{
"cells": [
{
"cell_type": "markdown",
"id": "281e8f65238cd05f",
"metadata": {
"collapsed": false
},
"source": [
"# Quatitative Study Replication\n",
"## Imports and Data"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "c7fa7823e0974f4f",
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-07T15:32:01.651786Z",
"start_time": "2024-08-07T15:32:01.608890100Z"
}
},
"outputs": [],
"source": [
"import warnings\n",
"warnings.filterwarnings(\"ignore\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "initial_id",
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
},
"ExecuteTime": {
"end_time": "2024-08-07T15:32:02.858145600Z",
"start_time": "2024-08-07T15:32:01.623812300Z"
}
},
"outputs": [],
"source": [
"import pandas as pd\n",
"dat = pd.read_csv('StudyDataset.csv', encoding='windows-1252', index_col=0)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "857c3f3e8cd89ba7",
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-07T15:32:02.906717600Z",
"start_time": "2024-08-07T15:32:02.863693700Z"
}
},
"outputs": [],
"source": [
"df = pd.read_csv('Quantitative Study Replication/Non-State Actors Dataset/nsa_v3.4_21November2013.asc', sep='\\t', encoding='windows-1252')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "cb6c6ee91a5fa5fc",
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-07T15:32:02.956800100Z",
"start_time": "2024-08-07T15:32:02.895241200Z"
}
},
"outputs": [],
"source": [
"df_3 = pd.read_csv('StudyDataset2.csv', encoding='windows-1252', index_col=0)"
]
},
{
"cell_type": "markdown",
"id": "831426af963ce602",
"metadata": {
"collapsed": false
},
"source": [
"## Rows with more than one Rebel Group\n",
"The DCJ dataset contains rows that list more than one rebel group in the 'Side B' column. In order to deal with this, first, sample weights were introduced. These rows were then split into a row for each group, and weighted such that the total weight of the newly created rows for each original rows sums to one. \n",
"\n",
"In other words, if a row lists 2 groups, it will be split into 2 rows, each with one of the two groups and otherwise identical, weighted at 0.5 each."
]
},
{
"cell_type": "code",
"execution_count": 5,
"outputs": [
{
"data": {
"text/plain": " acdid year gwno location sidea \\\n1 1 1946 145.0 Bolivia Bolivia \n2 1 1952 145.0 Bolivia Bolivia \n3 1 1967 145.0 Bolivia Bolivia \n4 2 1946 811.0 Cambodia (Kampuchea) France \n5 2 1947 811.0 Cambodia (Kampuchea) France \n... ... ... ... ... ... \n3406 271 2011 620.0 Libya Libya \n3407 271 2011 620.0 Libya Libya \n3408 271 2011 620.0 Libya Libya \n3409 271 2011 620.0 Libya Libya \n3410 271 2011 620.0 Libya Libya \n\n sideb incomp territory startdate \\\n1 Popular Revolutionary Movement 2 NaN 1946-06-30 \n2 MNR 2 NaN 1946-06-30 \n3 ELN 2 NaN 1946-06-30 \n4 Khmer Issarak 1 Cambodia 1946-08-31 \n5 Khmer Issarak 1 Cambodia 1946-08-31 \n... ... ... ... ... \n3406 NTC, Forces of Muammar Gaddafi 2 NaN 2011-01-28 \n3407 NTC, Forces of Muammar Gaddafi 2 NaN 2011-01-28 \n3408 NTC, Forces of Muammar Gaddafi 2 NaN 2011-01-28 \n3409 NTC, Forces of Muammar Gaddafi 2 NaN 2011-01-28 \n3410 NTC, Forces of Muammar Gaddafi 2 NaN 2011-01-28 \n\n epstartdate ... exile_erank exile_sender exile_scope exile_scount \\\n1 1946-06-30 ... NaN NaN NaN NaN \n2 1952-04-09 ... NaN NaN NaN NaN \n3 1967-03-31 ... NaN NaN NaN NaN \n4 1946-08-31 ... NaN NaN NaN NaN \n5 1946-08-31 ... NaN NaN NaN NaN \n... ... ... ... ... ... ... \n3406 2011-03-04 ... NaN NaN NaN NaN \n3407 2011-03-04 ... NaN NaN NaN NaN \n3408 2011-03-04 ... NaN NaN NaN NaN \n3409 2011-03-04 ... NaN NaN NaN NaN \n3410 2011-03-04 ... NaN NaN NaN NaN \n\n exile_implement exile_rDCJ exile_peaceagr exile_start exile_end \\\n1 NaN NaN NaN NaN NaN \n2 NaN NaN NaN NaN NaN \n3 NaN NaN NaN NaN NaN \n4 NaN NaN NaN NaN NaN \n5 NaN NaN NaN NaN NaN \n... ... ... ... ... ... \n3406 NaN NaN NaN NaN NaN \n3407 NaN NaN NaN NaN NaN \n3408 NaN NaN NaN NaN NaN \n3409 NaN NaN NaN NaN NaN \n3410 NaN NaN NaN NaN NaN \n\n exile_perm \n1 NaN \n2 NaN \n3 NaN \n4 NaN \n5 NaN \n... ... \n3406 NaN \n3407 NaN \n3408 NaN \n3409 NaN \n3410 NaN \n\n[3410 rows x 168 columns]",
"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>acdid</th>\n <th>year</th>\n <th>gwno</th>\n <th>location</th>\n <th>sidea</th>\n <th>sideb</th>\n <th>incomp</th>\n <th>territory</th>\n <th>startdate</th>\n <th>epstartdate</th>\n <th>...</th>\n <th>exile_erank</th>\n <th>exile_sender</th>\n <th>exile_scope</th>\n <th>exile_scount</th>\n <th>exile_implement</th>\n <th>exile_rDCJ</th>\n <th>exile_peaceagr</th>\n <th>exile_start</th>\n <th>exile_end</th>\n <th>exile_perm</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>1</th>\n <td>1</td>\n <td>1946</td>\n <td>145.0</td>\n <td>Bolivia</td>\n <td>Bolivia</td>\n <td>Popular Revolutionary Movement</td>\n <td>2</td>\n <td>NaN</td>\n <td>1946-06-30</td>\n <td>1946-06-30</td>\n <td>...</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>2</th>\n <td>1</td>\n <td>1952</td>\n <td>145.0</td>\n <td>Bolivia</td>\n <td>Bolivia</td>\n <td>MNR</td>\n <td>2</td>\n <td>NaN</td>\n <td>1946-06-30</td>\n <td>1952-04-09</td>\n <td>...</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>3</th>\n <td>1</td>\n <td>1967</td>\n <td>145.0</td>\n <td>Bolivia</td>\n <td>Bolivia</td>\n <td>ELN</td>\n <td>2</td>\n <td>NaN</td>\n <td>1946-06-30</td>\n <td>1967-03-31</td>\n <td>...</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>4</th>\n <td>2</td>\n <td>1946</td>\n <td>811.0</td>\n <td>Cambodia (Kampuchea)</td>\n <td>France</td>\n <td>Khmer Issarak</td>\n <td>1</td>\n <td>Cambodia</td>\n <td>1946-08-31</td>\n <td>1946-08-31</td>\n <td>...</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>5</th>\n <td>2</td>\n <td>1947</td>\n <td>811.0</td>\n <td>Cambodia (Kampuchea)</td>\n <td>France</td>\n <td>Khmer Issarak</td>\n <td>1</td>\n <td>Cambodia</td>\n <td>1946-08-31</td>\n <td>1946-08-31</td>\n <td>...</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>3406</th>\n <td>271</td>\n <td>2011</td>\n <td>620.0</td>\n <td>Libya</td>\n <td>Libya</td>\n <td>NTC, Forces of Muammar Gaddafi</td>\n <td>2</td>\n <td>NaN</td>\n <td>2011-01-28</td>\n <td>2011-03-04</td>\n <td>...</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>3407</th>\n <td>271</td>\n <td>2011</td>\n <td>620.0</td>\n <td>Libya</td>\n <td>Libya</td>\n <td>NTC, Forces of Muammar Gaddafi</td>\n <td>2</td>\n <td>NaN</td>\n <td>2011-01-28</td>\n <td>2011-03-04</td>\n <td>...</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>3408</th>\n <td>271</td>\n <td>2011</td>\n <td>620.0</td>\n <td>Libya</td>\n <td>Libya</td>\n <td>NTC, Forces of Muammar Gaddafi</td>\n <td>2</td>\n <td>NaN</td>\n <td>2011-01-28</td>\n <td>2011-03-04</td>\n <td>...</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>3409</th>\n <td>271</td>\n <td>2011</td>\n <td>620.0</td>\n <td>Libya</td>\n <td>Libya</td>\n <td>NTC, Forces of Muammar Gaddafi</td>\n <td>2</td>\n <td>NaN</td>\n <td>2011-01-28</td>\n <td>2011-03-04</td>\n <td>...</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>3410</th>\n <td>271</td>\n <td>2011</td>\n <td>620.0</td>\n <td>Libya</td>\n <td>Libya</td>\n <td>NTC, Forces of Muammar Gaddafi</td>\n <td>2</td>\n <td>NaN</td>\n <td>2011-01-28</td>\n <td>2011-03-04</td>\n <td>...</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n </tbody>\n</table>\n<p>3410 rows × 168 columns</p>\n</div>"
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dat"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-07T15:32:07.364909900Z",
"start_time": "2024-08-07T15:32:07.294715700Z"
}
},
"id": "dedfc0eb2e7a0fdc"
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{
"cell_type": "code",
"execution_count": 6,
"id": "45708832ae89f26f",
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-07T15:33:28.227116100Z",
"start_time": "2024-08-07T15:32:49.222920800Z"
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},
"outputs": [
{
"data": {
