1413 lines
728 KiB
Plaintext
1413 lines
728 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "95dcf546dfd256ab",
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"metadata": {},
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"source": [
|
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"# Overview"
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||
]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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||
"id": "80feb4d5",
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"metadata": {},
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"outputs": [],
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"source": [
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"import sys\n",
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"import os\n",
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"import math\n",
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"from typing import Dict\n",
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"\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "code",
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||
"execution_count": 2,
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||
"id": "initial_id",
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||
"metadata": {
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||
"ExecuteTime": {
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||
"end_time": "2025-10-03T09:49:28.626603Z",
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"start_time": "2025-10-03T09:49:28.450965Z"
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},
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"sys.path.append(os.path.abspath(\"..\"))\n",
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"from ners.core.config import setup_config\n",
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"from ners.core.utils.data_loader import DataLoader\n",
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"from ners.core.utils.region_mapper import RegionMapper"
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]
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},
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{
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"cell_type": "code",
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||
"execution_count": 3,
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||
"id": "a8fb2b6242a1d3ce",
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||
"metadata": {
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||
"ExecuteTime": {
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||
"end_time": "2025-10-03T09:49:28.637131Z",
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"start_time": "2025-10-03T09:49:28.633945Z"
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}
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"WARNING:root:Config file not found: config/pipeline.development.yaml. Using defaults.\n",
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||
"2025-10-05 22:55:20,981 - root - INFO - Ensured all required directories exist\n",
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"2025-10-05 22:55:20,987 - root - INFO - Loaded configuration: drc_names_pipeline v1.0.0\n",
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||
"2025-10-05 22:55:20,995 - root - INFO - Environment: development\n",
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"2025-10-05 22:55:21,002 - root - INFO - Config file: config/pipeline.development.yaml\n"
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||
]
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||
}
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],
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"source": [
|
||
"config = setup_config()\n",
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"loader = DataLoader(config)"
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]
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},
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||
{
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||
"cell_type": "code",
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||
"execution_count": 4,
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||
"id": "56b61bdd0628ebe4",
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||
"metadata": {
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||
"ExecuteTime": {
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||
"end_time": "2025-10-03T09:49:42.520725Z",
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"start_time": "2025-10-03T09:49:28.645041Z"
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}
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2025-10-05 22:55:21,044 - root - INFO - Reading /Users/bernard-ng/dev/community/analysis/drc-ners-nlp/data/dataset/names.csv with encoding: utf-8\n",
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||
"2025-10-05 22:57:35,223 - root - INFO - Successfully read /Users/bernard-ng/dev/community/analysis/drc-ners-nlp/data/dataset/names.csv with encoding: utf-8\n",
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"2025-10-05 22:57:35,237 - root - INFO - Concatenating 81 optimized chunks\n"
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||
]
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||
}
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||
],
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"source": [
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||
"df = loader.load_csv_complete(config.paths.data_dir / \"names.csv\")"
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]
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||
},
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||
{
