3 qd1@s UdZddlmZmZddlZddlZddlmZmZddl Z ddl Z ddl Z ddl m Z ddlmZmZmZmZmZmZmZddlZddlZddljjZddljjZddljj Z ddlm!Z!ddl"m#Z#dd l$m%Z%m&Z&dd l'm(Z(m)Z)m*Z*m+Z+dd l,m-Z-dd l.m/Z/dd l0m1Z1m2Z2m3Z3m4Z4m5Z5m6Z6m7Z7m8Z8m9Z9m:Z:m;Z;mZ>m?Z?m@Z@ddlAmBZBddlCmDZDddlEmFZFddlGmHZHddlImJZJddlKmLZLmMZMmNZNmOZOddlPmQZQddlRmSZTddlUmVZVmWZWmXZXmYZYddlZm[Z[dZ\de dj]e^e!ddddZ_dsdd Z`d!d"Zae%d#d$d%Zbddd&ejcd'd(dd)dddddddd'ddddd'ddd*d(d(d(dddd(dd(dd'd(d'd+%Zdd(d'd'd(d'd'dd,Zed)d-dd.Zfd/hZgd0d1hZhiZieejefiekZleejle-e_jmd2d3d4d5d6dd)dddd(dd'dddddd(ddddd'd'd(d'd(d(d(dd(d'd(dd)dd*dd&ejcd'ddddd'd'd(eed0d(df0e%ejd7d8d2Zne-e_jmd9d:d;d5dd?ZpGd@dAdAejqZrdBdCZsduee&eteeufdDdEdFZvdGdHZwdIdJZxdKdLZydMdNZzdOdPZ{GdQdRdRZ|GdSdTdTe|Z}dUdVZ~dWdXZGdYdZdZe|Zdvd[d\Zdwd]d^Zd_d`ZdxdadbZdcddZdydedfZdgdhZdidjZdkdlZdmdnZGdodpdpejqZGdqdrdreZdS)zzM Module contains tools for processing files into DataFrames or other objects )abc defaultdictN)StringIO TextIOWrapper)fill)AnyDictIterableListOptionalSequenceSet) STR_NA_VALUES)parsing)FilePathOrBufferUnion)AbstractMethodErrorEmptyDataError ParserError ParserWarning)Appender)astype_nansafe) ensure_object ensure_str is_bool_dtypeis_categorical_dtype is_dict_likeis_dtype_equalis_extension_array_dtype is_file_likeis_float is_integeris_integer_dtype is_list_likeis_object_dtype is_scalaris_string_dtype pandas_dtype)CategoricalDtype)isna) algorithms) Categorical) DataFrame)Index MultiIndex RangeIndexensure_index_from_sequences)Series) datetimes)get_filepath_or_buffer get_handleinfer_compressionvalidate_header_arg)generic_parserua {summary} Also supports optionally iterating or breaking of the file into chunks. Additional help can be found in the online docs for `IO Tools `_. Parameters ---------- filepath_or_buffer : str, path object or file-like object Any valid string path is acceptable. The string could be a URL. Valid URL schemes include http, ftp, s3, gs, and file. For file URLs, a host is expected. A local file could be: file://localhost/path/to/table.csv. If you want to pass in a path object, pandas accepts any ``os.PathLike``. By file-like object, we refer to objects with a ``read()`` method, such as a file handler (e.g. via builtin ``open`` function) or ``StringIO``. sep : str, default {_default_sep} Delimiter to use. If sep is None, the C engine cannot automatically detect the separator, but the Python parsing engine can, meaning the latter will be used and automatically detect the separator by Python's builtin sniffer tool, ``csv.Sniffer``. In addition, separators longer than 1 character and different from ``'\s+'`` will be interpreted as regular expressions and will also force the use of the Python parsing engine. Note that regex delimiters are prone to ignoring quoted data. Regex example: ``'\r\t'``. delimiter : str, default ``None`` Alias for sep. header : int, list of int, default 'infer' Row number(s) to use as the column names, and the start of the data. Default behavior is to infer the column names: if no names are passed the behavior is identical to ``header=0`` and column names are inferred from the first line of the file, if column names are passed explicitly then the behavior is identical to ``header=None``. Explicitly pass ``header=0`` to be able to replace existing names. The header can be a list of integers that specify row locations for a multi-index on the columns e.g. [0,1,3]. Intervening rows that are not specified will be skipped (e.g. 2 in this example is skipped). Note that this parameter ignores commented lines and empty lines if ``skip_blank_lines=True``, so ``header=0`` denotes the first line of data rather than the first line of the file. names : array-like, optional List of column names to use. If the file contains a header row, then you should explicitly pass ``header=0`` to override the column names. Duplicates in this list are not allowed. index_col : int, str, sequence of int / str, or False, default ``None`` Column(s) to use as the row labels of the ``DataFrame``, either given as string name or column index. If a sequence of int / str is given, a MultiIndex is