"text/plain": " acdid year gwno location sidea \\\n1 1 1946 145.0 Bolivia Bolivia \n2 1 1952 145.0 Bolivia Bolivia \n3 1 1967 145.0 Bolivia Bolivia \n4 2 1946 811.0 Cambodia (Kampuchea) France \n5 2 1947 811.0 Cambodia (Kampuchea) France \n... ... ... ... ... ... \n5861 271 2011 620.0 Libya Libya \n5862 271 2011 620.0 Libya Libya \n5863 271 2011 620.0 Libya Libya \n5864 271 2011 620.0 Libya Libya \n5865 271 2011 620.0 Libya Libya \n\n sideb incomp territory startdate \\\n1 Popular Revolutionary Movement 2 NaN 1946-06-30 \n2 MNR 2 NaN 1946-06-30 \n3 ELN 2 NaN 1946-06-30 \n4 Khmer Issarak 1 Cambodia 1946-08-31 \n5 Khmer Issarak 1 Cambodia 1946-08-31 \n... ... ... ... ... \n5861 Forces of Muammar Gaddafi 2 NaN 2011-01-28 \n5862 NTC 2 NaN 2011-01-28 \n5863 Forces of Muammar Gaddafi 2 NaN 2011-01-28 \n5864 NTC 2 NaN 2011-01-28 \n5865 Forces of Muammar Gaddafi 2 NaN 2011-01-28 \n\n epstartdate ... exile_sender exile_scope exile_scount exile_implement \\\n1 1946-06-30 ... NaN NaN NaN NaN \n2 1952-04-09 ... NaN NaN NaN NaN \n3 1967-03-31 ... NaN NaN NaN NaN \n4 1946-08-31 ... NaN NaN NaN NaN \n5 1946-08-31 ... NaN NaN NaN NaN \n... ... ... ... ... ... ... \n5861 2011-03-04 ... NaN NaN NaN NaN \n5862 2011-03-04 ... NaN NaN NaN NaN \n5863 2011-03-04 ... NaN NaN NaN NaN \n5864 2011-03-04 ... NaN NaN NaN NaN \n5865 2011-03-04 ... NaN NaN NaN NaN \n\n exile_rDCJ exile_peaceagr exile_start exile_end exile_perm \\\n1 NaN NaN NaN NaN NaN \n2 NaN NaN NaN NaN NaN \n3 NaN NaN NaN NaN NaN \n4 NaN NaN NaN NaN NaN \n5 NaN NaN NaN NaN NaN \n... ... ... ... ... ... \n5861 NaN NaN NaN NaN NaN \n5862 NaN NaN NaN NaN NaN \n5863 NaN NaN NaN NaN NaN \n5864 NaN NaN NaN NaN NaN \n5865 NaN NaN NaN NaN NaN \n\n model_weight \n1 1.0 \n2 1.0 \n3 1.0 \n4 1.0 \n5 1.0 \n... ... \n5861 0.5 \n5862 0.5 \n5863 0.5 \n5864 0.5 \n5865 0.5 \n\n[5865 rows x 169 columns]",
"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>acdid</th>\n <th>year</th>\n <th>gwno</th>\n <th>location</th>\n <th>sidea</th>\n <th>sideb</th>\n <th>incomp</th>\n <th>territory</th>\n <th>startdate</th>\n <th>epstartdate</th>\n <th>...</th>\n <th>exile_sender</th>\n <th>exile_scope</th>\n <th>exile_scount</th>\n <th>exile_implement</th>\n <th>exile_rDCJ</th>\n <th>exile_peaceagr</th>\n <th>exile_start</th>\n <th>exile_end</th>\n <th>exile_perm</th>\n <th>model_weight</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>1</th>\n <td>1</td>\n <td>1946</td>\n <td>145.0</td>\n <td>Bolivia</td>\n <td>Bolivia</td>\n <td>Popular Revolutionary Movement</td>\n <td>2</td>\n <td>NaN</td>\n <td>1946-06-30</td>\n <td>1946-06-30</td>\n <td>...</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>1.0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>1</td>\n <td>1952</td>\n <td>145.0</td>\n <td>Bolivia</td>\n <td>Bolivia</td>\n <td>MNR</td>\n <td>2</td>\n <td>NaN</td>\n <td>1946-06-30</td>\n <td>1952-04-09</td>\n <td>...</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>1.0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>1</td>\n <td>1967</td>\n <td>145.0</td>\n <td>Bolivia</td>\n <td>Bolivia</td>\n <td>ELN</td>\n <td>2</td>\n <td>NaN</td>\n <td>1946-06-30</td>\n <td>1967-03-31</td>\n <td>...</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>1.0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>2</td>\n <td>1946</td>\n <td>811.0</td>\n <td>Cambodia (Kampuchea)</td>\n <td>France</td>\n <td>Khmer Issarak</td>\n <td>1</td>\n <td>Cambodia</td>\n <td>1946-08-31</td>\n <td>1946-08-31</td>\n <td>...</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>1.0</td>\n </tr>\n <tr>\n <th>5</th>\n <td>2</td>\n <td>1947</td>\n <td>811.0</td>\n <td>Cambodia (Kampuchea)</td>\n <td>France</td>\n <td>Khmer Issarak</td>\n <td>1</td>\n <td>Cambodia</td>\n <td>1946-08-31</td>\n <td>1946-08-31</td>\n <td>...</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>1.0</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>5861</th>\n <td>271</td>\n <td>2011</td>\n <td>620.0</td>\n <td>Libya</td>\n <td>Libya</td>\n <td>Forces of Muammar Gaddafi</td>\n <td>2</td>\n <td>NaN</td>\n <td>2011-01-28</td>\n <td>2011-03-04</td>\n <td>...</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>0.5</td>\n </tr>\n <tr>\n <th>5862</th>\n <td>271</td>\n <td>2011</td>\n <td>620.0</td>\n <td>Libya</td>\n <td>Libya</td>\n <td>NTC</td>\n <td>2</td>\n <td>NaN</td>\n <td>2011-01-28</td>\n <td>2011-03-04</td>\n <td>...</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>0.5</td>\n </tr>\n <tr>\n <th>5863</th>\n <td>271</td>\n <td>2011</td>\n <td>620.0</td>\n <td>Libya</td>\n <td>Libya</td>\n <td>Forces of Muammar Gaddafi</td>\n <td>2</td>\n <td>NaN</td>\n <td>2011-01-28</td>\n <td>2011-03-04</td>\n <td>...</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>0.5</td>\n </tr>\n <tr>\n <th>5864</th>\n <td>271</td>\n <td>2011</td>\n <td>620.0</td>\n <td>Libya</td>\n <td>Libya</td>\n <td>NTC</td>\n <td>2</td>\n <td>NaN</td>\n <td>2011-01-28</td>\n <td>2011-03-04</td>\n <td>...</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>0.5</td>\n </tr>\n <tr>\n <th>5865</th>\n <td>271</td>\n <td>2011</td>\n <td>620.0</td>\n <td>Libya</td>\n <td>Libya</td>\n <td>Forces of Muammar Gaddafi</td>\n <td>2</td>\n <td>NaN</td>\n <td>2011-01-28</td>\n <td>2011-03-04</td>\n <td>...</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>0.5</td>\n </tr>\n </tbody>\n</table>\n<p>5865 rows × 169 columns</p>\n</div>"
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dat['model_weight'] = 1\n",
"indexes_to_delete = []\n",
"for i in dat.index:\n",
" if len(dat.loc[i]['sideb'].split(',')) > 1:\n",
" for actor in dat.loc[i]['sideb'].split(','):\n",
" new_row = dat.loc[i]\n",
" new_row['model_weight'] /= len(dat.loc[i]['sideb'].split(','))\n",
" new_row['sideb'] = actor.strip()\n",
" dat.loc[len(dat) + 1] = new_row\n",
" indexes_to_delete.append(i)\n",
"dat\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "e776b79508c3a43a",
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-07T15:33:28.248937700Z",
"start_time": "2024-08-07T15:33:28.229624500Z"
}
},
"outputs": [],
"source": [
"dat.drop(index=indexes_to_delete, inplace=True)"
]
},
{
"cell_type": "markdown",
"id": "ea8bcabd1bf4e769",
"metadata": {
"collapsed": false
},
"source": [
"## Different Spellings and Acronyms\n",
"Some of the spellings and acronyms used differ between the DCJ and NSA datasets. This step matches up the spellings so the datasets can be joined."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "c1e42d759d27dbca",
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-07T15:33:46.310790700Z",
"start_time": "2024-08-07T15:33:46.272174200Z"
}
},
"outputs": [],
"source": [
"dict = {\n",
"'Peoples Liberation Army': 'PLA',\n",
"'Republic of Kurdistan/KDPI': 'KDPI',\n",
"'Indonesian Peoples Army': \"Indonesian People's Army\",\n",
"'Communist Party of the Philippines': 'CPP',\n",
"'Military Faction (forces of Honasan, Abenina & Zumel)': 'CPP, Military Faction (forces of Honasan, Abenina & Zumel)',\n",
"'LTS[p]A': 'LTS(p)A',\n",
"'Viet Nam Doc Dong Min Hoi': 'Viet minh',\n",
"'Opposition coalition [Febreristas, Liberals and Communists]': 'Opposition coalition (Febreristas, Liberals and Communists)',\n",
"'Military Faction (forces of General Rodriguez)': 'Military faction (forces of Andres Rodriguez)',\n",
"'Military faction (forces of General Alfredo Stroessner)': 'Military faction (forces of Alfredo Stroessner)',\n",
"'PVO - White Band faction': 'PVO - \"White Band\" faction',\n",
"\"Arakan People's Liberation Party\": 'APLP',\n",
"'Communist Party of Arakan': 'CPA',\n",
"'Rohingya Solidarity Organisation': 'RSO',\n",
"'Arakan Rohingya Islamic Front': 'ARIF',\n",
"'Naxalites/PWG': 'PWG',\n",
"'Naxalites/CPI [-Marxist]': 'CPI-ML',\n",
"'CPI–Maoist': 'CPI-Maoist',\n",
"'Military Faction [Navy]': 'Military faction (Navy)',\n",
"'Military Faction - 26th of July Movement': 'M-26-7',\n",
"'National Revolutionary Council': 'Cuban Revolutionary Council',\n",
"'Darul Islam Movement': 'Darul Islam',\n",
"'Military faction (forces of Eduardo A. Lonardi Doucet)': 'Military faction (forces of Samuel Toranzo Calderón), Military faction (forces of Eduardo A. Lonardi Doucet)',\n",
"'Supreme Council for the Islamic Revolution in Iraq (SCIRI)': 'SCIRI',\n",
"'ISI/Jama\\'at Al-Tawhid wa Al-Jihad': 'ISI',\n",
