjust : Fills the left side of strings with an arbitrary character. Equivalent to ``Series.str.pad(side='left')``. Series.str.ljust : Fills the right side of strings with an arbitrary character. Equivalent to ``Series.str.pad(side='right')``. Series.str.center : Fills both sides of strings with an arbitrary character. Equivalent to ``Series.str.pad(side='both')``. Series.str.zfill : Pad strings in the Series/Index by prepending '0' character. Equivalent to ``Series.str.pad(side='left', fillchar='0')``. Examples -------- >>> s = pd.Series(["caribou", "tiger"]) >>> s 0 caribou 1 tiger dtype: object >>> s.str.pad(width=10) 0 caribou 1 tiger dtype: object >>> s.str.pad(width=10, side='right', fillchar='-') 0 caribou--- 1 tiger----- dtype: object >>> s.str.pad(width=10, side='both', fillchar='-') 0 -caribou-- 1 --tiger--- dtype: object z"fillchar must be a character, not rU z%fillchar must be a character, not strz#width must be of integer type, not )r� �fillchar) r@ rc �typer- r" ri r r4 r5 Z_str_padr6 )r# �widthr� r� r& r7 r* r* r+ �pad� s 6 zStringMethods.pada� Pad %(side)s side of strings in the Series/Index. Equivalent to :meth:`str.%(method)s`. Parameters ---------- width : int Minimum width of resulting string; additional characters will be filled with ``fillchar``. fillchar : str Additional character for filling, default is whitespace. Returns ------- filled : Series/Index of objects. Zstr_padzleft and right�centerc C s | j |d|d�S )NZboth)r� r� )r� )r# r� r� r* r* r+ r� s zStringMethods.center�right�ljustc C s | j |d|d�S )Nr� )r� r� )r� )r# r� r� r* r* r+ r� s zStringMethods.ljust�rjustc C s | j |d|d�S )Nr� )r� r� )r� )r# r� r� r* r* r+ r� s zStringMethods.rjustc C s | j |ddd�}| �|�S )a1 Pad strings in the Series/Index by prepending '0' characters. Strings in the Series/Index are padded with '0' characters on the left of the string to reach a total string length `width`. Strings in the Series/Index with length greater or equal to `width` are unchanged. Parameters ---------- width : int Minimum length of resulting string; strings with length less than `width` be prepended with '0' characters. Returns ------- Series/Index of objects. See Also -------- Series.str.rjust : Fills the left side of strings with an arbitrary character. Series.str.ljust : Fills the right side of strings with an arbitrary character. Series.str.pad : Fills the specified sides of strings with an arbitrary character. Series.str.center : Fills both sides of strings with an arbitrary character. Notes ----- Differs from :meth:`str.zfill` which has special handling for '+'/'-' in the string. Examples -------- >>> s = pd.Series(['-1', '1', '1000', 10, np.nan]) >>> s 0 -1 1 1 2 1000 3 10 4 NaN dtype: object Note that ``10`` and ``NaN`` are not strings, therefore they are converted to ``NaN``. The minus sign in ``'-1'`` is treated as a regular character and the zero is added to the left of it (:meth:`str.zfill` would have moved it to the left). ``1000`` remains unchanged as it is longer than `width`. >>> s.str.zfill(3) 0 0-1 1 001 2 1000 3 NaN 4 NaN dtype: object r� �0)r� r� )r� r6 )r# r� r7 r* r* r+ �zfill s =zStringMethods.zfillc C s | j j�|||�}| �|�S )au Slice substrings from each element in the Series or Index. Parameters ---------- start : int, optional Start position for slice operation. stop : int, optional Stop position for slice operation. step : int, optional Step size for slice operation. Returns ------- Series or Index of object Series or Index from sliced substring from original string object. See Also -------- Series.str.slice_replace : Replace a slice with a string. Series.str.get : Return element at position. Equivalent to `Series.str.slice(start=i, stop=i+1)` with `i` being the position. Examples -------- >>> s = pd.Series(["koala", "dog", "chameleon"]) >>> s 0 koala 1 dog 2 chameleon dtype: object >>> s.str.slice(start=1) 0 oala 1 og 2 hameleon dtype: object >>> s.str.slice(start=-1) 0 a 1 g 2 n dtype: object >>> s.str.slice(stop=2) 0 ko 1 do 2 ch dtype: object >>> s.str.slice(step=2) 0 kaa 1 dg 2 caeen dtype: object >>> s.str.slice(start=0, stop=5, step=3) 0 kl 1 d 2 cm dtype: object Equivalent behaviour to: >>> s.str[0:5:3] 