Python pandas.ExcelWriter() Examples
The following are 30
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Example #1
Source File: posterior_utils.py From scVI with MIT License | 7 votes |
def save_cluster_xlsx( filepath: str, de_results: List[pd.DataFrame], cluster_names: List ): """Saves multi-clusters DE in an xlsx sheet Parameters ---------- filepath xslx save path de_results list of pandas Dataframes for each cluster cluster_names list of cluster names """ writer = pd.ExcelWriter(filepath, engine="xlsxwriter") for i, x in enumerate(cluster_names): de_results[i].to_excel(writer, sheet_name=str(x)) writer.close()
Example #2
Source File: io.py From skan with BSD 3-Clause "New" or "Revised" License | 6 votes |
def write_excel(filename, **kwargs): """Write data tables to an Excel file, using kwarg names as sheet names. Parameters ---------- filename : str The filename to write to. kwargs : dict Mapping from sheet names to data. """ writer = pd.ExcelWriter(filename) for sheet_name, obj in kwargs.items(): if isinstance(obj, dict): obj = _params_dict_to_dataframe(obj) if isinstance(obj, pd.DataFrame): obj.to_excel(writer, sheet_name=sheet_name) writer.save() writer.close()
Example #3
Source File: text2xlsx.py From tweets-collector with Apache License 2.0 | 6 votes |
def export_text2xlsx(infile, outfile, field_delimiter, number): df = pd.read_csv(infile, delimiter=field_delimiter, engine='python') rows_number = df.shape[0] if rows_number > number: data_frames = split_dataframe(df, number) frame_number = 1 for frame in data_frames: filename, ext = os.path.splitext(outfile) excel_file = "{}_{}.xlsx".format(filename, frame_number) writer = ExcelWriter(excel_file, engine='xlsxwriter') frame.to_excel(writer, 'sheet1') writer.save() frame_number += 1 else: writer = ExcelWriter(outfile) df.to_excel(writer, 'sheet1') writer.save()
Example #4
Source File: spia.py From pybel with MIT License | 6 votes |
def spia_matrices_to_excel(spia_matrices: SPIADataFrames, path: str) -> None: """Export a SPIA data dictionary into an Excel sheet at the given path. .. note:: # The R import should add the values: # ["nodes"] from the columns # ["title"] from the name of the file # ["NumberOfReactions"] set to "0" """ writer = pd.ExcelWriter(path, engine='xlsxwriter') for relation, df in spia_matrices.items(): df.to_excel(writer, sheet_name=relation, index=False) # Save excel writer.save()
Example #5
Source File: Search.py From staramr with Apache License 2.0 | 6 votes |
def _resize_columns(self, sheetname_dataframe, writer, max_width, text_wrap=True): """ Resizes columns in workbook. :param sheetname_dataframe: A map mapping the sheet name to a dataframe. :param writer: The ExcelWriter, which the worksheets already added using writer.to_excel :param max_width: The maximum width of the columns. :param text_wrap: Whether or not to turn on text wrapping if columns surpass max_width. :return: None """ workbook = writer.book wrap_format = workbook.add_format({'text_wrap': text_wrap}) for name in sheetname_dataframe: for i, width in enumerate(self._get_col_widths(sheetname_dataframe[name])): if width > max_width: writer.sheets[name].set_column(i, i, width=max_width, cell_format=wrap_format) else: writer.sheets[name].set_column(i, i, width=width)
Example #6
Source File: recorder.py From RLs with Apache License 2.0 | 6 votes |
def __init__(self, kwargs, *, cp_dir, log_dir, excel_dir, logger2file): self.log_dir = log_dir self.writer = tf.summary.create_file_writer(log_dir) # self.writer.set_as_default() self.checkpoint = tf.train.Checkpoint(**kwargs) self.saver = tf.train.CheckpointManager(self.checkpoint, directory=cp_dir, max_to_keep=5, checkpoint_name='ckpt') self.excel_writer = pd.ExcelWriter(excel_dir + '/data.xlsx') self.logger = self.create_logger( name='logger', console_level=logging.INFO, console_format='%(levelname)s : %(message)s', logger2file=logger2file, file_name=log_dir + 'log.txt', file_level=logging.WARNING, file_format='%(lineno)d - %(asctime)s - %(module)s - %(funcName)s - %(levelname)s - %(message)s' )
