["']).*?(?P=quote)(?P=delim)sC (?:^| )(?P["']).*?(?P=quote)(?P[^\w "'])(?P ?)sD (?P >[^\w "'])(?P ?)(?P ["']).*?(?P=quote)(?:$| )s* (?:^| )(?P["']).*?(?P=quote)(?:$| )R i t quotei t delimt spacec S s | | | | k r | p | S( N( ( t at bt quotes( ( s /usr/lib64/python2.7/csv.pyts c S s | | | | k r | p | S( N( ( Re Rf t delims( ( s /usr/lib64/python2.7/csv.pyRh s s s] ((%(delim)s)|^)\W*%(quote)s[^%(delim)s\n]*%(quote)s[^%(delim)s\n]*%(quote)s\W*((%(delim)s)|$)( sF (?P [^\w "'])(?P ?)(?P ["']).*?(?P=quote)(?P=delim)sC (?:^| )(?P["']).*?(?P=quote)(?P[^\w "'])(?P ?)sD (?P >[^\w "'])(?P ?)(?P ["']).*?(?P=quote)(?:$| )s* (?:^| )(?P["']).*?(?P=quote)(?:$| )N( t ret compilet DOTALLt MULTILINEt findallR$ R% t groupindexRO t KeyErrorR t keyst searchR ( R t dataR` t matchest restrt regexpRg Ri t spacest mt nRC R' Rc R* t dq_regexpR) ( ( s /usr/lib64/python2.7/csv.pyR] s` c C s t d | j d } g t d D] } t | ^ q% } t d t | } d } i } i } i } d t | t | } } x| t | k r| d 7} xk | | | !D]\ } xS | D]K } | j | i } | j | } | j | d d | | <| | || d j | | d j d | k } | | f Sq>Wn g | j D] \ } } | | f ^ q} | j | d d } | d j | | d j d | k } | | f S( s 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. s i i i i c S s | d | d k r | p | S( Ni ( ( Re Rf ( ( s /usr/lib64/python2.7/csv.pyRh D s c S s d | d | d f S( Ni i ( ( Re Rf ( ( s /usr/lib64/python2.7/csv.pyRh J s g ?g?g{Gz?s %c R iN( R i ( t filterR% t splitt ranget chrt minR> RO t countRq t itemsR t removet floatR[ t sort( R Rs R` t ct asciit chunkLengtht iterationt charFrequencyt modesRi t startt endt linet chart metaFrequencyt freqR t modeListt totalt consistencyt thresholdRQ t vRc R* R@ ( ( s /usr/lib64/python2.7/csv.pyR^ sx % & ! + c C s t t | | j | } | j } t | } i } x t | D] } d | |R} R% Rq t intt longR t complexRI t OverflowErrorR t typeR ( R R_ t rdrRL t columnst columnTypest it checkedR? t colt thisTypet hasHeadert colType( ( s /usr/lib64/python2.7/csv.pyt has_header~ sN N( R! R" R R R% Ra R] R^ R ( ( ( s /usr/lib64/python2.7/csv.pyR s L i( ( ( ( ( R Rj t functoolsR t _csvR R R R R R R R R R R R R R R t cStringIOR t ImportErrort __all__R R R R R t NameErrorR R ( ( ( s /usr/lib64/python2.7/csv.pyt s2 ^ 2"