def func(n_nlicks,radio_value): if radio_value=='Fruit': fd1=fd[fd['Item Type']=='Fruit'] return send_data_frame(fd1.to_excel, "Fruit.xlsx", index=False) elif radio_value=='Vegitable': fd2=fd[fd['Item Type']=='Vegitable'] return send_data_frame(fd2.to_excel, "Vegitable.xlsx", index=False) else: return send_data_frame(fd.to_excel, "All_Data.xlsx", index=False)
def download_table(csvdownload, xlsdownload, jsondownload, data): df = pd.read_json(data, orient="records") if int(csvdownload) > int(xlsdownload) and int(csvdownload) > int( jsondownload): return send_data_frame(df.to_csv, "data.csv", index=False) elif int(xlsdownload) > int(csvdownload) and int(xlsdownload) > int( jsondownload): return send_data_frame(df.to_excel, r"data.xlsx", index=False) elif int(jsondownload) > int(csvdownload) and int(jsondownload) > int( xlsdownload): return send_data_frame(df.to_json, "data.json", orient="records")
def twoDownload(n_clicks, children): if (n_clicks is not None) and (n_clicks > 0) and (children is not None): searchDate = children.strip('Sun_') searchDate = searchDate.strip('.fits') try: specData = read_csv(two.find_one({'filename': {'$regex': '.*' + searchDate + '.*'}})) return send_data_frame(specData.to_csv, filename=searchDate + ".2d_spectrum.csv") except: entry = one.find_one({}) specData = read_csv(entry) return send_data_frame(specData.to_csv, filename="2D_sprectrum.csv")
def downloadCallback(n_clicks): if not n_clicks is None: dl = self.downloads['grid'] return send_data_frame(dl['data'].to_excel, dl['filename'] + '.xlsx', index=True) else: return no_update
def generate_csv(drtime, Locname): if Locname is not None and drtime is not None: df = pd.read_csv("currdf.csv") df["KeyTime"] = df.KeyTime.astype('datetime64[ns]') df = df[(df["Location"] == Locname) & (df["KeyTime"] == drtime)] #can do stuff to dataframe here (trim down columns or w/e) return send_data_frame(df.to_csv, filename="battdata.csv", index=False)
def generar_csv(n_clicks, data, titulo_reporte) : """ Callback para generar un archivo csv con los datos de la matrícula de todas las escuelas del reporte. Args: n_clicks (int): número de veces que se ha dado click al botón de descargar csv, o None si no se a presionado ninguna vez. data (dict): diccionario que contiene los datos de la sesión, incluidos los de las escuelas. titulo_reporte (str): título del reporte o None si no se a escrito nada en el input del título del reporte. Returns: Genera y descarga un archivo csv con los datos de la matrícula y la predicción de todas las escuelas con el nombre del título del reporte. """ titulo_reporte = titulo_reporte or 'Reporte sin titulo' if n_clicks : escuelas = data['escuelas'] primer_anio = min((escuelas[cct]['primer_anio'] for cct in escuelas)) ultimo_anio = max((len(escuelas[cct]['matricula']) + escuelas[cct]['primer_anio'] for cct in escuelas)) + 5 nombre_columnas = ["cct"] + ["%d-%d" % (anio, anio + 1) for anio in range(primer_anio, ultimo_anio)] matricula = [ [cct] + [ (escuelas[cct]['matricula'] + escuelas[cct]['pred'])[anio - escuelas[cct]['primer_anio']] if anio >= escuelas[cct]['primer_anio'] else '' for anio in range(primer_anio, ultimo_anio)] for cct in escuelas] dataframe = pd.DataFrame(matricula, columns = nombre_columnas) return send_data_frame(dataframe.to_csv, filename = "%s.csv" % (titulo_reporte)) else : raise PreventUpdate
def func(n_clicks): if os.path.exists('tmp.csv'): print('exists') df = pd.read_csv('tmp.csv') fn = 'poem_' + datetime.now().strftime('%Y_%m_%d_%H_%M_%S') + '.csv' os.remove('tmp.csv') return send_data_frame(df.to_csv, fn, index=False)
def func(n_clicks, prod_cons_matrix, region_lvl): #temp_dff = load_matrix(str(prod_cons_path), str(prod_cons_matrix)) fpath = set_filepath(auth.get_current_username(), results_path, results_filepath) + 'output.csv' return send_data_frame( download_df.to_csv, fpath ) #results_filepath)#"mydf.csv") # dash_extensions.snippets: send_data_frame
def generate_csv(n_clicks, raw): # I had to write this line otherwise it would automatically download the file even if you didn't click the download button...seems hacky if n_clicks is None or raw == None: raise PreventUpdate json_data = json.loads(raw) df = pd.DataFrame.from_dict(json_data, orient='columns') return send_data_frame(df.to_csv, filename="raw_data.csv")