"'RJF/Al-Jaysh al-Islami fi Iraq': 'RJF',\n",
"'Independent Nasserite Movement /Mourabitoun militia': 'Independent Nasserite Movement /Mourabitoun militia',\n",
"'Lebanese National Movement': 'LNM',\n",
"'Shan State Army - South (SSA-S)': 'SSA',\n",
"'Independent Mining State of South Kasai': 'Independent Mining State of South Kasai',\n",
"'Military faction (forces of Amsha Desta and Merid Negusie)': 'EPRDF, Military faction (forces of Amsha Desta and Merid Negusie)',\n",
"'CPN-M/UPF': 'CPN-M',\n",
"'KDP/DPK': 'KDP',\n",
"'North Kalimantan Liberation Army': 'North Kalimantan Liberation Army',\n",
"'Military faction (forces of Hugo Chávez)': 'Military faction (forces of Hugo Chávez)',\n",
"\"Military faction (forces loyal to Léon M'Ba)\": \"Military faction (forces loyal to Léon M'Ba)\",\n",
"'Military faction (forces loyal to Gervais Nyangoma)': 'Military faction (forces loyal to Gervais Nyangoma)',\n",
"'First Liberation Army': 'First Liberation Army',\n",
"'Second Liberation Army (Frolinat)': 'Second Liberation Army',\n",
"'Military faction (forces of Maldoum Bada Abbas)': 'MDD, Military faction (forces of Maldoum Bada Abbas)',\n",
"'Military faction (Constitutionalists)': 'Military faction (Constitutionalists)',\n",
"'Military faction (forces of Jerry John Rawlings)': 'Military faction (forces of Jerry John Rawlings)',\n",
"'Military faction (forces of Ekow Dennis and Edward Adjei-Ampofo) ': 'Military faction (forces of Ekow Dennis and Edward Adjei-Ampofo)',\n",
"'Military faction (forces of Patrick Nzeogwu)': 'Military faction (forces of Patrick Nzeogwu)',\n",
"'Boko Haram': \"Jama'atu Ahlis Sunna Lidda'awati wal-Jihad\",\n",
"'Military faction (forces loyal to Nureddin Atassi and Youssef Zeayen)': 'Military faction (forces loyal to Nureddin Atassi and Youssef Zeayen)',\n",
"'Khmer Rouge/PDK': 'KR',\n",
"'FUNCINPEC/ANS': 'FUNCINPEC',\n",
"'MIM/Mindanao Independence Movement': 'MIM',\n",
"'MNLF – NM': 'MNLF - NM',\n",
"'MNLF – HM': 'MNLF - HM',\n",
"'SLM/A': 'SLM/A',\n",
"'SLM/A – MM': 'SLM/A - MM',\n",
"'SPLM/A-North': 'SPLM/A-North',\n",
"'Military faction (forces of Mohamed Madbouh)': 'Military faction (forces of Mohamed Madbouh)',\n",
"'Mukti Bahini: Liberation Force': 'Mukti Bahini',\n",
"'Military faction (forces of Idi Amin)': 'Military faction (forces of Idi Amin)',\n",
"'Military faction (forces of Charles Arube)': 'Military faction (forces of Charles Arube)',\n",
"\"Lord's Army\": \"Lord's Army\",\n",
"'PIRA/IRA': 'PIRA',\n",
"'Military faction (forces of Benjamin Mejia)': 'Military faction (forces of Benjamin Mejia)',\n",
"'MLN or Tupamaros': 'MLN/Tupamaros',\n",
"'Military faction (forces of Augusto Pinochet, Toribio Merino and Leigh Guzman)': 'Military faction (forces of Augusto Pinochet, Toribio Merino and Leigh Guzman)',\n",
"'JSS/SB/Shanti Bahini': 'JSS/SB',\n",
"'BLA/Baluchistan Liberation Army': 'BLA',\n",
"'BRA/Baluchistan Republican Army': 'BRA',\n",
"'Hezb-i-Islami': 'Hizb-i Islami-yi Afghanistan',\n",
"'Hezb-i-Wahdat': 'Hizb-i Wahdat',\n",
"'Jamiat-i-Islami': \"Jam'iyyat-i Islami-yi Afghanistan\",\n",
"'Junbish-i Milli-yi Islami': 'Junbish-i Milli-yi Islami',\n",
"'Hizb-i Demokratik-i Khalq-i Afghanistan': 'PDPA',\n",
"'Harakat-i Inqilab-i Islami-yi Afghanistan': 'Harakat-i Inqilab-i Islami-yi Afghanistan',\n",
"'Mahaz-i Milli-yi Islami-yi Afghanistan': 'Mahaz-i Milli-yi Afghanistan',\n",
"'Jabha-yi Nijat-i Milli-yi Afghanistan': 'Jabha-yi Nijat-i Milli-yi Afghanistan',\n",
"'Ittihad-i Islami Bara-yi Azadi-yi Afghanistan': 'Ittihad-i Is',\n",
"'Harakat-i Islami-yi Afghanistan': 'Harakat-i Islami-yi Afghanistan',\n",
"'Hizb-i Islami-yi Afghanistan - Khalis faction': 'Hizb-i Islami-yi Afghanistan - Khalis faction',\n",
"'FDN/Contras': 'Contras/FDN',\n",
"'USC Faction': 'USC/SNA',\n",
"'ARS/UIC': 'ARS/UIC',\n",
"'Mujahideen e Khalq': 'MEK',\n",
"'Military faction (forces of Samuel Doe)': 'Military faction (forces of Samuel Doe)',\n",
"'Resistance Armee Tunisienne': 'Résistance Armée Tunisienne',\n",
"'Military faction (forces of Hezekiah Ochuka)': 'Military faction (forces of Hezekiah Ochuka)',\n",
"'PKK/Kadek': 'PKK',\n",
"'Yemenite Socialist Party - Abdul Fattah Ismail faction': 'Yemenite Socialist Party - Abdul Fattah Ismail faction',\n",
"'Military faction (forces of Moisés Giroldi)': 'Military faction (forces of Moisés Giroldi)',\n",
"'Military faction (forces of Himmler Rebu and Guy Francois)': 'Military faction (forces of Himmler Rebu and Guy Francois)',\n",
"'Military faction (forces of Raol Cédras)': 'Military faction (forces of Raol Cédras)',\n",
"'OP Lavalas (Chimères)': 'OP Lavalas (Chimères)',\n",
"'Government of Armenia and ANM': 'Republic of Armenia',\n",
"'Azerbaijani Popular Front': 'APF',\n",
"'FRUD – AD': 'FRUD - AD',\n",
"'Croatian irregulars': 'Croatian irregulars',\n",
"'Exile and Redemption': \"Takfir wa'l Hijra\",\n",
"'FLEC–FAC': 'FLEC-FAC',\n",
"'Republic of Nagorno-Karabakh': 'Republic of Nagorno-Karabakh',\n",
"'Serbian Republic of Bosnia and Herzegovina': 'Serbian Republic of Bosnia-Herzegovina',\n",
"'Serbian Republic of Krajina': 'Serbian Republic of Krajina',\n",
"'al-Gamaa al-Islamiyya': \"al-Gama'a al-Islamiyya\",\n",
"'Republic of Abkhazia': 'Republic of Abkhazia',\n",
"'Republic of South Ossetia': 'Republic of South Ossetia',\n",
"'Dniestr Republic': 'PMR',\n",
"'Movement for Peace in Tajikistan': 'Movement for Peace in Tajikistan',\n",
"'Husseinov Military Faction': 'Military faction (forces of Suret Husseinov)',\n",
"'OPON forces': 'OPON forces',\n",
"'Autonomous Province of Western Bosnia': 'Autonomous Province of Western Bosnia',\n",
"'Croatian Republic of Bosnia and Herzegovina': 'Croatian Republic of Bosnia-Herzegovina',\n",
"'Republic of Chechnya': 'Chechen Republic of Ichkeria',\n",
"'Military Junta for the Consolidation of Democracy, Peace and Justice': 'Military Junta for the Consolidation of Democracy, Peace and Justice',\n",
"'National Liberation Army (UCK)': 'UCK',\n",
"'al-Qaida [The Base]': 'al-Qaida',\n",
"'Forces of the Caucasus Emirate': 'Forces of the Caucasus Emirate',\n",
"'NDFB – RD': 'NDFB - RD',\n",
"'Republic of South Sudan': 'Republic of South Sudan',\n",
"'Forces of Muammar Gaddafi': 'Forces of Muammar Gaddafi'\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "5450adfbf40b0aa8",
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-07T15:33:48.850315500Z",
"start_time": "2024-08-07T15:33:48.798368400Z"
}
},
"outputs": [],
"source": [
"df = df.groupby(['ucdpid', 'side_b']).first().reset_index()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "9e0c952d59e7e20f",
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-07T15:34:07.051133300Z",
"start_time": "2024-08-07T15:34:06.986475300Z"
}
},
"outputs": [],
"source": [
"df.replace(dict, inplace=True)"
]
},
{
"cell_type": "markdown",
"id": "3f68fbd2a36ea7ac",
"metadata": {
"collapsed": false
},
"source": [
"## Joining the Data\n",
"This cell merges the three datasets into one complete dataset"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "7f5e802deef62ae0",
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-07T15:35:26.803327800Z",
"start_time": "2024-08-07T15:35:26.735106700Z"
}
},
"outputs": [],
"source": [
"data = pd.merge(left=dat, right=df, left_on=['acdid','sideb'], right_on=['ucdpid', 'side_b'], suffixes=('_DCJ', '_NSA'), how='inner')\n",
"data = data.join(df_3.set_index(['acdid', 'styear', 'endyear', 'year']), on=['acdid', 'styear', 'endyear', 'year'], rsuffix='_DCJ2', how='left')"
]
},
{
"cell_type": "markdown",
"id": "1bc06941b0334c51",
"metadata": {
"collapsed": false
},
"source": [
"## Data Cleaning\n",
"There are a couple of data cleaning steps to get the data in the form that's described in the study:"
]
},
{
"cell_type": "markdown",
"id": "15c93a8120022e04",
"metadata": {
"collapsed": false
},
"source": [
"### is_leftist_group\n",
"The original paper mentions 36 leftist groups. The full list of involved groups was given to Claude Sonnet 3.5, and it was asked to identify leftist groups. The list was then confirmed using basic google searches. Data was labeled 1/True if a group is a leftist group, or 0 if not."
