Create app.py

app.py

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+import pandas as pd
+import numpy as np
+
+from dataprep.clean import clean_country
+import plotly.graph_objects as go
+
+gdp_per_capita = pd.read_csv('./data/country_gdp_per_capita_worldbank.csv')
+gdp_per_capita = gdp_per_capita[['Country Name', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019', '2020']]
+
+usa = pd.read_csv('./data/USAData_RenewableInvestment_2010-2020.csv').set_index('Country')
+
+## read plotly data ##
+
+temp_delta_plotly = pd.read_csv('./data/Annual_Surface_Temperature_Change.csv')
+invest_plotly = pd.read_csv('data/Environmental_Protection_Expenditures.csv')
+gdp_per_capita_plotly = gdp_per_capita.copy()
+
+## prepare data for plotly ##
+
+gdp_per_capita_plotly = gdp_per_capita_plotly.rename(columns={'Country Name': 'Country'})
+
+# look up ISO3 codes for countries
+gdp_per_capita_plotly = clean_country(gdp_per_capita_plotly, "Country", output_format="alpha-3").rename(columns={'Country_clean': 'ISO3'})
+gdp_per_capita_plotly = gdp_per_capita_plotly[['Country', 'ISO3', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019', '2020']]
+gdp_per_capita_plotly = gdp_per_capita_plotly.rename(columns={'2010': 2010, '2011': 2011, '2012': 2012, '2013': 2013, '2014': 2014, '2015': 2015, '2016': 2016, '2017': 2017, '2018': 2018, '2019': 2019, '2020': 2020})
+
+temp_delta_plotly = temp_delta_plotly[['Country', 'ISO3', 'F2010', 'F2011', 'F2012', 'F2013', 'F2014', 'F2015', 'F2016', 'F2017', 'F2018', 'F2019', 'F2020']]
+temp_delta_plotly = temp_delta_plotly.rename(columns={'F2010': 2010, 'F2011': 2011, 'F2012': 2012, 'F2013': 2013, 'F2014': 2014, 'F2015': 2015, 'F2016': 2016, 'F2017': 2017, 'F2018': 2018, 'F2019': 2019, 'F2020': 2020})
+
+usa2 = usa.copy().reset_index()
+usa2['ISO3'] = 'USA'
+invest_plotly = invest_plotly[invest_plotly['Unit'] == 'Percent of GDP'].fillna(int(0))
+invest_plotly = invest_plotly[['Country', 'ISO3', 'F2010', 'F2011', 'F2012', 'F2013', 'F2014', 'F2015', 'F2016', 'F2017', 'F2018', 'F2019', 'F2020']].reset_index(drop=True)
+invest_plotly = pd.concat([invest_plotly, usa2])
+invest_plotly = invest_plotly.groupby(['ISO3']).agg('sum').reset_index()
+invest_plotly = invest_plotly.rename(columns={'F2010': 2010, 'F2011': 2011, 'F2012': 2012, 'F2013': 2013, 'F2014': 2014, 'F2015': 2015, 'F2016': 2016, 'F2017': 2017, 'F2018': 2018, 'F2019': 2019, 'F2020': 2020})
+
+temp_delta_plotly = temp_delta_plotly.melt(id_vars=['ISO3'], value_vars=[2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020], var_name='Year', value_name='Temp_Change')
+gdp_per_capita_plotly = gdp_per_capita_plotly.melt(id_vars=['ISO3', 'Country'], value_vars=[2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020], var_name='Year', value_name='GDP_Per_Capita')
+invest_plotly = invest_plotly.melt(id_vars=['ISO3'], value_vars=[2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020], var_name='Year', value_name='Investment_Percent')
+
+plotly_data = pd.merge(temp_delta_plotly, invest_plotly, on=['ISO3', 'Year'])
+plotly_data = pd.merge(gdp_per_capita_plotly, plotly_data, on=['ISO3', 'Year'])
+
+# drop all YEMEN data, as there is missing GDP data and no temp_change data
+plotly_data = plotly_data.drop(plotly_data.loc[plotly_data['ISO3']=='YEM'].index)
+
+# drop 2010-2012 data for SOMALIA, as there is no GDP data
+plotly_data = plotly_data.drop(plotly_data.loc[(plotly_data['ISO3']=='SOM') & (plotly_data['Year']<2013)].index)
+
+new_country_data = plotly_data.copy()