def update_uploads( n_clicks, techSheetFilename, techSheetContents, ): if n_clicks is not None: if techSheetFilename is not None: list_of_df = [ parseContents(c, n) for c, n in zip(techSheetContents, techSheetFilename) ] """ Do something cool with df """ outputDF = [ send_data_frame( df.to_csv, filename=df_name, ) for df, df_name in zip(list_of_df, techSheetFilename) ] + [None for i in range(5 - len(techSheetFilename))] return outputDF else: return [None for i in range(5)] else: return [None for i in range(5)]
def func(n_clicks, selected_subs, selected_sig1): selected_sig1.append('Substation') selected_sig1.append('datetime') sel_customer = customer[selected_sig1] sel_customer = sel_customer[sel_customer['Substation'].isin(selected_subs)] return send_data_frame(sel_customer.to_csv, "substations.csv", index=False)
def func(n_clicks, selected_sites, selected_sig2): selected_sig2.append('Site') selected_sig2.append('datetime') sel_weather = weather[selected_sig2] sel_weather = sel_weather[sel_weather['Site'].isin(selected_sites)] return send_data_frame(sel_weather.to_csv, "weather.csv", index=False)
def export_button_click(n_clicks, data): if n_clicks is None: return None df = pd.DataFrame(data) df.drop(columns=['PointsHistory', 'Similarity'], inplace=True) df['Points'] = df['Points'].apply(lambda x: x[0]) return send_data_frame(df.to_csv, 'points.csv', sep=';', index=False)
def generate_csv(data, n1): if data is not None: data = json.loads(data) data = pd.DataFrame.from_dict(data) if n1 is not None: return send_data_frame(data.to_csv, filename=f"sample_predictions_{datetime.today().strftime('%Y-%m-%d')}.csv", index = False) else: return None
def download_strengths_csv(n_clicks: Union[int, None]) -> Union[dict, None]: """ダウンロードボタンがクリックされたら資質ファイルをダウンロード""" if type(n_clicks) == int: df = pd.read_csv(strengths_path) dataframe_content = send_data_frame(df.to_csv, filename="member_strengths.csv") return dataframe_content else: return None
def generate_csv(n_clicks, jsonified_cleaned_data, jsonified_cleaned_data2, jsonified_cleaned_data3): if n_clicks is None: raise PreventUpdate else: if jsonified_cleaned_data: df = pd.read_json(jsonified_cleaned_data, orient='split') return send_data_frame(df.to_csv, filename="some_name.csv") else: if jsonified_cleaned_data2: df = pd.read_json(jsonified_cleaned_data2, orient='split') return send_data_frame(df.to_csv, filename="some_name.csv") else: if jsonified_cleaned_data3: df = pd.read_json(jsonified_cleaned_data3, orient='split') return send_data_frame(df.to_csv, filename="some_name.csv") else: raise PreventUpdate
def generate_csv(n_clicks, data): if n_clicks < 1: raise PreventUpdate # df = cache.get(session_id) df = pd.DataFrame(data) if df is None: raise PreventUpdate else: df['Sample_Date'] = pd.to_datetime(df['Sample_Date']) df['Sample_Date'] = df['Sample_Date'].dt.strftime('%m/%d/%Y') return send_data_frame(df.to_csv, filename='querydata.csv')
def __download_graph_data(self, *inputs): """Download data associated with a figure""" # prep data file to_concat = [] for trace_name, df in self.data.items(): df_to_concat = df.copy() df_to_concat.insert(0, "trace", trace_name) to_concat.append(df_to_concat) df = pd.concat(to_concat).reset_index() return send_data_frame(df.to_excel, f"{df.columns[-1]}.xlsx")
def csv_download(cb_clicks, time, HRR, jobidd): #---------UNPACK JSON DATA flat_time = json.loads(time) flat_HRR = json.loads(HRR) #---------CREATE PANDAS DATAFRAME int_dict = {'Time (s)': flat_time, 'HRR (kW/s^2)': flat_HRR} t2_df = pd.DataFrame(int_dict) csv_title = 't_squard - ' + str(jobidd) + '.csv' return send_data_frame(t2_df.to_csv, filename=csv_title)
def download(n_clicks, years_range, nbds): if n_clicks: nbds = json.loads(nbds) if nbds else [default_nbd_id] df_nbh = df[df["nbhid"].isin(nbds)] # filter chosen states nbhname = df_nbh['nbh_name'].iloc[0] df_nbh = df_nbh[(df_nbh['CREATION YEAR'] <= years_range[1]) & (df_nbh['CREATION YEAR'] >= years_range[0])] return send_data_frame(df_nbh.to_csv, "".join([ "kc311_", "_".join(nbds), '_', nbhname, '_', str(years_range[0]), '-', str(years_range[1]), ".csv" ]), index=False)
def export_to_csv(self, n_clicks): try: team = int(request.cookies["team_metrics_idx"]) except BadRequestKeyError: team = 0 tm = self.projects[team] if n_clicks > 0: logging.debug("Downloading CSV file") return send_data_frame( tm.cycle_data.to_csv, filename=f"{tm.name}.csv", )
def export_range_csv(n_clicks, start_date, end_date, plot, sensor_tag, df_save): if n_clicks > 0: out_filename = str(start_date) + "_" + str(end_date) + "_data.csv" df = pd.read_json(df_save, orient="split") visible_traces = [] for key in plot["data"]: if key.get("visible") == 1 or str(key.get("visible")) == "None": visible_traces.append(key.get(sensor_tag)) df = df[df[sensor_tag].isin(visible_traces)] return send_data_frame(df.sort_values(by=["ip", "datetime"]).to_csv, out_filename, index=False)