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "7b7e7bc92cad0a19",
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-07T15:36:00.998850800Z",
"start_time": "2024-08-07T15:35:58.287693700Z"
}
},
"outputs": [],
"source": [
"data['is_leftist_group'] = pd.Series()\n",
"for i in data.index:\n",
" if data.loc[i]['sideb'] in ['CPP', 'CPI', 'CPI-ML', 'PWG', 'CPI-Maoist', 'CPT', 'CPM', 'CPN-M', 'FARC', 'Sendero Luminoso', 'JVP', 'MEK', 'MCC', 'Viet minh', 'Pathet Lao', 'TPLF', 'EPRDF', 'Frelimo', 'MPLA', 'SWAPO', 'ZAPU', 'ZANU', 'MLN/Tupamaros', 'FSLN', 'PDPA', 'UNLF', 'PFLP', 'PFLP-GC', 'ELN', 'PLA', 'DSE', 'Huk', 'M-26-7', 'EPL', 'MIR', 'EPRP']:\n",
" data['is_leftist_group'][i] = 1\n",
" else:\n",
" data['is_leftist_group'][i] = 0"
]
},
{
"cell_type": "markdown",
"id": "1fa8a7a7eddf76ba",
"metadata": {
"collapsed": false
},
"source": [
"### Trimming the Fat\n",
"'Dataset' contains just the necessary elements from 'data', and includes all the information needed for this analysis."
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "ba2017724d99258b",
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-07T15:36:05.740770900Z",
"start_time": "2024-08-07T15:36:05.695608300Z"
}
},
"outputs": [],
"source": [
"dataset = data[['acdid', 'location', 'sidea', 'sideb', 'model_weight', 'mobcap', 'fightcap', 'intens', 'polity', 'is_leftist_group', 'trial', 'truth', 'rep', 'amnesty', 'purge', 'exile', 'incomp', 'terrcont', 'year']] "
]
},
{
"cell_type": "markdown",
"id": "848b3dd8a10b4b45",
"metadata": {
"collapsed": false
},
"source": [
"### year_of_conflict\n",
"This variable represents how many years a conflict has been ongoing, as described in the original study. It is increased by 1 for every consecutive year that the same conflict appears in the dataset."
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "84aa3e3ffc1b73f9",
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-07T15:37:04.119663600Z",
"start_time": "2024-08-07T15:36:58.034743100Z"
}
},
"outputs": [],
"source": [
"dataset['year_of_conflict'] = pd.Series()\n",
"dataset['year_of_conflict'][0] = 1\n",
"i = 1\n",
"for idx in dataset.index[1:]:\n",
" if list(dataset.loc[idx][['acdid', 'location', 'sidea', 'sideb']]) == list(dataset.loc[idx-1][['acdid', 'location', 'sidea', 'sideb']]) and dataset.loc[idx, 'year'] == dataset.loc[idx-1]['year'] + 1:\n",
" i+=1\n",
" elif list(dataset.loc[idx][['acdid', 'location', 'sidea', 'sideb']]) == list(dataset.loc[idx-1][['acdid', 'location', 'sidea', 'sideb']]) and dataset.loc[idx, 'year'] == dataset.loc[idx-1]['year']:\n",
" pass\n",
" else: \n",
" i = 1\n",
" dataset['year_of_conflict'][idx] = i "
]
},
{
"cell_type": "code",
"execution_count": 18,
"outputs": [
{
"data": {
"text/plain": " acdid location sidea sideb \\\n0 1 Bolivia Bolivia Popular Revolutionary Movement \n1 1 Bolivia Bolivia MNR \n2 1 Bolivia Bolivia ELN \n3 2 Cambodia (Kampuchea) France Khmer Issarak \n4 2 Cambodia (Kampuchea) France Khmer Issarak \n... ... ... ... ... \n4302 271 Libya Libya Forces of Muammar Gaddafi \n4303 271 Libya Libya Forces of Muammar Gaddafi \n4304 271 Libya Libya Forces of Muammar Gaddafi \n4305 271 Libya Libya Forces of Muammar Gaddafi \n4306 271 Libya Libya Forces of Muammar Gaddafi \n\n model_weight mobcap fightcap intens polity is_leftist_group ... \\\n0 1.0 moderate moderate 2 0 0 ... \n1 1.0 moderate moderate 1 0 0 ... \n2 1.0 low low 1 0 1 ... \n3 1.0 low low 1 0 0 ... \n4 1.0 low low 1 0 0 ... \n... ... ... ... ... ... ... ... \n4302 0.5 low moderate 2 0 0 ... \n4303 0.5 low moderate 2 0 0 ... \n4304 0.5 low moderate 2 0 0 ... \n4305 0.5 low moderate 2 0 0 ... \n4306 0.5 low moderate 2 0 0 ... \n\n amnesty purge exile incomp terrcont year year_of_conflict \\\n0 0 0 0 2 no 1946 1 \n1 0 0 0 2 no 1952 1 \n2 0 0 0 2 no 1967 1 \n3 0 0 0 1 yes 1946 1 \n4 0 0 0 1 yes 1947 2 \n... ... ... ... ... ... ... ... \n4302 0 0 0 2 yes 2011 1 \n4303 0 0 0 2 yes 2011 1 \n4304 0 0 0 2 yes 2011 1 \n4305 1 0 0 2 yes 2011 1 \n4306 0 0 0 2 yes 2011 1 \n\n conciliatory coercive DCJ_used \n0 0 0 0 \n1 0 0 0 \n2 0 0 0 \n3 0 0 0 \n4 0 0 0 \n... ... ... ... \n4302 1 0 1 \n4303 0 1 1 \n4304 0 1 1 \n4305 1 0 1 \n4306 0 1 1 \n\n[4307 rows x 23 columns]",
"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>acdid</th>\n <th>location</th>\n <th>sidea</th>\n <th>sideb</th>\n <th>model_weight</th>\n <th>mobcap</th>\n <th>fightcap</th>\n <th>intens</th>\n <th>polity</th>\n <th>is_leftist_group</th>\n <th>...</th>\n <th>amnesty</th>\n <th>purge</th>\n <th>exile</th>\n <th>incomp</th>\n <th>terrcont</th>\n <th>year</th>\n <th>year_of_conflict</th>\n <th>conciliatory</th>\n <th>coercive</th>\n <th>DCJ_used</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1</td>\n <td>Bolivia</td>\n <td>Bolivia</td>\n <td>Popular Revolutionary Movement</td>\n <td>1.0</td>\n <td>moderate</td>\n <td>moderate</td>\n <td>2</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>2</td>\n <td>no</td>\n <td>1946</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1</td>\n <td>Bolivia</td>\n <td>Bolivia</td>\n <td>MNR</td>\n <td>1.0</td>\n <td>moderate</td>\n <td>moderate</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>2</td>\n <td>no</td>\n <td>1952</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>1</td>\n <td>Bolivia</td>\n <td>Bolivia</td>\n <td>ELN</td>\n <td>1.0</td>\n <td>low</td>\n <td>low</td>\n <td>1</td>\n <td>0</td>\n <td>1</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>2</td>\n <td>no</td>\n <td>1967</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>2</td>\n <td>Cambodia (Kampuchea)</td>\n <td>France</td>\n <td>Khmer Issarak</td>\n <td>1.0</td>\n <td>low</td>\n <td>low</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>yes</td>\n <td>1946</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>2</td>\n <td>Cambodia (Kampuchea)</td>\n <td>France</td>\n <td>Khmer Issarak</td>\n <td>1.0</td>\n <td>low</td>\n <td>low</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>yes</td>\n <td>1947</td>\n <td>2</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>4302</th>\n <td>271</td>\n <td>Libya</td>\n <td>Libya</td>\n <td>Forces of Muammar Gaddafi</td>\n <td>0.5</td>\n <td>low</td>\n <td>moderate</td>\n <td>2</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>2</td>\n <td>yes</td>\n <td>2011</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>4303</th>\n <td>271</td>\n <td>Libya</td>\n <td>Libya</td>\n <td>Forces of Muammar Gaddafi</td>\n <td>0.5</td>\n <td>low</td>\n <td>moderate</td>\n <td>2</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>2</td>\n <td>yes</td>\n <td>2011</td>\n <td>1</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n </tr>\n <tr>\n <th>4304</th>\n <td>271</td>\n <td>Libya</td>\n <td>Libya</td>\n <td>Forces of Muammar Gaddafi</td>\n <td>0.5</td>\n <td>low</td>\n <td>moderate</td>\n <td>2</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>2</td>\n <td>yes</td>\n <td>2011</td>\n <td>1</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n </tr>\n <tr>\n <th>4305</th>\n <td>271</td>\n <td>Libya</td>\n <td>Libya</td>\n <td>Forces of Muammar Gaddafi</td>\n <td>0.5</td>\n <td>low</td>\n <td>moderate</td>\n <td>2</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>2</td>\n <td>yes</td>\n <td>2011</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>4306</th>\n <td>271</td>\n <td>Libya</td>\n <td>Libya</td>\n <td>Forces of Muammar Gaddafi</td>\n <td>0.5</td>\n <td>low</td>\n <td>moderate</td>\n <td>2</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>2</td>\n <td>yes</td>\n <td>2011</td>\n <td>1</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n </tr>\n </tbody>\n</table>\n<p>4307 rows × 23 columns</p>\n</div>"
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-07T15:38:43.692956600Z",
"start_time": "2024-08-07T15:38:43.638831200Z"
}
},
"id": "8b0a6b80b62fee37"
},
{
"cell_type": "markdown",
"id": "4427a736a704d1e9",
"metadata": {
"collapsed": false
},
"source": [
"### Democratic Regime\n",
"The authors code their 'democratic regime' variable based on the polity2 scores found in the data, with 6 and up being considered a democratic regime. This process is repeated in the cell below, returning the results as a binary variable in place in the polity column."