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||
"cell_type": "markdown",
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||
"id": "db9f5c2b2e9f9c0f",
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"metadata": {},
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"source": [
|
||
"## Quantitative"
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||
]
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||
},
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{
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||
"cell_type": "code",
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"execution_count": 5,
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||
"id": "7000df8794ddfaad",
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||
"metadata": {
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||
"ExecuteTime": {
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"end_time": "2025-10-03T09:49:49.680636Z",
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"start_time": "2025-10-03T09:49:48.608824Z"
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}
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>count</th>\n",
|
||
" <th>mean</th>\n",
|
||
" <th>std</th>\n",
|
||
" <th>min</th>\n",
|
||
" <th>25%</th>\n",
|
||
" <th>50%</th>\n",
|
||
" <th>75%</th>\n",
|
||
" <th>max</th>\n",
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||
" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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||
" <tr>\n",
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||
" <th>id</th>\n",
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||
" <td>8069950.0</td>\n",
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" <td>16.231662</td>\n",
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||
" <td>20.139369</td>\n",
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" <td>0.0</td>\n",
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||
" <td>4.0</td>\n",
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||
" <td>10.0</td>\n",
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||
" <td>21.0</td>\n",
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||
" <td>939.0</td>\n",
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||
" </tr>\n",
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||
" <tr>\n",
|
||
" <th>percentage</th>\n",
|
||
" <td>8069950.0</td>\n",
|
||
" <td>56.188421</td>\n",
|
||
" <td>5.403735</td>\n",
|
||
" <td>10.0</td>\n",
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||
" <td>52.0</td>\n",
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||
" <td>55.0</td>\n",
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||
" <td>59.0</td>\n",
|
||
" <td>96.0</td>\n",
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||
" </tr>\n",
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||
" <tr>\n",
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||
" <th>year</th>\n",
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||
" <td>8069950.0</td>\n",
|
||
" <td>2017.599677</td>\n",
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||
" <td>4.371243</td>\n",
|
||
" <td>2008.0</td>\n",
|
||
" <td>2015.0</td>\n",
|
||
" <td>2019.0</td>\n",
|
||
" <td>2021.0</td>\n",
|
||
" <td>2023.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>line</th>\n",
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||
" <td>8069950.0</td>\n",
|
||
" <td>118021.649797</td>\n",
|
||
" <td>195762.213691</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>6957.0</td>\n",
|
||
" <td>19192.0</td>\n",
|
||
" <td>117146.0</td>\n",
|
||
" <td>826262.0</td>\n",
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||
" </tr>\n",
|
||
" </tbody>\n",
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||
"</table>\n",
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||
"</div>"
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||
],
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||
"text/plain": [
|
||
" count mean std min 25% 50% \\\n",
|
||
"id 8069950.0 16.231662 20.139369 0.0 4.0 10.0 \n",
|
||
"percentage 8069950.0 56.188421 5.403735 10.0 52.0 55.0 \n",
|
||
"year 8069950.0 2017.599677 4.371243 2008.0 2015.0 2019.0 \n",
|
||
"line 8069950.0 118021.649797 195762.213691 4.0 6957.0 19192.0 \n",
|
||
"\n",
|
||
" 75% max \n",
|
||
"id 21.0 939.0 \n",
|
||
"percentage 59.0 96.0 \n",
|
||
"year 2021.0 2023.0 \n",
|
||
"line 117146.0 826262.0 "
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||
]
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||
},
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||
"execution_count": 5,
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||
"metadata": {},
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||
"output_type": "execute_result"
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||
}
|
||
],
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||
"source": [
|
||
"df.describe().T"
|
||
]
|
||
},
|
||
{
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||
"cell_type": "markdown",
|
||
"id": "572574873e443bda",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Regions"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
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||
"execution_count": 6,
|
||
"id": "e02c8023d43b3466",
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||
"metadata": {
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||
"ExecuteTime": {
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||
"end_time": "2025-10-03T09:49:50.186633Z",
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||
"start_time": "2025-10-03T09:49:49.727757Z"
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}
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
|
