1331 lines
482 KiB
Plaintext
Vendored
1331 lines
482 KiB
Plaintext
Vendored
{
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"cells": [
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{
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"metadata": {},
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"cell_type": "markdown",
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"source": "# Names Analysis & Modeling",
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"id": "ae0a818eafd9bd24"
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},
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{
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"cell_type": "code",
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"id": "initial_id",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-09-28T14:57:07.646210Z",
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"start_time": "2025-09-28T14:57:07.643563Z"
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}
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},
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"source": [
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"import sys \n",
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"import os\n",
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"from collections import Counter"
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],
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"outputs": [],
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"execution_count": 1
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-09-28T14:57:07.915456Z",
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"start_time": "2025-09-28T14:57:07.719242Z"
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}
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},
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"cell_type": "code",
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"source": [
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"sys.path.append(os.path.abspath(\"..\"))\n",
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"\n",
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"from core.utils.data_loader import DataLoader\n",
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"from core.utils.region_mapper import RegionMapper\n",
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"from core.config.pipeline_config import PipelineConfig\n",
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"\n",
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"from research.statistics.utils import LETTERS\n",
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"from research.statistics.utils import identified_category_dist\n",
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"from research.statistics.utils import explode_words_token\n",
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"from research.statistics.utils import build_transition_probabilities\n",
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"from research.statistics.utils import build_transition_comparisons\n",
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"from research.statistics.utils import build_letter_frequencies\n",
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"from research.statistics.plots import plot_transition_matrix"
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],
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"id": "584a6fcfcbea71e4",
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"outputs": [],
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"execution_count": 2
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},
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{
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"cell_type": "code",
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"id": "f1a69290-a9c0-40d0-9fe8-a06d8a466671",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-09-28T14:57:08.401248Z",
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"start_time": "2025-09-28T14:57:08.396493Z"
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}
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},
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"source": [
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"config = PipelineConfig(\n",
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" paths={\n",
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" \"root_dir\": \"../data\",\n",
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" \"data_dir\": \"../data/dataset\",\n",
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" \"models_dir\": \"../models\",\n",
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" \"outputs_dir\": \"../data/processed\",\n",
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" \"logs_dir\": \"../logs\",\n",
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" \"configs_dir\": \"../configs\",\n",
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" \"checkpoints_dir\": \"../checkpoints\"\n",
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" }\n",
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")\n",
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"\n",
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"loader = DataLoader(config)"
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],
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"outputs": [],
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"execution_count": 3
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-09-28T14:57:36.335481Z",
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"start_time": "2025-09-28T14:57:08.553275Z"
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}
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},
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"cell_type": "code",
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"source": [
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"df = loader.load_csv_complete(config.paths.data_dir / \"names_featured.csv\")\n",
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"df['province'] = RegionMapper.clean_province(df['province'])\n",
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"df.columns"
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],
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"id": "e48c6fd9a213bcd2",
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Index(['name', 'sex', 'region', 'words', 'length', 'probable_native',\n",