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "a877eb395306e936",
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-07T15:37:19.088952100Z",
"start_time": "2024-08-07T15:37:19.047765600Z"
}
},
"outputs": [],
"source": [
"dataset['polity'] = dataset['polity'].apply(lambda x : 1 if x >= 6 else 0)"
]
},
{
"cell_type": "markdown",
"id": "999f735aa2901c3f",
"metadata": {
"collapsed": false
},
"source": [
"### Target Variables\n",
"Creates columns for coercive judicial processes (1/True if trial, exile, or purge was used), conciliatory (1/True if truth, rep, or amnesty was used), and DCJ_used (1/True if coercive or conciliatory is true)."
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "166eadae534fb213",
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-07T15:37:42.427963100Z",
"start_time": "2024-08-07T15:37:38.749579700Z"
}
},
"outputs": [],
"source": [
"dataset['conciliatory'], dataset['coercive'] = pd.Series(), pd.Series()\n",
"dataset['DCJ_used'] = pd.Series()\n",
"for idx in dataset.index:\n",
" if dataset.loc[idx, 'truth'] == 1\\\n",
" or dataset.loc[idx, 'rep'] == 1\\\n",
" or dataset.loc[idx, 'amnesty'] == 1:\n",
" dataset['conciliatory'][idx] = 1\n",
" dataset['coercive'][idx] = 0\n",
" dataset['DCJ_used'][idx] = 1\n",
" elif dataset.loc[idx, 'trial'] == 1\\\n",
" or dataset.loc[idx, 'exile'] == 1\\\n",
" or dataset.loc[idx, 'purge'] == 1:\n",
" dataset['coercive'][idx] = 1\n",
" dataset['conciliatory'][idx] = 0\n",
" dataset['DCJ_used'][idx] = 1\n",
" else:\n",
" dataset['coercive'][idx] = 0\n",
" dataset['conciliatory'][idx] = 0\n",
" dataset['DCJ_used'][idx] = 0"
]
},
{
"cell_type": "markdown",
"id": "13ae523a4a149e56",
"metadata": {
"collapsed": false
},
"source": [
"### mobcap\n",
"Mobility Capacity is coded, per the authors, as a binary variable with moderate and above being 1/True."
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "dde162498210fbef",
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-06T12:27:32.258608900Z",
"start_time": "2024-08-06T12:27:32.242778900Z"
}
},
"outputs": [],
"source": [
"mobcap_dict = {\n",
" 'low': 0,\n",
" 'no': 0,\n",
" 'moderate': 1,\n",
" 'high': 1\n",
"}\n",
"dataset['mobcap'].replace(mobcap_dict, inplace=True)"
]
},
{
"cell_type": "markdown",
"id": "305bf58693437461",
"metadata": {
"collapsed": false
},
"source": [
"### Incompatability\n",
"Incomp is rated 1 in the original data if the incompatability is territory, and 2 if it is government. Because we're only interested in territory as the 'True' category, we'll just change the 2s to 0s."
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "917d16342cbac5d5",
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-06T12:27:32.304297800Z",
"start_time": "2024-08-06T12:27:32.260847400Z"
}
},
"outputs": [],
"source": [
"dataset['incomp'].replace({2:0}, inplace=True)"
]
},
{
"cell_type": "markdown",
"id": "2c6486d58a256a7c",
"metadata": {
"collapsed": false
},
"source": [
"### Territorial Control\n",
"This variable just needs to be converted from 'yes'/'no' to 1/0 notation"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "8cd1a6dd8dae140b",
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-06T12:27:32.306497200Z",
"start_time": "2024-08-06T12:27:32.281622Z"
}
},
"outputs": [],
"source": [
"dataset['terrcont'].replace({'yes': 1, 'no': 0}, inplace=True)"
]
},
{
"cell_type": "markdown",
"id": "20c9ee1454144782",
"metadata": {
"collapsed": false
},
"source": [
"### Cold War\n",
"Adds a cold_war column based on whether the year is before or after 1989, as per the study"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "1aecee4f48bc5da",
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-06T12:27:33.257081200Z",
"start_time": "2024-08-06T12:27:32.293071800Z"
}
},
"outputs": [],
"source": [
"dataset['cold_war'] = pd.Series()\n",
"for idx in dataset.index:\n",
" if dataset['year'][idx] >= 1989: dataset['cold_war'][idx] = 0\n",
" else: dataset['cold_war'][idx] = 1\n",
" "
]
},
{
"cell_type": "markdown",
"id": "a65ed0af83b5d317",
"metadata": {
"collapsed": false
},
"source": [
"### Intensity\n",
"The authors use a binary variable for intensity, which is coded as 1- not intense and 2- intense in the original data. We can just subtract 1 from each row to get to the binary notation we need."
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "7427aa502e3f19db",
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-06T12:27:33.268916600Z",
"start_time": "2024-08-06T12:27:33.259382700Z"
}
},
"outputs": [],
"source": [
"dataset['intens'] -= 1"
]
},
{
"cell_type": "markdown",
"id": "3879c47321fe5d50",
"metadata": {
"collapsed": false
},
"source": [
"### Making everything True/False\n",
"This cell codes all binary variables as boolean true/false variables to prepare the data for regression."
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "85e17c7477cf8b09",
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-06T12:27:33.599542400Z",
"start_time": "2024-08-06T12:27:33.274478100Z"
}
},
"outputs": [],
"source": [
"for i in ['mobcap', 'intens', 'polity', 'is_leftist_group', 'trial', 'truth', 'rep', 'amnesty', 'exile', 'purge', 'conciliatory', 'coercive', 'cold_war', 'incomp', 'terrcont', 'DCJ_used']:\n",
" dataset[i].replace({1: True, 0: False}, inplace=True)"
]
},
{
"cell_type": "markdown",
"id": "b0f0d7c99366dad1",
"metadata": {
"collapsed": false
},
"source": [
"### Getting dummy columns for fightcap\n",
"Fighting capacity remains as a 4 category column in the data, so we'll get dummies to one-hot encode the data. Each category will end up in it's own column."
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "ba39db8c27722360",
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-06T12:27:33.809379800Z",
"start_time": "2024-08-06T12:27:33.602927400Z"
}
},
"outputs": [],
"source": [
"dataset = pd.get_dummies(dataset, columns=['fightcap'])"
]
},
{
"cell_type": "markdown",
"id": "ca9a86f732310265",
"metadata": {
"collapsed": false
},
"source": [
"### Saving the clean dataset"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "8f4e1eb751d9af7e",
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-06T12:27:43.867250900Z",
"start_time": "2024-08-06T12:27:43.831227800Z"
}
},
"outputs": [],
"source": [
"dataset.to_csv('CleanData.csv')"
]
},
{
"cell_type": "markdown",
"id": "a1d8e28ba2eaef98",
"metadata": {
"collapsed": false
},
"source": [
"## Modeling"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "ee7873a5f890c861",
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-06T12:27:36.173272700Z",
"start_time": "2024-08-06T12:27:33.929615700Z"
}
},
"outputs": [],
"source": [
"from sklearn.linear_model import LogisticRegression\n",
"logreg = LogisticRegression(penalty=None)"
]
},
{
"cell_type": "markdown",
"id": "a926c22ca21b2b5b",
"metadata": {
"collapsed": false
},
"source": [
"### Filling NAs\n",
"There are just a couple of NA rows in the data, we'll fill them with False."