used. Note: ``index_col=False`` can be used to force pandas to *not* use the first column as the index, e.g. when you have a malformed file with delimiters at the end of each line. usecols : list-like or callable, optional Return a subset of the columns. If list-like, all elements must either be positional (i.e. integer indices into the document columns) or strings that correspond to column names provided either by the user in `names` or inferred from the document header row(s). For example, a valid list-like `usecols` parameter would be ``[0, 1, 2]`` or ``['foo', 'bar', 'baz']``. Element order is ignored, so ``usecols=[0, 1]`` is the same as ``[1, 0]``. To instantiate a DataFrame from ``data`` with element order preserved use ``pd.read_csv(data, usecols=['foo', 'bar'])[['foo', 'bar']]`` for columns in ``['foo', 'bar']`` order or ``pd.read_csv(data, usecols=['foo', 'bar'])[['bar', 'foo']]`` for ``['bar', 'foo']`` order. If callable, the callable function will be evaluated against the column names, returning names where the callable function evaluates to True. An example of a valid callable argument would be ``lambda x: x.upper() in ['AAA', 'BBB', 'DDD']``. Using this parameter results in much faster parsing time and lower memory usage. squeeze : bool, default False If the parsed data only contains one column then return a Series. prefix : str, optional Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, ... mangle_dupe_cols : bool, default True Duplicate columns will be specified as 'X', 'X.1', ...'X.N', rather than 'X'...'X'. Passing in False will cause data to be overwritten if there are duplicate names in the columns. dtype : Type name or dict of column -> type, optional Data type for data or columns. E.g. {{'a': np.float64, 'b': np.int32, 'c': 'Int64'}} Use `str` or `object` together with suitable `na_values` settings to preserve and not interpret dtype. If converters are specified, they will be applied INSTEAD of dtype conversion. engine : {{'c', 'python'}}, optional Parser engine to use. The C engine is faster while the python engine is currently more feature-complete. converters : dict, optional Dict of functions for converting values in certain columns. Keys can either be integers or column labels. true_values : list, optional Values to consider as True. false_values : list, optional Values to consider as False. skipinitialspace : bool, default False Skip spaces after delimiter. skiprows : list-like, int or callable, optional Line numbers to skip (0-indexed) or number of lines to skip (int) at the start of the file. If callable, the callable function will be evaluated against the row indices, returning True if the row should be skipped and False otherwise. An example of a valid callable argument would be ``lambda x: x in [0, 2]``. skipfooter : int, default 0 Number of lines at bottom of file to skip (Unsupported with engine='c'). nrows : int, optional Number of rows of file to read. Useful for reading pieces of large files. na_values : scalar, str, list-like, or dict, optional Additional strings to recognize as NA/NaN. If dict passed, specific per-column NA values. By default the following values are interpreted as NaN: 'z', 'Fz )subsequent_indenta"'. keep_default_na : bool, default True Whether or not to include the default NaN values when parsing the data. Depending on whether `na_values` is passed in, the behavior is as follows: * If `keep_default_na` is True, and `na_values` are specified, `na_values` is appended to the default NaN values used for parsing. * If `keep_default_na` is True, and `na_values` are not specified, only the default NaN values are used for parsing. * If `keep_default_na` is False, and `na_values` are specified, only the NaN values specified `na_values` are used for parsing. * If `keep_default_na` is False, and `na_values` are not specified, no strings will be parsed as NaN. Note that if `na_filter` is passed in as False, the `keep_default_na` and `na_values` parameters will be ignored. na_filter : bool, default True Detect missing value markers (empty