Every line of 'pandas to csv without index' code snippets is scanned for vulnerabilities by our powerful machine learning engine that combs millions of open source libraries, ensuring your Python code is secure.
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')