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B �5�g�G � @ sT d dl mZ d dlZd dlmZmZ d dlZd dlZd dl m Z m Z d dl m Z mZ d dlmZ d dlmZ d dlm mZ d dlmZ d dlm mZ d d l m Z m Z d d l m Z d d l!m"Z" d d l#m$Z$ d d l%m&Z& er�d dl'm(Z(m)Z) e e$d ddd� �d$dddd�dd��Z*e ddd�d%dddd�d d ��Z+d&dd d dd!�d"d#�Z,dS )'� )� annotationsN)� TYPE_CHECKING�cast)�Appender�deprecate_kwarg)�is_extension_array_dtype� is_list_like)� concat_compat)�notna)� Categorical)�Index� MultiIndex)�concat)� tile_compat)� _shared_docs)� to_numeric)� DataFrame�Series�meltz pd.melt(df, zDataFrame.melt)Zcaller�other�valueTr �bool)�frame� ignore_index�returnc C s t | jt�r dd� | jD �}n t| j�}|| jkrBtjdtdd� |d k r�t|�sZ|g}q�t | jt�rzt |t�sztd��q�t|�}t t � |��� |�}|j s�tdt|�� ���ng }|d k �rpt|�s�|g}nVt | jt�r�t |t�s�td��n6t|�}t t � |��� |�}|j �s$td t|�� ���|d k �rF| j�|��|| �} nt�| j�|| ��} | jd d �| f } n| �� } |d k �r�| j�|�| _|d k�rt | jt��r�t| jj�tt| jj��k�r�| jj}nd d� tt| jj��D �}n | jjd k �r| jjnd g}t |t��r|g}| j \} } | t|�8 } i } xR|D ]J} | � | �}t |��rlt d t |g| d d��}nt!�"|j#| �}|| | < �q:W || |g }| j#�$d�| |< x2t%|�D ]&\}} t!�&| j�'|���(| �| | < �q�W | j)| |d�}|�s�t*| j+| �|_+|S )Nc S s g | ]}|D ]}|�q qS � r )�.0�c�xr r �N/home/digitalm-up/venv/lib/python3.7/site-packages/pandas/core/reshape/melt.py� <listcomp>8 s zmelt.<locals>.<listcomp>z�This dataframe has a column name that matches the 'value_name' column name of the resulting Dataframe. In the future this will raise an error, please set the 'value_name' parameter of DataFrame.melt to a unique name.� )� stacklevelz>id_vars must be a list of tuples when columns are a MultiIndexz:The following 'id_vars' are not present in the DataFrame: zAvalue_vars must be a list of tuples when columns are a MultiIndexz=The following 'value_vars' are not present in the DataFrame: c S s g | ]}d |� ��qS )Z variable_r )r �ir r r r | s �variabler T)r �F)�columns),� isinstancer& r �list�warnings�warn� FutureWarningr � ValueErrorr �com�flatten� difference�empty�KeyErrorZget_level_valuesZ get_indexer�algos�uniqueZget_indexer_forZiloc�copy�len�names�set�range�name�str�shape�popr r r �np�tile�_valuesZravel� enumerateZ asanyarrayZ_get_level_values�repeat� _constructorr �index)r �id_vars� value_vars�var_name� value_nameZ col_levelr �cols�missing�idx�N�K�mdata�colZid_dataZmcolumnsr# �resultr r r r + s� �label)Z old_arg_nameZ new_arg_name)�data�dropnar c sd t |t�r$t|�� �}t|�� �}n t|� \}}ttjdd� |D �� �}t� j� |��}t |d �}x |D ]} t | �|krjt d��qjW i } g } x>t||�D ]0\} } � fdd�| D �}t |�| | < | � | � q�W x"|D ]}t�� | j|�| |< q�W |�rRtjt | | d �td��x | D ]}�t| | �M ��qW ��� �sR�fdd�| �� D �} � j| || d �S ) a Reshape wide-format data to long. Generalized inverse of DataFrame.pivot. Accepts a dictionary, ``groups``, in which each key is a new column name and each value is a list of old column names that will be "melted" under the new column name as part of the reshape. Parameters ---------- data : DataFrame The wide-format DataFrame. groups : dict {new_name : list_of_columns}. dropna : bool, default True Do not include columns whose entries are all NaN. label : None