-
Notifications
You must be signed in to change notification settings - Fork 206
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
feat: update gapminder.json and add source information (#580)
* docs: add gapminder source details to SOURCES.md * feat: update gapminder.json dataset from source * feat: update gapminder.json dataset from source * feat: Add Gapminder dataset updater script This commit introduces a Python script that updates the gapminder.json file in the vega-datasets repository. The script: - Fetches current data from Gapminder's Google Sheets - Processes and combines data for life expectancy, population, fertility, and regions - Filters results to match countries in the existing dataset - Updates consistent with a minor release The script maintains data consistency by referencing a specific version of the existing dataset. This update allows for refreshed Gapminder data while preserving the column structure and scope of countries/years expected by dependent visualizations. Related: #580 * fix: typo
- Loading branch information
Showing
3 changed files
with
148 additions
and
2 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Large diffs are not rendered by default.
Oops, something went wrong.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,117 @@ | ||
| """ | ||
| Gapminder Dataset Updater | ||
| This script updates the gapminder.json file in the vega-datasets repository | ||
| in a manner consistent with a minor release. It fetches current data from the source, | ||
| processes it, and then filters the results to match the countries and years in the | ||
| existing dataset. To ensure reproducibility and data consistency, the script fetches | ||
| the existing gapminder dataset from a specific commit. | ||
| The generated dataset is used in the following PR: | ||
| https://github.com/vega/vega-datasets/pull/580 | ||
| Data sources: | ||
| - Google Sheets: Multiple sheets containing updated Gapminder data | ||
| - Vega-Datasets: Raw GitHub URL (commit: 05fcb7c07b1d76206856e75129fc1e79dc61735c) | ||
| """ | ||
|
|
||
| import pandas as pd | ||
| import json | ||
| import re | ||
|
|
||
| def google_sheet_to_pandas(sheet_url): | ||
| key_match = re.search(r'/d/([a-zA-Z0-9-_]+)', sheet_url) | ||
| gid_match = re.search(r'gid=(\d+)', sheet_url) | ||
| sheet_key, gid = key_match.group(1), gid_match.group(1) | ||
| csv_export_url = f"https://docs.google.com/spreadsheets/d/{sheet_key}/export?format=csv&gid={gid}" | ||
| return pd.read_csv(csv_export_url) | ||
|
|
||
|
|
||
| # Gapminder datasets (as of July 2024) are stored in individual Google Sheets files. | ||
| # These files are linked from dataset-specific reference pages: | ||
| # - Life Expectancy: https://www.gapminder.org/data/documentation/gd004/ | ||
| # - Population: https://www.gapminder.org/data/documentation/gd003/ | ||
| # - Fertility: https://www.gapminder.org/data/documentation/gd008/ | ||
| # - Data Geographies: https://www.gapminder.org/data/geo/ | ||
|
|
||
| urls = [ | ||
| "https://docs.google.com/spreadsheets/d/1RehxZjXd7_rG8v2pJYV6aY0J3LAsgUPDQnbY4dRdiSs/edit?gid=176703676#gid=176703676", #life expectancy v14 (retrieved July 11, 2024) | ||
| "https://docs.google.com/spreadsheets/d/1c1luQNdpH90tNbMIeU7jD__59wQ0bdIGRFpbMm8ZBTk/edit?gid=176703676#gid=176703676", #population v7 (retrieved July 11, 2024) | ||
| "https://docs.google.com/spreadsheets/d/1aLtIpAWvDGGa9k2XXEz6hZugWn0wCd5nmzaRPPjbYNA/edit?gid=176703676#gid=176703676", #fertility v14 (retrieved July 11, 2024) | ||
| "https://docs.google.com/spreadsheets/d/1qHalit8sXC0R8oVXibc2wa2gY7bkwGzOybEMTWp-08o/edit?gid=1597424158#gid=1597424158" #data geographies v2 (retrieved July 11, 2024) | ||
| ] | ||
|
|
||
| # Load dataframes from Google Sheets | ||
| df_life, df_pop, df_fert, df_region = [google_sheet_to_pandas(url) for url in urls] | ||
|
|
||
| # Load gapminder dataset directly from the raw GitHub URL | ||
| gapminder_url = "https://raw.githubusercontent.com/vega/vega-datasets/05fcb7c07b1d76206856e75129fc1e79dc61735c/data/gapminder.json" | ||
| df_gapminder = pd.read_json(gapminder_url) | ||
|
|
||
| # Prepare main dataframe | ||
| df_main = df_pop[['name', 'time', 'Population']].rename(columns={'Population': 'pop'}) | ||
|
|
||
| # Merge other dataframes | ||
| df_main = df_main.merge(df_life[['name', 'time', 'Life expectancy ']], on=['name', 'time']) | ||
| df_main = df_main.merge(df_fert[['name', 'time', 'Babies per woman']], on=['name', 'time']) | ||
| df_main = df_main.merge(df_region[['name', 'six_regions']], on='name') | ||
|
|
||
| # Rename columns | ||
| df_main = df_main.rename(columns={ | ||
| 'name': 'country', | ||
| 'time': 'year', | ||
| 'Life expectancy ': 'life_expect', | ||
| 'Babies per woman': 'fertility', | ||
| 'six_regions': 'region' | ||
| }) | ||
|
|
||
| # Reorder columns | ||
| df_main = df_main[['year', 'country', 'region', 'pop', 'life_expect', 'fertility']] | ||
|
|
||
| # Convert year to int and filter years from 1955 to 2005 in increments of 5 | ||
| df_main['year'] = df_main['year'].astype(int) | ||
| df_main = df_main[df_main['year'].between(1955, 2005) & (df_main['year'] % 5 == 0)] | ||
|
|
||
| # Sort the dataframe | ||
| df_main = df_main.sort_values(['country', 'year']) | ||
|
|
||
| # Create the cluster mapping | ||
| cluster_map = { | ||
| 'south_asia': 0, | ||
| 'europe_central_asia': 1, | ||
| 'sub_saharan_africa': 2, | ||
| 'america': 3, | ||
| 'east_asia_pacific': 4, | ||
| 'middle_east_north_africa': 5 | ||
| } | ||
|
|
||
| # Add cluster column and drop region column | ||
| df_main['cluster'] = df_main['region'].map(cluster_map) | ||
| df_main = df_main.drop('region', axis=1) | ||
|
|
||
| # Reorder columns | ||
| column_order = ['year', 'country', 'cluster', 'pop', 'life_expect', 'fertility'] | ||
| df_main = df_main[column_order] | ||
|
|
||
| # Rename Hong Kong to Hong Kong, China in df_gapminder | ||
| df_gapminder.loc[df_gapminder['country'] == 'Hong Kong', 'country'] = 'Hong Kong, China' | ||
|
|
||
| # Get the list of countries in df_gapminder | ||
| gapminder_countries = set(df_gapminder['country']) | ||
|
|
||
| # Keep only rows in df_main that have a country in gapminder_countries | ||
| df_main = df_main[df_main['country'].isin(gapminder_countries)] | ||
|
|
||
| # Convert population to integer to match data type of original version of the dataset (and handle potential errors) | ||
| df_main['pop'] = df_main['pop'].astype(int, errors='ignore') | ||
|
|
||
| # Convert DataFrame to list of dictionaries | ||
| data_list = df_main.to_dict(orient='records') | ||
|
|
||
| # Convert the list of dictionaries to JSON | ||
| json_data = json.dumps(data_list) | ||
|
|
||
| print(json_data) | ||
| with open('gapminder.json', 'w') as f: | ||
| json.dump(data_list, f) |