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| def cagr(series): | |
| start_val = series.iloc[0] | |
| end_val = series.iloc[-1] | |
| num_vals = len(series) | |
| CAGR = (end_val/start_val)**(1/num_vals)-1 | |
| prefix = '' | |
| if CAGR > 0: | |
| prefix = '+' | |
| return prefix + '{:.1%}'.format(CAGR) |
ccerv1
/ pretty_production_chart_v3.py
Created
February 16, 2019 12:22
Adds CAGR to the labels and a title that explains the key message
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| labels = [] | |
| y_vals = [] | |
| cum_sum = 0 | |
| for col_num, country_name in enumerate(production_table.columns): | |
| label = country_name + " " + cagr(production_table[country_name]) | |
| labels.append(label) | |
| prod_val = production_table.iloc[-1,col_num] | |
| y_vals.append((cum_sum * 2 + prod_val)/2) | |
| cum_sum += prod_val |
ccerv1
/ geopandas_basemap.py
Created
February 23, 2019 12:48
Display the default GeoPandas base map of the world
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| import geopandas | |
| world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres')) | |
| world.plot() |
ccerv1
/ world_map.py
Created
February 23, 2019 13:14
Create a new table with relevant features only
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| world_map = world[world.name != 'Antarctica'] | |
| world_map = world_map[['name', 'geometry']] | |
| world_map.set_index('name', inplace=True) |
ccerv1
/ pretty_world_map.py
Created
February 23, 2019 13:16
Pretty version of the GeoPandas world map
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| fig, ax = plt.subplots(figsize=(20,8)) | |
| ax.set_aspect('equal') | |
| world_map.plot(color='#f2f2f2', edgecolor="Grey", ax=ax) |
ccerv1
/ world_production.py
Created
February 23, 2019 13:21
Extract world production over the last 25 years from USDA table
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| world_production = attribute_query('Production') | |
| world_production.fillna(0,inplace=True) | |
| world_production = world_production.iloc[:,-YEARS_IN_SERIES:] |
ccerv1
/ country_rename.py
Created
February 23, 2019 13:30
Rename country indices that have alternative spellings
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| world_production.rename({ | |
| "Cote d'Ivoire": "Côte d'Ivoire", | |
| "Central African Republic": "Central African Rep.", | |
| "Congo (Kinshasa)": "Dem. Rep. Congo", | |
| "Dominican Republic": "Dominican Rep.", | |
| "Laos": "Lao PDR" | |
| }, inplace=True) |
ccerv1
/ world_production_cagr.py
Created
February 23, 2019 13:43
Apply a CAGR function to each country's production series
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| def cagr(series): | |
| start_val = series.iloc[0] | |
| if not start_val: | |
| return None | |
| end_val = series.iloc[-1] | |
| num_vals = len(series) | |
| CAGR = (end_val/start_val)**(1/num_vals)-1 | |
| return CAGR | |
| world_production['CAGR'] = world_production.apply(cagr, axis=1) |
ccerv1
/ cagr_choropleth.py
Created
February 23, 2019 13:49
V1 choropleth map of coffee production growth rates
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| cagr_map = pd.concat([world_map, world_production], axis=1, sort=True) | |
| cagr_map.fillna(0, inplace=True) | |
| cagr_map.plot(column='CAGR', cmap='Blues') |
ccerv1
/ cagr_choropleth_v2.py
Created
February 23, 2019 17:34
Pretty version of the CAGR choropleth map of coffee production
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| growing_map = cagr_map[cagr_map.CAGR > 0] | |
| growing_map.plot(column='CAGR', cmap='YlGn', scheme='fisher_jenks', edgecolor='black', ax=ax) | |
| shrinking_map = cagr_map[cagr_map.CAGR < 0] | |
| shrinking_map.plot(column='CAGR', cmap='Reds_r', scheme='fisher_jenks', edgecolor='black', ax=ax) | |
| ax.set_ylim([-23.5,23.5]) | |
| ax.set_xlim([-120,160]) | |
| fig |