Example #7
Source File: sct_analyze_lesion.py From spinalcordtoolbox with MIT License | 6 votes |
def pack_measures(self): writer = pd.ExcelWriter(self.excel_name, engine='xlwt') self.measure_pd.to_excel(writer, sheet_name='measures', index=False, engine='xlwt') # Add the total column and row if self.path_template is not None: for sheet_name in self.distrib_matrix_dct: if '#' in sheet_name: df = self.distrib_matrix_dct[sheet_name].copy() df = df.append(df.sum(numeric_only=True, axis=0), ignore_index=True) df['total'] = df.sum(numeric_only=True, axis=1) df.iloc[-1, df.columns.get_loc('vert')] = 'total' df.to_excel(writer, sheet_name=sheet_name, index=False, engine='xlwt') else: self.distrib_matrix_dct[sheet_name].to_excel(writer, sheet_name=sheet_name, index=False, engine='xlwt') # Save pickle self.distrib_matrix_dct['measures'] = self.measure_pd with open(self.pickle_name, 'wb') as handle: pickle.dump(self.distrib_matrix_dct, handle) # Save Excel writer.save()
Example #8
Source File: export.py From pymongo-schema with GNU Lesser General Public License v3.0 | 6 votes |
def write_data(self, file_descr): """ Use dataframe to_excel to write into file_descr (filename) - open first if file exists. """ if os.path.isfile(file_descr): print(file_descr, 'exists') # Solution to keep existing data book = load_workbook(file_descr) writer = pd.ExcelWriter(file_descr, engine='openpyxl') writer.book = book writer.sheets = dict((ws.title, ws) for ws in book.worksheets) self.data_df.to_excel(writer, sheet_name='Mongo_Schema', index=True, float_format='%.2f') writer.save() else: self.data_df.to_excel(file_descr, sheet_name='Mongo_Schema', index=True, float_format='%.2f')
Example #9
Source File: results.py From SDGym with MIT License | 6 votes |
def write_results(results, summary, output): writer = pd.ExcelWriter(output, engine='xlsxwriter') cell_fmt = writer.book.add_format({ "font_name": "Arial", "font_size": "10" }) index_fmt = writer.book.add_format({ "font_name": "Arial", "font_size": "10", "bold": True, }) header_fmt = writer.book.add_format({ "font_name": "Arial", "font_size": "10", "bold": True, "bottom": 1 }) add_sheet(summary, 'Number of wins per version', writer, cell_fmt, index_fmt, header_fmt) for version in reversed(sorted(results.keys())): add_sheet(results[version], version, writer, cell_fmt, index_fmt, header_fmt) writer.save()
Example #10
Source File: export.py From patzilla with GNU Affero General Public License v3.0 | 6 votes |
def create(self): # A memory buffer as ExcelWriter storage backend buffer = BytesIO() self.workbook.filename = buffer # Create "cover" sheet self.write_cover_sheet() # Create "queries" sheet self.write_queries_sheet() # Create numberlist sheets self.write_numberlist_sheets() # Create "comments" sheet self.write_comments_sheet() # Save/persist ExcelWriter model self.writer.save() # Get hold of buffer content payload = buffer.getvalue() return payload
Example #11
Source File: create_excel.py From ditto with BSD 3-Clause "New" or "Revised" License | 6 votes |