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "d705837c4f2d38f7",
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-06T12:27:36.192775600Z",
"start_time": "2024-08-06T12:27:36.176238400Z"
}
},
"outputs": [],
"source": [
"dataset['terrcont'].fillna(False, inplace=True)\n",
"dataset['mobcap'].fillna(False, inplace=True)"
]
},
{
"cell_type": "markdown",
"id": "1257f1fbf2717137",
"metadata": {
"collapsed": false
},
"source": [
"### Model 1: All DCJ Processes"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "78151264e23b60d8",
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-06T12:27:54.796279600Z",
"start_time": "2024-08-06T12:27:54.678904800Z"
}
},
"outputs": [
{
"data": {
"text/plain": "LogisticRegression(penalty=None)",
"text/html": "<style>#sk-container-id-1 {\n /* Definition of color scheme common for light and dark mode */\n --sklearn-color-text: black;\n --sklearn-color-line: gray;\n /* Definition of color scheme for unfitted estimators */\n --sklearn-color-unfitted-level-0: #fff5e6;\n --sklearn-color-unfitted-level-1: #f6e4d2;\n --sklearn-color-unfitted-level-2: #ffe0b3;\n --sklearn-color-unfitted-level-3: chocolate;\n /* Definition of color scheme for fitted estimators */\n --sklearn-color-fitted-level-0: #f0f8ff;\n --sklearn-color-fitted-level-1: #d4ebff;\n --sklearn-color-fitted-level-2: #b3dbfd;\n --sklearn-color-fitted-level-3: cornflowerblue;\n\n /* Specific color for light theme */\n --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n --sklearn-color-icon: #696969;\n\n @media (prefers-color-scheme: dark) {\n /* Redefinition of color scheme for dark theme */\n --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n --sklearn-color-icon: #878787;\n }\n}\n\n#sk-container-id-1 {\n color: var(--sklearn-color-text);\n}\n\n#sk-container-id-1 pre {\n padding: 0;\n}\n\n#sk-container-id-1 input.sk-hidden--visually {\n border: 0;\n clip: rect(1px 1px 1px 1px);\n clip: rect(1px, 1px, 1px, 1px);\n height: 1px;\n margin: -1px;\n overflow: hidden;\n padding: 0;\n position: absolute;\n width: 1px;\n}\n\n#sk-container-id-1 div.sk-dashed-wrapped {\n border: 1px dashed var(--sklearn-color-line);\n margin: 0 0.4em 0.5em 0.4em;\n box-sizing: border-box;\n padding-bottom: 0.4em;\n background-color: var(--sklearn-color-background);\n}\n\n#sk-container-id-1 div.sk-container {\n /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n but bootstrap.min.css set `[hidden] { display: none !important; }`\n so we also need the `!important` here to be able to override the\n default hidden behavior on the sphinx rendered scikit-learn.org.\n See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n display: inline-block !important;\n position: relative;\n}\n\n#sk-container-id-1 div.sk-text-repr-fallback {\n display: none;\n}\n\ndiv.sk-parallel-item,\ndiv.sk-serial,\ndiv.sk-item {\n /* draw centered vertical line to link estimators */\n background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n background-size: 2px 100%;\n background-repeat: no-repeat;\n background-position: center center;\n}\n\n/* Parallel-specific style estimator block */\n\n#sk-container-id-1 div.sk-parallel-item::after {\n content: \"\";\n width: 100%;\n border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n flex-grow: 1;\n}\n\n#sk-container-id-1 div.sk-parallel {\n display: flex;\n align-items: stretch;\n justify-content: center;\n background-color: var(--sklearn-color-background);\n position: relative;\n}\n\n#sk-container-id-1 div.sk-parallel-item {\n display: flex;\n flex-direction: column;\n}\n\n#sk-container-id-1 div.sk-parallel-item:first-child::after {\n align-self: flex-end;\n width: 50%;\n}\n\n#sk-container-id-1 div.sk-parallel-item:last-child::after {\n align-self: flex-start;\n width: 50%;\n}\n\n#sk-container-id-1 div.sk-parallel-item:only-child::after {\n width: 0;\n}\n\n/* Serial-specific style estimator block */\n\n#sk-container-id-1 div.sk-serial {\n display: flex;\n flex-direction: column;\n align-items: center;\n background-color: var(--sklearn-color-background);\n padding-right: 1em;\n padding-left: 1em;\n}\n\n\n/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\nclickable and can be expanded/collapsed.\n- Pipeline and ColumnTransformer use this feature and define the default style\n- Estimators will overwrite some part of the style using the `sk-estimator` class\n*/\n\n/* Pipeline and ColumnTransformer style (default) */\n\n#sk-container-id-1 div.sk-toggleable {\n /* Default theme specific background. It is overwritten whether we have a\n specific estimator or a Pipeline/ColumnTransformer */\n background-color: var(--sklearn-color-background);\n}\n\n/* Toggleable label */\n#sk-container-id-1 label.sk-toggleable__label {\n cursor: pointer;\n display: block;\n width: 100%;\n margin-bottom: 0;\n padding: 0.5em;\n box-sizing: border-box;\n text-align: center;\n}\n\n#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n /* Arrow on the left of the label */\n content: \"▸\";\n float: left;\n margin-right: 0.25em;\n color: var(--sklearn-color-icon);\n}\n\n#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n color: var(--sklearn-color-text);\n}\n\n/* Toggleable content - dropdown */\n\n#sk-container-id-1 div.sk-toggleable__content {\n max-height: 0;\n max-width: 0;\n overflow: hidden;\n text-align: left;\n /* unfitted */\n background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-1 div.sk-toggleable__content.fitted {\n /* fitted */\n background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-1 div.sk-toggleable__content pre {\n margin: 0.2em;\n border-radius: 0.25em;\n color: var(--sklearn-color-text);\n /* unfitted */\n background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n /* unfitted */\n background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n /* Expand drop-down */\n max-height: 200px;\n max-width: 100%;\n overflow: auto;\n}\n\n#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n content: \"▾\";\n}\n\n/* Pipeline/ColumnTransformer-specific style */\n\n#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n color: var(--sklearn-color-text);\n background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator-specific style */\n\n/* Colorize estimator box */\n#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n /* unfitted */\n background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n /* fitted */\n background-color: var(--sklearn-color-fitted-level-2);\n}\n\n#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n#sk-container-id-1 div.sk-label label {\n /* The background is the default theme color */\n color: var(--sklearn-color-text-on-default-background);\n}\n\n/* On hover, darken the color of the background */\n#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n color: var(--sklearn-color-text);\n background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n/* Label box, darken color on hover, fitted */\n#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n color: var(--sklearn-color-text);\n background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator label */\n\n#sk-container-id-1 div.sk-label label {\n font-family: monospace;\n font-weight: bold;\n display: inline-block;\n line-height: 1.2em;\n}\n\n#sk-container-id-1 div.sk-label-container {\n text-align: center;\n}\n\n/* Estimator-specific */\n#sk-container-id-1 div.sk-estimator {\n font-family: monospace;\n border: 1px dotted var(--sklearn-color-border-box);\n border-radius: 0.25em;\n box-sizing: border-box;\n margin-bottom: 0.5em;\n /* unfitted */\n background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-1 div.sk-estimator.fitted {\n /* fitted */\n background-color: var(--sklearn-color-fitted-level-0);\n}\n\n/* on hover */\n#sk-container-id-1 div.sk-estimator:hover {\n /* unfitted */\n background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-1 div.sk-estimator.fitted:hover {\n /* fitted */\n background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Specification for estimator info (e.g. \"i\" and \"?\") */\n\n/* Common style for \"i\" and \"?\" */\n\n.sk-estimator-doc-link,\na:link.sk-estimator-doc-link,\na:visited.sk-estimator-doc-link {\n float: right;\n font-size: smaller;\n line-height: 1em;\n font-family: monospace;\n background-color: var(--sklearn-color-background);\n border-radius: 1em;\n height: 1em;\n width: 1em;\n text-decoration: none !important;\n margin-left: 1ex;\n /* unfitted */\n border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n color: var(--sklearn-color-unfitted-level-1);\n}\n\n.sk-estimator-doc-link.fitted,\na:link.sk-estimator-doc-link.fitted,\na:visited.sk-estimator-doc-link.fitted {\n /* fitted */\n border: var(--sklearn-color-fitted-level-1) 1pt solid;\n color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\ndiv.sk-estimator:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover {\n /* unfitted */\n background-color: var(--sklearn-color-unfitted-level-3);\n color: var(--sklearn-color-background);\n text-decoration: none;\n}\n\ndiv.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover {\n /* fitted */\n background-color: var(--sklearn-color-fitted-level-3);\n color: var(--sklearn-color-background);\n text-decoration: none;\n}\n\n/* Span, style for the box shown on hovering the info icon */\n.sk-estimator-doc-link span {\n display: none;\n z-index: 9999;\n position: relative;\n font-weight: normal;\n right: .2ex;\n padding: .5ex;\n margin: .5ex;\n width: min-content;\n min-width: 20ex;\n max-width: 50ex;\n color: var(--sklearn-color-text);\n box-shadow: 2pt 2pt 4pt #999;\n /* unfitted */\n background: var(--sklearn-color-unfitted-level-0);\n border: .5pt solid var(--sklearn-color-unfitted-level-3);\n}\n\n.sk-estimator-doc-link.fitted span {\n /* fitted */\n background: var(--sklearn-color-fitted-level-0);\n border: var(--sklearn-color-fitted-level-3);\n}\n\n.sk-estimator-doc-link:hover span {\n display: block;\n}\n\n/* \"?\"-specific style due to the `<a>` HTML tag */\n\n#sk-container-id-1 a.estimator_doc_link {\n float: right;\n font-size: 1rem;\n line-height: 1em;\n font-family: monospace;\n background-color: var(--sklearn-color-background);\n border-radius: 1rem;\n height: 1rem;\n width: 1rem;\n text-decoration: none;\n /* unfitted */\n color: var(--sklearn-color-unfitted-level-1);\n border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n}\n\n#sk-container-id-1 a.estimator_doc_link.fitted {\n /* fitted */\n border: var(--sklearn-color-fitted-level-1) 1pt solid;\n color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\n#sk-container-id-1 a.estimator_doc_link:hover {\n /* unfitted */\n background-color: var(--sklearn-color-unfitted-level-3);\n color: var(--sklearn-color-background);\n text-decoration: none;\n}\n\n#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n /* fitted */\n background-color: var(--sklearn-color-fitted-level-3);\n}\n</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(penalty=None)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\"> LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(penalty=None)</pre></div> </div></div></div></div>"
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"logreg.fit(dataset[['mobcap', 'fightcap_high', 'fightcap_moderate', 'fightcap_low', 'intens', 'polity', 'is_leftist_group', 'incomp', 'terrcont', 'year_of_conflict', 'cold_war']], dataset['DCJ_used'], sample_weight=dataset['model_weight'])"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "8b70f303fbf9451c",
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-06T12:27:54.976560300Z",
"start_time": "2024-08-06T12:27:54.954159600Z"
}
},
"outputs": [],
"source": [
"coefs = pd.DataFrame(logreg.coef_)\n",
"for i in range(11):\n",
" coefs.rename({i: ['mobcap', 'fightcap_high', 'fightcap_moderate', 'fightcap_low', 'intens', 'polity', 'is_leftist_group', 'incomp', 'terrcont', 'year_of_conflict', 'cold_war'][i]}, axis=1, inplace=True)\n",
"coefs.index = ['All DCJ Processes']"
]
},
{
"cell_type": "markdown",
"id": "84f123c126099fbc",
"metadata": {
"collapsed": false
},
"source": [
"### Model 2: Conciliatory Process Use"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "97fe74e0f6453079",
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-06T12:27:55.478557800Z",
"start_time": "2024-08-06T12:27:55.432200500Z"
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},
"outputs": [],
"source": [
"logreg2 = LogisticRegression(penalty=None)\n",
"logreg2.fit(dataset[['mobcap', 'fightcap_high', 'fightcap_moderate', 'fightcap_low', 'intens', 'polity', 'is_leftist_group', 'incomp', 'terrcont', 'year_of_conflict', 'cold_war']], dataset['conciliatory'], sample_weight=dataset['model_weight'])\n",
"coefs2 = pd.DataFrame(logreg2.coef_)\n",
"for i in range(11):\n",
" coefs2.rename({i: ['mobcap', 'fightcap_high', 'fightcap_moderate', 'fightcap_low', 'intens', 'polity', 'is_leftist_group', 'incomp', 'terrcont', 'year_of_conflict', 'cold_war'][i]}, axis=1, inplace=True)\n",
"coefs2.index = ['Conciliatory Processes']"
]
},
{
"cell_type": "markdown",
"id": "6a09b2ec23b84199",
"metadata": {
"collapsed": false
},
"source": [
"### Model 3: Coercive Process Use"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "de770af16ab8e3a",
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-06T12:27:55.840764800Z",
"start_time": "2024-08-06T12:27:55.791184500Z"
}
},
"outputs": [],
"source": [
"logreg3 = LogisticRegression(penalty=None)\n",
"logreg3.fit(dataset[['mobcap', 'fightcap_high', 'fightcap_moderate', 'fightcap_low', 'intens', 'polity', 'is_leftist_group', 'incomp', 'terrcont', 'year_of_conflict', 'cold_war']], dataset['coercive'], sample_weight=dataset['model_weight'])\n",
"coefs3 = pd.DataFrame(logreg3.coef_)\n",
"for i in range(11):\n",
" coefs3.rename({i: ['mobcap', 'fightcap_high', 'fightcap_moderate', 'fightcap_low', 'intens', 'polity', 'is_leftist_group', 'incomp', 'terrcont', 'year_of_conflict', 'cold_war'][i]}, axis=1, inplace=True)\n",
"coefs3.index = ['Coercive Processes']"
]
},
{
"cell_type": "markdown",
"id": "52735e49b7d89bde",
"metadata": {
"collapsed": false
},
"source": [
"### Model 4: Conciliatory Processes vs. Coercive Processes\n",
"These next two analyses only includes rows where a DCJ was used, and in order to compare conciliatory process use directly to coercive process use among conflict years where a DCJ was used."