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" 'probable_surname', 'identified_name', 'identified_surname',\n",
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" 'ner_entities', 'ner_tagged', 'annotated', 'identified_category',\n",
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" 'province'],\n",
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" dtype='object')"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"execution_count": 4
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},
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{
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"metadata": {},
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"cell_type": "markdown",
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"source": "## Name category distribution",
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"id": "46429fc98b67a89f"
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-09-28T14:57:43.780783Z",
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"start_time": "2025-09-28T14:57:43.246962Z"
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}
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},
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"cell_type": "code",
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"source": [
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"df_name_categories = identified_category_dist(df)\n",
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"df_name_categories.head(12)\n",
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"\n",
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"# save data\n",
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"df_name_categories.to_csv(\"../assets/identified_category_distribution.csv\")"
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],
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"id": "378147d2abc9ab24",
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"outputs": [],
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"execution_count": 5
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},
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{
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"metadata": {},
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"cell_type": "markdown",
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"source": "### Simple vs Compose (all provinces)",
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"id": "3c99d846cb37c469"
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-09-28T14:57:45.847127Z",
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"start_time": "2025-09-28T14:57:43.788533Z"
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}
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},
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"cell_type": "code",
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"source": [
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"labels = ['Simple', 'Compose']\n",
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"values = [\n",
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" len(df.query(\"identified_category == 'simple'\")),\n",
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" len(df.query(\"identified_category == 'compose'\"))\n",
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"]\n",
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"\n",
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"plt.figure(figsize=(6, 6))\n",
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"plt.pie(values, labels=labels, autopct='%1.1f%%')\n",
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"plt.axis(\"equal\")\n",
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"\n",
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"# save figures\n",
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"plt.savefig(\"../assets/identified_category_distribution.png\")\n",
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"plt.savefig(\"../assets/identified_category_distribution.svg\")\n",
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"\n",
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"plt.show()"
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],
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"id": "ae30e79a975010d4",
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<Figure size 600x600 with 1 Axes>"
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],
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"image/png": 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"
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data",
|
||
"jetTransient": {
|
||
"display_id": null
|
||
}
|
||
}
|
||
],
|
||
"execution_count": 6
|
||
},
|
||
{
|
||
"metadata": {},
|
||
"cell_type": "markdown",
|
||
"source": "### Simple vs Compose by Province",
|
||
"id": "d31d8ae890cdfece"
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-09-28T14:57:46.356962Z",
|
||
"start_time": "2025-09-28T14:57:45.874356Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": [
|
||
"df_name_categories_pct = df_name_categories.div(df_name_categories.sum(axis=1), axis=0) * 100\n",
|
||
"ax = df_name_categories_pct.plot.barh(\n",
|
||
" stacked=True,\n",
|
||
" figsize=(12, 6),\n",
|
||
" colormap=\"tab20c\",\n",
|
||
" width=0.85\n",
|
||
")\n",
|
||
"\n",
|
||
"ax.set_xlabel(\"Proportion (%)\")\n",
|
||
"ax.set_ylabel(\"Province\")\n",
|
||
"ax.set_xlim(0, 100)\n",
|
||
"ax.grid(axis=\"x\", linestyle=\"--\", alpha=0.6)\n",
|
||
"\n",
|
||
"plt.legend(title=\"Category\", bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n",
|
||
"plt.tight_layout()\n",
|
||
"\n",
|
||
"# save figures\n",
|
||
"plt.savefig(\"../assets/identified_category_distribution_by_province.png\")\n",
|
||
"plt.savefig(\"../assets/identified_category_distribution_by_province.svg\")\n",
|
||
"\n",
|
||
"plt.show()"
|
||
],
|
||
"id": "6d5c1abb55b7076a",
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1200x600 with 1 Axes>"
|
||
],
|
||
"image/png": 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|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data",
|
||
"jetTransient": {
|
||
"display_id": null
|
||
}
|
||
}
|
||
],
|
||
"execution_count": 7
|
||
},
|
||
{
|
||
"metadata": {},
|
||
"cell_type": "markdown",
|
||
"source": "## Native Names vs Surnames",
|
||
"id": "1973b08d18c2258e"
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-09-28T14:57:47.131714Z",
|
||