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" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Value</th>\n",
|
||
" <th>Count</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>bandundu</td>\n",
|
||
" <td>165090</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>bandundu-1</td>\n",
|
||
" <td>37755</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>bandundu-2</td>\n",
|
||
" <td>75006</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>bandundu-3</td>\n",
|
||
" <td>27907</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>bas-congo</td>\n",
|
||
" <td>45604</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>120</th>\n",
|
||
" <td>tshopo-1</td>\n",
|
||
" <td>49612</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>121</th>\n",
|
||
" <td>tshopo-2</td>\n",
|
||
" <td>23624</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>122</th>\n",
|
||
" <td>tshuapa</td>\n",
|
||
" <td>13717</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>123</th>\n",
|
||
" <td>tshuapa-1</td>\n",
|
||
" <td>8206</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>124</th>\n",
|
||
" <td>tshuapa-2</td>\n",
|
||
" <td>11220</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>125 rows × 2 columns</p>\n",
|
||
"</div>"
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||
],
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||
"text/plain": [
|
||
" Value Count\n",
|
||
"0 bandundu 165090\n",
|
||
"1 bandundu-1 37755\n",
|
||
"2 bandundu-2 75006\n",
|
||
"3 bandundu-3 27907\n",
|
||
"4 bas-congo 45604\n",
|
||
".. ... ...\n",
|
||
"120 tshopo-1 49612\n",
|
||
"121 tshopo-2 23624\n",
|
||
"122 tshuapa 13717\n",
|
||
"123 tshuapa-1 8206\n",
|
||
"124 tshuapa-2 11220\n",
|
||
"\n",
|
||
"[125 rows x 2 columns]"
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||
]
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||
},
|
||
"execution_count": 6,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
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||
}
|
||
],
|
||
"source": [
|
||
"table = (\n",
|
||
" df[\"region\"]\n",
|
||
" .value_counts() # counts by frequency\n",
|
||
" .rename_axis(\"Value\") # name the index\n",
|
||
" .reset_index(name=\"Count\") # make it a DataFrame\n",
|
||
" .sort_values(\"Value\") # sort alphabetically\n",
|
||
" .reset_index(drop=True) # clean row index\n",
|
||
")\n",
|
||
"\n",
|
||
"table"
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||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "2c6b460a94f3ca9d",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Subregions by Parent Region"
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||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "8f8a9bb068152311",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-10-03T09:49:50.879683Z",
|
||
"start_time": "2025-10-03T09:49:50.867657Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def build_subregion_tables(\n",
|
||
" series: pd.Series, mapper: RegionMapper\n",
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||
") -> Dict[str, pd.DataFrame]:\n",
|
||
" \"\"\"\n",
|
||
" Returns { PARENT -> DataFrame[subregion, count] }.\n",
|
||
" Parent is the mapped province produced by RegionMapper.map().\n",
|
||
" \"\"\"\n",
|
||
" s = series.astype(str).str.strip().str.lower()\n",
|
||
" parents = mapper.map(s)\n",
|
||
" df_regions = pd.DataFrame({\"subregion\": s, \"parent\": parents})\n",
|
||
" counts = (\n",
|
||
" df_regions.groupby([\"parent\", \"subregion\"], as_index=False)\n",
|
||
" .size()\n",
|
||
" .rename(columns={\"size\": \"count\"})\n",
|
||
" )\n",
|
||
"\n",
|
||
" out: Dict[str, pd.DataFrame] = {}\n",
|
||
" for parent, g in counts.groupby(\"parent\"):\n",
|
||
" g = g.sort_values([\"count\", \"subregion\"], ascending=[False, True])\n",
|
||
" out[parent] = g.reset_index(drop=True)\n",
|
||
" return out\n",
|
||
"\n",
|
||
"\n",
|
||
"def plot_all_parents_grid(series: pd.Series, mapper: RegionMapper, per_row: int = 4):\n",
|
||
" \"\"\"\n",
|
||
" Plot horizontal bar charts of subregion counts for every parent in\n",
|
||
" RegionMapper.get_provinces(), arranged as a grid with `per_row` columns.\n",
|
||
" \"\"\"\n",
|
||
"\n",
|
||
" def truncate_label(l: str, max_len: int = 15) -> str:\n",
|
||
" return l if len(l) <= max_len else l[: max_len - 3] + \"...\"\n",
|
||
"\n",
|
||
" # Build subregion tables once\n",
|
||
" tables = build_subregion_tables(series, mapper)\n",
|
||
"\n",
|
||
" # Respect the explicit list of parents from RegionMapper\n",
|
||
" parents = [\n",
|
||
" p.upper() for p in mapper.get_provinces()\n",
|
||
" ] # mapper.get_provinces() are lowercase\n",
|
||
" n = len(parents)\n",
|
||
" rows = math.ceil(n / per_row)\n",
|
||
"\n",
|
||
" # Make figure size scale with grid size\n",
|
||
" fig_w = 6 * per_row\n",
|
||
" fig_h = max(4.8 * rows, 3.5) # minimum height\n",
|
||
" fig, axes = plt.subplots(rows, per_row, figsize=(fig_w, fig_h))\n",
|
||
" if rows == 1 and per_row == 1:\n",
|
||
" axes = [[axes]]\n",
|
||
" elif rows == 1:\n",
|
||
" axes = [axes]\n",
|
||
" elif per_row == 1:\n",
|
||
" axes = [[ax] for ax in axes]\n",
|
||
"\n",
|
||
" # Draw each parent's subplot\n",
|
||
" for idx, parent in enumerate(parents):\n",
|
||
" r = idx // per_row\n",
|
||
" c = idx % per_row\n",
|
||
" ax = axes[r][c]\n",
|
||
"\n",
|
||
" dfp = tables.get(parent)\n",
|
||
" if dfp is None or dfp.empty:\n",
|
||
" ax.set_title(parent)\n",
|
||
" ax.text(0.5, 0.5, \"No data\", ha=\"center\", va=\"center\")\n",
|
||
" ax.set_axis_off()\n",
|
||
" continue\n",
|
||
"\n",
|
||
" labels = [truncate_label(lbl, 15) for lbl in dfp[\"subregion\"]]\n",
|
||
"\n",
|
||
" # Horizontal bar chart: y=subregion, x=count (sorted by count desc already)\n",
|
||
" ax.barh(labels, dfp[\"count\"])\n",
|
||
" ax.set_title(parent)\n",
|
||
" ax.set_xlabel(\"Count\")\n",
|
||