strings and the value of na_values). In data without any NAs, passing na_filter=False can improve the performance of reading a large file. verbose : bool, default False Indicate number of NA values placed in non-numeric columns. skip_blank_lines : bool, default True If True, skip over blank lines rather than interpreting as NaN values. parse_dates : bool or list of int or names or list of lists or dict, default False The behavior is as follows: * boolean. If True -> try parsing the index. * list of int or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3 each as a separate date column. * list of lists. e.g. If [[1, 3]] -> combine columns 1 and 3 and parse as a single date column. * dict, e.g. {{'foo' : [1, 3]}} -> parse columns 1, 3 as date and call result 'foo' If a column or index cannot be represented as an array of datetimes, say because of an unparseable value or a mixture of timezones, the column or index will be returned unaltered as an object data type. For non-standard datetime parsing, use ``pd.to_datetime`` after ``pd.read_csv``. To parse an index or column with a mixture of timezones, specify ``date_parser`` to be a partially-applied :func:`pandas.to_datetime` with ``utc=True``. See :ref:`io.csv.mixed_timezones` for more. Note: A fast-path exists for iso8601-formatted dates. infer_datetime_format : bool, default False If True and `parse_dates` is enabled, pandas will attempt to infer the format of the datetime strings in the columns, and if it can be inferred, switch to a faster method of parsing them. In some cases this can increase the parsing speed by 5-10x. keep_date_col : bool, default False If True and `parse_dates` specifies combining multiple columns then keep the original columns. date_parser : function, optional Function to use for converting a sequence of string columns to an array of datetime instances. The default uses ``dateutil.parser.parser`` to do the conversion. Pandas will try to call `date_parser` in three different ways, advancing to the next if an exception occurs: 1) Pass one or more arrays (as defined by `parse_dates`) as arguments; 2) concatenate (row-wise) the string values from the columns defined by `parse_dates` into a single array and pass that; and 3) call `date_parser` once for each row using one or more strings (corresponding to the columns defined by `parse_dates`) as arguments. dayfirst : bool, default False DD/MM format dates, international and European format. cache_dates : bool, default True If True, use a cache of unique, converted dates to apply the datetime conversion. May produce significant speed-up when parsing duplicate date strings, especially ones with timezone offsets. .. versionadded:: 0.25.0 iterator : bool, default False Return TextFileReader object for iteration or getting chunks with ``get_chunk()``. chunksize : int, optional Return TextFileReader object for iteration. See the `IO Tools docs `_ for more information on ``iterator`` and ``chunksize``. compression : {{'infer', 'gzip', 'bz2', 'zip', 'xz', None}}, default 'infer' For on-the-fly decompression of on-disk data. If 'infer' and `filepath_or_buffer` is path-like, then detect compression from the following extensions: '.gz', '.bz2', '.zip', or '.xz' (otherwise no decompression). If using 'zip', the ZIP file must contain only one data file to be read in. Set to None for no decompression. thousands : str, optional Thousands separator. decimal : str, default '.' Character to recognize as decimal point (e.g. use ',' for European data). lineterminator : str (length 1), optional Character to break file into lines. Only valid with C parser. quotechar : str (length 1), optional The character used to denote the start and end of a quoted item. Quoted items can include the delimiter and it will be ignored. quoting : int or csv.QUOTE_* instance, default 0 Control field quoting behavior per ``csv.QUOTE_*`` constants. Use one of QUOTE_MINIMAL (0), QUOTE_ALL (1), QUOTE_NONNUMERIC (2) or QUOTE_NONE (3). doublequote : bool, default ``True`` When quotechar is specified and quoting is not ``QUOTE_NONE``, indicate whether or not to interpret two consecutive quotechar elements INSIDE a field as a single ``quotechar`` element. escapechar : str (length 1), optional One-character string used to escape other characters. comment : str, optional Indicates remainder of line should not be parsed. If found at the beginning of a line, the line will be ignored altogether. This parameter must be a single character. Like empty lines (as long as ``skip_blank_lines=True``), fully commented lines are ignored by the parameter `header` but not by `skiprows`. For example, if ``comment='#'``, parsing ``#empty\na,b,c\n1,2,3`` with ``header=0`` will result in 'a,b,c' being treated as the header. encoding : str, optional Encoding to use for UTF when reading/writing (ex. 'utf-8'). `List of Python standard encodings `_ . dialect : str or csv.Dialect, optional If provided, this parameter will override values (default or not) for the following parameters: `delimiter`, `doublequote`, `escapechar`, `skipinitialspace`, `quotechar`, and `quoting`. If it is necessary to override values, a ParserWarning will be issued. See csv.Dialect documentation for more details. error_bad_lines : bool, default True Lines with too many fields (e.g. a csv line with too many commas) will by default cause an exception to be raised, and no DataFrame will be returned. If False, then these "bad lines" will dropped from the DataFrame that is returned. warn_bad_lines : bool, default True If error_bad_lines is False, and warn_bad_lines is True, a warning for each "bad line" will be output. delim_whitespace : bool, default False Specifies whether or not whitespace (e.g. ``' '`` or ``' '``) will be used as the sep. Equivalent to setting ``sep='\s+'``. If this option is set to True, nothing should be passed in for the ``delimiter`` parameter. low_memory : bool, default True Internally process the file in chunks, resulting in lower memory use while parsing, but possibly mixed type inference. To ensure no mixed types either set False, or specify the type with the `dtype` parameter. Note that the entire file is read into a single DataFrame regardless, use the `chunksize` or `iterator` parameter to return the data in chunks. (Only valid with C parser). memory_map : bool, default False If a filepath is provided for `filepath_or_buffer`, map the file object directly onto memory and access the data directly from there. Using this option can improve performance because there is no longer any I/O overhead. float_precision : str, optional Specifies which converter the C engine should use for floating-point values. The options are `None` for the ordinary converter, `high` for the high-precision converter, and `round_trip` for the round-trip converter. Returns ------- DataFrame or TextParser A comma-separated values (csv) file is returned as two-dimensional data structure with labeled axes. See Also -------- DataFrame.to_csv : Write DataFrame to a comma-separated values (csv) file. read_csv : Read a comma-separated values (csv) file into DataFrame. read_fwf : Read a table of fixed-width formatted lines into DataFrame. Examples -------- >>> pd.{func_name}('data.csv') # doctest: +SKIP cCs^d|dd|d}|dk rZt|rBt||kr8t|t|}nt|oP||ksZt||S)a Checks whether the 'name' parameter for parsing is either an integer OR float that can SAFELY be cast to an integer without losing accuracy. Raises a ValueError if that is not the case. Parameters ---------- name : string Parameter name (used for error reporting) val : int or float The value to check min_val : int Minimum allowed value (val < min_val will result in a ValueError) 'sz' must be an integer >=dN)r int ValueErrorr!)namevalZmin_valmsgrB3/tmp/pip-build-7vycvbft/pandas/pandas/io/parsers.py_validate_integerqs  rDcCsH|dk rDt|tt|kr$tdt|ddp:t|tjsDtddS)aZ Raise ValueError if the `names` parameter contains duplicates or has an invalid data type. Parameters ---------- names : array-like or None An array containing a list of the names used for the output DataFrame. Raises ------ ValueError If names are not unique or are not ordered (e.g. set). 