0 kl 1 d 2 cm dtype: object )r4 r5 Z _str_slicer6 )r# �start�stop�stepr7 r* r* r+ �sliceQ s HzStringMethods.slicec C s | j j�|||�}| �|�S )a� Replace a positional slice of a string with another value. Parameters ---------- start : int, optional Left index position to use for the slice. If not specified (None), the slice is unbounded on the left, i.e. slice from the start of the string. stop : int, optional Right index position to use for the slice. If not specified (None), the slice is unbounded on the right, i.e. slice until the end of the string. repl : str, optional String for replacement. If not specified (None), the sliced region is replaced with an empty string. Returns ------- Series or Index Same type as the original object. See Also -------- Series.str.slice : Just slicing without replacement. Examples -------- >>> s = pd.Series(['a', 'ab', 'abc', 'abdc', 'abcde']) >>> s 0 a 1 ab 2 abc 3 abdc 4 abcde dtype: object Specify just `start`, meaning replace `start` until the end of the string with `repl`. >>> s.str.slice_replace(1, repl='X') 0 aX 1 aX 2 aX 3 aX 4 aX dtype: object Specify just `stop`, meaning the start of the string to `stop` is replaced with `repl`, and the rest of the string is included. >>> s.str.slice_replace(stop=2, repl='X') 0 X 1 X 2 Xc 3 Xdc 4 Xcde dtype: object Specify `start` and `stop`, meaning the slice from `start` to `stop` is replaced with `repl`. Everything before or after `start` and `stop` is included as is. >>> s.str.slice_replace(start=1, stop=3, repl='X') 0 aX 1 aX 2 aX 3 aXc 4 aXde dtype: object )r4 r5 Z_str_slice_replacer6 )r# r� r� r� r7 r* r* r+ � slice_replace� s IzStringMethods.slice_replace�strictc sL �t kr��fdd�}nt���� � �fdd�}| jj}|�|�}| �|�S )aO Decode character string in the Series/Index using indicated encoding. Equivalent to :meth:`str.decode` in python2 and :meth:`bytes.decode` in python3. Parameters ---------- encoding : str errors : str, optional Returns ------- Series or Index c s | � � ��S )N)�decode)re )�encoding�errorsr* r+ �<lambda>� � z&StringMethods.decode.<locals>.<lambda>c s � | ��d S )Nr r* )re )�decoderr� r* r+ r� � r� )�_cpython_optimized_decoders�codecs� getdecoderr4 r5 Z_str_mapr6 )r# r� r� �f�arrr7 r* )r� r� r� r+ r� � s zStringMethods.decodec C s | j j�||�}| j|dd�S )a* Encode character string in the Series/Index using indicated encoding. Equivalent to :meth:`str.encode`. Parameters ---------- encoding : str errors : str, optional Returns ------- encoded : Series/Index of objects F)r| )r4 r5 Z _str_encoder6 )r# r� r� r7 r* r* r+ �encode s zStringMethods.encodeat Remove %(position)s characters. Strip whitespaces (including newlines) or a set of specified characters from each string in the Series/Index from %(side)s. Equivalent to :meth:`str.%(method)s`. Parameters ---------- to_strip : str or None, default None Specifying the set of characters to be removed. All combinations of this set of characters will be stripped. If None then whitespaces are removed. Returns ------- Series or Index of object See Also -------- Series.str.strip : Remove leading and trailing characters in Series/Index. Series.str.lstrip : Remove leading characters in Series/Index. Series.str.rstrip : Remove trailing characters in Series/Index. Examples -------- >>> s = pd.Series(['1. Ant. ', '2. Bee!\n', '3. Cat?\t', np.nan]) >>> s 0 1. Ant. 1 2. Bee!\n 2 3. Cat?\t 3 NaN dtype: object >>> s.str.strip() 0 1. Ant. 1 2. Bee! 2 3. Cat? 3 NaN dtype: object >>> s.str.lstrip('123.') 0 Ant. 1 Bee!\n 2 Cat?\t 3 NaN dtype: object >>> s.str.rstrip('.!? \n\t') 0 1. Ant 1 2. Bee 2 3. Cat 3 NaN dtype: object >>> s.str.strip('123.!? \n\t') 0 Ant 1 Bee 2 Cat 3 NaN dtype: object Z str_stripzleft and right sides�stripzleading and trailing)r� rp �positionc C s | j j�|�}| �|�S )N)r4 r5 Z _str_stripr6 )r# �to_stripr7 r* r* r+ r� X s zStringMethods.stripz left side�lstrip�leadingc C s | j j�|�}| �|�S )N)r4 r5 Z _str_lstripr6 )r# r� r7 r* r* r+ r� e s zStringMethods.lstripz right side�rstripZtrailingc C s | j j�|�}| �|�S )N)r4 r5 Z _str_rstripr6 )r# r� r7 r* r* r+ r� n s zStringMethods.rstripc K s | j jj|f|�}| �|�S )a� Wrap strings in Series/Index at specified line width. This method