" ax.set_ylabel(\"Subregion\")\n",
|
||
" ax.invert_yaxis() # largest at top\n",
|
||
" # Improve readability with many labels\n",
|
||
" for label in ax.get_yticklabels():\n",
|
||
" label.set_fontsize(8)\n",
|
||
"\n",
|
||
" # Turn off any leftover empty axes\n",
|
||
" total_axes = rows * per_row\n",
|
||
" for j in range(n, total_axes):\n",
|
||
" r = j // per_row\n",
|
||
" c = j % per_row\n",
|
||
" axes[r][c].set_axis_off()\n",
|
||
"\n",
|
||
" plt.tight_layout()\n",
|
||
" plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "4083480dfe1df962",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-10-03T09:50:00.460640Z",
|
||
"start_time": "2025-10-03T09:49:51.239623Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1800x1920 with 12 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plot_all_parents_grid(df[\"region\"], RegionMapper(), per_row=3)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "5242e5b15acb277c",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-10-03T09:50:02.908297Z",
|
||
"start_time": "2025-10-03T09:50:00.544028Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"df[\"province\"] = RegionMapper().map(df[\"region\"])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "d3e3268c111c5c22",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-10-03T09:50:03.004308Z",
|
||
"start_time": "2025-10-03T09:50:02.998149Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Index(['id', 'name', 'sex', 'percentage', 'year', 'region', 'filename', 'line',\n",
|
||
" 'province'],\n",
|
||
" dtype='object')"
|
||
]
|
||
},
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df.columns"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"id": "4d673e975665827f",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-10-03T09:50:03.423521Z",
|
||
"start_time": "2025-10-03T09:50:03.159435Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"import pandas as pd\n",
|
||
"import networkx as nx\n",
|
||
"from matplotlib.patches import Circle\n",
|
||
"\n",
|
||
"\n",
|
||
"def _province_palette(provinces):\n",
|
||
" \"\"\"Map each province to a color using matplotlib's default cycle.\"\"\"\n",
|
||
" colors = (\n",
|
||
" plt.rcParams[\"axes.prop_cycle\"]\n",
|
||
" .by_key()\n",
|
||
" .get(\"color\", [\"C0\", \"C1\", \"C2\", \"C3\", \"C4\", \"C5\", \"C6\", \"C7\", \"C8\", \"C9\"])\n",
|
||
" )\n",
|
||
" return {p: colors[i % len(colors)] for i, p in enumerate(sorted(provinces))}\n",
|
||
"\n",
|
||
"\n",
|
||
"def plot_region_clusters_by_province(\n",
|
||
" df: pd.DataFrame,\n",
|
||
" year_col: str = \"year\",\n",
|
||
" region_col: str = \"region\",\n",
|
||
" province_col: str = \"province\",\n",
|
||
" figsize=(14, 9),\n",
|
||
" node_size_min: float = 30,\n",
|
||
" node_size_max: float = 600,\n",
|
||
" province_padding: float = 1.6, # spacing between province cluster centers\n",
|
||
" hull_alpha: float = 0.08, # alpha for province “enclosing” circles\n",
|
||
" label_regions: bool = True,\n",
|
||
" label_max_chars: int = 18,\n",
|
||
" random_seed: int = 42,\n",
|
||
"):\n",
|
||
" \"\"\"\n",
|
||
" Visualize how regions cluster inside provinces.\n",
|
||
" - One node per region, grouped by its province.\n",
|
||
" - Node size ~ count of (province, region) across all years.\n",
|
||
" - Node color = province; legend shows province colors.\n",
|
||
" \"\"\"\n",
|
||
" # --- Prepare aggregated weights per (province, region) ---\n",
|
||
" dfx = df[[year_col, region_col, province_col]].dropna().copy()\n",
|
||
" dfx[province_col] = dfx[province_col].astype(str).str.strip()\n",
|
||
" dfx[region_col] = dfx[region_col].astype(str).str.strip()\n",
|
||
" agg = (\n",
|
||
" dfx.groupby([province_col, region_col], as_index=False)\n",
|
||
" .size()\n",
|
||
" .rename(columns={\"size\": \"count\"})\n",
|
||
" )\n",
|
||
"\n",
|
||
" if agg.empty:\n",
|
||
" raise ValueError(\"No data after cleaning; check inputs.\")\n",
|
||
"\n",
|
||
" provinces = sorted(agg[province_col].unique())\n",
|
||
" color_map = _province_palette(provinces)\n",
|
||
"\n",
|
||
" # Scale node sizes from counts\n",
|
||
" cmin, cmax = agg[\"count\"].min(), agg[\"count\"].max()\n",
|
||
" if cmax == cmin:\n",
|
||
" sizes = np.full(len(agg), (node_size_min + node_size_max) / 2.0)\n",
|
||
" else:\n",
|
||
" sizes = node_size_min + (agg[\"count\"] - cmin) * (\n",
|
||
" node_size_max - node_size_min\n",
|
||
" ) / (cmax - cmin)\n",
|
||
" agg[\"_size\"] = sizes\n",
|
||
"\n",
|
||
" # --- Lay out provinces on a grid; do a small spring layout per province around its center ---\n",
|
||
" rng = np.random.default_rng(random_seed)\n",
|
||
"\n",
|
||
" # Choose grid dimensions close to square\n",
|
||
" nP = len(provinces)\n",
|
||
" ncols = math.ceil(math.sqrt(nP))\n",
|
||
" math.ceil(nP / ncols)\n",
|
||
"\n",
|
||
" # Province centers on grid\n",
|
||
" centers = {}\n",
|
||
" for idx, p in enumerate(provinces):\n",
|
||
" r, c = divmod(idx, ncols)\n",
|
||
" centers[p] = (\n",
|
||
" c * province_padding,\n",
|
||
" -r * province_padding,\n",
|
||
" ) # y negative to go \"down\"\n",
|
||
"\n",
|
||
" # Build positions\n",
|
||
" pos = {} # (province, region) -> (x,y)\n",
|
||
" hull_radii = {} # province -> radius used for background circle\n",
|
||
"\n",
|
||
" for p in provinces:\n",
|
||
" sub = agg[agg[province_col] == p].reset_index(drop=True)\n",
|
||
" center = np.array(centers[p], dtype=float)\n",
|
||
"\n",
|
||
" # Make a tiny graph with one hub (province) pulling region nodes inward\n",
|
||
" G = nx.Graph()\n",
|
||
" hub = f\"__hub__::{p}\"\n",
|
||
" G.add_node(hub)\n",
|
||
"\n",
|
||
" for i, row in sub.iterrows():\n",
|
||
" node = (p, row[region_col])\n",
|
||
" G.add_node(node)\n",
|
||
" # connect to hub with weight inversely related to count (heavier count -> shorter spring)\n",
|
||