def write_to_excel(comp_values): current_dir = os.path.realpath(os.path.dirname(__file__)) df = pd.DataFrame(comp_values) with pd.ExcelWriter(os.path.join(current_dir, "output" + ".xlsx")) as writer: for col in df: df1 = pd.concat([df[col], df[col].apply(pd.Series)], axis=1).drop( [col], axis=1 ) df1.to_excel(writer, sheet_name=col) worksheet = writer.sheets[col] worksheet.set_column(0, 19, 16) # Tests for running the plots independently # if __name__ == "__main__": # write_to_excel({'ieee_4node': {'cyme_output': {'1': [1.796043190045738, 5.388117303585387, 0.10784685850195197, 0.5904760366014294], '2': [2.0202605985010313, 6.518985500570162, 0.22820004614399358, 0.8158267453203445], '3': [0.2536631703405771, 0.8984713888134145, 0.06581202655980385, 0.24831713333607722], '4': [0.5340964354229437, 2.3119539172019614, 0.23396047634518685, 0.46239954705589636]}, 'demo_output': {}, 'gridlabd_output': {'sourcebus': [1.7960358301233543, 5.388107490370064, 1.6037668205527515, 6.415067282211007], 'node2': [11.615625751028661, 11.520529778987775, 9.073598525409436, 5.8609512435066655], 'node3': [1.2918305507375225, 1.2769209930818217, 1.0089298663668416, 0.6470699623879508], 'load4': [0.8640026377752124, 0.41845221416471984, 0.8640056993976751, 0.4184497947877163], 'node1': [11.95788550650638, 12.207304856874984, 9.189536257036401, 6.043847565567804]}, 'opendss_output': {'sourcebus': [0.0002463564270509644, 0.0007390394604267333, 0.00018858267905109123, 0.0007542723977164494], 'n2': [0.29564583680274237, 0.7167450994489304, 0.11638654713225582, 0.2358768460293939], 'n3': [0.06870713919610347, 0.23863115122119738, 0.048858783362332986, 0.18910852255356034], 'n4': [0.5051857600366919, 0.7781653125896516, 0.21133259350889166, 0.4141945608522559]}, 'synergi_output': {'node_61746791326': [0.000999999415454687, 0.0009999923015324673, 0.0009999996415073831, 0.0009999921771822826], 'node_61746791327': [1.4052433405517772, 0.8534261345199505, 1.100207413576314, 0.09925701086833777], 'node_61746839533': [0.5767771891663984, 1.2140898058567755, 0.5400898631419725, 1.138540681909626], 'node_61746842036': [2.2802968553458975, 1.9930774142792114, 1.8176517572334512, 1.1452767401123385]}}, 'ieee_13node': {'cyme_output': {'650': [1.6442002249862027, 2.525071217368887, 0.00175590064769382, 0.013818660541591976], '633': [2.191962671993786, 2.7645145724914206, 0.13502678340376117, 0.30258402028428943], '634': [0.03373885959967424, 0.0437948202860033, 0.006851233217346727, 0.01281928879314106], '7': [1.6444948113338582, 2.5248296649991535, 0.0027565424317121945, 0.014811194353743895], 'rg60': [1.6444956621823732, 2.524829864977333, 0.002756707027920302, 0.014813876873885912], '632': [2.1103087225772716, 2.527515777000864, 0.0791161472443358, 0.2320482533916044], '652': [1.2506186991395154, 1.2878722947329384, 1.2506186991395154, 1.2878722947329384], '611': [1.1239472985549395, 1.3770646713358636, 1.1239472985549395, 1.3770646713358636], '692': [2.631742710227492, 2.70681263013228, 0.16404792757812026, 0.44179116013620445], '645': [2.2388323604857887, 2.784026210466048, 0.1850730276433944, 0.31739303544051045], '646':[2.317657028721549, 2.949917446377885, 0.24850513527121498, 0.3676808102436452], '671': [2.630682474394549, 2.706364834017407, 0.16301316760562778, 0.44085146992520197], '675': [2.7067614833052813, 2.7173749384388346, 0.23870615585157873, 0.47708961405564254], '684': [2.6970360888981926, 2.8036701407791496, 0.23157910972791085, 0.5317115549334911], '680': [2.7200580191836536, 3.2662254613120227, 0.19824054093535326, 0.5540558678888127]}, 'demo_output': {}, 'gridlabd_output': {}, 'opendss_output': {'sourcebus': [0.17960357997560872, 0.5388107499674782, 0.16044213136336458, 0.6414216083753025], '650': [3.46173977571697e-05, 0.00027688611330264445, 0.00024463297015852746, 0.0011161493255213788], 'rg60': [0.0003803596442662037, 0.0006225803505498496, 0.0005903829452526287, 0.0014616392165030185], '633': [0.38047984831844595, 0.8729366035169337, 0.13271664569004354, 0.28972479926771977], '634': [0.010133922766079825, 0.020570453379602505, 0.006830751758597249, 