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csv.cpython-37.pyc
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For example: ,'some text', The quote with the most wins, same with the delimiter. If there is no quotechar the delimiter can't be determined this way. )zI(?P<delim>[^\w\n"\'])(?P<space> ?)(?P<quote>["\']).*?(?P=quote)(?P=delim)zG(?:^|\n)(?P<quote>["\']).*?(?P=quote)(?P<delim>[^\w\n"\'])(?P<space> ?)zG(?P<delim>[^\w\n"\'])(?P<space> ?)(?P<quote>["\']).*?(?P=quote)(?:$|\n)z-(?:^|\n)(?P<quote>["\']).*?(?P=quote)(?:$|\n))r FNr r �quote� �delimNZspace)rD r2 r z]((%(delim)s)|^)\W*%(quote)s[^%(delim)s\n]*%(quote)s[^%(delim)s\n]*%(quote)s\W*((%(delim)s)|$))rh rf TF) �re�compile�DOTALL� MULTILINE�findall� groupindexrU �KeyError�max�escape�search)r �datard ZmatchesZrestrZregexpZquotes�delimsZspacesrn �m�nrD r* rh r, Z dq_regexpr+ r r r ra � sZ z"Sniffer._guess_quote_and_delimiterc C s t td|�d���}dd� td�D �}tdt|��}d}i }i }i }d| } } �x| t|�k �rj|d7 }xT|| | � D ]D} x>|D ]6} |�| i �} | �| �}| �|d�d | |< | || < q�W qvW x�|�� D ]�} t || � � �}t|�dkr�|d d dkr�q�t|�dk�rZt |d d � d �|| < |� || � || d || d t d d � |D �� f|| < q�|d || < q�W |� � }t t|| t|���}d}d}x�t|�dk�r||k�rx\|D ]T\}}|d dk�r�|d dk�r�|d | |k�r�|dk�s�||k�r�|||< �q�W |d8 }�q�W t|�dk�r\t |�� �d }|d �|�|d �d| �k}||fS | } | |7 } qRW |�svdS t|�dk�r�xF| jD ]<}||�� k�r�|d �|�|d �d| �k}||fS �q�W dd� |� � D �}|�� |d d }|d �|�|d �d| �k}||fS )a� The delimiter /should/ occur the same number of times on each row. However, due to malformed data, it may not. We don't want an all or nothing approach, so we allow for small variations in this number. 1) build a table of the frequency of each character on every line. 2) build a table of frequencies of this frequency (meta-frequency?), e.g. 'x occurred 5 times in 10 rows, 6 times in 1000 rows, 7 times in 2 rows' 3) use the mode of the meta-frequency to determine the /expected/ frequency for that character 4) find out how often the character actually meets that goal 5) the character that best meets its goal is the delimiter For performance reasons, the data is evaluated in chunks, so it can try and evaluate the smallest portion of the data possible, evaluating additional chunks as necessary. Nr2 c S s g | ] }t |��qS r )�chr)rR �cr r r rT . s z,Sniffer._guess_delimiter.<locals>.<listcomp>� � r rg c S s | d S )Nrg r )rS r r r �<lambda>H � z*Sniffer._guess_delimiter.<locals>.<lambda>)rD c s s | ]}|d V qdS )rg Nr )rR �itemr r r rW M s z+Sniffer._guess_delimiter.<locals>.<genexpr>g �?g�������?g{�G�z�?z%c )r r c S s g | ]\}}||f�qS r r )rR �k�vr r r rT w s ���)�list�filter�split�range�minrA rU �countrX �itemsrp �remove�sum�floatr` �sort)r rs rd �asciiZ chunkLengthZ iterationZ charFrequencyZmodesrt �start�end�line�charZ metaFrequencyZfreqr� ZmodeListZtotalZ consistencyZ thresholdr~ r rh r, rC r r r rb st zSniffer._guess_delimiterc C s� t t|�| �|��}t|�}t|�}i }xt|�D ] }d ||< q2W d}x�|D ]�}|dkrZP |d7 }t|�|krpqLx�t|�� �D ]x} xJtt t gD ]0} y| || � P W q� t t fk r� Y q�X q�W t|| �} | || kr~|| d kr�| || < q~|| = q~W qLW d} x�|� � D ]~\} } t| �td�k�rLt|| �| k�rB| d7 } n| d8 } n<y| || � W n" t tfk �r~ | d7 } Y n X | d8 } �q W | dkS )Nr � rg )r r re r= rA r� r� rX �intr� �complexrK � OverflowErrorr� �typer" ) r rc ZrdrrO �columnsZ columnTypes�i�checkedrB �colZthisTypeZ hasHeaderZcolTyper r r � has_header� sJ zSniffer.has_header)N) r% r& r' r r re ra rb r� r r r r r � s Lg) r ri Z_csvr r r r r r r r r r r r r r r! � collectionsr �ior �__all__r r r r r r� � NameErrorr� r r r r r �<module> s2 @ 2
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