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Also supports optionally iterating or breaking of the file into chunks. Additional help can be found in the `online docs for IO Tools `_. Parameters ---------- filepath_or_buffer : str, path object or file-like object Any valid string path is acceptable. The string could be a URL. Valid URL schemes include http, ftp, s3, and file. For file URLs, a host is expected. A local file could be: ``file://localhost/path/to/table.csv``. If you want to pass in a path object, pandas accepts any ``os.PathLike``. By file-like object, we refer to objects with a ``read()`` method, such as a file handler (e.g. via builtin ``open`` function) or ``StringIO``. colspecs : list of tuple (int, int) or 'infer'. optional A list of tuples giving the extents of the fixed-width fields of each line as half-open intervals (i.e., [from, to[ ). String value 'infer' can be used to instruct the parser to try detecting the column specifications from the first 100 rows of the data which are not being skipped via skiprows (default='infer'). widths : list of int, optional A list of field widths which can be used instead of 'colspecs' if the intervals are contiguous. infer_nrows : int, default 100 The number of rows to consider when letting the parser determine the `colspecs`. .. versionadded:: 0.24.0 **kwds : optional Optional keyword arguments can be passed to ``TextFileReader``. Returns ------- DataFrame or TextParser A comma-separated values (csv) file is returned as two-dimensional data structure with labeled axes. See Also -------- DataFrame.to_csv : Write DataFrame to a comma-separated values (csv) file. read_csv : Read a comma-separated values (csv) file into DataFrame. 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Parameters ---------- columns : array-like Object which may or may not be convertible into a MultiIndex index_col : None, bool or list, optional Column or columns to use as the (possibly hierarchical) index Returns ------- boolean : Whether or not columns could become a MultiIndex Nc3s$|]}|tkrt|tVqdS)N)rrGr).0r)rmrBrC sz,_is_potential_multi_index..)rGr[rEr.all)rrmrB)rmrC_is_potential_multi_indexs  rcs"trfddt|DSS)z Check whether or not the 'usecols' parameter is a callable. If so, enumerates the 'names' parameter and returns a set of indices for each entry in 'names' that evaluates to True. If not a callable, returns 'usecols'. csh|]\}}|r|qSrBrB)rir?)r}rBrC sz$_evaluate_usecols..)r enumerate)r}rIrB)r}rC_evaluate_usecolssrcs0fdd|D}t|dkr,td||S)a% Validates that all usecols are present in a given list of names. If not, raise a ValueError that shows what usecols are missing. Parameters ---------- usecols : iterable of usecols The columns to validate are present in names. names : iterable of names The column names to check against. Returns ------- usecols : iterable of usecols The `usecols` parameter if the validation succeeds. Raises ------ ValueError : Columns were missing. Error message will list them. csg|]}|kr|qSrBrB)rr)rIrBrC sz+_validate_usecols_names..rz>Usecols do not match columns, columns expected but not found: )rEr>)r}rImissingrB)rIrC_validate_usecols_namess   rcCs$t|std|dkr td|S)a Validate the 'skipfooter' parameter. Checks whether 'skipfooter' is a non-negative integer. Raises a ValueError if that is not the case. Parameters ---------- skipfooter : non-negative integer The number of rows to skip at the end of the file. Returns ------- validated_skipfooter : non-negative integer The original input if the validation succeeds. 