def update_table(the_location, the_benchmark, clicks):
if clicks % 2 == 0:
if not all((the_benchmark, the_location)):
the_location = location_start
the_benchmark = bench_start
df_perf_summary = fm.fin_met(ticker_start, the_benchmark)
else:
df_perf_summary = fm.fin_met(ticker_start, the_benchmark)
else:
df_perf_summary = fm.fin_met(tickers_bench[the_benchmark],
ticker_start)
modifed_perf_table = make_dash_table(df_perf_summary)
modifed_perf_table.insert(
0,
html.Tr([
html.Td([]),
html.Td(['Company'], colSpan=4, style={'text-align': "center"}),
html.Td(['Benchmark'], colSpan=4, style={'text-align': "center"})
],
style={
'background': 'white',
'font-weight': '600'
}))
return modifed_perf_table
def dadr(diffy):
diffy = json.loads(diffy)
dict_items = global_store(diffy["the_location"], diffy["the_benchmark"],
diffy["clicks"])
df_perf_summary = fm.fin_met(dict_items["Benchmark"],
dict_items["Company"])
def make_dash_table(df):
''' Return a dash definitio of an HTML table for a Pandas dataframe '''
table = []
for index, row in df.iterrows():
html_row = []
for i in range(len(row)):
html_row.append(html.Td([row[i]]))
table.append(html.Tr(html_row))
return table
modifed_perf_table = make_dash_table(df_perf_summary)
modifed_perf_table.insert(
0,
html.Tr([
html.Td([]),
html.Td(['Company'], colSpan=4, style={'text-align': "center"}),
html.Td(['Benchmark'], colSpan=4, style={'text-align': "center"})
],
style={
'background': 'white',
'font-weight': '600'
}))
return modifed_perf_table
diffy["first_option_target_code"]]["Company Metrics"]
dict_info_output = ext_info_dict[
diffy["first_option_target_code"]]["Stakeholder Description"]
c_metrics_df_output_1 = ext_info_dict[
diffy["first_option_target_code"]]["Company Metrics"]
stock_plot_desc_output, _ = describe(diffy["first_option_target_code"],
diffy["first_option_bench_code"])
title_output = str(diffy["first_option_target_long_name"]) + " 4-D Report"
location_output = str(diffy["target_location_address"]) + " Location"
profile_output = str(
diffy["first_option_target_location_small_name"]) + " Profile"
df_perf_summary_output = fm.fin_met(diffy["first_option_bench_code"],
diffy["first_option_target_code"])
first_dict = {}
#first_dict[""]
first_dict[
"target_location_small_drop_down_options"] = target_location_small_drop_down_options
first_dict["bench_code_drop_down_options"] = bench_code_drop_down_options
first_dict[
"first_option_target_location_small_name"] = first_option_target_location_small_name
first_dict["first_option_target_code"] = first_option_target_code
first_dict["first_option_bench_code"] = first_option_bench_code
first_dict["s_metrics_df_output"] = s_metrics_df_output
first_dict["dict_info_output"] = dict_info_output
first_dict["c_metrics_df_output"] = c_metrics_df_output
def make_dash_table(df):
''' Return a dash definitio of an HTML table for a Pandas dataframe '''
table = []
for index, row in df.iterrows():
html_row = []
for i in range(len(row)):
html_row.append(html.Td([row[i]]))
table.append(html.Tr(html_row))
return table
##
#df_perf_summary = pd.read_csv("17530.csv")
df_perf_summary = fm.fin_met(ticker_start, bench_start)
modifed_perf_table = make_dash_table(df_perf_summary)
modifed_perf_table.insert(
0,
html.Tr([
html.Td([]),
html.Td(['Company'], colSpan=4, style={'text-align': "center"}),
html.Td(['Benchmark'], colSpan=4, style={'text-align': "center"})
],
style={
'background': 'white',
'font-weight': '600'
}))
desc = describe(diffy["code_start"], diffy["the_benchmark"])
stock_plot_desc_output = desc
title = str(diffy["code_start_small"]) + " 4-D Report"
title_output = title
title = str(diffy["long_addy"]) + " Location"
location_output = title
title = str(diffy["the_location"]) + " Profile"
profile_output = title
df_perf_summary_output = fm.fin_met(diffy["the_benchmark"],
diffy["code_start"])
df_perf_summary_output = fm.fin_met(diffy["the_benchmark"],
diffy["code_start"])
first_dict = {}
first_dict["locas_output"] = locas_output
first_dict["benchy_output"] = benchy_output
first_dict["first_option_coy_output"] = first_option_coy_output
first_dict["code_start"] = code_start
first_dict["the_benchmark"] = the_benchmark
first_dict["s_metrics_df_output"] = s_metrics_df_output
first_dict["c_metrics_df_output"] = c_metrics_df_output
first_dict["stock_plot_desc_output"] = stock_plot_desc_output