0.012931935773494248], '671': [0.6108771018713602, 1.3265863413420522, 0.16093756369535386, 0.42423496513300524], '645': [0.4373080269407039, 0.8584390204064538, 0.18280284255143675, 0.32024566175842095], '646': [0.536560726260114, 0.9883607573047537, 0.24564544687076215, 0.36912074589189336], '692': [0.6119751654440972, 1.3274110796789444, 0.16196524206540175, 0.4251679482288616], '675': [0.7447836834016821, 1.3496142794655475, 0.20882248763406844, 0.46165856154276824], '611': [0.47109807838681994, 0.8597942885112269, 0.47109807838681994, 0.8597942885112269], '652': [0.5900525671135924, 0.8394468662430931, 0.5900525671135924, 0.8394468662430931], '670': [0.3852338293138269, 0.8975161336344963, 0.1039477429886314, 0.2879817616207214], '632': [0.2799093266146708, 0.6803831271514527, 0.07670340676752073, 0.21819919790776035], '680': [0.7346431542735055, 1.6877642140995293, 0.19615949605780691, 0.5372620670065573], '684': [0.7244901189090343, 1.397819420078607, 0.22833210323410577, 0.4698019455814719]}, 'synergi_output': {}}})
Example #12
Source File: pandas_bridge.py From evo with GNU General Public License v3.0 | 6 votes |
def save_df_as_table(df, path, format_str=SETTINGS.table_export_format, transpose=SETTINGS.table_export_transpose, confirm_overwrite=False): if confirm_overwrite and not user.check_and_confirm_overwrite(path): return if transpose: df = df.T if format_str == "excel": # requires xlwt and/or openpyxl to be installed with pd.ExcelWriter(path) as writer: df.to_excel(writer) else: getattr(df, "to_" + format_str)(path) logger.debug("{} table saved to: {}".format( format_str, path))
Example #13
Source File: test_io.py From modin with Apache License 2.0 | 6 votes |
def test_to_excel(): modin_df = create_test_modin_dataframe() pandas_df = create_test_pandas_dataframe() TEST_EXCEL_DF_FILENAME = "test_df.xlsx" TEST_EXCEL_pandas_FILENAME = "test_pandas.xlsx" modin_writer = pandas.ExcelWriter(TEST_EXCEL_DF_FILENAME) pandas_writer = pandas.ExcelWriter(TEST_EXCEL_pandas_FILENAME) modin_df.to_excel(modin_writer) pandas_df.to_excel(pandas_writer) modin_writer.save() pandas_writer.save() assert assert_files_eq(TEST_EXCEL_DF_FILENAME, TEST_EXCEL_pandas_FILENAME) teardown_test_file(TEST_EXCEL_DF_FILENAME) teardown_test_file(TEST_EXCEL_pandas_FILENAME)
Example #14
Source File: test_io.py From pyam with Apache License 2.0 | 6 votes |
def test_io_xlsx(test_df, meta_args): # add column to `meta` test_df.set_meta(['a', 'b'], 'string') # write to xlsx (direct file name and ExcelWriter, see bug report #300) file = 'testing_io_write_read.xlsx' for f in [file, pd.ExcelWriter(file)]: test_df.to_excel(f, **meta_args[0]) if isinstance(f, pd.ExcelWriter): f.close() # read from xlsx import_df = IamDataFrame(file, **meta_args[1]) # assert that IamDataFrame instances are equal and delete file assert_iamframe_equal(test_df, import_df) os.remove(file)
Example #15
Source File: core.py From pyam with Apache License 2.0 | 6 votes |
def export_meta(self, excel_writer, sheet_name='meta'): """Write the 'meta' indicators of this object to an Excel sheet Parameters ---------- excel_writer : str, path object or ExcelWriter object any valid string path, :class:`pathlib.Path` or :class:`pandas.ExcelWriter` sheet_name : str name of sheet which will contain dataframe of 'meta' indicators """ if not isinstance(excel_writer, pd.ExcelWriter): close = True excel_writer = pd.ExcelWriter(excel_writer) write_sheet(excel_writer, sheet_name, self.meta, index=True) if close: excel_writer.close()
Example #16
Source File: sync.py From CO2MPAS-TA with European Union Public License 1.1 | 6 votes |