+new_country_data = new_country_data.groupby('Country').agg({'Temp_Change': 'mean', 'Investment_Percent': 'mean', 'GDP_Per_Capita': 'mean'})
+new_country_data['Temp_Change'] = new_country_data['Temp_Change'] + .25
+new_country_data = new_country_data.rename(columns={'Temp_Change': 'temp_delta_avg', 'Investment_Percent': 'renew_invest_avg', 'GDP_Per_Capita': 'gdp_per_capita_avg'})
+
+g20 = ['Argentina', 'Australia', 'Brazil', 'Canada', 'China', 'France', 'Germany', 'India', 'Indonesia', 'Italy', 'Japan', 'Republic of Korea', 'Mexico', 'Russia', 'Saudi Arabia', 'South Africa', 'Turkey', 'United Kingdom', 'United States', 'Austria', 'Belgium', 'Bulgaria', 'Croatia', 'Cyprus', 'Czech Republic', 'Denmark', 'Estonia', 'Finland', 'Greece', 'Hungary', 'Ireland', 'Latvia', 'Lithuania', 'Luxembourg', 'Malta', 'Netherlands', 'Poland', 'Portugal', 'Romania', 'Slovakia', 'Slovenia', 'Spain', 'Sweden']
+new_country_data['g20'] = new_country_data.index.isin(g20).tolist()
+
+g20_countries = new_country_data.loc[new_country_data['g20'] == True].index.to_list()
+
+
+plotly_data['Temp_Change'] = plotly_data['Temp_Change'] + .25
+
+p1 = (plotly_data['Investment_Percent'] > 2)
+p2 = (plotly_data['Temp_Change'] < 1.5)
+
+plotly_data['color_code'] = np.where(p1 & p2, '#46725D', "False")
+plotly_data['color_code'] = np.where(p1 & ~p2, '#A46D13', plotly_data['color_code'])
+plotly_data['color_code'] = np.where(~p1 & p2, '#505693', plotly_data['color_code'])
+plotly_data['color_code'] = np.where(~p1 & ~p2, '#9A381D', plotly_data['color_code'])
+
+# make plotly figure
+
+dataset = plotly_data.copy()
+
+years = [2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020]
+
+# make figure
+fig_dict = {
+    "data": [],
+    "layout": {},
+    "frames": []
+}
+
+min_x_val = dataset['Temp_Change'].min()-.2
+max_x_val = dataset['Temp_Change'].max()+.2
+min_y_val = dataset['Investment_Percent'].min()-.2
+max_y_val = dataset['Investment_Percent'].max()+.2
+
+# fill in most of layout
+fig_dict["layout"]["xaxis"] = {"range": [min_x_val, max_x_val], "title": f'Annual Temperature Above Pre-industrial Levels ({chr(176)}C)'}
+fig_dict["layout"]["yaxis"] = {"range": [min_y_val, 4.5], "title": "Investment in Renewable Energy (% GDP)"}  # "type": "log" makes y-axis log scale
+fig_dict["layout"]["hovermode"] = "closest"
+fig_dict["layout"]["updatemenus"] = [
+    {
+        "buttons": [
+            {
+                "args": [None, {"frame": {"duration": 700, "redraw": False},
+                                "fromcurrent": True, "transition": {"duration": 500,
+                                                                    "easing": "quadratic-in-out"}}],
+                "label": "Play",
+                "method": "animate"
+            },
+            {
+                "args": [[None], {"frame": {"duration": 0, "redraw": False},
+                                  "mode": "immediate",
+                                  "transition": {"duration": 0}}],
+                "label": "Pause",
+                "method": "animate"
+            }
+        ],
+        "direction": "left",
+        "pad": {"r": 10, "t": 87},
+        "showactive": False,
+        "type": "buttons",
+        "x": 0.1,
+        "xanchor": "right",
+        "y": 0,
+        "yanchor": "top"
+    }
+]
+
+sliders_dict = {
+    "active": 0,
+    "yanchor": "top",
+    "xanchor": "left",
+    "currentvalue": {
+        "font": {"size": 20},
+        "prefix": "Year:",
+        "visible": True,
+        "xanchor": "right"
+    },
+    "transition": {"duration": 300, "easing": "cubic-in-out"},
+    "pad": {"b": 10, "t": 50},