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only the converter will be used)rF) try_num_boolrz$Bool column has NA values in column zFilled z NA values in column )rrWrGrrrCrrrFrrrr Z map_inferr>r*isinrviewrZuint8Zmap_infer_maskrDr&rrrrrr _cast_typesprint)rdctrqrr~ruZdtypesrrrZconv_f cast_typerFrGmaskZcvalsna_countZis_str_or_ea_dtyperJrBrBrC_convert_to_ndarrayssX        zParserBase._convert_to_ndarraysc Csd}t|jjtjtjfrftj|t|}|j }|dkr^t |rN|j tj }tj ||tj||fS|rt|jrytj||d}Wn*ttfk r|}tj||d}YqXt|j }n|}|jtjkrtj||d}|jtjko|r tjtj||j|jd}||fS)aU Infer types of values, possibly casting Parameters ---------- values : ndarray na_values : set try_num_bool : bool, default try try to cast values to numeric (first preference) or boolean Returns ------- converted : ndarray na_count : int rF)rsrt) issubclassrvrrnumberZbool_r*rKrsumr"ZastypeZfloat64Zputmasknanr$r Zmaybe_convert_numericr>rparsersZsanitize_objectsr)Zobject_libopsZmaybe_convert_boolZasarrayrsrt)rrrqrJrRrQrrBrBrCrD?s2   zParserBase._infer_typescCst|rbt|to|jdk }t| r6| r6t|t}t|jj }t j ||j |||j d}nt|rt|}|j}y|j||dStk r}ztd|d|WYdd}~XnXnPyt||ddd}Wn:tk r}ztd|d ||WYdd}~XnX|S) aF Cast values to specified type Parameters ---------- values : ndarray cast_type : string or np.dtype dtype to cast values to column : string column name - 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Currently, any column that is involved with date parsing will not undergo such conversions. r rNcs:dk rt|r|}t|s*j|}jj|dS)N)r!rrdZ set_noconvert)r))rIrr}rBrC_setHs  z3CParserWrapper._set_noconvert_columns.._set)rN) rr rr}sortrrIrGrRrrrm)rrir@krB)rIrr}rCrf3s4            z%CParserWrapper._set_noconvert_columnscCs|jjt|dS)N)rdset_error_bad_linesr=)rstatusrBrBrCrlhsz"CParserWrapper.set_error_bad_linesNc sy|jj|}Wntk r|jrd|_|j|j}t||j|j|j j dd\}}|j |j |j dk r||jttfdd|j}||fSYnXd|_|j}|jjr|jrtdg}xTt|jjD]D}|jdkr|j|}n|j|j|}|j||dd}|j|qWt|}|j dk rD|j|}|j|}t|j}d d t||D}|j||\}}nzt|j}t|j}|j|}|j dk r|j|}d d |D} d d t||D}|j||\}}|j|| |\}}|j ||j }|||fS)NFrv)rvcs |dkS)NrrB)item)rrBrC~sz%CParserWrapper.read..z file structure not yet supportedT)r5cSsi|]\}\}}||qSrBrB)rrkrvrBrBrC sz'CParserWrapper.read..cSsg|] }|dqS)rUrB)rr)rBrBrCrsz'CParserWrapper.read..cSsi|]\}\}}||qSrBrB)rrkrrprBrBrCrqs) rdr]rrr2r_get_empty_metarmrr_rWr4rr}_filter_usecolsrfilterrrIrgr!r\rr_maybe_parse_datesrr0rr,r_rr;) rrVrarIrrrErrr9rB)rrCr]ks`                zCParserWrapper.readcs>t|j|dk r:t|tkr:fddt|D}|S)Ncs$g|]\}}|ks|kr|qSrBrB)rrr?)r}rBrCrsz2CParserWrapper._filter_usecols..)rr}rEr)rrIrB)r}rCrss zCParserWrapper._filter_usecolscCsJt|jjd}d}|jjdkrB|jdk rBt||j|j\}}|_||fS)Nr)rrdrlrgrmr+r)rrIZ idx_namesrBrBrC_get_index_namess zCParserWrapper._get_index_namesTcCs|r|j|r|j|}|S)N)r#r)rrrr5rBrBrCrus z!CParserWrapper._maybe_parse_dates)N)T) rrrrrr^rfrlr]rsrvrurBrBrBrCrsj 5 U  rcOsd|d<t||S)a6 Converts lists of lists/tuples into DataFrames with proper type inference and optional (e.g. string to datetime) conversion. Also enables iterating lazily over chunks of large files Parameters ---------- data : file-like object or list delimiter : separator character to use dialect : str or csv.Dialect instance, optional Ignored if delimiter is longer than 1 character names : sequence, default header : int, default 0 Row to use to parse column labels. Defaults to the first row. Prior rows will be discarded index_col : int or list, optional Column or columns to use as the (possibly hierarchical) index has_index_names: bool, default False True if the cols defined in index_col have an index name and are not in the header. na_values : scalar, str, list-like, or dict, optional Additional strings to recognize as NA/NaN. keep_default_na : bool, default True thousands : str, optional Thousands separator comment : str, optional Comment out remainder of line parse_dates : bool, default False keep_date_col : bool, default False date_parser : function, optional skiprows : list of integers Row numbers to skip skipfooter : int Number of line at bottom of file to skip converters : dict, optional Dict of functions for converting values in certain columns. Keys can either be integers or column labels, values are functions that take one input argument, the cell (not column) content, and return the transformed content. encoding : str, optional Encoding to use for UTF when reading/writing (ex. 