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "b9ae824a7610a445",
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-06T12:27:56.163794900Z",
"start_time": "2024-08-06T12:27:56.081042300Z"
}
},
"outputs": [],
"source": [
"dataset2 = dataset[dataset['DCJ_used'] == True]\n",
"logreg4 = LogisticRegression(penalty=None)\n",
"logreg4.fit(dataset2[['mobcap', 'fightcap_high', 'fightcap_moderate', 'fightcap_low', 'intens', 'polity', 'is_leftist_group', 'incomp', 'terrcont', 'year_of_conflict', 'cold_war']], dataset2['conciliatory'], sample_weight=dataset2['model_weight'])\n",
"coefs4 = pd.DataFrame(logreg4.coef_)\n",
"for i in range(11):\n",
" coefs4.rename({i: ['mobcap', 'fightcap_high', 'fightcap_moderate', 'fightcap_low', 'intens', 'polity', 'is_leftist_group', 'incomp', 'terrcont', 'year_of_conflict', 'cold_war'][i]}, axis=1, inplace=True)\n",
"coefs4.index = ['Conciliatory Processes vs. Coercive']"
]
},
{
"cell_type": "markdown",
"id": "cfac3c94ffbadaa0",
"metadata": {
"collapsed": false
},
"source": [
"### Model 5: Coercive processes vs. Conciliatory processes"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "4f82bb422f643d5d",
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-06T12:27:56.470157800Z",
"start_time": "2024-08-06T12:27:56.396326900Z"
}
},
"outputs": [],
"source": [
"logreg5 = LogisticRegression(penalty=None)\n",
"logreg5.fit(dataset2[['mobcap', 'fightcap_high', 'fightcap_moderate', 'fightcap_low', 'intens', 'polity', 'is_leftist_group', 'incomp', 'terrcont', 'year_of_conflict', 'cold_war']], dataset2['coercive'], sample_weight=dataset2['model_weight'])\n",
"coefs5 = pd.DataFrame(logreg5.coef_)\n",
"for i in range(11):\n",
" coefs5.rename({i: ['mobcap', 'fightcap_high', 'fightcap_moderate', 'fightcap_low', 'intens', 'polity', 'is_leftist_group', 'incomp', 'terrcont', 'year_of_conflict', 'cold_war'][i]}, axis=1, inplace=True)\n",
"coefs5.index = ['Coercive Processes vs. Conciliatory']"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "258d9352cadfba76",
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-06T12:27:56.570906100Z",
"start_time": "2024-08-06T12:27:56.559680200Z"
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},
"outputs": [],
"source": [
"coefs['constant'] = logreg.intercept_\n",
"coefs2['constant'] = logreg2.intercept_\n",
"coefs3['constant'] = logreg3.intercept_\n",
"coefs4['constant'] = logreg4.intercept_\n",
"coefs5['constant'] = logreg5.intercept_"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "c2da3e658a014245",
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-06T12:27:56.752230800Z",
"start_time": "2024-08-06T12:27:56.720077200Z"
}
},
"outputs": [],
"source": [
"from sklearn.metrics import log_loss\n",
"coefs['log_likelihood'] = -log_loss(logreg.predict(dataset[['mobcap', 'fightcap_high', 'fightcap_moderate', 'fightcap_low', 'intens', 'polity', 'is_leftist_group', 'incomp', 'terrcont', 'year_of_conflict', 'cold_war']]), dataset['DCJ_used'])\n",
"coefs2['log_likelihood'] = -log_loss(logreg2.predict(dataset[['mobcap', 'fightcap_high', 'fightcap_moderate', 'fightcap_low', 'intens', 'polity', 'is_leftist_group', 'incomp', 'terrcont', 'year_of_conflict', 'cold_war']]), dataset['conciliatory'])\n",
"coefs3['log_likelihood'] = -log_loss(logreg3.predict(dataset[['mobcap', 'fightcap_high', 'fightcap_moderate', 'fightcap_low', 'intens', 'polity', 'is_leftist_group', 'incomp', 'terrcont', 'year_of_conflict', 'cold_war']]), dataset['coercive'])\n",
"coefs4['log_likelihood'] = -log_loss(logreg4.predict(dataset2[['mobcap', 'fightcap_high', 'fightcap_moderate', 'fightcap_low', 'intens', 'polity', 'is_leftist_group', 'incomp', 'terrcont', 'year_of_conflict', 'cold_war']]), dataset2['conciliatory'])\n",
"coefs5['log_likelihood'] = -log_loss(logreg5.predict(dataset2[['mobcap', 'fightcap_high', 'fightcap_moderate', 'fightcap_low', 'intens', 'polity', 'is_leftist_group', 'incomp', 'terrcont', 'year_of_conflict', 'cold_war']]), dataset2['coercive'])"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "310ca1c3a0879f4f",
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-06T12:27:56.941387200Z",
"start_time": "2024-08-06T12:27:56.923974900Z"
}
},
"outputs": [],
"source": [
"results = pd.concat([coefs, coefs2, coefs3, coefs4, coefs5]).T"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "b1c38ff3eb412c4a",
"metadata": {
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"ExecuteTime": {
"end_time": "2024-08-06T12:27:57.492291200Z",
"start_time": "2024-08-06T12:27:57.460786600Z"
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"outputs": [
{
"data": {
"text/plain": " 1 2 3 4 5\nmobcap 0.661976 0.388605 0.241040 0.240883 -0.240883\nfightcap_high -0.449099 0.457280 -0.829236 0.958880 -0.958879\nfightcap_moderate 0.103389 -0.053578 0.222631 -0.200837 0.200837\nfightcap_low 0.167188 -0.516068 0.515424 -0.650171 0.650172\nintens 0.634839 0.191754 0.336639 -0.070971 0.070971\npolity 1.551596 -0.125052 1.304639 -0.743485 0.743485\nis_leftist_group -0.472057 -0.310735 -0.119309 -0.307852 0.307852\nincomp -0.976216 -0.347354 -0.527997 -0.059748 0.059748\nterrcont -0.769757 0.134406 -0.792893 0.599034 -0.599034\nyear_of_conflict 0.079885 0.024635 0.030991 0.014480 -0.014480\ncold_war -1.734773 -0.812434 -0.942777 0.049247 -0.049247\nconstant 0.788109 -0.868502 -0.627620 -0.231353 0.231352\nlog_likelihood -7.054748 -7.121697 -10.276667 -10.238452 -10.238452",
"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>1</th>\n <th>2</th>\n <th>3</th>\n <th>4</th>\n <th>5</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>mobcap</th>\n <td>0.661976</td>\n <td>0.388605</td>\n <td>0.241040</td>\n <td>0.240883</td>\n <td>-0.240883</td>\n </tr>\n <tr>\n <th>fightcap_high</th>\n <td>-0.449099</td>\n <td>0.457280</td>\n <td>-0.829236</td>\n <td>0.958880</td>\n <td>-0.958879</td>\n </tr>\n <tr>\n <th>fightcap_moderate</th>\n <td>0.103389</td>\n <td>-0.053578</td>\n <td>0.222631</td>\n <td>-0.200837</td>\n <td>0.200837</td>\n </tr>\n <tr>\n <th>fightcap_low</th>\n <td>0.167188</td>\n <td>-0.516068</td>\n <td>0.515424</td>\n <td>-0.650171</td>\n <td>0.650172</td>\n </tr>\n <tr>\n <th>intens</th>\n <td>0.634839</td>\n <td>0.191754</td>\n <td>0.336639</td>\n <td>-0.070971</td>\n <td>0.070971</td>\n </tr>\n <tr>\n <th>polity</th>\n <td>1.551596</td>\n <td>-0.125052</td>\n <td>1.304639</td>\n <td>-0.743485</td>\n <td>0.743485</td>\n </tr>\n <tr>\n <th>is_leftist_group</th>\n <td>-0.472057</td>\n <td>-0.310735</td>\n <td>-0.119309</td>\n <td>-0.307852</td>\n <td>0.307852</td>\n </tr>\n <tr>\n <th>incomp</th>\n <td>-0.976216</td>\n <td>-0.347354</td>\n <td>-0.527997</td>\n <td>-0.059748</td>\n <td>0.059748</td>\n </tr>\n <tr>\n <th>terrcont</th>\n <td>-0.769757</td>\n <td>0.134406</td>\n <td>-0.792893</td>\n <td>0.599034</td>\n <td>-0.599034</td>\n </tr>\n <tr>\n <th>year_of_conflict</th>\n <td>0.079885</td>\n <td>0.024635</td>\n <td>0.030991</td>\n <td>0.014480</td>\n <td>-0.014480</td>\n </tr>\n <tr>\n <th>cold_war</th>\n <td>-1.734773</td>\n <td>-0.812434</td>\n <td>-0.942777</td>\n <td>0.049247</td>\n <td>-0.049247</td>\n </tr>\n <tr>\n <th>constant</th>\n <td>0.788109</td>\n <td>-0.868502</td>\n <td>-0.627620</td>\n <td>-0.231353</td>\n <td>0.231352</td>\n </tr>\n <tr>\n <th>log_likelihood</th>\n <td>-7.054748</td>\n <td>-7.121697</td>\n <td>-10.276667</td>\n <td>-10.238452</td>\n <td>-10.238452</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"results"