def template(output_file, cycle_type, gear_box_type, wltp_class): """ Writes a sample template OUTPUT_FILE. OUTPUT_FILE: SYNCING input template file (.xlsx). [default: ./datasync.xlsx] """ import pandas as pd from co2mpas.core.model.physical.cycle import dsp theoretical = sh.selector(['times', 'velocities'], dsp(inputs=dict( cycle_type=cycle_type.upper(), gear_box_type=gear_box_type, wltp_class=wltp_class, downscale_factor=0 ), outputs=['times', 'velocities'], shrink=True)) base = dict.fromkeys(( 'times', 'velocities', 'target gears', 'engine_speeds_out', 'engine_coolant_temperatures', 'co2_normalization_references', 'alternator_currents', 'battery_currents', 'target fuel_consumptions', 'target co2_emissions', 'target engine_powers_out' ), []) data = dict(theoretical=theoretical, dyno=base, obd=base) os.makedirs(osp.dirname(output_file), exist_ok=True) with pd.ExcelWriter(output_file) as writer: for k, v in data.items(): pd.DataFrame(v).to_excel(writer, k, index=False) return data
Example #17
Source File: excel.py From CO2MPAS-TA with European Union Public License 1.1 | 6 votes |
def _clone_excel(file_name): from urllib.error import URLError import openpyxl try: from urllib.request import urlopen book = openpyxl.load_workbook(urlopen(file_name)) except (ValueError, URLError): with open(file_name, 'rb') as file: book = openpyxl.load_workbook(file) import io import pandas as pd fd = io.BytesIO() # noinspection PyTypeChecker writer = pd.ExcelWriter( fd, engine='openpyxl', optimized_write=True, write_only=True ) writer.book = book writer.sheets.update(dict((ws.title, ws) for ws in book.worksheets)) return writer, fd
Example #18
Source File: runner_analyzer.py From workload-collocation-agent with Apache License 2.0 | 5 votes |
def multiple_dfs(self, df_list, sheets, file_name, spaces): writer = pd.ExcelWriter(file_name, engine='xlsxwriter') row = 0 for dataframe in df_list: dataframe.to_excel(writer, sheet_name=sheets, startrow=row, startcol=0) row = row + len(dataframe.index) + spaces + 1 writer.save()
Example #19
Source File: runner_analyzer.py From workload-collocation-agent with Apache License 2.0 | 5 votes |
def export_to_xls(self): logging.debug("Saving xls to {} for experiment {}".format(self.export_file_path, self.experiment_index)) runner_analyzer_results_dir = os.path.join(self.experiment_meta.data_path, 'runner_analyzer') if not os.path.isdir(runner_analyzer_results_dir): os.mkdir(runner_analyzer_results_dir) with pd.ExcelWriter(os.path.join(runner_analyzer_results_dir, 'tasks_summaries_{}.xlsx'.format( self.experiment_index))) as writer: self.tasks_summaries[0].to_excel(writer, sheet_name='BASELINE') self.tasks_summaries[1].to_excel(writer, sheet_name='KUBERNETES_BASELINE') self.tasks_summaries[2].to_excel(writer, sheet_name='WCA-SCHEDULER') with pd.ExcelWriter(os.path.join(runner_analyzer_results_dir, 'node_summaries_{}.xlsx'.format( self.experiment_index))) as writer: self.node_summaries[0].to_excel(writer, sheet_name='BASELINE') self.node_summaries[1].to_excel(writer, sheet_name='KUBERNETES_BASELINE') self.node_summaries[2].to_excel(writer, sheet_name='WCA-SCHEDULER') with pd.ExcelWriter(os.path.join(runner_analyzer_results_dir, 'workloads_summaries_{}.xlsx'.format( self.experiment_index))) as writer: self.workloads_summaries[0].to_excel(writer, sheet_name='BASELINE') self.workloads_summaries[1].to_excel(writer, sheet_name='KUBERNETES_BASELINE') self.workloads_summaries[2].to_excel(writer, sheet_name='WCA-SCHEDULER')
Example #20
Source File: dash-download-file-link-server.py From dash-recipes with MIT License | 5 votes |
def update_href(dropdown_value): df = pd.DataFrame({dropdown_value: [1, 2, 3]}) relative_filename = os.path.join( 'downloads', '{}-download.xlsx'.format(dropdown_value) ) absolute_filename = os.path.join(os.getcwd(), relative_filename) writer = pd.ExcelWriter(absolute_filename) df.to_excel(writer, 'Sheet1') writer.save() return '/{}'.format(relative_filename)