'utf-8') squeeze : bool, default False returns Series if only one column. infer_datetime_format: bool, default False If True and `parse_dates` is True for a column, try to infer the datetime format based on the first datetime string. If the format can be inferred, there often will be a large parsing speed-up. float_precision : str, optional Specifies which converter the C engine should use for floating-point values. The options are None for the ordinary converter, 'high' for the high-precision converter, and 'round_trip' for the round-trip converter. rr)r\)argsr_rBrBrC TextParsers6rxcCstdd|DS)Ncss"|]}|dks|dkrdVqdS)rNrUrB)rrprBrBrCr sz#count_empty_vals..)rV)valsrBrBrCcount_empty_vals srzc@seZdZddZddZddZd3dd Zd d Zd4d d ZddZ ddZ ddZ ddZ ddZ ddZddZddZddZd d!Zd"d#Zd$d%Zd&d'Zd(d)Zd*d+Zd,Zd-d.Zd/d0Zd5d1d2ZdS)6rc stj|d_g_d_d_|d_|d_|d_|d_ t j r`j _ nfdd_ t |d _ |d _|d _tjtrtj_|d _|d _|d_|d_|d_t|d\_}|d_|d_|d_|dpd_d_d|kr4|d_|d_|d_|d_ |d_!|d_"|d_#g_$t%|djjjd\}}j&j'|t(|d rj)|n|_d_*yj+\_,_-_.Wn$t/t0fk rj1YnXt2j,d!kr@j3j,j4j5\_,_4_5}t2j,_-n j,d_,t6j,_7j8sj9j,\}_7_,d"_:j4dkr|_4j;j,j<rj=_>nd_>t2j"d!krt0d#j!dkrt?j@d$j"d%_Ant?j@d$j!d&j"d%_AdS)'zN Workhorse function for processing nested list into DataFrame NrrLrOrrocs |jkS)N)ro)r))rrBrCro, sz'PythonParser.__init__..rprergrfrirjrkrhr}rrrrIFrr~rurvrxrzryr$)rLrOrreadlinerUTz'Only length-1 decimal markers supportedz[^-^0-9^z]+^)Brrrabufposline_posrLrOrrorskipfuncrrprergrGrrfrirjrkrhr r}rrrZ names_passedrr~rurvrxrzryZ_comment_linesr4rextendr _make_reader _col_indices_infer_columnsrnum_original_columnsrrr>r^rEr/rrrrr!_get_index_namerr rR_set_no_thousands_columns_no_thousands_columnsrXcompilenonnum)rrr_rMrrrB)rrCr s                                    zPythonParser.__init__cstfdd}tjtr\xƈjD].}t|trNx|D] }||q._set)rFrGrRrrrrm)rrir@rkrB)rrrCr s*           z&PythonParser._set_no_thousands_columnsc s:jdkstdkrjr*tdGfdddtj}|}dk rT|_nj}j|ggd}x>jj s| rj d7_ j}j|ggd}qpW|d}j d7_ j d7_ tj j |}|j|_tj t||d}jjt|tj |dd}nfd d } | }|_dS) NrUz.MyDialect N) rrrrergrfrirjrhrkrB)rrBrC MyDialect srr)rT)rstrictc3sDj}tj}|j|jVxD]}|j|jVq(WdS)N)r{rXrsplitstrip)linepat)rrrBrCrb s   z(PythonParser._make_reader.._read)rerErkr>rDialectr{_check_commentsrr~rSniffersniffreaderrr}rrra) rrrZdiarlinessniffedZline_rdrrrbrB)rrrrCr s4  zPythonParser._make_readerNc Csy|j|}Wn"tk r0|jr*g}nYnXd|_t|j}t|s|j|j}t||j|j |j \}}}|j ||j }|||fSt |d}d}|jr|t|kr|d}|dd}|j|} |j| } |j|j}|j|| \}} |j| } |j| | ||\}}||| fS)NFrrU) _get_linesrrrrrEr2rrrmrrvr4rrzr _rows_to_cols_exclude_implicit_indexrr_ _convert_datar;) rrowscontentrrIrrZcount_empty_content_valsr:r9rarBrBrCr] s4         zPythonParser.readcCsz|j|j}|jrb|j}i}d}xTt|D]2\}}x|||krJ|d7}q4W|||||<q*Wnddt||D}|S)NrrUcSsi|]\}}||qSrBrB)rrkrprBrBrCrq1 sz8PythonParser._exclude_implicit_index..)r2rr>rmrr,)rr9rIZ excl_indicesraoffsetrrrBrBrCr$ s  z$PythonParser._exclude_implicit_indexcCs|dkr|j}|j|dS)N)r)rTr])rrrBrBrCr6 szPythonParser.get_chunkc sfdd}|j}tjts*j}n |j}i}i}tjtrx^jD]F}j|}j|} t|tr|jkrj|}|||<| ||<qPWn j}j}j|||j ||S)NcsDi}x:|jD].\}}t|tr4|jkr4j|}|||<qW|S)zconverts col numbers to names)rrGr=r)mappingcleanrrp)rrBrC_clean_mapping= s   z2PythonParser._convert_data.._clean_mapping) rurGrvrrqrr=rrSr~) rrarZ clean_convZ clean_dtypesZclean_na_valuesZclean_na_fvaluesrZna_valueZ na_fvaluerB)rrCr; s0        zPythonParser._convert_datacsj}d}d}t}jdk rj}t|tttjfr`t|dk}|rjt||ddg}n d}|g}g}x|t |D]n\}} y$j } xj | krj } qWWnt k rR} zj | krtd| dj dd| |o| dkr"|rj|jdgt|d|||fSjs4td| jdd} WYdd} ~ XnXgg} xbt | D]V\} }|d kr|rd | d |}n d | }| j| j|n j|qfW| rBjrBtt}xt D]Z\} }||}x2|dkr$|d||<|d |}||}qW|| <|d||<qWnr|r| |dkrt}jdk rttjnd}t| }||kr|||krd}dg|jdg_|j|jfd d| Dt|dkrzt}qzW|rj|dk rjdk r&t|tjksHjdkrPt|t|dkrPtdt|dkrftdjdk rj||nd_t|}|g}nj||d}ny j } Wn@t k r} z"|std| |dd} WYdd} ~ XnXt| }|}|sLjr,fddt|Dg}ntt|g}j||d}ntjdksft||kr~j|g|}t|}nBtj rt|tjkrtdj|g||g}|}|||fS)NrTrUFzPassed header=z but only z lines in filezNo columns to parse from filerz Unnamed: Z_level_rdcsh|] }|qSrBrB)rr) this_columnsrBrCr sz.PythonParser._infer_columns..zHNumber of passed names did not match number of header fields in the filez*Cannot pass names with multi-index columnscsg|]}j|qSrB)rn)rr)rrBrCr sz/PythonParser._infer_columns..r*r*r*r*)rIrFrlrGrrrrrEr_buffered_liner _next_linerr> _clear_bufferrrrrr=rmr}rr}r_handle_usecolsrrnrr)rrIrZ clear_bufferrrlZhave_mi_columnsrlevelhrrrZthis_unnamed_colsrrrHr0rr1lcr.Z