Not used. .. deprecated:: 1.0.0 Returns ------- DataFrame Reshaped DataFrame. See Also -------- melt : Unpivot a DataFrame from wide to long format, optionally leaving identifiers set. pivot : Create a spreadsheet-style pivot table as a DataFrame. DataFrame.pivot : Pivot without aggregation that can handle non-numeric data. DataFrame.pivot_table : Generalization of pivot that can handle duplicate values for one index/column pair. DataFrame.unstack : Pivot based on the index values instead of a column. wide_to_long : Wide panel to long format. Less flexible but more user-friendly than melt. Examples -------- >>> data = pd.DataFrame({'hr1': [514, 573], 'hr2': [545, 526], ... 'team': ['Red Sox', 'Yankees'], ... 'year1': [2007, 2007], 'year2': [2008, 2008]}) >>> data hr1 hr2 team year1 year2 0 514 545 Red Sox 2007 2008 1 573 526 Yankees 2007 2008 >>> pd.lreshape(data, {'year': ['year1', 'year2'], 'hr': ['hr1', 'hr2']}) team year hr 0 Red Sox 2007 514 1 Yankees 2007 573 2 Red Sox 2008 545 3 Yankees 2008 526 c S s g | ] }t |��qS r )r7 )r r r r r r � s z lreshape.<locals>.<listcomp>r z$All column lists must be same lengthc s g | ]}� | j �qS r )r? )r rN )rQ r r r � s )Zdtypec s i | ]\}}|� |�qS r r )r �k�v)�maskr r � <dictcomp> s z lreshape.<locals>.<dictcomp>)r& )r' �dictr( �keys�values�zipr7 �unionr& r/ r5 r, r �appendr= r> r? Zonesr r �all�itemsrB )rQ �groupsrR rP rX rY Zall_colsZid_colsrL �seqrM Z pivot_cols�targetr6 Z to_concatrN r r )rQ rU r �lreshape� s2 : rb � �\d+r: )�df�sep�suffixr c sP ddddd�dd��ddd�dd��t ��s4�g�nt���t�fd d �� jD ��r\td ��t ��sl�g�nt���� � �� �� r�td ��� ���fd d��D �}dd� |D �}tt� j�� ��|��}� ����fdd�t �|�D �} | d j | dd� dd�} t ��dk�r*� | � ��� | �} | S � | j | �� �d�� ��g �} | S )a` Wide panel to long format. Less flexible but more user-friendly than melt. With stubnames ['A', 'B'], this function expects to find one or more group of columns with format A-suffix1, A-suffix2,..., B-suffix1, B-suffix2,... You specify what you want to call this suffix in the resulting long format with `j` (for example `j='year'`) Each row of these wide variables are assumed to be uniquely identified by `i` (can be a single column name or a list of column names) All remaining variables in the data frame are left intact. Parameters ---------- df : DataFrame The wide-format DataFrame. stubnames : str or list-like The stub name(s). The wide format variables are assumed to start with the stub names. i : str or list-like Column(s) to use as id variable(s). j : str The name of the sub-observation variable. What you wish to name your suffix in the long format. sep : str, default "" A character indicating the separation of the variable names in the wide format, to be stripped from the names in the long format. For example, if your column names are A-suffix1, A-suffix2, you can strip the hyphen by specifying `sep='-'`. suffix : str, default '\\d+' A regular expression capturing the wanted suffixes. '\\d+' captures numeric suffixes. Suffixes with no numbers could be specified with the negated character class '\\D+'. You can also further disambiguate suffixes, for