Example #21
Source File: JSON-to-Excel.py From Table-Extractor-From-Image with Apache License 2.0 | 5 votes |
def exportInExcel(dataFrame,noOfRows,f): try: #Copy the text in a list a = copy.copy(dataFrame) for i in range(0, noOfRows): for j in range(0, len(dataFrame[i])): if(dataFrame[i][j]=='-'): a[i][j]='-' else: a[i][j]=dataFrame[i][j].text print(' ') #Convert the list into a pandas.DataFrame df = pd.DataFrame(a) print(df) if os.path.exists(exportPath): #If any excel sheet exists book = load_workbook(exportPath) writer = pd.ExcelWriter(exportPath, engine='openpyxl') writer.book = book writer.sheets = dict((ws.title, ws) for ws in book.worksheets) df.to_excel(writer, f) writer.save() else: #If it doesn't, then create a new file writer = ExcelWriter(exportPath) df.to_excel(writer,f) writer.save() except Exception as e: print("Exception in exportInExcel():", e) #Main Function
Example #22
Source File: pandas_mongo_bridge.py From kobo-predict with BSD 2-Clause "Simplified" License | 5 votes |
def export_to(self, file_path, batchsize=1000): self.xls_writer = ExcelWriter(file_path) # get record count record_count = self._query_mongo(count=True) # query in batches and for each batch create an XLSDataFrameWriter and # write to existing xls_writer object start = 0 header = True while start < record_count: cursor = self._query_mongo(self.filter_query, start=start, limit=batchsize) data = self._format_for_dataframe(cursor) # write all cursor's data to their respective sheets for section_name, section in self.sections.iteritems(): records = data[section_name] # TODO: currently ignoring nested repeats # so ignore sections that have 0 records if len(records) > 0: # use a different group delimiter if needed columns = section["columns"] if self.group_delimiter != DEFAULT_GROUP_DELIMITER: columns = [self.group_delimiter.join(col.split("/")) for col in columns] columns = columns + self.EXTRA_COLUMNS writer = XLSDataFrameWriter(records, columns) writer.write_to_excel(self.xls_writer, section_name, header=header, index=False) header = False # increment counter(s) start += batchsize time.sleep(0.1) self.xls_writer.save()
Example #23
Source File: os.py From Pointer-Generator with MIT License | 5 votes |
def list_excel(columns, data, file_name): """ # Arguments - columns {list}: ["a","b"] - data {list}:[["a-v","b-v"]["a-v1","a-v2"]] """ df = pd.DataFrame(columns=columns,data=data) writer = pd.ExcelWriter(file_name+'.xlsx') df.to_excel(writer,'Sheet1') return "Done"
Example #24
Source File: excel.py From financial_life with Apache License 2.0 | 5 votes |
def report(simulation, filename='report.xls'): """ This function generates a report as an excel sheet. simulation the simualation that should be exported to excel filename filename of the excel file """ writer = pd.ExcelWriter(filename) for account in simulation.accounts: df = account.report.as_df() df.to_excel(writer, sheet_name=account.name) writer.save()
Example #25
Source File: export.py From SniffAir with MIT License | 5 votes |
def main(workspace, path, name): table_name = ['accessPoints', 'ProbeRequests', 'ProbeRequests', 'EAP', 'Hidden_SSID', 'inscope_accessPoints', 'inscope_ProbeRequests', 'inscope_ProbeResponses'] sheet_name = ['AccessPoints', 'ProbeRequests', 'ProbeRequests', 'EAP', 'Hidden_SSID', 'Inscope_AccessPoints', 'Inscope_ProbeRequests', 'Inscope_ProbeResponses'] ws = workspace q = queries() ws1 = q.db_connect(ws) writer = dp.ExcelWriter(path+name+'.xlsx', engine='xlsxwriter') j = 0 print "Exporting: "+path+name+'.xlsx' for tbn in table_name: try: td = dp.read_sql('select * from '+tbn+'', ws1) if td.empty: pass j +=1 print colors.RD + "[-]" + colors.NRM + " Skipping: " + sheet_name[j] + ". No Data in table." else: td.to_excel(writer, sheet_name=''+sheet_name[j]+'', index=False) j +=1 print colors.GRN + "[+]" + colors.NRM + " Exporting: " + sheet_name[j] + "." except ValueError: continue except pandas.io.sql.DatabaseError: continue writer.save() print "Export Completed"