" # spring_layout interprets 'weight' so larger weight pulls nodes closer\n",
|
||
" w = float(row[\"count\"])\n",
|
||
" G.add_edge(hub, node, weight=max(1.0, w))\n",
|
||
"\n",
|
||
" # Initial positions: jitter around center\n",
|
||
" init_pos = {hub: center}\n",
|
||
" for node in G.nodes:\n",
|
||
" if node == hub:\n",
|
||
" continue\n",
|
||
" init_pos[node] = center + rng.normal(scale=0.15, size=2)\n",
|
||
"\n",
|
||
" # Small local layout\n",
|
||
" # Note: spring_layout is deterministic with seed\n",
|
||
" ploc = nx.spring_layout(\n",
|
||
" G,\n",
|
||
" k=0.25,\n",
|
||
" iterations=60,\n",
|
||
" weight=\"weight\",\n",
|
||
" seed=random_seed,\n",
|
||
" pos=init_pos,\n",
|
||
" fixed=[hub],\n",
|
||
" )\n",
|
||
"\n",
|
||
" # Save region node positions; track cluster radius\n",
|
||
" pts = []\n",
|
||
" for node in G.nodes:\n",
|
||
" if node == hub:\n",
|
||
" continue\n",
|
||
" pos[node] = ploc[node]\n",
|
||
" pts.append(ploc[node])\n",
|
||
"\n",
|
||
" if pts:\n",
|
||
" pts = np.vstack(pts)\n",
|
||
" # radius ~ 1.2 * std distance to center, with minimum\n",
|
||
" d = np.linalg.norm(pts - center, axis=1)\n",
|
||
" hull_radii[p] = max(0.35, 1.2 * (np.median(d) if len(d) > 4 else d.mean()))\n",
|
||
" else:\n",
|
||
" hull_radii[p] = 0.4\n",
|
||
"\n",
|
||
" # --- Plot ---\n",
|
||
" fig, ax = plt.subplots(figsize=figsize)\n",
|
||
"\n",
|
||
" # Province hulls (soft circles)\n",
|
||
" for p in provinces:\n",
|
||
" cx, cy = centers[p]\n",
|
||
" circ = Circle(\n",
|
||
" (cx, cy),\n",
|
||
" radius=hull_radii[p],\n",
|
||
" facecolor=color_map[p],\n",
|
||
" edgecolor=\"none\",\n",
|
||
" alpha=hull_alpha,\n",
|
||
" )\n",
|
||
" ax.add_patch(circ)\n",
|
||
"\n",
|
||
" # Region nodes\n",
|
||
" handles = []\n",
|
||
" labels = []\n",
|
||
" for p in provinces:\n",
|
||
" sub = agg[agg[province_col] == p]\n",
|
||
" xs, ys, ss = [], [], []\n",
|
||
" for _, row in sub.iterrows():\n",
|
||
" node = (p, row[region_col])\n",
|
||
" x, y = pos[node]\n",
|
||
" xs.append(x)\n",
|
||
" ys.append(y)\n",
|
||
" ss.append(row[\"_size\"])\n",
|
||
" sc = ax.scatter(\n",
|
||
" xs, ys, s=ss, alpha=0.85, edgecolors=\"none\", label=p, color=color_map[p]\n",
|
||
" )\n",
|
||
" handles.append(sc)\n",
|
||
" labels.append(p)\n",
|
||
"\n",
|
||
" # Labels (optional)\n",
|
||
" if label_regions:\n",
|
||
" for _, row in sub.iterrows():\n",
|
||
" node = (p, row[region_col])\n",
|
||
" x, y = pos[node]\n",
|
||
" name = row[region_col]\n",
|
||
" if len(name) > label_max_chars:\n",
|
||
" name = name[: label_max_chars - 1] + \"…\"\n",
|
||
" ax.text(x, y, name, fontsize=7, ha=\"center\", va=\"center\")\n",
|
||
"\n",
|
||
" # Axes aesthetics\n",
|
||
" ax.set_aspect(\"equal\", adjustable=\"datalim\")\n",
|
||
" ax.set_xticks([])\n",
|
||
" ax.set_yticks([])\n",
|
||
" ax.set_xlabel(\"\")\n",
|
||
" ax.set_ylabel(\"\")\n",
|
||
" ax.set_title(\n",
|
||
" \"Regions clustered within Provinces (node size = frequency across years)\"\n",
|
||
" )\n",
|
||
"\n",
|
||
" # Province legend\n",
|
||
" ncols = 4 if len(labels) > 10 else 2\n",
|
||
" ax.legend(\n",
|
||
" handles,\n",
|
||
" labels,\n",
|
||
" title=\"Province\",\n",
|
||
" loc=\"upper right\",\n",
|
||
" bbox_to_anchor=(1.0, 1.0),\n",
|
||
" ncol=ncols,\n",
|
||
" )\n",
|
||
"\n",
|
||
" # Expand limits to fit hulls nicely\n",
|
||
" xs, ys = zip(*centers.values())\n",
|
||
" ax.set_xlim(min(xs) - province_padding, max(xs) + province_padding)\n",
|
||
" ax.set_ylim(min(ys) - province_padding, max(ys) + province_padding)\n",
|
||
"\n",
|
||
" plt.tight_layout()\n",
|
||
" plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"id": "595487c63277e585",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-10-03T09:58:09.712122Z",
|
||
"start_time": "2025-10-03T09:58:09.686613Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"import matplotlib.patheffects as path_effects\n",
|
||
"\n",
|
||
"\n",
|
||
"def _province_palette(provinces):\n",
|
||
" colors = (\n",
|
||
" plt.rcParams[\"axes.prop_cycle\"]\n",
|
||
" .by_key()\n",
|
||
" .get(\"color\", [\"C0\", \"C1\", \"C2\", \"C3\", \"C4\", \"C5\", \"C6\", \"C7\", \"C8\", \"C9\"])\n",
|
||
" )\n",
|
||
" return {p: colors[i % len(colors)] for i, p in enumerate(sorted(provinces))}\n",
|
||
"\n",
|
||
"\n",
|
||
"def _separate_points(points, radii, iters=60, step=0.02):\n",
|
||
" \"\"\"\n",
|
||
" Simple collision-avoidance: iteratively push circles (points with radii)\n",
|
||
" apart until overlaps are (mostly) gone.\n",
|
||
" points: (N,2) array; radii: (N,) array in same coordinate units\n",
|
||
" \"\"\"\n",
|
||
" P = points.copy().astype(float)\n",
|
||
" R = radii.astype(float)\n",
|
||
" N = len(P)\n",
|
||
" for _ in range(iters):\n",
|
||
" moved = 0.0\n",
|
||
" for i in range(N):\n",
|
||
" for j in range(i + 1, N):\n",
|
||
" d = P[j] - P[i]\n",
|
||
" dist = np.hypot(d[0], d[1])\n",
|
||
" min_d = R[i] + R[j]\n",
|
||
" if dist < 1e-6:\n",
|
||
" # tiny random kick to avoid division by zero\n",
|
||
" d = np.random.default_rng().normal(scale=1e-3, size=2)\n",
|
||
" dist = np.hypot(d[0], d[1])\n",
|
||
" if dist < min_d:\n",
|
||
" # push each point away from the other\n",
|
||
" push = (min_d - dist) / 2.0\n",
|
||
" delta = (d / dist) * (push + 1e-3)\n",
|
||
" P[i] -= delta * step\n",
|
||
" P[j] += delta * step\n",
|
||
" moved += push\n",
|
||
" if moved < 1e-3:\n",
|
||
" break\n",
|
||
" return P\n",
|
||
"\n",
|
||
"\n",
|
||
"def plot_region_clusters_by_province_readable(\n",
|
||
" df: pd.DataFrame,\n",
|
||
" year_col: str = \"year\",\n",
|
||
" region_col: str = \"region\",\n",
|
||
" province_col: str = \"province\",\n",
|
||
" figsize=(18, 11),\n",
|
||
" node_size_min: float = 60,\n",
|
||
" node_size_max: float = 1100,\n",
|
||
" label_top_k: int = 12, # show labels only for the top-K regions per province\n",