unnamed_countZncolsrB)rrrCrh s                   "    "zPythonParser._infer_columnsc s|jdk rt|jr"t|j|ntdd|jDrt|dkrJtdgxb|jD]P}t|tryj|j |Wqtk rt |j|YqXqVj|qVWn|jfdd|D}|_ |S)zb Sets self._col_indices usecols_key is used if there are string usecols. Ncss|]}t|tVqdS)N)rGr)rurBrBrCr sz/PythonParser._handle_usecols..rUz4If using multiple headers, usecols must be integers.cs"g|]}fddt|DqS)csg|]\}}|kr|qSrBrB)rrr() col_indicesrBrCr sz;PythonParser._handle_usecols...)r)rr])rrBrCr sz0PythonParser._handle_usecols..) r}rrrrEr>rGrrrrr)rrZ usecols_keyrrB)rrCr s(      zPythonParser._handle_usecolscCs$t|jdkr|jdS|jSdS)zH Return a line from buffer, filling buffer if required. rN)rEr}r)rrBrBrCr& s zPythonParser._buffered_linecCs|s|St|dts|S|ds&|S|dd}|tkr>|S|d}t|dkr|d|jkrd}|d}|ddj|d}|||}t||dkr|||dd7}|g|ddSt|dkr|ddgSdgSdS)a- Checks whether the file begins with the BOM character. If it does, remove it. In addition, if there is quoting in the field subsequent to the BOM, remove it as well because it technically takes place at the beginning of the name, not the middle of it. rrUrNr)rGr_BOMrErgr)rZ first_rowZ first_eltZ first_row_bomstartquoteendnew_rowrBrBrC_check_for_bom/ s*    zPythonParser._check_for_bomcCs| ptdd|DS)z Check if a line is empty or not. 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N) rrarErrorrrrrpr)rrerAreasonrBrBrCr s      zPythonParser._next_iter_linecCs|jdkr|Sg}xv|D]n}g}xZ|D]R}t|t s@|j|krL|j|q&|d|j|j}t|dkrv|j|Pq&W|j|qW|S)Nr)ryrGrrfindrE)rrrlrlr)rBrBrCr s      zPythonParser._check_commentscCsTg}xJ|D]B}t|dksBt|dkr t|dt sB|djr |j|q W|S)a} Iterate through the lines and remove any that are either empty or contain only one whitespace value Parameters ---------- lines : array-like The array of lines that we are to filter. 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( -" 54'@Grcsfdd}|S)Ncsdkr\tj|}ytjt|dddjStk rXtjtj|ddSXny,tj|dd}t|t j rt d|St k ry tjtjtj|dddSt k rt f|SXYnXdS) Nignore)utcr|errorsrcache)r|)r)rrz scalar parser)r`r|)r) rZconcat_date_colstoolsZ to_datetimerZto_numpyr>r5rGdatetime Exceptionr7) date_colsstrsr)rwrQr|rrBrC converter s:   z'_make_date_converter..converterrB)rQr|rrwrrB)rwrQr|rrCr s'rcsfdd}g}i} |} t|}t} |dks:t|trB||fSt|trx|D]} t| rt| trx| |krx| | } || rqR||| || <qRt|| || \} }}| |krtd| || | <|j| | j |qRWnlt|t rLx^|j D]R\} } | |krtd| dt|| || \}}}|| | <|j| | j |qW|j | |j ||sx&t| D]}|j ||j|qpW||fS)Ncs$ttr|kp"tto"|kS)N)rGr)colspec)rmrrBrC_isindex? sz*_process_date_conversion.._isindexz New date column already in dict z Date column z already in dict)rrFrGr[r%r=_try_convert_datesr>rrrrrrrB) data_dictrZ parse_specrmrrr{rZnew_colsZnew_datarrrnew_namerZ old_namesrMrrB)rmrrCr^6 sN          r^c st|}g}xL|D]D}||kr*|j|qt|trL||krL|j||q|j|qWdjdd|D}fdd|D}||} || |fS)NrMcss|]}t|VqdS)N)r)rr)rBrBrCr sz%_try_convert_dates..csg|]}|kr|qSrBrB)rr)rrBrCr sz&_try_convert_dates..)rFrrGr=r) r`rrrcolsetcolnamesrrZto_parseZnew_colrB)rrCr{ s  rcCs|dkr |rt}nt}t}nt|tr|j}i}x:|jD].\}}t|sV|g}|rft|tB}|||<q@Wdd|jD}n*t|s|g}t|}|r|tB}t|}||fS)NcSsi|]\}}t||qSrB)_floatify_na_values)rrkrprBrBrCrq sz$_clean_na_values..) rrFrGrrrr#_stringify_na_valuesr)rqrrrZ old_na_valuesrkrprBrBrCr s,   rc Cst|sd||fSt|}t|}g}t|}xxt|D]l\}}t|tr|j|xNt|D]$\}}||kr^|||<|j|Pq^Wq8||}|j||j|q8Wx.t|D]"\}}t|tr||krd||<qW|||fS)N)rrrrGrrrB) rrmrZcp_colsrrrr"r?rBrBrCr+ s*      r+c st|}tts,pttfddnFj}tddx0|jD]$\}}t|rb||n|}||<qJW|dks|dks|dkrtg}nJfdd|D} t | |d}|j x"t |D]\} } |j | | qWfdd |D} ||| fS) NcsS)NrBrB) default_dtyperBrCro sz!_get_empty_meta..cSstS)N)objectrBrBrBrCro sFcsg|]}tg|dqS))rv)r1)rr?)rvrBrCr sz#_get_empty_meta..)rIcsi|]}tg|d|qS))rv)r1)rrH)rvrBrCrq sz#_get_empty_meta..) rrGrrrrrr!r-r0rjrr) rrmrrvZ_dtyperkrprrrarr(rrB)rrvrCrr s$     rrcCsTt}xH|D]@}y t|}tj|s.|j|Wq tttfk rJYq Xq W|S)N)rFfloatrisnanrrr> OverflowError)rqrrprBrBrCrs   rcCsg}x|D]}|jt||j|yHt|}|t|krbt|}|j|d|jt||j|Wntttfk rYnXy|jt|Wq tttfk rYq Xq Wt|S)z3 return a stringified and numeric for these values z.0)rrrr=rr>rrF)rqrr)rprBrBrCr s$    rcCsJt|tr>||kr"||||fS|r0ttfSttfSn||fSdS)a Get the NaN values for a given column. 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