argument description path string indicating filesystem locationurlor file-like object sep or delimiter character sequence or regular expression to use to split fields in each row header row number to use as column names defaults to (first row)but should be none if there is no header row index_col column numbers or names to use as the row index in the result can be single name/number or list of them for hierarchical index names list of column names for resultcombine with header=none skiprows number of rows at beginning of file to ignore or list of row numbers (starting from to skip na_values sequence of values to replace with na comment character or characters to split comments off the end of lines parse_dates attempt to parse data to datetimefalse by default if truewill attempt to parse all columns otherwise can specify list of column numbers or name to parse if element of list is tuple or listwill combine multiple columns together and parse to date (for example if date/time split across two columnskeep_date_col if joining columns to parse datedrop the joined columns default true converters dict containing column number of name mapping to functions for example {'foo'fwould apply the function to all values in the 'foocolumn dayfirst when parsing potentially ambiguous datestreat as international format ( -june default false date_parser function to use to parse dates nrows number of rows to read from beginning of file iterator return textparser object for reading file piecemeal chunksize for iterationsize of file chunks skip_footer number of lines to ignore at end of file verbose print various parser output informationlike the number of missing values placed in non-numeric columns encoding text encoding for unicode for example 'utf- for utf- encoded text squeeze if the parsed data only contains one column return series thousands separator for thousandse ',or reading text files in pieces when processing very large files or figuring out the right set of arguments to correctly process large fileyou may only want to read in small piece of file or iterate through smaller chunks of the file in [ ]result pd read_csv('ch /ex csv'in [ ]result out[ ] data loadingstorageand file formats www it-ebooks info