3 examples of 'pandas to csv without index' in Python
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1153 def to_csv(self, file_name, sep=',', encoding=None): 1154 start = time.time() 1155 init = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss 1156 1157 self._data.to_csv(file_name, sep, encoding) 1158 1159 finish = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss 1160 self._last_operation_dict['time'] = time.time() - start 1161 self._last_operation_dict['name'] = 'to_csv' 1162 self._last_operation_dict['mem_usage'] = finish - init
554 def _csv_to_pandas_df(filepath, 555 separator=DEFAULT_SEPARATOR, 556 quote_char=DEFAULT_QUOTE_CHARACTER, 557 escape_char=DEFAULT_ESCAPSE_CHAR, 558 contain_headers=True, 559 lines_to_skip=0, 560 date_columns=None, 561 rowIdAndVersionInIndex=True): 562 test_import_pandas() 563 import pandas as pd 564 565 # DATEs are stored in csv as unix timestamp in milliseconds 566 def datetime_millisecond_parser(milliseconds): return pd.to_datetime(milliseconds, unit='ms', utc=True) 567 568 if not date_columns: 569 date_columns = [] 570 571 line_terminator = str(os.linesep) 572 573 df = pd.read_csv(filepath, 574 sep=separator, 575 lineterminator=line_terminator if len(line_terminator) == 1 else None, 576 quotechar=quote_char, 577 escapechar=escape_char, 578 header=0 if contain_headers else None, 579 skiprows=lines_to_skip, 580 parse_dates=date_columns, 581 date_parser=datetime_millisecond_parser) 582 if rowIdAndVersionInIndex and "ROW_ID" in df.columns and "ROW_VERSION" in df.columns: 583 # combine row-ids (in index) and row-versions (in column 0) to 584 # make new row labels consisting of the row id and version 585 # separated by a dash. 586 zip_args = [df["ROW_ID"], df["ROW_VERSION"]] 587 if "ROW_ETAG" in df.columns: 588 zip_args.append(df['ROW_ETAG']) 589 590 df.index = row_labels_from_id_and_version(zip(*zip_args)) 591 del df["ROW_ID"] 592 del df["ROW_VERSION"] 593 if "ROW_ETAG" in df.columns: 594 del df['ROW_ETAG'] 595 596 return df
57 def _dataframe_to_txt(writer, dataframe): 58 encoding_writer = codecs.getwriter('utf-8')(writer) 59 for row in dataframe.iterrows(): 60 encoding_writer.write("".join(row[1].tolist())) 61 encoding_writer.write('\n')