has the same keyword parameters and defaults as :class:`textwrap.TextWrapper`. Parameters ---------- width : int Maximum line width. expand_tabs : bool, optional If True, tab characters will be expanded to spaces (default: True). replace_whitespace : bool, optional If True, each whitespace character (as defined by string.whitespace) remaining after tab expansion will be replaced by a single space (default: True). drop_whitespace : bool, optional If True, whitespace that, after wrapping, happens to end up at the beginning or end of a line is dropped (default: True). break_long_words : bool, optional If True, then words longer than width will be broken in order to ensure that no lines are longer than width. If it is false, long words will not be broken, and some lines may be longer than width (default: True). break_on_hyphens : bool, optional If True, wrapping will occur preferably on whitespace and right after hyphens in compound words, as it is customary in English. If false, only whitespaces will be considered as potentially good places for line breaks, but you need to set break_long_words to false if you want truly insecable words (default: True). Returns ------- Series or Index Notes ----- Internally, this method uses a :class:`textwrap.TextWrapper` instance with default settings. To achieve behavior matching R's stringr library str_wrap function, use the arguments: - expand_tabs = False - replace_whitespace = True - drop_whitespace = True - break_long_words = False - break_on_hyphens = False Examples -------- >>> s = pd.Series(['line to be wrapped', 'another line to be wrapped']) >>> s.str.wrap(12) 0 line to be\nwrapped 1 another line\nto be\nwrapped dtype: object )r4 r5 Z _str_wrapr6 )r# r� r% r7 r* r* r+ �wrapw s 8zStringMethods.wrap�|c C s$ | j j�|�\}}| j||ddd�S )aS Return DataFrame of dummy/indicator variables for Series. Each string in Series is split by sep and returned as a DataFrame of dummy/indicator variables. Parameters ---------- sep : str, default "|" String to split on. Returns ------- DataFrame Dummy variables corresponding to values of the Series. See Also -------- get_dummies : Convert categorical variable into dummy/indicator variables. Examples -------- >>> pd.Series(['a|b', 'a', 'a|c']).str.get_dummies() a b c 0 1 1 0 1 1 0 0 2 1 0 1 >>> pd.Series(['a|b', np.nan, 'a|c']).str.get_dummies() a b c 0 1 1 0 1 0 0 0 2 1 0 1 TF)r. r_ r| )r4 r5 Z_str_get_dummiesr6 )r# r� r7 r. r* r* r+ � get_dummies� s 'zStringMethods.get_dummiesc C s | j j�|�}| �|�S )a Map all characters in the string through the given mapping table. Equivalent to standard :meth:`str.translate`. Parameters ---------- table : dict Table is a mapping of Unicode ordinals to Unicode ordinals, strings, or None. Unmapped characters are left untouched. Characters mapped to None are deleted. :meth:`str.maketrans` is a helper function for making translation tables. Returns ------- Series or Index )r4 r5 Z_str_translater6 )r# �tabler7 r* r* r+ � translate� s zStringMethods.translatec C s | j j�||�}| j|dd�S )aG Count occurrences of pattern in each string of the Series/Index. This function is used to count the number of times a particular regex pattern is repeated in each of the string elements of the :class:`~pandas.Series`. Parameters ---------- pat : str Valid regular expression. flags : int, default 0, meaning no flags Flags for the `re` module. For a complete list, `see here <https://docs.python.org/3/howto/regex.html#compilation-flags>`_. **kwargs For compatibility with other string methods. Not used. Returns ------- Series or Index Same type as the calling object containing the integer counts. See Also -------- re : Standard library module for regular expressions. str.count : Standard library version, without regular expression support. Notes ----- Some characters need to be escaped when passing in `pat`. eg. ``'$'`` has a special meaning in regex and must be escaped when finding this literal character. Examples -------- >>> s = pd.Series(['A', 'B', 'Aaba', 'Baca', np.nan, 'CABA', 'cat']) >>> s.str.count('a') 0 0.0 1 0.0 2 2.0 3 2.0 4 NaN 5 0.0 6 1.0 dtype: float64 Escape ``'$'`` to find the literal dollar sign. >>> s = pd.Series(['$', 'B', 'Aab$', '$$ca', 'C$B$', 'cat']) >>> s.str.count('\\$') 0 1 1 0 2 1 3 2 4 2 5 0 dtype: int64 This is also available on Index >>> pd.Index(['A', 'A', 'Aaba', 'cat']).str.count('a') Int64Index([0, 0, 2, 1], dtype='int64') F)r| )r4 r5 Z _str_countr6 )r# r� r� r7 r* r* r+ �count� s AzStringMethods.countc C s | j jj||d�}| j|dd�S )a� Test if the start of each string element matches a pattern. Equivalent to :meth:`str.startswith`. Parameters ---------- pat : str Character sequence. Regular expressions are not accepted. na : object, default NaN Object shown if element tested is not a string. The default depends on dtype of the array. For object-dtype, ``numpy.nan`` is used. For ``StringDtype``, ``pandas.NA`` is used. Returns ------- Series or Index of bool A Series of booleans indicating whether the given pattern matches the start of each string element. See Also -------- str.startswith : Python standard library string method. Series.str.endswith : Same as startswith, but tests the end of string. Series.str.contains : Tests if string element contains a pattern. Examples -------- >>> s = pd.Series(['bat', 'Bear', 'cat', np.nan]) >>> s 0 bat 1 Bear 2 cat 3 NaN dtype: object >>> s.str.startswith('b') 0 True 1 False 2 False 3 NaN dtype: object Specifying `na` to be `False` instead of `NaN`. >>> s.str.startswith('b', na=False) 0 True 1 False 2 False 3 False dtype: bool )r� F)r| )r4 r5 Z_str_startswithr6 )r# r� r� r7 r* r* r+ � startswith; s 6zStringMethods.startswithc C s | j jj||d�}| j|dd�S )a� Test if the end of each string element matches a pattern. Equivalent to :meth:`str.endswith`. Parameters ---------- pat : str Character sequence. Regular expressions are not accepted. na : object, default NaN Object shown if element tested is not a string. The default depends on dtype of the array. For object-dtype, ``numpy.nan`` is used. For ``StringDtype``, ``pandas.NA`` is used. Returns ------- Series or Index of bool A Series of booleans indicating whether the given pattern matches the end of each string element. See Also -------- str.endswith : Python standard library string method. Series.str.startswith : Same as endswith, but tests the start of string. Series.str.contains : Tests if string element contains a pattern. Examples -------- >>> s = pd.Series(['bat', 'bear', 'caT', np.nan]) >>> s 0 bat 1 bear 2 caT 3 NaN dtype: object >>> s.str.endswith('t') 0 True 1 False 2 False 3 NaN dtype: object Specifying `na` to be `False` instead of `NaN`. >>> s.str.endswith('t', na=False) 0 True 1 False 2 False 3 False dtype: bool )r� F)r| )r4 r5 Z _str_endswithr6 )r# r� r� r7 r* r* r+ �endswitht s 6zStringMethods.endswithc C s | j j�||�}| j|dd�S )aP Find all occurrences of pattern or regular expression in the Series/Index. Equivalent to applying :func:`re.findall` to all the elements in the Series/Index. Parameters ---------- pat : str Pattern or regular expression. flags : int, default 0 Flags from ``re`` module, e.g. `re.IGNORECASE` (default is 0, which means no flags). Returns ------- Series/Index of lists of strings All non-overlapping matches of pattern or regular expression in each string of this Series/Index. See Also -------- count : Count occurrences of pattern or regular expression in each string of the Series/Index. extractall : For each string in the Series, extract groups from all matches of regular expression and return a DataFrame with one row for each match and one column for each group. re.findall : The equivalent ``re`` function to all non-overlapping matches of pattern or regular expression in string, as a list of strings. Examples -------- >>> s = pd.Series(['Lion', 'Monkey', 'Rabbit']) The search for the pattern 'Monkey' returns one match: >>> s.str.findall('Monkey') 0 [] 1 [Monkey] 2 [] dtype: object On the other hand, the search for the pattern 'MONKEY' doesn't return any match: >>> s.str.findall('MONKEY') 0 [] 1 [] 2 [] dtype: object Flags can be added to the pattern or regular expression. For instance, to find the pattern 'MONKEY' ignoring the case: >>> import re >>> s.str.findall('MONKEY', flags=re.IGNORECASE) 0 [] 1 [Monkey] 2 [] dtype: object When the pattern matches more than one