"start_time": "2025-09-28T14:57:46.364153Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": "df_base = df.query(\"identified_category == 'simple'\")",
|
||
"id": "1e7dde234bb3504f",
|
||
"outputs": [],
|
||
"execution_count": 8
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-09-28T14:58:19.334139Z",
|
||
"start_time": "2025-09-28T14:57:47.146184Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": [
|
||
"df_names = explode_words_token(df_base, 'identified_name', 'name')\n",
|
||
"df_names = df_names[['name', 'province', 'sex']].reset_index(drop=True)\n",
|
||
"df_names.describe().T"
|
||
],
|
||
"id": "24a4ad40319f441b",
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
" count unique top freq\n",
|
||
"name 10015595 644336 ilunga 82342\n",
|
||
"province 10015595 12 KINSHASA 2106077\n",
|
||
"sex 10015595 2 m 6033856"
|
||
],
|
||
"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>count</th>\n",
|
||
" <th>unique</th>\n",
|
||
" <th>top</th>\n",
|
||
" <th>freq</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>name</th>\n",
|
||
" <td>10015595</td>\n",
|
||
" <td>644336</td>\n",
|
||
" <td>ilunga</td>\n",
|
||
" <td>82342</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>province</th>\n",
|
||
" <td>10015595</td>\n",
|
||
" <td>12</td>\n",
|
||
" <td>KINSHASA</td>\n",
|
||
" <td>2106077</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>sex</th>\n",
|
||
" <td>10015595</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>m</td>\n",
|
||
" <td>6033856</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
]
|
||
},
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"execution_count": 9
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-09-28T14:58:41.141954Z",
|
||
"start_time": "2025-09-28T14:58:19.869428Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": [
|
||
"df_surnames = explode_words_token(df_base, 'identified_surname', 'name')\n",
|
||
"df_surnames = df_surnames[['name', 'province', 'sex']].reset_index(drop=True)\n",
|
||
"df_surnames.describe().T"
|
||
],
|
||
"id": "736b0d787b9a6809",
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
" count unique top freq\n",
|
||
"name 5007877 253743 jean 89564\n",
|
||
"province 5007877 12 KINSHASA 1053047\n",
|
||
"sex 5007877 2 m 3017009"
|
||
],
|
||
"text/html": [
|
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"<div>\n",
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"<style scoped>\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",
|
||
" }\n",
|
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|
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" .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>count</th>\n",
|
||
" <th>unique</th>\n",
|
||
" <th>top</th>\n",
|
||
" <th>freq</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>name</th>\n",
|
||
" <td>5007877</td>\n",
|
||
" <td>253743</td>\n",
|
||
" <td>jean</td>\n",
|
||
" <td>89564</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>province</th>\n",
|
||
" <td>5007877</td>\n",
|
||
" <td>12</td>\n",
|
||
" <td>KINSHASA</td>\n",
|
||
" <td>1053047</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>sex</th>\n",
|
||
" <td>5007877</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>m</td>\n",
|
||
" <td>3017009</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
]
|
||
},
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"execution_count": 10
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "98ba6e48-a7c4-4aae-8b46-872489c95faf",
|
||
"metadata": {},
|
||
"source": "### Transition probabilities"
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-09-28T14:59:04.922560Z",
|
||
"start_time": "2025-09-28T14:58:41.384503Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": [
|
||
"names_transitions = build_transition_probabilities(df_names['name'])\n",
|
||
"names_transitions_males = build_transition_probabilities(df_names.query(\"sex == 'm'\")['name'])\n",
|
||
"names_transitions_females = build_transition_probabilities(df_names.query(\"sex == 'f'\")['name'])\n",
|
||
"\n",
|
||
"names_transitions['df_probs'].to_csv(\"../assets/names_transition_probs.csv\")"
|
||
],
|
||
"id": "12a7094d94ad519f",
|
||
"outputs": [],
|
||
"execution_count": 11
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-09-28T14:59:17.020745Z",
|
||
"start_time": "2025-09-28T14:59:04.964198Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": [
|
||
"surnames_transitions = build_transition_probabilities(df_surnames['name'])\n",
|
||
"surnames_transitions_males = build_transition_probabilities(df_surnames.query(\"sex == 'm'\")['name'])\n",
|
||
"surnames_transitions_females = build_transition_probabilities(df_surnames.query(\"sex == 'f'\")['name'])\n",
|
||
"\n",
|
||
"surnames_transitions['df_probs'].to_csv(\"../assets/surnames_transition_probs.csv\")"
|
||
],
|
||
"id": "684b467b17955d65",
|
||
"outputs": [],
|
||
"execution_count": 12
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-09-28T14:59:21.075645Z",
|
||
"start_time": "2025-09-28T14:59:17.790264Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": [
|
||
"fig, axes = plt.subplots(1, 3, figsize=(20, 6), sharex=True, sharey=True)\n",
|
||
"hm1 = plot_transition_matrix(axes[0], names_transitions['df_probs'], \"Native - Male\")\n",
|
||
"hm2 = plot_transition_matrix(axes[1], names_transitions_females['df_probs'], \"Native - Female\")\n",
|
||
"hm3 = plot_transition_matrix(axes[2], names_transitions_males['df_probs'], \"Native\")\n",
|
||
"cbar = fig.colorbar(hm3.collections[0], ax=axes, orientation=\"vertical\", fraction=0.03, pad=0.02)\n",
|
||
"cbar.set_label(\"Transition probability\")\n",
|
||
"plt.savefig(\"../assets/names_transition_probabilities.png\")\n",
|
||
"plt.savefig(\"../assets/names_transition_probabilities.svg\")\n",
|
||
"\n",
|
||
"fig, axes = plt.subplots(1, 3, figsize=(20, 6), sharex=True, sharey=True)\n",
|
||
"hm4 = plot_transition_matrix(axes[0], surnames_transitions['df_probs'], \"Surnames - Male\")\n",
|
||
"hm5 = plot_transition_matrix(axes[1], surnames_transitions_females['df_probs'], \"Surnames - Female\")\n",
|
||
"hm6 = plot_transition_matrix(axes[2], surnames_transitions_males['df_probs'], \"Surnames\")\n",
|
||
"cbar = fig.colorbar(hm6.collections[0], ax=axes, orientation=\"vertical\", fraction=0.03, pad=0.02)\n",
|
||
"cbar.set_label(\"Transition probability\")\n",
|
||
"plt.savefig(\"../assets/surnames_transition_probabilities.png\")\n",
|
||
"plt.savefig(\"../assets/surnames_transition_probabilities.svg\")\n",
|
||
"plt.show()"
|
||
],
|
||
"id": "f15ad6eb4df27527",