+    "len": 0.9,
+    "x": 0.1,
+    "y": 0,
+    "steps": []
+}
+
+Countries = list(plotly_data['Country'].unique())
+Countries = sorted(Countries)
+
+# make data
+year = 2010
+for Country in g20_countries:
+    dataset_by_year = dataset[dataset["Year"] == year]
+    dataset_by_year_and_country = dataset_by_year[
+        dataset_by_year["Country"] == Country]
+
+    data_dict = {
+        "x": list(dataset_by_year_and_country["Temp_Change"]),
+        "y": list(dataset_by_year_and_country["Investment_Percent"]),
+        "mode": "markers",
+        "marker": {
+            "sizemode": "area",
+            "sizeref": 300,
+            "size": list(dataset_by_year_and_country["GDP_Per_Capita"]),
+            "color": dataset_by_year_and_country.loc[dataset_by_year_and_country['Country']==Country].color_code[dataset_by_year_and_country['Year']==year]
+            },
+            "name": Country
+            }
+    fig_dict["data"].append(data_dict)
+
+# make frames
+for year in years:
+    frame = {"data": [], "name": str(year)}
+    for Country in g20_countries:
+        dataset_by_year = dataset[dataset["Year"] == int(year)]
+        dataset_by_year_and_country = dataset_by_year[
+            dataset_by_year["Country"] == Country]
+
+        data_dict = {
+            "x": list(dataset_by_year_and_country["Temp_Change"]),
+            "y": list(dataset_by_year_and_country["Investment_Percent"]),
+            "mode": "markers",
+            "marker": {
+                "sizemode": "area",
+                "sizeref": 300,
+                "size": list(dataset_by_year_and_country["GDP_Per_Capita"]),
+                "color": dataset_by_year_and_country.loc[dataset_by_year_and_country['Country']==Country].color_code[dataset_by_year_and_country['Year']==year]
+                },
+                "name": Country
+                }
+        frame["data"].append(data_dict)
+
+    fig_dict["frames"].append(frame)
+    slider_step = {"args": [
+        [year],
+        {"frame": {"duration": 1500, "redraw": False},
+         "mode": "immediate",
+         "transition": {"duration": 1500}}
+    ],
+        "label": year,
+        "method": "animate"}
+    sliders_dict["steps"].append(slider_step)
+
+
+fig_dict["layout"]["sliders"] = [sliders_dict]
+
+fig = go.Figure(fig_dict)
+
+fig.add_hline(y=2, line_dash="dash", line_color="black", annotation_text="Investment Needed to Fully Transition to Renewable Energy by 2050", annotation_position="bottom right")
+fig.add_vline(x=1.5, line_dash="dash", line_color="black", annotation_text="2050 Target Temperature Increase", annotation_position="top right")
+fig.add_annotation(x=3.75, y=-.35, text="Urgent Action Needed", showarrow=False, font_size=12, bordercolor='#9A381D', font=dict(color='#9A381D'), borderpad=3)
+fig.add_annotation(x=3.67, y=4.1, text="Continued Progress Needed", showarrow=False, font_size=12, bordercolor='#A46D13', font=dict(color='#A46D13'), borderpad=3)
+fig.add_annotation(x=0.2, y=4.1, text="Meeting 2050 Climate Goals", showarrow=False, font_size=12, bordercolor='#46725D', font=dict(color='#46725D'), borderpad=3)
+fig.add_annotation(x=0.17, y=-.35, text="Investments Falling Short", showarrow=False, font_size=12, bordercolor='#505693', font=dict(color='#505693'), borderpad=3)
+
+fig.update_layout(
+    title={
+        'text': "G20 Countries Have Invested Little as Temperatures Dramatically Increased Over the Last Decade",
+        'y':0.9,
+        'x':0.5,
+        'xanchor': 'center',
+        'yanchor': 'top'},
+    showlegend=False
+    )
+
+fig.show()