string in the Series, all matches are returned: >>> s.str.findall('on') 0 [on] 1 [on] 2 [] dtype: object Regular expressions are supported too. For instance, the search for all the strings ending with the word 'on' is shown next: >>> s.str.findall('on$') 0 [on] 1 [] 2 [] dtype: object If the pattern is found more than once in the same string, then a list of multiple strings is returned: >>> s.str.findall('b') 0 [] 1 [] 2 [b, b] dtype: object F)r| )r4 r5 Z _str_findallr6 )r# r� r� r7 r* r* r+ �findall� s ZzStringMethods.findallrc zFrameOrSeriesUnion | Index)r� r� r_ r� c C s ddl m} t|t�s td��tj||d�}|jdkr>td��|s`|jdkr`t| jt �r`td��| j}t |�}|jdkpz|}|r�d} t |�} |j j dkr�|| |d �} q�| jj j|||d �} t|t�r�|j} nd} || | | |d �} n t|�} | jj j|||d �} | j| | d �S ) a) Extract capture groups in the regex `pat` as columns in a DataFrame. For each subject string in the Series, extract groups from the first match of regular expression `pat`. Parameters ---------- pat : str Regular expression pattern with capturing groups. flags : int, default 0 (no flags) Flags from the ``re`` module, e.g. ``re.IGNORECASE``, that modify regular expression matching for things like case, spaces, etc. For more details, see :mod:`re`. expand : bool, default True If True, return DataFrame with one column per capture group. If False, return a Series/Index if there is one capture group or DataFrame if there are multiple capture groups. Returns ------- DataFrame or Series or Index A DataFrame with one row for each subject string, and one column for each group. Any capture group names in regular expression pat will be used for column names; otherwise capture group numbers will be used. The dtype of each result column is always object, even when no match is found. If ``expand=False`` and pat has only one capture group, then return a Series (if subject is a Series) or Index (if subject is an Index). See Also -------- extractall : Returns all matches (not just the first match). Examples -------- A pattern with two groups will return a DataFrame with two columns. Non-matches will be NaN. >>> s = pd.Series(['a1', 'b2', 'c3']) >>> s.str.extract(r'([ab])(\d)') 0 1 0 a 1 1 b 2 2 NaN NaN A pattern may contain optional groups. >>> s.str.extract(r'([ab])?(\d)') 0 1 0 a 1 1 b 2 2 NaN 3 Named groups will become column names in the result. >>> s.str.extract(r'(?P<letter>[ab])(?P<digit>\d)') letter digit 0 a 1 1 b 2 2 NaN NaN A pattern with one group will return a DataFrame with one column if expand=True. >>> s.str.extract(r'[ab](\d)', expand=True) 0 0 1 1 2 2 NaN A pattern with one group will return a Series if expand=False. >>> s.str.extract(r'[ab](\d)', expand=False) 0 1 1 2 2 NaN dtype: object r )r~ z expand must be True or False)r� z"pattern contains no capture groupsrU z,only one regex group is supported with IndexN)ro r? )r� r_ )ro rD r? )r. )rq r~ r@ r^ rv r� r� r� r4 r � _result_dtype�_get_group_namesr5 �sizeZ _str_extractr rD �_get_single_group_namer6 )r# r� r� r_ r~ r� �objZ result_dtypeZ returns_dfr. ro r7 Z result_listZ result_indexr* r* r+ �extract s4 T zStringMethods.extractc C s t | j||�S )a� Extract capture groups in the regex `pat` as columns in DataFrame. For each subject string in the Series, extract groups from all matches of regular expression pat. When each subject string in the Series has exactly one match, extractall(pat).xs(0, level='match') is the same as extract(pat). Parameters ---------- pat : str Regular expression pattern with capturing groups. flags : int, default 0 (no flags) A ``re`` module flag, for example ``re.IGNORECASE``. These allow to modify regular expression matching for things like case, spaces, etc. Multiple flags can be combined with the bitwise OR operator, for example ``re.IGNORECASE | re.MULTILINE``. Returns ------- DataFrame A ``DataFrame`` with one row for each match, and one column for each group. Its rows have a ``MultiIndex`` with first levels that come from the subject ``Series``. The last level is named 'match' and indexes the matches in each item of the ``Series``. Any capture group names in regular expression pat will be used for column names; otherwise capture group numbers will be used. See Also -------- extract : Returns first match only (not all matches). Examples -------- A pattern with one group will return a DataFrame with one column. Indices with no matches will not appear in the result. >>> s = pd.Series(["a1a2", "b1", "c1"], index=["A", "B", "C"]) >>> s.str.extractall(r"[ab](\d)") 0 match A 0 1 1 2 B 0 1 Capture group names are used for column names of the result. >>> s.str.extractall(r"[ab](?P<digit>\d)") digit match A 0 1 1 2 B 0 1 A pattern with two groups will return a DataFrame with two columns. >>> s.str.extractall(r"(?P<letter>[ab])(?P<digit>\d)") letter digit match A 0 a 1 1 a 2 B 0 b 1 Optional groups that do not match are NaN in the result. >>> s.str.extractall(r"(?P<letter>[ab])?(?P<digit>\d)") letter digit match A 0 a 1 1 a 2 B 0 b 1 C 0 NaN 1 )�str_extractallrH )r# r� r� r* r* r+ � extractall� s LzStringMethods.extractalla Return %(side)s indexes in each strings in the Series/Index. Each of returned indexes corresponds to the position where the substring is fully contained between [start:end]. Return -1 on failure. Equivalent to standard :meth:`str.%(method)s`. Parameters ---------- sub : str Substring being searched. start : int Left edge index. end : int Right edge index. Returns ------- Series or Index of int. See Also -------- %(also)s �findZlowestz/rfind : Return highest indexes in each strings.)r� rp r� c C sB t |t�s"dt|�j� �}t|��| jj�|||�}| j|dd�S )Nz expected a string object, not F)r| ) r@ rc r� r- r" r4 r5 Z _str_findr6 )r# �subr� r� r& r7 r* r* r+ r� � s zStringMethods.findZhighest�rfindz-find : Return lowest indexes in each strings.c C sD t |t�s"dt|�j� �}t|��| jjj|||d�}| j|dd�S )Nz expected a string object, not )r� r� F)r| ) r@ rc r� r- r" r4 r5 Z _str_rfindr6 )r# r� r� r� r& r7 r* r* r+ r� s zStringMethods.rfindc C s | j j�|�}| �|�S )am Return the Unicode normal form for the strings in the Series/Index. For more information on the forms, see the :func:`unicodedata.normalize`. Parameters ---------- form : {'NFC', 'NFKC', 'NFD', 'NFKD'} Unicode form. Returns ------- normalized : Series/Index of objects )r4 r5 Z_str_normalizer6 )r# �formr7 r* r* r+ � normalize s zStringMethods.normalizeau Return %(side)s indexes in each string in Series/Index. Each of the returned indexes corresponds to the position where the substring is fully contained between [start:end]. This is the same as ``str.%(similar)s`` except instead of returning -1, it raises a ValueError when the substring is not found. Equivalent to standard ``str.%(method)s``. Parameters ---------- sub : str Substring being searched. start : int Left edge index. end : int Right edge index. Returns ------- Series or Index of object See Also -------- %(also)s rD z0rindex : Return highest indexes in each strings.)r� Zsimilarrp r� c C sD t |t�s"dt|�j� �}t|��| jjj|||d�}| j|dd�S )Nz expected a string object, not )r� r� F)r| ) r@ rc r� r- r" r4 r5 Z _str_indexr6 )r# r� r� r� r& r7 r* r* r+ rD F s zStringMethods.index�rindexz.index : Return lowest indexes in each strings.c C sD t |t�s"dt|�j� �}t|��| jjj|||d�}| j|dd�S )Nz expected a string object, not )r� r� F)r| ) r@ rc r� r- r" r4 r5 Z _str_rindexr6 )r# r� r� r� r& r7 r* r* r+ r� X s zStringMethods.rindexc C s | j j�� }| j|dd�S )a Compute the length of each element in the Series/Index. The element may be a sequence (such as a string, tuple or list) or a collection (such as a dictionary). Returns ------- Series or Index of int A Series or Index of integer values indicating the length of each element in the Series or Index. See Also -------- str.len : Python built-in function returning the length of an object. Series.size : Returns the length of the Series. Examples -------- Returns the length (number of characters) in a string. Returns the number of entries for dictionaries, lists or tuples. >>> s = pd.Series(['dog', ... '', ... 5, ... {'foo' : 'bar'}, ... [2, 3, 5, 7], ... ('one', 'two', 'three')]) >>> s 0 dog 1 2 5 3 {'foo': 'bar'} 4 [2, 3, 5, 7] 5 (one, two, three) dtype: object >>> s.str.len() 0 3.0 1 0.0 2 NaN 3 1.0 4 4.0 5 3.0 dtype: float64 F)r| )r4 r5 Z_str_lenr6 )r# r7 r* r* r+ ri j s . zStringMethods.lena� Convert strings in the Series/Index to %(type)s. %(version)s Equivalent to :meth:`str.