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 2000x600 with 4 Axes>"
|
||
],
|
||
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|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data",
|
||
"jetTransient": {
|
||
"display_id": null
|
||
}
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 2000x600 with 4 Axes>"
|
||
],
|
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"image/png": 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|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data",
|
||
"jetTransient": {
|
||
"display_id": null
|
||
}
|
||
}
|
||
],
|
||
"execution_count": 13
|
||
},
|
||
{
|
||
"metadata": {},
|
||
"cell_type": "markdown",
|
||
"source": "### Transition Probability Differences",
|
||
"id": "1f59136fe68b02a8"
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-09-28T14:59:22.072443Z",
|
||
"start_time": "2025-09-28T14:59:21.120458Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": [
|
||
"names_transitions_diff = names_transitions_males['df_probs'] - names_transitions_females['df_probs']\n",
|
||
"surnames_transitions_diff = surnames_transitions_males['df_probs'] - surnames_transitions_females['df_probs']\n",
|
||
"\n",
|
||
"fig, axes = plt.subplots(1, 2, figsize=(14, 6), sharex=True, sharey=True)\n",
|
||
"\n",
|
||
"hm1 = sns.heatmap(names_transitions_diff.loc[list(LETTERS), list(LETTERS)], cmap=\"RdBu_r\", center=0, ax=axes[0], cbar=False)\n",
|
||
"axes[0].set_title(\"Names Transition Difference (Male − Female)\")\n",
|
||
"axes[0].set_xlabel(\"Next letter\"); axes[0].set_ylabel(\"Current letter\")\n",
|
||
"\n",
|
||
"hm2 = sns.heatmap(surnames_transitions_diff.loc[list(LETTERS), list(LETTERS)], cmap=\"RdBu_r\", center=0, ax=axes[1], cbar=False)\n",
|
||
"axes[1].set_title(\"Surnames Transition Difference (Male − Female)\")\n",
|
||
"axes[1].set_xlabel(\"Next letter\"); axes[1].set_ylabel(\"\")\n",
|
||
"\n",
|
||
"# Shared colorbar\n",
|
||
"cbar = fig.colorbar(hm2.collections[0], ax=axes, orientation=\"vertical\", fraction=0.03, pad=0.02)\n",
|
||
"cbar.set_label(\"Difference in transition probability (Male − Female)\")\n",
|
||
"\n",
|
||
"plt.savefig(\"../assets/transition_difference.png\")\n",
|
||
"plt.show()"
|
||
],
|
||
"id": "51ec0792317364fc",
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1400x600 with 3 Axes>"
|
||
],
|
||
"image/png": 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"
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data",
|
||
"jetTransient": {
|
||
"display_id": null
|
||
}
|
||
}
|
||
],
|
||
"execution_count": 14
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-09-28T14:59:24.145603Z",
|
||
"start_time": "2025-09-28T14:59:22.140871Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": [
|
||
"df_comparisons = build_transition_comparisons(\n",
|
||
" {\n",
|
||
" 'm': names_transitions_males,\n",
|
||
" 'f': names_transitions_females\n",
|
||
" },\n",
|
||
" {\n",
|
||
" 'm': surnames_transitions_males,\n",
|
||
" 'f': surnames_transitions_females\n",
|
||
" }\n",
|
||
")\n",
|
||
"df_comparisons.head(3)"
|
||
],
|
||
"id": "dae836cd8a6c26e6",
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
" l2 kl_mf kl_fm jsd permutation_p_value\n",
|
||
"names 0.318904 0.043201 0.021538 0.032370 0.979\n",
|
||
"surnames 1.277002 0.293619 0.239895 0.266757 0.001"
|
||
],
|
||
"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>l2</th>\n",
|
||
" <th>kl_mf</th>\n",
|
||
" <th>kl_fm</th>\n",
|
||
" <th>jsd</th>\n",
|
||
" <th>permutation_p_value</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>names</th>\n",
|
||
" <td>0.318904</td>\n",
|
||
" <td>0.043201</td>\n",
|
||
" <td>0.021538</td>\n",
|
||
" <td>0.032370</td>\n",
|
||
" <td>0.979</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>surnames</th>\n",
|
||
" <td>1.277002</td>\n",
|
||
" <td>0.293619</td>\n",
|
||
" <td>0.239895</td>\n",
|
||
" <td>0.266757</td>\n",
|
||
" <td>0.001</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
]
|
||
},
|
||
"execution_count": 15,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"execution_count": 15
|
||
},
|
||
{
|
||
"metadata": {},
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"l2 (Euclidean Distance): This metric shows the overall magnitude of the difference between the two matrices. The value for surnames (1.277) is significantly higher than for names (0.3189), indicating that the male and female surname transition matrices are farther apart than those for first names.\n",
|
||
"\n",
|
||
"kl_mf and kl_fm (Kullback-Leibler Divergence): These values measure the information lost when one gender's probability distribution is used to approximate the other. The values for surnames (0.2936 and 0.2399) are much larger than for names (0.0432 and 0.0215). This reinforces the L2 distance's finding: there's a greater difference in the letter patterns between male and female surnames.\n",
|
||
"\n",
|
||
"jsd (Jensen-Shannon Divergence): This is a symmetric, smoothed version of KL divergence. The jsd for surnames (0.2668) is much higher than for names (0.0324), providing a single, clear measure of the greater divergence in surname patterns.\n",
|
||
"\n",
|
||
"The permutation_p_value is the most crucial part of this analysis. It determines whether the observed differences are statistically significant or likely due to random chance.\n",
|
||
"\n",
|
||
"names (p-value = 0.979): A p-value of 0.979 is very high, far greater than the common significance level of 0.05. This means there's a 97.9% chance of observing a difference this large or larger even if there were no real difference in letter transition patterns between male and female first names. Therefore, you cannot reject the null hypothesis that the patterns are the same. The observed small difference (JSD = 0.032) is not statistically significant.\n",
|
||
"\n",
|
||
"surnames (p-value = 0.001): A p-value of 0.001 is very low, well below the significance level of 0.05. This means there's only a 0.1% chance of observing a difference this large or larger if there were no real difference. This provides strong statistical evidence to reject the null hypothesis. The significant difference (JSD = 0.266) is not random; it indicates a genuine, statistically significant difference in the letter transition patterns between male and female surnames.\n",
|
||
"\n",
|
||
"Conclusion: The analysis confirms that male and female surnames have distinct, gender-specific letter transition patterns, while the patterns in first names are not statistically different."