Example #26
Source File: export.py From tridesclous with MIT License | 5 votes |
def write_out_data(self, out_data, filename): assert HAS_PANDAS writer = pd.ExcelWriter(filename+'.xlsx') for key, (spike_indexes, spike_labels) in out_data.items(): df = pd.DataFrame() df['index'] = spike_indexes df['label'] = spike_labels df.to_excel(writer, sheet_name=key, index=False) writer.save()
Example #27
Source File: export_spreadsheet.py From airbnb-data-collection with MIT License | 5 votes |
def export_city_summary(ab_config, city, project, start_date): logging.info(" ---- Exporting summary spreadsheet" + " for " + city + " using project " + project) city_bar = city.replace(" ", "_").lower() today = dt.date.today().isoformat() xlsxfile = ("./{project}/slee_{project}_{city_bar}_summary_{today}.xlsx" ).format(project=project, city_bar=city_bar, today=today) writer = pd.ExcelWriter(xlsxfile, engine="xlsxwriter") df = survey_df(ab_config, city, start_date) city_view = city_view_name(ab_config, city) logging.info("Total listings...") df = total_listings(ab_config, city_view) df.to_excel(writer, sheet_name="Total Listings", index=False) logging.info("Listings by room type...") df = by_room_type(ab_config, city_view) df["Listings"].to_excel(writer, sheet_name="Listings by room type", index=True) df["Reviews"].to_excel(writer, sheet_name="Reviews by room type", index=True) logging.info("Listings by host type...") df = by_host_type(ab_config, city_view) df["Hosts"].to_excel(writer, sheet_name="Hosts by host type", index=True) df["Listings"].to_excel(writer, sheet_name="Listings by host type", index=True) df["Reviews"].to_excel(writer, sheet_name="Reviews by host type", index=True) logging.info("Listings by neighborhood...") df = by_neighborhood(ab_config, city_view) df["Listings"].to_excel(writer, sheet_name="Listings by Neighborhood", index=True) df["Reviews"].to_excel(writer, sheet_name="Reviews by Neighborhood", index=True) logging.info("Saving " + xlsxfile) writer.save()
Example #28
Source File: evaluation.py From MOTDT with MIT License | 5 votes |
def save_summary(summary, filename): import pandas as pd writer = pd.ExcelWriter(filename) summary.to_excel(writer) writer.save()
Example #29
Source File: inputs.py From CityEnergyAnalyst with MIT License | 5 votes |
def database_dict_to_file(db_dict, db_path): with pandas.ExcelWriter(db_path) as writer: for sheet_name, data in db_dict.items(): df = pandas.DataFrame(data).dropna(axis=0, how='all') df.to_excel(writer, sheet_name=sheet_name, index=False) print('Database file written to {}'.format(db_path))
Example #30
Source File: reporting.py From CityEnergyAnalyst with MIT License | 5 votes |
def full_report_to_xls(tsd, output_folder, basename): """ this function is to write a full report to an ``*.xls`` file containing all intermediate and final results of a single building thermal loads calculation""" df = pd.DataFrame(tsd) # Create a Pandas Excel writer using XlsxWriter as the engine. #timestamp = datetime.datetime.now().strftime('%Y-%m-%d-%H-%M-%S') #output_path = os.path.join(output_folder,"%(basename)s-%(timestamp)s.xls" % locals()) output_path = os.path.join(output_folder, "%(basename)s.xls" % locals()) writer = pd.ExcelWriter(output_path, engine='xlwt') df.to_excel(writer, na_rep='NaN') # Close the Pandas Excel writer and output the Excel file. writer.save() writer.close()