example, if your wide variables are of the form A-one, B-two,.., and you have an unrelated column A-rating, you can ignore the last one by specifying `suffix='(!?one|two)'`. When all suffixes are numeric, they are cast to int64/float64. Returns ------- DataFrame A DataFrame that contains each stub name as a variable, with new index (i, j). See Also -------- melt : Unpivot a DataFrame from wide to long format, optionally leaving identifiers set. pivot : Create a spreadsheet-style pivot table as a DataFrame. DataFrame.pivot : Pivot without aggregation that can handle non-numeric data. DataFrame.pivot_table : Generalization of pivot that can handle duplicate values for one index/column pair. DataFrame.unstack : Pivot based on the index values instead of a column. Notes ----- All extra variables are left untouched. This simply uses `pandas.melt` under the hood, but is hard-coded to "do the right thing" in a typical case. Examples -------- >>> np.random.seed(123) >>> df = pd.DataFrame({"A1970" : {0 : "a", 1 : "b", 2 : "c"}, ... "A1980" : {0 : "d", 1 : "e", 2 : "f"}, ... "B1970" : {0 : 2.5, 1 : 1.2, 2 : .7}, ... "B1980" : {0 : 3.2, 1 : 1.3, 2 : .1}, ... "X" : dict(zip(range(3), np.random.randn(3))) ... }) >>> df["id"] = df.index >>> df A1970 A1980 B1970 B1980 X id 0 a d 2.5 3.2 -1.085631 0 1 b e 1.2 1.3 0.997345 1 2 c f 0.7 0.1 0.282978 2 >>> pd.wide_to_long(df, ["A", "B"], i="id", j="year") ... # doctest: +NORMALIZE_WHITESPACE X A B id year 0 1970 -1.085631 a 2.5 1 1970 0.997345 b 1.2 2 1970 0.282978 c 0.7 0 1980 -1.085631 d 3.2 1 1980 0.997345 e 1.3 2 1980 0.282978 f 0.1 With multiple id columns >>> df = pd.DataFrame({ ... 'famid': [1, 1, 1, 2, 2, 2, 3, 3, 3], ... 'birth': [1, 2, 3, 1, 2, 3, 1, 2, 3], ... 'ht1': [2.8, 2.9, 2.2, 2, 1.8, 1.9, 2.2, 2.3, 2.1], ... 'ht2': [3.4, 3.8, 2.9, 3.2, 2.8, 2.4, 3.3, 3.4, 2.9] ... }) >>> df famid birth ht1 ht2 0 1 1 2.8 3.4 1 1 2 2.9 3.8 2 1 3 2.2 2.9 3 2 1 2.0 3.2 4 2 2 1.8 2.8 5 2 3 1.9 2.4 6 3 1 2.2 3.3 7 3 2 2.3 3.4 8 3 3 2.1 2.9 >>> l = pd.wide_to_long(df, stubnames='ht', i=['famid', 'birth'], j='age') >>> l ... # doctest: +NORMALIZE_WHITESPACE ht famid birth age 1 1 1 2.8 2 3.4 2 1 2.9 2 3.8 3 1 2.2 2 2.9 2 1 1 2.0 2 3.2 2 1 1.8 2 2.8 3 1 1.9 2 2.4 3 1 1 2.2 2 3.3 2 1 2.3 2 3.4 3 1 2.1 2 2.9 Going from long back to wide just takes some creative use of `unstack` >>> w = l.unstack() >>> w.columns = w.columns.map('{0[0]}{0[1]}'.format) >>> w.reset_index() famid birth ht1 ht2 0 1 1 2.8 3.4 1 1 2 2.9 3.8 2 1 3 2.2 2.9 3 2 1 2.0 3.2 4 2 2 1.8 2.8 5 2 3 1.9 2.4 6 3 1 2.2 3.3 7 3 2 2.3 3.4 8 3 3 2.1 2.9 Less wieldy column names are also handled >>> np.random.seed(0) >>> df = pd.DataFrame({'A(weekly)-2010': np.random.rand(3), ... 'A(weekly)-2011': np.random.rand(3), ... 'B(weekly)-2010': np.random.rand(3), ... 'B(weekly)-2011': np.random.rand(3), ... 