|
||
],
|
||
"id": "30078f474d5f456e"
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-09-28T14:59:24.290360Z",
|
||
"start_time": "2025-09-28T14:59:24.286314Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": "df_comparisons.to_csv(\"../assets/transition_comparisons.csv\")",
|
||
"id": "b5d674fb3a7f1c83",
|
||
"outputs": [],
|
||
"execution_count": 16
|
||
},
|
||
{
|
||
"metadata": {},
|
||
"cell_type": "markdown",
|
||
"source": "### Letters frequency",
|
||
"id": "c49254df7eee9ae2"
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-09-28T14:59:24.854294Z",
|
||
"start_time": "2025-09-28T14:59:24.348456Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": [
|
||
"def plot_letter_grid(df_all: pd.DataFrame, use=\"freq\", sort_values=False):\n",
|
||
" \"\"\"\n",
|
||
" Plot a 1×3 grid of letter distributions for Male, Female, and Both.\n",
|
||
" `use` ∈ {\"freq\",\"count\"} controls y-axis.\n",
|
||
" \"\"\"\n",
|
||
" # Slice datasets (adapt values if your labels are 'M'/'F', etc.)\n",
|
||
" df_male = df_all[df_all['sex'].str.lower() == str('m').lower()]\n",
|
||
" df_female = df_all[df_all['sex'].str.lower() == str('f').lower()]\n",
|
||
"\n",
|
||
" L_m = build_letter_frequencies(df_male['name'])\n",
|
||
" L_f = build_letter_frequencies(df_female['name'])\n",
|
||
"\n",
|
||
" y = \"freq\" if use == \"freq\" else \"count\"\n",
|
||
" if sort_values:\n",
|
||
" L_m = L_m.sort_values(y, ascending=False)\n",
|
||
" L_f = L_f.sort_values(y, ascending=False)\n",
|
||
"\n",
|
||
" ymax = max(L_m[y].max(), L_f[y].max()) * 1.10\n",
|
||
"\n",
|
||
" fig, axes = plt.subplots(1, 2, figsize=(18, 5), sharey=True, constrained_layout=True)\n",
|
||
" for ax, data, title in zip(axes, [L_m, L_f], [\"Male\", \"Female\"]):\n",
|
||
" ax.bar(data[\"letter\"], data[y], color=\"steelblue\", alpha=0.8)\n",
|
||
" ax.set_title(title)\n",
|
||
" ax.set_xlabel(\"Letter\")\n",
|
||
" ax.set_ylim(0, ymax)\n",
|
||
" ax.grid(axis=\"y\", alpha=0.3)\n",
|
||
" axes[0].set_ylabel(\"Frequency\" if y == \"freq\" else \"Count\")\n",
|
||
" plt.show()"
|
||
],
|
||
"id": "4a0d8e2a74a6406c",
|
||
"outputs": [
|
||
{
|
||
"ename": "NameError",
|
||
"evalue": "name 'pd' is not defined",
|
||
"output_type": "error",
|
||
"traceback": [
|
||
"\u001B[31m---------------------------------------------------------------------------\u001B[39m",
|
||
"\u001B[31mNameError\u001B[39m Traceback (most recent call last)",
|
||
"\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[17]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mplot_letter_grid\u001B[39m(df_all: \u001B[43mpd\u001B[49m.DataFrame, use=\u001B[33m\"\u001B[39m\u001B[33mfreq\u001B[39m\u001B[33m\"\u001B[39m, sort_values=\u001B[38;5;28;01mFalse\u001B[39;00m):\n\u001B[32m 2\u001B[39m \u001B[38;5;250m \u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m 3\u001B[39m \u001B[33;03m Plot a 1×3 grid of letter distributions for Male, Female, and Both.\u001B[39;00m\n\u001B[32m 4\u001B[39m \u001B[33;03m `use` ∈ {\"freq\",\"count\"} controls y-axis.\u001B[39;00m\n\u001B[32m 5\u001B[39m \u001B[33;03m \"\"\"\u001B[39;00m\n\u001B[32m 6\u001B[39m \u001B[38;5;66;03m# Slice datasets (adapt values if your labels are 'M'/'F', etc.)\u001B[39;00m\n",
|
||
"\u001B[31mNameError\u001B[39m: name 'pd' is not defined"
|
||
]
|
||
}
|
||
],
|
||
"execution_count": 17
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-09-28T14:59:24.856757Z",
|
||
"start_time": "2025-09-28T14:52:15.817423Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": "plot_letter_grid(df_names, use=\"freq\")",
|
||
"id": "9265e47639c4319d",
|
||
"outputs": [
|
||
{
|
||
"ename": "NameError",
|
||
"evalue": "name 'plot_letter_grid' is not defined",
|
||
"output_type": "error",
|
||
"traceback": [
|
||
"\u001B[31m---------------------------------------------------------------------------\u001B[39m",
|
||
"\u001B[31mNameError\u001B[39m Traceback (most recent call last)",
|
||
"\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[19]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m \u001B[43mplot_letter_grid\u001B[49m(df_names, use=\u001B[33m\"\u001B[39m\u001B[33mfreq\u001B[39m\u001B[33m\"\u001B[39m)\n",
|
||
"\u001B[31mNameError\u001B[39m: name 'plot_letter_grid' is not defined"
|
||
]
|
||
}
|
||
],
|
||
"execution_count": 19