|
||
" min_count_for_dot: int = 1, # drop ultra-rare regions if needed (e.g., 2 or 3)\n",
|
||
" base_padding: float = 1.6, # base spacing; will be auto-scaled per province\n",
|
||
" hull_alpha: float = 0.10,\n",
|
||
" random_seed: int = 42,\n",
|
||
"):\n",
|
||
" \"\"\"\n",
|
||
" Readable 'regions clustered within provinces' visualization.\n",
|
||
"\n",
|
||
" - Dots: one per (province, region), size ~ total count across years.\n",
|
||
" - Color: province.\n",
|
||
" - Labels: top-K per province only, with white halo for contrast.\n",
|
||
" - Collision-avoidance keeps dots separate and text readable.\n",
|
||
" - Province spacing grows with number of regions in the province.\n",
|
||
" \"\"\"\n",
|
||
" # --- aggregate ---\n",
|
||
" dfx = df[[year_col, region_col, province_col]].dropna().copy()\n",
|
||
" dfx[province_col] = dfx[province_col].astype(str).str.strip()\n",
|
||
" dfx[region_col] = dfx[region_col].astype(str).str.strip()\n",
|
||
"\n",
|
||
" agg = (\n",
|
||
" dfx.groupby([province_col, region_col], as_index=False)\n",
|
||
" .size()\n",
|
||
" .rename(columns={\"size\": \"count\"})\n",
|
||
" )\n",
|
||
" if min_count_for_dot > 1:\n",
|
||
" agg = agg.loc[agg[\"count\"] >= min_count_for_dot]\n",
|
||
"\n",
|
||
" if agg.empty:\n",
|
||
" raise ValueError(\"No data after filtering; relax thresholds.\")\n",
|
||
"\n",
|
||
" provinces = sorted(agg[province_col].unique())\n",
|
||
" color_map = _province_palette(provinces)\n",
|
||
"\n",
|
||
" # sizes scaled per global min/max\n",
|
||
" cmin, cmax = agg[\"count\"].min(), agg[\"count\"].max()\n",
|
||
" if cmax == cmin:\n",
|
||
" agg[\"_size\"] = (node_size_min + node_size_max) / 2.0\n",
|
||
" else:\n",
|
||
" agg[\"_size\"] = node_size_min + (agg[\"count\"] - cmin) * (\n",
|
||
" node_size_max - node_size_min\n",
|
||
" ) / (cmax - cmin)\n",
|
||
"\n",
|
||
" # radius used for collision (approximate pixel-independent unit)\n",
|
||
" # convert marker size (points^2) into a rough radius in data coords by picking a scale;\n",
|
||
" # since our layout is arbitrary units, a simple proportional mapping works:\n",
|
||
" agg[\"_radius\"] = np.sqrt(agg[\"_size\"]) * 0.02\n",
|
||
"\n",
|
||
" # --- province centers on grid with adaptive spacing ---\n",
|
||
" # provinces with many regions need more room\n",
|
||
" counts_per_prov = (\n",
|
||
" agg.groupby(province_col)[\"region\"].count().reindex(provinces, fill_value=1)\n",
|
||
" )\n",
|
||
" scale_per_prov = (\n",
|
||
" (counts_per_prov / counts_per_prov.median()).clip(0.8, 2.0).to_dict()\n",
|
||
" )\n",
|
||
"\n",
|
||
" nP = len(provinces)\n",
|
||
" ncols = math.ceil(math.sqrt(nP))\n",
|
||
" math.ceil(nP / ncols)\n",
|
||
"\n",
|
||
" centers = {}\n",
|
||
" for idx, p in enumerate(provinces):\n",
|
||
" r, c = divmod(idx, ncols)\n",
|
||
" pad = base_padding * scale_per_prov[p]\n",
|
||
" centers[p] = (c * pad, -r * pad)\n",
|
||
"\n",
|
||
" rng = np.random.default_rng(random_seed)\n",
|
||
" pos = {}\n",
|
||
" hull_radii = {}\n",
|
||
"\n",
|
||
" # --- per-province local layout + collision-avoidance ---\n",
|
||
" for p in provinces:\n",
|
||
" sub = agg[agg[province_col] == p].reset_index(drop=True)\n",
|
||
" center = np.array(centers[p], dtype=float)\n",
|
||
"\n",
|
||
" # start with small random jitter around center\n",
|
||
" pts = center + rng.normal(scale=0.1, size=(len(sub), 2))\n",
|
||
" pts = _separate_points(pts, sub[\"_radius\"].to_numpy(), iters=80, step=0.8)\n",
|
||
"\n",
|
||
" # optional spring-like pull to center to keep bunch tight\n",
|
||
" pts = center + 0.85 * (pts - center)\n",
|
||
"\n",
|
||
" for (x, y), (_, row) in zip(pts, sub.iterrows()):\n",
|
||
" pos[(p, row[region_col])] = (float(x), float(y))\n",
|
||
"\n",
|
||
" # cluster hull radius based on spread\n",
|
||
" d = np.linalg.norm(pts - center, axis=1)\n",
|
||
" hull_radii[p] = max(0.35, 1.25 * (np.median(d) if len(d) > 4 else d.mean()))\n",
|
||
"\n",
|
||
" # --- draw ---\n",
|
||
" fig, ax = plt.subplots(figsize=figsize)\n",
|
||
"\n",
|
||
" # soft province circles\n",
|
||
" for p in provinces:\n",
|
||
" cx, cy = centers[p]\n",
|
||
" circ = Circle(\n",
|
||
" (cx, cy),\n",
|
||
" radius=hull_radii[p],\n",
|
||
" facecolor=color_map[p],\n",
|
||
" edgecolor=\"none\",\n",
|
||
" alpha=hull_alpha,\n",
|
||
" )\n",
|
||
" ax.add_patch(circ)\n",
|
||
"\n",
|
||
" # dots + labels (top-K)\n",
|
||
" handles, labels = [], []\n",
|
||
" for p in provinces:\n",
|
||
" sub = (\n",
|
||
" agg[agg[province_col] == p]\n",
|
||
" .sort_values(\"count\", ascending=False)\n",
|
||
" .reset_index(drop=True)\n",
|
||
" )\n",
|
||
" xs, ys, ss = [], [], []\n",
|
||
" for _, row in sub.iterrows():\n",
|
||
" x, y = pos[(p, row[region_col])]\n",
|
||
" xs.append(x)\n",
|
||
" ys.append(y)\n",
|
||
" ss.append(row[\"_size\"])\n",
|
||
" sc = ax.scatter(\n",
|
||
" xs, ys, s=ss, alpha=0.9, edgecolors=\"none\", label=p, color=color_map[p]\n",
|
||
" )\n",
|
||
" handles.append(sc)\n",
|
||
" labels.append(p)\n",
|
||
"\n",
|
||
" # labels only for top-K\n",
|
||
" top = sub.head(label_top_k)\n",
|
||
" for _, row in top.iterrows():\n",
|
||
" x, y = pos[(p, row[region_col])]\n",
|
||
" txt = row[region_col]\n",
|
||
" if len(txt) > 18:\n",
|
||
" txt = txt[:17] + \"…\"\n",
|
||
" t = ax.text(x, y, txt, fontsize=8, ha=\"center\", va=\"center\", color=\"black\")\n",
|
||
" # white halo for contrast\n",
|
||
" t.set_path_effects(\n",
|
||
" [\n",
|
||
" path_effects.Stroke(linewidth=2.5, foreground=\"white\"),\n",
|
||
" path_effects.Normal(),\n",
|
||
" ]\n",
|
||
" )\n",
|
||
"\n",
|
||
" # legend, axes, limits\n",
|
||
" ax.legend(handles, labels, title=\"Province\", loc=\"lower right\", ncol=3)\n",
|
||
"\n",
|
||
" ax.set_aspect(\"equal\", adjustable=\"datalim\")\n",
|
||
" ax.set_xticks([])\n",