%(method)s`. Returns ------- Series or Index of object See Also -------- Series.str.lower : Converts all characters to lowercase. Series.str.upper : Converts all characters to uppercase. Series.str.title : Converts first character of each word to uppercase and remaining to lowercase. Series.str.capitalize : Converts first character to uppercase and remaining to lowercase. Series.str.swapcase : Converts uppercase to lowercase and lowercase to uppercase. Series.str.casefold: Removes all case distinctions in the string. Examples -------- >>> s = pd.Series(['lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe']) >>> s 0 lower 1 CAPITALS 2 this is a sentence 3 SwApCaSe dtype: object >>> s.str.lower() 0 lower 1 capitals 2 this is a sentence 3 swapcase dtype: object >>> s.str.upper() 0 LOWER 1 CAPITALS 2 THIS IS A SENTENCE 3 SWAPCASE dtype: object >>> s.str.title() 0 Lower 1 Capitals 2 This Is A Sentence 3 Swapcase dtype: object >>> s.str.capitalize() 0 Lower 1 Capitals 2 This is a sentence 3 Swapcase dtype: object >>> s.str.swapcase() 0 LOWER 1 capitals 2 THIS IS A SENTENCE 3 sWaPcAsE dtype: object Z casemethodszdict[str, dict[str, str]]� _doc_argsZ lowercase�lowerr� )r� rp �versionZ uppercase�upperZ titlecase�titlezbe capitalized� capitalizez be swapcased�swapcasez be casefolded�casefoldz .. versionadded:: 0.25.0 c C s | j j�� }| �|�S )N)r4 r5 Z _str_lowerr6 )r# r7 r* r* r+ r� � s zStringMethods.lowerc C s | j j�� }| �|�S )N)r4 r5 Z _str_upperr6 )r# r7 r* r* r+ r� � s zStringMethods.upperc C s | j j�� }| �|�S )N)r4 r5 Z _str_titler6 )r# r7 r* r* r+ r� s zStringMethods.titlec C s | j j�� }| �|�S )N)r4 r5 Z_str_capitalizer6 )r# r7 r* r* r+ r� s zStringMethods.capitalizec C s | j j�� }| �|�S )N)r4 r5 Z _str_swapcaser6 )r# r7 r* r* r+ r� s zStringMethods.swapcasec C s | j j�� }| �|�S )N)r4 r5 Z _str_casefoldr6 )r# r7 r* r* r+ r� s zStringMethods.casefoldu� Check whether all characters in each string are %(type)s. This is equivalent to running the Python string method :meth:`str.%(method)s` for each element of the Series/Index. If a string has zero characters, ``False`` is returned for that check. Returns ------- Series or Index of bool Series or Index of boolean values with the same length as the original Series/Index. See Also -------- Series.str.isalpha : Check whether all characters are alphabetic. Series.str.isnumeric : Check whether all characters are numeric. Series.str.isalnum : Check whether all characters are alphanumeric. Series.str.isdigit : Check whether all characters are digits. Series.str.isdecimal : Check whether all characters are decimal. Series.str.isspace : Check whether all characters are whitespace. Series.str.islower : Check whether all characters are lowercase. Series.str.isupper : Check whether all characters are uppercase. Series.str.istitle : Check whether all characters are titlecase. Examples -------- **Checks for Alphabetic and Numeric Characters** >>> s1 = pd.Series(['one', 'one1', '1', '']) >>> s1.str.isalpha() 0 True 1 False 2 False 3 False dtype: bool >>> s1.str.isnumeric() 0 False 1 False 2 True 3 False dtype: bool >>> s1.str.isalnum() 0 True 1 True 2 True 3 False dtype: bool Note that checks against characters mixed with any additional punctuation or whitespace will evaluate to false for an alphanumeric check. >>> s2 = pd.Series(['A B', '1.5', '3,000']) >>> s2.str.isalnum() 0 False 1 False 2 False dtype: bool **More Detailed Checks for Numeric Characters** There are several different but overlapping sets of numeric characters that can be checked for. >>> s3 = pd.Series(['23', '³', '⅕', '']) The ``s3.str.isdecimal`` method checks for characters used to form numbers in base 10. >>> s3.str.isdecimal() 0 True 1 False 2 False 3 False dtype: bool The ``s.str.isdigit`` method is the same as ``s3.str.isdecimal`` but also includes special digits, like superscripted and subscripted digits in unicode. >>> s3.str.isdigit() 0 True 1 True 2 False 3 False dtype: bool The ``s.str.isnumeric`` method is the same as ``s3.str.isdigit`` but also includes other characters that can represent quantities such as unicode fractions. >>> s3.str.isnumeric() 0 True 1 True 2 True 