def generate_csv(n_clicks, plotted_data): """ Callback to download graph data to a CSV file. :param n_clicks: number of times the download button was clicked. Used here to simply detect if the button was clicked. :param plotted_data: the data that is currently displayed :return: an instruction for the browser to initiate a download of the DataFrame """ if n_clicks: data = pd.read_json(plotted_data, orient="split") return send_data_frame(data.to_csv, "ukbb_metadata_variable_subset.csv", index=False)
def export_csv(n_clicks, date, plot, sensor_tag, df_save): if n_clicks > 0: data_source = get_sensor_datafile_name(date) df = pd.read_json(df_save, orient="split") out_filename = data_source.split("_")[0] + "_data.csv" visible_traces = [] for key in plot["data"]: if key.get("visible") == 1 or str(key.get("visible")) == "None": visible_traces.append(key.get(sensor_tag)) df = df[df[sensor_tag].isin(visible_traces)] return send_data_frame(df.sort_values(by=["ip", "datetime"]).to_csv, out_filename, index=False) return dash.no_update
def save_to_csv(n_clicks, n_intervals, sec): no_notification = html.Plaintext("", style={'margin': "0px"}) notification_text = html.Plaintext("The Shown Table Data has been saved to the excel sheet.", style={'color': 'green', 'font-weight': 'bold', 'font-size': 'large'}) input_triggered = dash.callback_context.triggered[0]["prop_id"].split(".")[0] if input_triggered == "excel_btn" and n_clicks: sec = 10 return send_data_frame(df_table_content.to_csv, filename="Labeled_Eye_Images.csv"), notification_text, sec elif input_triggered == 'excel_notification_interval' and sec > 0: sec = sec - 1 if sec > 0: return None, notification_text, sec else: return None, no_notification, sec elif sec == 0: return None, no_notification, sec
def func(n_nlicks,url,dados): ctx = dash.callback_context trigger_id = ctx.triggered[0]["prop_id"].split(".")[0] if trigger_id != 'btn-exp-tabela': raise PreventUpdate else: nome_arquivo = 'arquivo.xlsx' if url == '/' or url == '/contratos': nome_arquivo = 'tabela_contratos.xlsx' if url == '/balanco': nome_arquivo = 'tabela_balanço.xlsx' if url == '/custos': nome_arquivo = 'tabela_custos.xlsx' df = pd.DataFrame(dados) if n_nlicks != None: return send_data_frame(df.to_excel, nome_arquivo, index=False)
def func(n_nlicks,url,fig): ctx = dash.callback_context trigger_id = ctx.triggered[0]["prop_id"].split(".")[0] if trigger_id != 'btn-exp-grafico': raise PreventUpdate else: nome_arquivo = 'arquivo.xlsx' if url == '/' or url == '/contratos': nome_arquivo = 'grafico_contratos.xlsx' if url == '/balanco': nome_arquivo = 'grafico_balanço.xlsx' if url == '/custos': nome_arquivo = 'grafico_custos.xlsx' if n_nlicks != None: df2 = pd.DataFrame(gf.retorna_grafico(fig)) return send_data_frame(df2.to_excel, nome_arquivo, index=False)
def generar_csv(n_clicks, data, id_escuela): """ Callback para generar un archivo csv con los datos de la matrícula de la escuela del reporte. Args: n_clicks (int): número de veces que se ha dado click al botón de descargar csv, o None si no se a presionado ninguna vez. data (dict): diccionario que contiene los datos de la sesión, incluidos los de las escuelas. id_escuela (dict): diccionario que contiene el id del objeto para descargar el archivo csv, que a su vez contiene la cct de la escuela. Returns: Genera y descarga un archivo csv con los datos de la matrícula y la predicción de todas las escuelas con el nombre de la cct de la escuela. """ if n_clicks: cct = id_escuela['index'] escuelas = data['escuelas'] primer_anio = escuelas[cct]['primer_anio'] ultimo_anio = len( escuelas[cct]['matricula']) + escuelas[cct]['primer_anio'] + 5 nombre_columnas = ["cct"] + [ "%d-%d" % (anio, anio + 1) for anio in range(primer_anio, ultimo_anio) ] matricula = [cct] + [(escuelas[cct]['matricula'] + escuelas[cct]['pred'])[anio - primer_anio] for anio in range(primer_anio, ultimo_anio)] dataframe = pd.DataFrame([matricula], columns=nombre_columnas) return send_data_frame(dataframe.to_csv, filename="%s.csv" % (cct)) else: raise PreventUpdate
def generate_csv(n_clicks, final_df, EpitopeDB_type): if n_clicks != 0: if final_df is not None: final_df = pd.read_json(final_df, orient='split') return send_data_frame(final_df.to_csv, filename=f"DESA, {EpitopeDB_type}.csv")
def func(n_clicks): return send_data_frame(df.to_excel, "mydf.xls")