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-09-28T14:59:24.857123Z",
|
||
"start_time": "2025-09-25T21:39:45.174312Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": "plot_letter_grid(df_names, use=\"freq\", sort_values=True)",
|
||
"id": "395dace523f1bed4",
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1800x500 with 2 Axes>"
|
||
],
|
||
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|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data",
|
||
"jetTransient": {
|
||
"display_id": null
|
||
}
|
||
}
|
||
],
|
||
"execution_count": 209
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"id": "98c7ed61-5c2d-4d9a-9d85-e4efe4bfc402",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-09-28T14:59:24.857443Z",
|
||
"start_time": "2025-09-25T21:35:00.831997Z"
|
||
}
|
||
},
|
||
"source": [
|
||
"def count_ngrams(df: pd.DataFrame, n: int, col: str = \"name\") -> pd.DataFrame:\n",
|
||
" # Normalize and clean\n",
|
||
" _names = (\n",
|
||
" df[col]\n",
|
||
" .astype(str)\n",
|
||
" .str.lower()\n",
|
||
" .str.replace(r\"[^a-z]\", \"\", regex=True)\n",
|
||
" )\n",
|
||
"\n",
|
||
" # Collect n-grams from all names\n",
|
||
" ngrams = []\n",
|
||
" for name in _names:\n",
|
||
" if len(name) >= n:\n",
|
||
" ngrams.extend(name[i:i+n] for i in range(len(name) - n + 1))\n",
|
||
"\n",
|
||
" # Count\n",
|
||
" counter = Counter(ngrams)\n",
|
||
"\n",
|
||
" # Build DataFrame\n",
|
||
" df_ngrams = (\n",
|
||
" pd.DataFrame(counter.items(), columns=[f\"{n}-gram\", \"count\"])\n",
|
||
" .sort_values(\"count\", ascending=False)\n",
|
||
" .reset_index(drop=True)\n",
|
||
" )\n",
|
||
" df_ngrams[\"freq\"] = df_ngrams[\"count\"] / df_ngrams[\"count\"].sum()\n",
|
||
" return df_ngrams\n",
|
||
"\n",
|
||
"def plot_ngrams_grid(df: pd.DataFrame, n: int, top_k: int = 20,\n",
|
||
" gender_col=\"sex\", male_value=\"m\", female_value=\"f\", name_col=\"name\"):\n",
|
||
" \"\"\"\n",
|
||
" Plot top n-grams for Male, Female, and All in a 1×3 grid.\n",
|
||
" \"\"\"\n",
|
||
" # Split datasets\n",
|
||
" df_male = df[df[gender_col].str.lower() == str(male_value).lower()]\n",
|
||
" df_female = df[df[gender_col].str.lower() == str(female_value).lower()]\n",
|
||
"\n",
|
||
" # Compute n-grams\n",
|
||
" ng_male = count_ngrams(df_male, n, col=name_col)\n",
|
||
" ng_female = count_ngrams(df_female, n, col=name_col)\n",
|
||
"\n",
|
||
" # Plot in a grid\n",
|
||
" fig, axes = plt.subplots(1, 2, figsize=(18, 6), sharey=True, constrained_layout=True)\n",
|
||
" for ax, data, title in zip(\n",
|
||
" axes, [ng_male, ng_female], [\"Male\", \"Female\"]\n",
|
||
" ):\n",
|
||
" sns.barplot(\n",
|
||
" data=data.head(top_k),\n",
|
||
" x=f\"{n}-gram\", y=\"count\",\n",
|
||
" ax=ax\n",
|
||
" )\n",
|
||
" ax.set_title(f\"{title} – Top {top_k} {n}-grams\")\n",
|
||
" ax.set_xlabel(f\"{n}-gram\")\n",
|
||
" ax.set_ylabel(\"Count\")\n",
|
||
" ax.tick_params(axis=\"x\", rotation=45)\n",
|
||
"\n",
|
||
" plt.show()"
|
||
],
|
||
"outputs": [],
|
||
"execution_count": 202
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"id": "412883b2-c1fd-483f-b61a-ba2bfc8b04e7",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-09-28T14:59:24.857737Z",
|
||
"start_time": "2025-09-25T21:35:03.778209Z"
|
||
}
|
||
},
|
||
"source": "plot_ngrams_grid(df_names, n=3, top_k=20, gender_col=\"sex\")",
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1800x600 with 2 Axes>"
|
||
],
|
||
"image/png": 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"
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data",
|
||
"jetTransient": {
|
||
"display_id": null
|
||
}
|
||
}
|
||
],
|
||
"execution_count": 203
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-09-28T14:59:24.858071Z",
|
||
"start_time": "2025-09-25T21:35:45.428602Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": "plot_ngrams_grid(df_names, n=4, top_k=20, gender_col=\"sex\")",
|
||
"id": "b4eda839e6cc5d8f",
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1800x600 with 2 Axes>"
|
||
],
|
||
"image/png": 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"
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data",
|
||
"jetTransient": {
|
||
"display_id": null
|
||
}
|
||
}
|
||
],
|
||
"execution_count": 204
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-09-28T14:59:24.858335Z",
|
||
