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@ -5,15 +5,25 @@ import dash_table
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import dash_core_components as dcc
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import dash_html_components as html
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import pandas as pd
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import numpy as np
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csv_name = os.path.dirname(__file__) + '../statistics.csv'
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df = pd.read_csv(csv_name, index_col=0).T
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df = pd.read_csv(csv_name)
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# Round floats
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df.iloc[:, 1:] = np.round(df.iloc[:, 1:] * 100) / 100
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var_list = ['H_sig', 'H_1%', 'H_max', 'Hm0', 'Tp', 'Tp1']
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df = df[['location'] + var_list]
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# add an id column and set it as the index
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# in this case the unique ID is just the country name, so we could have just
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# renamed 'country' to 'id' (but given it the display name 'country'), but
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# here it's duplicated just to show the more general pattern.
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df['id'] = df.index
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# df.set_index('location', inplace=True, drop=False)
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# df.set_index('id', inplace=True, drop=False)
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app = dash.Dash(__name__)
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@ -21,7 +31,7 @@ app.layout = html.Div([
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dash_table.DataTable(
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id='datatable-row-ids',
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columns=[
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{'name': i, 'id': i} for i in df.columns
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{'name': i, 'id': i, 'deletable': True} for i in df.columns
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# omit the id column
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if i != 'id'
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],
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@ -78,7 +88,7 @@ def update_graphs(row_ids, selected_row_ids, active_cell):
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figure={
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'data': [
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{
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'x': dff.index,
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'x': dff['id'],
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'y': dff[column],
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'type': 'bar',
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'marker': {'color': colors},
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@ -98,7 +108,7 @@ def update_graphs(row_ids, selected_row_ids, active_cell):
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# check if column exists - user may have deleted it
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# If `column.deletable=False`, then you don't
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# need to do this check.
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for column in dff.columns
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for column in ['pop', 'lifeExp', 'gdpPercap'] if column in dff
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]
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