|
||
" ax.set_yticks([])\n",
|
||
" ax.set_xlabel(\"\")\n",
|
||
" ax.set_ylabel(\"\")\n",
|
||
"\n",
|
||
" xs, ys = zip(*[centers[p] for p in provinces])\n",
|
||
" pad = max(base_padding, 1.2)\n",
|
||
" ax.set_xlim(min(xs) - pad, max(xs) + pad)\n",
|
||
" # ax.set_ylim(min(ys) - pad, max(ys) + pad)\n",
|
||
"\n",
|
||
" plt.savefig(\"../../assets/regions-clusters-readable.png\")\n",
|
||
" plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"id": "20e16282a8eebc97",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-10-03T09:58:17.182810Z",
|
||
"start_time": "2025-10-03T09:58:10.971477Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"2025-10-05 23:10:16,306 - matplotlib.axes._base - WARNING - Ignoring fixed x limits to fulfill fixed data aspect with adjustable data limits.\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 2200x1400 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plot_region_clusters_by_province_readable(\n",
|
||
" df, label_top_k=5, min_count_for_dot=2, base_padding=0.5, figsize=(22, 14)\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "530d424405c49287",
|
||
"metadata": {},
|
||
"source": [
|
||
"# Year"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "73eac09aa9dd3d5",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-10-03T09:50:35.543474Z",
|
||
"start_time": "2025-10-03T09:50:35.484794Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Value</th>\n",
|
||
" <th>Count</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>2008</td>\n",
|
||
" <td>183051</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>2009</td>\n",
|
||
" <td>256209</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>2010</td>\n",
|
||
" <td>280484</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>2011</td>\n",
|
||
" <td>323867</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>2012</td>\n",
|
||
" <td>322043</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>2013</td>\n",
|
||
" <td>270742</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>2014</td>\n",
|
||
" <td>330042</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>2015</td>\n",
|
||
" <td>729980</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8</th>\n",
|
||
" <td>2016</td>\n",
|
||
" <td>387432</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9</th>\n",
|
||
" <td>2017</td>\n",
|
||
" <td>402320</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>10</th>\n",
|
||
" <td>2018</td>\n",
|
||
" <td>483758</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>11</th>\n",
|
||
" <td>2019</td>\n",
|
||
" <td>519065</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>12</th>\n",
|
||
" <td>2020</td>\n",
|
||
" <td>632869</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>13</th>\n",
|
||
" <td>2021</td>\n",
|
||
" <td>956634</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>14</th>\n",
|
||
" <td>2022</td>\n",
|
||
" <td>1221970</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>15</th>\n",
|
||
" <td>2023</td>\n",
|
||
" <td>769484</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Value Count\n",
|
||
"0 2008 183051\n",
|
||
"1 2009 256209\n",
|
||
"2 2010 280484\n",
|
||
"3 2011 323867\n",
|
||
"4 2012 322043\n",
|
||
"5 2013 270742\n",
|
||
"6 2014 330042\n",
|
||
"7 2015 729980\n",
|
||
"8 2016 387432\n",
|
||
"9 2017 402320\n",
|
||
"10 2018 483758\n",
|
||
"11 2019 519065\n",
|
||
"12 2020 632869\n",
|
||
"13 2021 956634\n",
|
||
"14 2022 1221970\n",
|
||
"15 2023 769484"
|
||
]
|
||
},
|
||
"execution_count": 19,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"table = (\n",
|
||
" df[\"year\"]\n",
|
||
" .value_counts() # counts by frequency\n",
|
||
" .rename_axis(\"Value\") # name the index\n",
|
||
" .reset_index(name=\"Count\") # make it a DataFrame\n",
|
||
" .sort_values(\"Value\") # sort alphabetically\n",
|
||
" .reset_index(drop=True) # clean row index\n",
|
||
")\n",
|
||
"\n",
|
||
"table"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "d19d077267fbcff0",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-10-03T09:50:35.915484Z",
|
||
"start_time": "2025-10-03T09:50:35.621736Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1200x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"table.plot(x=\"Value\", y=\"Count\", kind=\"bar\", figsize=(12, 6), legend=False)\n",
|
||
"\n",
|
||
"plt.xlabel(\"Year\")\n",
|
||
"plt.ylabel(\"Count\")\n",
|
||
"plt.title(\"Counts by Year\")\n",
|
||
"plt.xticks(rotation=45) # rotate x labels for readability\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"id": "c3432b78cb51dba9",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-10-03T09:50:36.020895Z",
|
||
"start_time": "2025-10-03T09:50:36.004598Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def build_parent_year_tables(\n",
|
||
" regions: pd.Series, years: pd.Series, mapper: RegionMapper\n",
|
||
") -> Dict[str, pd.DataFrame]:\n",
|
||
" \"\"\"\n",
|
||
" Returns { PARENT -> DataFrame[year, count] } where count is the number of rows\n",
|
||
" in that parent for each year (all subregions summed).\n",
|
||
" \"\"\"\n",
|
||
" r = regions.astype(str).str.strip().str.lower()\n",
|
||
" pd.to_numeric(years, errors=\"coerce\").dropna().astype(int)\n",
|
||
" # align after coercion\n",
|
||
" df_regions = pd.DataFrame({\"region\": r, \"year\": years}).dropna(subset=[\"year\"])\n",
|
||
" df_regions[\"year\"] = pd.to_numeric(df_regions[\"year\"], errors=\"coerce\").astype(\n",
|
||
" \"Int64\"\n",
|
||
" )\n",
|
||
" df_regions = df_regions.dropna(subset=[\"year\"])\n",
|
||
" df_regions[\"year\"] = df_regions[\"year\"].astype(int)\n",
|
||
"\n",
|
||
" # Map to parent provinces via RegionMapper\n",
|
||
" df_regions[\"parent\"] = mapper.map(df_regions[\"region\"])\n",
|
||
"\n",
|
||
" # Compute global year range so every parent gets the same y-axis domain\n",