3 False dtype: bool **Checks for Whitespace** >>> s4 = pd.Series([' ', '\t\r\n ', '']) >>> s4.str.isspace() 0 True 1 True 2 False dtype: bool **Checks for Character Case** >>> s5 = pd.Series(['leopard', 'Golden Eagle', 'SNAKE', '']) >>> s5.str.islower() 0 True 1 False 2 False 3 False dtype: bool >>> s5.str.isupper() 0 False 1 False 2 True 3 False dtype: bool The ``s5.str.istitle`` method checks for whether all words are in title case (whether only the first letter of each word is capitalized). Words are assumed to be as any sequence of non-numeric characters separated by whitespace characters. >>> s5.str.istitle() 0 False 1 True 2 False 3 False dtype: bool Z ismethodsZ alphanumeric�isalnum)r� rp Z alphabetic�isalpha�digits�isdigit� whitespace�isspace�islower�isupper�istitle�numeric� isnumeric�decimal� isdecimal)r9 )NNNr� )Nr� F)Nr� F)r� T)r� T)Tr NT)Tr N)Tr N)r� Nr N)r� r� )r� )r� )r� )NNN)NNN)r� )r� )N)N)N)r� )r )N)N)r )r T)r )r N)r N)r N)r N)Ir- � __module__� __qualname__r8 rJ � staticmethodr> rR r] rk rl r6 r� r2 r� r r r� r� r� r� rY r� r� r� r� r� r� r� r� r� r� r� r� r� r� r� r� r� r� r� r� r� r� r� r� r� r� r� r� r� r� rD r� ri r� �__annotations__r� r� r� r� r� r� r: r r r r r r r r r r* r* r* r+ r; � s� ,h< qT 6B *, U@ K K Q ; . C 8 8 \~ f / t r; rw rc )�list_of_columnsr� c C s\ yt | |�}W nH tk rV x2| D ]*}tj|dd�}|dkr$td|� ��d�q$W Y nX |S )a" Auxiliary function for :meth:`str.cat`. Same signature as cat_core, but handles TypeErrors in concatenation, which happen if the arrays in list_of columns have the wrong dtypes or content. Parameters ---------- list_of_columns : list of numpy arrays List of arrays to be concatenated with sep; these arrays may not contain NaNs! sep : string The separator string for concatenating the columns. Returns ------- nd.array The concatenation of list_of_columns with sep. T)rL )r r zpConcatenation requires list-likes containing only strings (or missing values). Offending values found in column N)�cat_corer" rN rO )r r� r7 �columnr? r* r* r+ r� � s r� c C sd |dkr$t j| td�}t j|dd�S |gdt| � d }| |ddd�< t j|td�}t j|dd�S )a� Auxiliary function for :meth:`str.cat` Parameters ---------- list_of_columns : list of numpy arrays List of arrays to be concatenated with sep; these arrays may not contain NaNs! sep : string The separator string for concatenating the columns. Returns ------- nd.array The concatenation of list_of_columns with sep. r� )r? r )r� rS rU N)rk Zasarrayr� �sumri )r r� Z arr_of_colsZ list_with_sepZ arr_with_sepr* r* r+ r � s r c C s& ddl m} t| j|�r | jS tS d S )Nr )r< )r= r< r@ r? r� )r� r< r* r* r+ r� s r� z re.Patternr )r� r� c C s | j rtt| j ��S d S d S )N)� groupindex�next�iter)r� r* r* r+ r� s r� zlist[Hashable]c s, dd� | j �� D �� � fdd�t| j�D �S )z� Get named groups from compiled regex. Unnamed groups are numbered. Parameters ---------- regex : compiled regex Returns ------- list of column labels c S s i | ]\}}||�qS r* r* )rg �k�vr* r* r+ � <dictcomp>3 s z$_get_group_names.<locals>.<dictcomp>c s g | ]}� � d | |��qS )rU )rY )rg r[ )rn r* r+ rh 4 s z$_get_group_names.<locals>.<listcomp>)r �itemsr� r� )r� r* )rn r+ r� % s r� c C s t j||d�}|jdkr td��t| t�r:| �� jdd�} t|�}g }g }| j j dk}x�| � � D ]x\}} t| t �r`|s||f}xZt |�| ��D ]H\} } t| t �r�| f} dd� | D �} |�| � t|| f �} |�| � q�W q`W dd lm} |j|| j jd g d �}t| �}| j||||d �}|S ) N)r� r z"pattern contains no capture groupsT)ZdroprU c S s g | ]}|d krt jn|�qS )r� )rk �NaN)rg �groupr* r* r+ rh N s z"str_extractall.<locals>.<listcomp>)ra r� )rn )rD ro r? )r� r� r� rv r@ r Z to_seriesZ reset_indexr� rD ry r rc � enumerater� �append�tuplerq ra rx rn r� rz )r� r� r� r� ro Z match_listZ index_listZis_miZ subject_key�subjectZmatch_iZ match_tupleZna_tupleZ result_keyra rD r? r7 r* r* r+ r� 7 s4 r� )N)r )6� __future__r r� �collections.abcr � functoolsr r� �typingr r rV �numpyrk Zpandas._libs.libZ_libsrN Zpandas._typingr r Zpandas.util._decoratorsr Zpandas.core.dtypes.commonr r r r r r r Zpandas.core.dtypes.genericr r r r Zpandas.core.dtypes.missingr Zpandas.core.baser rq r r r Z_cpython_optimized_encodersr� r2 r: r; r� r r� r� r� r� r* r* r* r+ �<module> sf $ C c$
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