"start_time": "2025-09-25T21:36:21.247703Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": "plot_ngrams_grid(df_names, n=5, top_k=20, gender_col=\"sex\")",
|
||
"id": "55605ab05b9a10bb",
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1800x600 with 2 Axes>"
|
||
],
|
||
"image/png": 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"
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data",
|
||
"jetTransient": {
|
||
"display_id": null
|
||
}
|
||
}
|
||
],
|
||
"execution_count": 205
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "28e65639-9082-464d-9330-71e2a028c878",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Name generation"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"id": "28fe77f9-f9b0-4267-9dbb-3969e04b83fb",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-09-28T14:59:24.858635Z",
|
||
"start_time": "2025-09-25T21:43:42.798968Z"
|
||
}
|
||
},
|
||
"source": [
|
||
"# tokens must include '^' (start) and '$' (end)\n",
|
||
"tokens = [\"^\"] + list(LETTERS) + [\"$\"]\n",
|
||
"token_to_idx = {t: i for i, t in enumerate(tokens)}\n",
|
||
"idx_to_token = np.array(tokens)\n",
|
||
"V = len(tokens)\n",
|
||
"\n",
|
||
"# Prepare a well-formed probability matrix (rows sum to 1, no NaNs/negatives)\n",
|
||
"def prepare_prob_matrix(df_probs, tokens):\n",
|
||
" import pandas as pd, numpy as np\n",
|
||
" if isinstance(df_probs, pd.DataFrame):\n",
|
||
" P = df_probs.loc[tokens, tokens].to_numpy(dtype=float, copy=True)\n",
|
||
" else:\n",
|
||
" P = np.array(df_probs, dtype=float, copy=True)\n",
|
||
" if P.shape[0] != len(tokens) or P.shape[1] != len(tokens):\n",
|
||
" raise ValueError(\n",
|
||
" f\"Matrix shape {P.shape} does not match tokens length {len(tokens)}. \"\n",
|
||
" \"Use the same tokens used to build the matrix (e.g., transitions['tokens']).\"\n",
|
||
" )\n",
|
||
" # clean & renormalize\n",
|
||
" P[~np.isfinite(P) | (P < 0)] = 0.0\n",
|
||
" rs = P.sum(axis=1, keepdims=True)\n",
|
||
" P = np.divide(P, np.where(rs == 0, 1.0, rs), out=np.zeros_like(P), where=True)\n",
|
||
" return P\n",
|
||
"\n",
|
||
"\n",
|
||
"def generate_name(P, token_to_idx, idx_to_token, *,\n",
|
||
" target_len=None, # exact character length (letters only), if provided\n",
|
||
" min_len=1, # minimum character length\n",
|
||
" max_len=12, # hard cap on steps\n",
|
||
" temperature=1.0):\n",
|
||
" start = token_to_idx['^']\n",
|
||
" end = token_to_idx['$']\n",
|
||
" cur = start\n",
|
||
" out = []\n",
|
||
"\n",
|
||
" for _ in range(max_len):\n",
|
||
" row = P[cur]\n",
|
||
"\n",
|
||
" # Temperature scaling (τ<1 = sharper, τ>1 = flatter)\n",
|
||
" if temperature != 1.0:\n",
|
||
" row = np.power(row, 1.0 / temperature)\n",
|
||
" s = row.sum()\n",
|
||
" row = row / s if s > 0 else row\n",
|
||
"\n",
|
||
" row_mod = row.copy()\n",
|
||
"\n",
|
||
" # 1) Prevent early stop before min_len\n",
|
||
" if len(out) < min_len:\n",
|
||
" row_mod[end] = 0.0\n",
|
||
"\n",
|
||
" # 2) If target_len reached or exceeded, force end\n",
|
||
" if target_len is not None and len(out) >= target_len:\n",
|
||
" row_mod[:] = 0.0\n",
|
||
" row_mod[end] = 1.0\n",
|
||
"\n",
|
||
" s = row_mod.sum()\n",
|
||
" if s == 0.0:\n",
|
||
" # Fallback: uniform over valid next tokens\n",
|
||
" candidates = np.arange(V)\n",
|
||
" # exclude '^'\n",
|
||
" candidates = candidates[candidates != start]\n",
|
||
" # exclude '$' if below min_len\n",
|
||
" if len(out) < min_len:\n",
|
||
" candidates = candidates[candidates != end]\n",
|
||
" probs = np.ones(len(candidates)) / len(candidates)\n",
|
||
" nxt_idx = np.random.choice(candidates, p=probs)\n",
|
||
" else:\n",
|
||
" row_mod = row_mod / s\n",
|
||
" nxt_idx = np.random.choice(V, p=row_mod)\n",
|
||
"\n",
|
||
" if nxt_idx == end:\n",
|
||
" break\n",
|
||
" out.append(idx_to_token[nxt_idx])\n",
|
||
" cur = nxt_idx\n",
|
||
"\n",
|
||
" return \"\".join(out).capitalize()"
|
||
],
|
||
"outputs": [],
|
||
"execution_count": 210
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-09-28T14:59:24.858969Z",
|
||
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|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# Example preparation and usage\n",
|
||
"P = prepare_prob_matrix(transitions_male['df_probs'], tokens)\n",
|
||
"generated_var = [generate_name(P, token_to_idx, idx_to_token, min_len=5, max_len=12, temperature=0.5) for _ in range(10)]\n",