|
||
" if df_regions.empty:\n",
|
||
" return {}\n",
|
||
" year_min, year_max = df_regions[\"year\"].min(), df_regions[\"year\"].max()\n",
|
||
" full_year_index = pd.Index(range(year_min, year_max + 1), name=\"year\")\n",
|
||
"\n",
|
||
" # Group and build individual parent tables with missing years filled as 0\n",
|
||
" g = df_regions.groupby([\"parent\", \"year\"]).size().rename(\"count\").reset_index()\n",
|
||
" tables: Dict[str, pd.DataFrame] = {}\n",
|
||
" for parent, gp in g.groupby(\"parent\"):\n",
|
||
" gp = (\n",
|
||
" gp.set_index(\"year\")\n",
|
||
" .reindex(full_year_index, fill_value=0)\n",
|
||
" .rename_axis(\"year\")\n",
|
||
" .reset_index()[[\"year\", \"count\"]]\n",
|
||
" )\n",
|
||
" tables[parent] = gp.sort_values(\"year\").reset_index(drop=True)\n",
|
||
" return tables\n",
|
||
"\n",
|
||
"\n",
|
||
"def plot_all_parents_by_year_grid(\n",
|
||
" regions: pd.Series, years: pd.Series, mapper: RegionMapper, per_row: int = 4\n",
|
||
"):\n",
|
||
" \"\"\"\n",
|
||
" Plot horizontal bar charts of yearly counts (summing all subregions) for each parent\n",
|
||
" from RegionMapper.get_provinces(), arranged as a grid with `per_row` columns.\n",
|
||
" \"\"\"\n",
|
||
" tables = build_parent_year_tables(regions, years, mapper)\n",
|
||
" parents = [\n",
|
||
" p.upper() for p in mapper.get_provinces()\n",
|
||
" ] # keep your canonical ordering\n",
|
||
" n = len(parents)\n",
|
||
" rows = math.ceil(n / per_row)\n",
|
||
"\n",
|
||
" fig_w = 6 * per_row\n",
|
||
" fig_h = max(4.8 * rows, 3.5)\n",
|
||
" fig, axes = plt.subplots(rows, per_row, figsize=(fig_w, fig_h))\n",
|
||
" if rows == 1 and per_row == 1:\n",
|
||
" axes = [[axes]]\n",
|
||
" elif rows == 1:\n",
|
||
" axes = [axes]\n",
|
||
" elif per_row == 1:\n",
|
||
" axes = [[ax] for ax in axes]\n",
|
||
"\n",
|
||
" for i, parent in enumerate(parents):\n",
|
||
" r, c = divmod(i, per_row)\n",
|
||
" ax = axes[r][c]\n",
|
||
"\n",
|
||
" dfp = tables.get(parent)\n",
|
||
" if dfp is None or dfp.empty:\n",
|
||
" ax.set_title(parent)\n",
|
||
" ax.text(0.5, 0.5, \"No data\", ha=\"center\", va=\"center\")\n",
|
||
" ax.set_axis_off()\n",
|
||
" continue\n",
|
||
"\n",
|
||
" # Horizontal bars: y = year (string for tidy ticks), x = count\n",
|
||
" ylabels = dfp[\"year\"].astype(str)\n",
|
||
" ax.barh(ylabels, dfp[\"count\"])\n",
|
||
" ax.set_title(parent)\n",
|
||
" ax.set_xlabel(\"Count\")\n",
|
||
" ax.set_ylabel(\"Year\")\n",
|
||
" ax.invert_yaxis() # most recent year at top if desired; remove if you prefer ascending top->bottom\n",
|
||
"\n",
|
||
" # compact y tick labels\n",
|
||
" for label in ax.get_yticklabels():\n",
|
||
" label.set_fontsize(8)\n",
|
||
"\n",
|
||
" # Turn off any unused axes\n",
|
||
" total_axes = rows * per_row\n",
|
||
" for j in range(n, total_axes):\n",
|
||
" r, c = divmod(j, per_row)\n",
|
||
" axes[r][c].set_axis_off()\n",
|
||
"\n",
|
||
" plt.tight_layout()\n",
|
||
" plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"id": "3e9c97741b7f976f",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-10-03T09:50:45.775083Z",
|
||
"start_time": "2025-10-03T09:50:36.076073Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"2025-10-05 23:15:29,930 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n",
|
||
"2025-10-05 23:15:29,966 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n",
|
||
"2025-10-05 23:15:30,223 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n",
|
||
"2025-10-05 23:15:30,265 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n",
|
||
"2025-10-05 23:15:30,419 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n",
|
||
"2025-10-05 23:15:30,425 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n",
|
||
"2025-10-05 23:15:30,645 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n",
|
||
"2025-10-05 23:15:30,669 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n",
|
||
"2025-10-05 23:15:30,905 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n",
|
||
"2025-10-05 23:15:30,922 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n",
|
||
"2025-10-05 23:15:31,157 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n",
|
||
"2025-10-05 23:15:31,162 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n",
|
||
"2025-10-05 23:15:31,453 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n",
|
||
"2025-10-05 23:15:31,484 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n",
|
||
"2025-10-05 23:15:31,722 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n",
|
||
"2025-10-05 23:15:31,736 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n",
|
||
"2025-10-05 23:15:31,907 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n",
|
||
"2025-10-05 23:15:31,919 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n",
|
||
"2025-10-05 23:15:32,177 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n",
|
||
"2025-10-05 23:15:32,186 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n",
|
||
"2025-10-05 23:15:32,411 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n",
|
||
"2025-10-05 23:15:32,468 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n",
|
||
"2025-10-05 23:15:32,772 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n",
|
||
"2025-10-05 23:15:32,782 - matplotlib.category - INFO - Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
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|
||
"text/plain": [
|
||
"<Figure size 2400x1440 with 12 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plot_all_parents_by_year_grid(df[\"region\"], df[\"year\"], RegionMapper(), per_row=4)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "fcdf0854",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": ".venv",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.11.13"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|