'X' : np.random.randint(3, size=3)}) >>> df['id'] = df.index >>> df # doctest: +NORMALIZE_WHITESPACE, +ELLIPSIS A(weekly)-2010 A(weekly)-2011 B(weekly)-2010 B(weekly)-2011 X id 0 0.548814 0.544883 0.437587 0.383442 0 0 1 0.715189 0.423655 0.891773 0.791725 1 1 2 0.602763 0.645894 0.963663 0.528895 1 2 >>> pd.wide_to_long(df, ['A(weekly)', 'B(weekly)'], i='id', ... j='year', sep='-') ... # doctest: +NORMALIZE_WHITESPACE X A(weekly) B(weekly) id year 0 2010 0 0.548814 0.437587 1 2010 1 0.715189 0.891773 2 2010 1 0.602763 0.963663 0 2011 0 0.544883 0.383442 1 2011 1 0.423655 0.791725 2 2011 1 0.645894 0.528895 If we have many columns, we could also use a regex to find our stubnames and pass that list on to wide_to_long >>> stubnames = sorted( ... set([match[0] for match in df.columns.str.findall( ... r'[A-B]\(.*\)').values if match != []]) ... ) >>> list(stubnames) ['A(weekly)', 'B(weekly)'] All of the above examples have integers as suffixes. It is possible to have non-integers as suffixes. >>> df = pd.DataFrame({ ... 'famid': [1, 1, 1, 2, 2, 2, 3, 3, 3], ... 'birth': [1, 2, 3, 1, 2, 3, 1, 2, 3], ... 'ht_one': [2.8, 2.9, 2.2, 2, 1.8, 1.9, 2.2, 2.3, 2.1], ... 'ht_two': [3.4, 3.8, 2.9, 3.2, 2.8, 2.4, 3.3, 3.4, 2.9] ... }) >>> df famid birth ht_one ht_two 0 1 1 2.8 3.4 1 1 2 2.9 3.8 2 1 3 2.2 2.9 3 2 1 2.0 3.2 4 2 2 1.8 2.8 5 2 3 1.9 2.4 6 3 1 2.2 3.3 7 3 2 2.3 3.4 8 3 3 2.1 2.9 >>> l = pd.wide_to_long(df, stubnames='ht', i=['famid', 'birth'], j='age', ... sep='_', suffix=r'\w+') >>> l ... # doctest: +NORMALIZE_WHITESPACE ht famid birth age 1 1 one 2.8 two 3.4 2 one 2.9 two 3.8 3 one 2.2 two 2.9 2 1 one 2.0 two 3.2 2 one 1.8 two 2.8 3 one 1.9 two 2.4 3 1 one 2.2 two 3.3 2 one 2.3 two 3.4 3 one 2.1 two 2.9 r: z list[str])�stubrf rg r c s> dt �|�� t �|�� |� d�}t �|�� � fdd�| jD �S )N�^�$c s g | ]}� � |�r|�qS r )�match)r rN )�patternr r r � s z7wide_to_long.<locals>.get_var_names.<locals>.<listcomp>)�re�escape�compiler& )re rh rf rg �regexr )rl r � get_var_names� s z#wide_to_long.<locals>.get_var_names)rh rf c S sp t | |||�|�|d�}t|| �||< || jjt�|| �ddd�||< t|| dd�||< |�||g �S )N)rD rE rG rF rc T)rp �ignore)�errors) r �rstripr r: �replacerm rn r � set_index)re rh r# �jrE rf Znewdfr r r � melt_stub� s $z wide_to_long.<locals>.melt_stubc 3 s | ]}|� kV qd S )Nr )r rN )� stubnamesr r � <genexpr> s z wide_to_long.<locals>.<genexpr>z,stubname can't be identical to a column namez3the id variables need to uniquely identify each rowc s g | ]}�� |����qS r r )r rh )re rq rf rg r r r s z wide_to_long.<locals>.<listcomp>c S s g | ]}|D ]}|�q qS r r )r Zsublist�er r r r s c s" g | ]\}}�� |��|���qS r r )r �srT )re r# rw rx rf r r r s r � N�outer)�how)�on)r r( �anyr& r, Z duplicatedr7 �tolistr/ rZ �joinr5 rv �mergeZ reset_index) re ry r# rw rf rg rE Zvalue_vars_flattenedrD Z_meltedZmelted�newr )re rq r# rw rx rf ry rg r � wide_to_long s. m "r� )NNNr NT)TN)rc rd )-� __future__r rm �typingr r r) �numpyr= Zpandas.util._decoratorsr r Zpandas.core.dtypes.commonr r Zpandas.core.dtypes.concatr Zpandas.core.dtypes.missingr Zpandas.core.algorithms�coreZ algorithmsr2 Zpandas.core.arraysr Zpandas.core.common�commonr- Zpandas.core.indexes.apir r Zpandas.core.reshape.concatr Zpandas.core.reshape.utilr Zpandas.core.shared_docsr Zpandas.core.tools.numericr Zpandasr r r rb r� r r r r �<module> s8 s _
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