|
||
"\n",
|
||
"names = pd.DataFrame(generated_var, columns=[\"name\"])"
|
||
],
|
||
"id": "a01eb547985d5a62",
|
||
"outputs": [],
|
||
"execution_count": 212
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-09-28T14:59:24.859246Z",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
"data": {
|
||
"text/plain": [
|
||
" name\n",
|
||
"0 Tolemba\n",
|
||
"1 Kalanga\n",
|
||
"2 Mbayo\n",
|
||
"3 Kukasa\n",
|
||
"4 Kadamu\n",
|
||
"5 Bongalula\n",
|
||
"6 Kanga\n",
|
||
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|
||
"8 Kayalela\n",
|
||
"9 Mbalo"
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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||
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|
||
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||
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||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" </tr>\n",
|
||
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|
||
" <th>1</th>\n",
|
||
" <td>Kalanga</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>Mbayo</td>\n",
|
||
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|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>Kukasa</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>Kadamu</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>Bongalula</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>Kanga</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>Mangonga</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8</th>\n",
|
||
" <td>Kayalela</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9</th>\n",
|
||
" <td>Mbalo</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
]
|
||
},
|
||
"execution_count": 213,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
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|
||
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|
||
"execution_count": 213
|
||
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|
||
{
|
||
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|
||
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||
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||
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|
||
"cell_type": "code",
|
||
"source": [
|
||
"P = prepare_prob_matrix(transitions_female['df_probs'], tokens)\n",
|
||
"generated_var = [generate_name(P, token_to_idx, idx_to_token, min_len=5, max_len=12, temperature=0.5) for _ in range(10)]\n",
|
||
"\n",
|
||
"names = pd.DataFrame(generated_var, columns=[\"name\"])"
|
||
],
|
||
"id": "5d5ec37da189b7a8",
|
||
"outputs": [],
|
||
"execution_count": 216
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-09-28T14:59:24.860061Z",
|
||
"start_time": "2025-09-25T21:44:33.284314Z"
|
||
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|
||
},
|
||
"cell_type": "code",
|
||
"source": "names",
|
||
"id": "f9da3dad4317ac10",
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
" name\n",
|
||
"0 Ngone\n",
|
||
"1 Kandi\n",
|
||
"2 Kangonga\n",
|
||
"3 Dangangandi\n",
|
||
"4 Kukale\n",
|
||
"5 Mando\n",
|
||
"6 Kasaba\n",
|
||
"7 Mambo\n",
|
||
"8 Mpolanga\n",
|
||
"9 Kangena"
|
||
],
|
||
"text/html": [
|
||
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|
||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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|
||
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|
||
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|
||
" <td>Kangonga</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <tr>\n",
|
||
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|
||
" <td>Kukale</td>\n",
|
||
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|
||
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|
||
" <th>5</th>\n",
|
||
" <td>Mando</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>Kasaba</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>Mambo</td>\n",
|
||
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|
||
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|
||
" <th>8</th>\n",
|
||
" <td>Mpolanga</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
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|
||
" <td>Kangena</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
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