]
},
{
"cell_type": "code",
"execution_count": 38,
"outputs": [],
"source": [
"dataset3 = dataset[dataset['conciliatory'] == True]\n",
"logreg6 = LogisticRegression(penalty=None)\n",
"logreg6.fit(dataset3[\n",
" ['mobcap', 'fightcap_high', 'fightcap_moderate', 'fightcap_low', 'intens', 'polity', 'is_leftist_group',\n",
" 'incomp', 'terrcont', 'year_of_conflict', 'cold_war']], dataset3['truth'],\n",
" sample_weight=dataset3['model_weight'])\n",
"coefs6 = pd.DataFrame(logreg6.coef_)\n",
"for i in range(11):\n",
" coefs6.rename({i: ['mobcap', 'fightcap_high', 'fightcap_moderate', 'fightcap_low', 'intens', 'polity',\n",
" 'is_leftist_group', 'incomp', 'terrcont', 'year_of_conflict', 'cold_war'][i]}, axis=1,\n",
" inplace=True)\n",
"coefs6.index = ['Truth Commissions']"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-06T12:39:09.528207200Z",
"start_time": "2024-08-06T12:39:09.467856400Z"
}
},
"id": "456b2a60df1ffaa7"
},
{
"cell_type": "code",
"execution_count": 39,
"outputs": [],
"source": [
"logreg7 = LogisticRegression(penalty=None)\n",
"logreg7.fit(dataset3[\n",
" ['mobcap', 'fightcap_high', 'fightcap_moderate', 'fightcap_low', 'intens', 'polity', 'is_leftist_group',\n",
" 'incomp', 'terrcont', 'year_of_conflict', 'cold_war']], dataset3['rep'],\n",
" sample_weight=dataset3['model_weight'])\n",
"coefs7 = pd.DataFrame(logreg7.coef_)\n",
"for i in range(11):\n",
" coefs7.rename({i: ['mobcap', 'fightcap_high', 'fightcap_moderate', 'fightcap_low', 'intens', 'polity',\n",
" 'is_leftist_group', 'incomp', 'terrcont', 'year_of_conflict', 'cold_war'][i]}, axis=1,\n",
" inplace=True)\n",
"coefs7.index = ['Reparations']"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-06T12:39:10.244148700Z",
"start_time": "2024-08-06T12:39:10.196017900Z"
}
},
"id": "5c4e4b2e5d1737cf"
},
{
"cell_type": "code",
"execution_count": 40,
"outputs": [],
"source": [
"logreg8 = LogisticRegression(penalty=None)\n",
"logreg8.fit(dataset3[\n",
" ['mobcap', 'fightcap_high', 'fightcap_moderate', 'fightcap_low', 'intens', 'polity', 'is_leftist_group',\n",
" 'incomp', 'terrcont', 'year_of_conflict', 'cold_war']], dataset3['amnesty'],\n",
" sample_weight=dataset3['model_weight'])\n",
"coefs8 = pd.DataFrame(logreg8.coef_)\n",
"for i in range(11):\n",
" coefs8.rename({i: ['mobcap', 'fightcap_high', 'fightcap_moderate', 'fightcap_low', 'intens', 'polity',\n",
" 'is_leftist_group', 'incomp', 'terrcont', 'year_of_conflict', 'cold_war'][i]}, axis=1,\n",
" inplace=True)\n",
"coefs8.index = ['Amnesty']"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-06T12:39:10.374730400Z",
"start_time": "2024-08-06T12:39:10.330745300Z"
}
},
"id": "3dd316ba17d721ec"
},
{
"cell_type": "code",
"execution_count": 41,
"outputs": [],
"source": [
"dataset4 = dataset[dataset['coercive'] == True]\n",
"logreg9 = LogisticRegression(penalty=None)\n",
"logreg9.fit(dataset4[\n",
" ['mobcap', 'fightcap_high', 'fightcap_moderate', 'fightcap_low', 'intens', 'polity', 'is_leftist_group',\n",
" 'incomp', 'terrcont', 'year_of_conflict', 'cold_war']], dataset4['trial'],\n",
" sample_weight=dataset4['model_weight'])\n",
"coefs9 = pd.DataFrame(logreg9.coef_)\n",
"for i in range(11):\n",
" coefs9.rename({i: ['mobcap', 'fightcap_high', 'fightcap_moderate', 'fightcap_low', 'intens', 'polity',\n",
" 'is_leftist_group', 'incomp', 'terrcont', 'year_of_conflict', 'cold_war'][i]}, axis=1,\n",
" inplace=True)\n",
"coefs9.index = ['Trials']"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-06T12:39:10.549633100Z",
"start_time": "2024-08-06T12:39:10.480849700Z"
}
},
"id": "9a91509167d64ef9"
},
{
"cell_type": "code",
"execution_count": 42,
"outputs": [],
"source": [
"logreg10 = LogisticRegression(penalty=None)\n",
"logreg10.fit(dataset4[\n",
" ['mobcap', 'fightcap_high', 'fightcap_moderate', 'fightcap_low', 'intens', 'polity', 'is_leftist_group',\n",
" 'incomp', 'terrcont', 'year_of_conflict', 'cold_war']], dataset4['exile'],\n",
" sample_weight=dataset4['model_weight'])\n",
"coefs10 = pd.DataFrame(logreg10.coef_)\n",
"for i in range(11):\n",
" coefs10.rename({i: ['mobcap', 'fightcap_high', 'fightcap_moderate', 'fightcap_low', 'intens', 'polity',\n",
" 'is_leftist_group', 'incomp', 'terrcont', 'year_of_conflict', 'cold_war'][i]}, axis=1,\n",
" inplace=True)\n",
"coefs10.index = ['Exiles']"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-06T12:39:10.736878300Z",
"start_time": "2024-08-06T12:39:10.655150900Z"
}
},
"id": "d64e1c3ac05946e8"
},
{
"cell_type": "code",
"execution_count": 43,
"outputs": [],
"source": [
"logreg11 = LogisticRegression(penalty=None)\n",
"logreg11.fit(dataset4[\n",
" ['mobcap', 'fightcap_high', 'fightcap_moderate', 'fightcap_low', 'intens', 'polity', 'is_leftist_group',\n",
" 'incomp', 'terrcont', 'year_of_conflict', 'cold_war']], dataset4['purge'],\n",
" sample_weight=dataset4['model_weight'])\n",
"coefs11 = pd.DataFrame(logreg11.coef_)\n",
"for i in range(11):\n",
" coefs11.rename({i: ['mobcap', 'fightcap_high', 'fightcap_moderate', 'fightcap_low', 'intens', 'polity',\n",
" 'is_leftist_group', 'incomp', 'terrcont', 'year_of_conflict', 'cold_war'][i]}, axis=1,\n",
" inplace=True)\n",
"coefs11.index = ['Purges']"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-06T12:39:10.878848700Z",
"start_time": "2024-08-06T12:39:10.836761900Z"
}
},
"id": "abfcba61a50926d1"
},
{
"cell_type": "code",
"execution_count": 44,
"outputs": [],
"source": [
"coefs6['constant'] = logreg6.intercept_\n",
"coefs7['constant'] = logreg7.intercept_\n",
"coefs8['constant'] = logreg8.intercept_\n",
"coefs9['constant'] = logreg9.intercept_\n",
"coefs10['constant'] = logreg10.intercept_\n",
"coefs11['constant'] = logreg11.intercept_"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-06T12:39:10.999932100Z",
"start_time": "2024-08-06T12:39:10.978360800Z"
}
},
"id": "b963b63141b61d41"
},
{
"cell_type": "code",
"execution_count": 47,
"outputs": [
{
"data": {
"text/plain": " acdid location sidea sideb model_weight mobcap \\\n31 6 Iran Iran KDPI 1.0 False \n32 6 Iran Iran KDPI 1.0 False \n33 6 Iran Iran KDPI 1.0 False \n34 6 Iran Iran KDPI 1.0 False \n43 6 Iran Iran KDPI 1.0 False \n... ... ... ... ... ... ... \n4295 271 Libya Libya NTC 0.5 True \n4298 271 Libya Libya NTC 0.5 True \n4301 271 Libya Libya Forces of Muammar Gaddafi 0.5 False \n4302 271 Libya Libya Forces of Muammar Gaddafi 0.5 False \n4305 271 Libya Libya Forces of Muammar Gaddafi 0.5 False \n\n intens polity is_leftist_group trial ... terrcont year \\\n31 True False False False ... False 1979 \n32 True False False False ... True 1979 \n33 True False False False ... False 1979 \n34 True False False False ... True 1979 \n43 False False False False ... False 1984 \n... ... ... ... ... ... ... ... \n4295 True False False False ... True 2011 \n4298 True False False False ... True 2011 \n4301 True False False False ... True 2011 \n4302 True False False False ... True 2011 \n4305 True False False False ... True 2011 \n\n year_of_conflict conciliatory coercive DCJ_used cold_war \\\n31 1 True False True True \n32 1 True False True True \n33 1 True False True True \n34 1 True False True True \n43 6 True False True True \n... ... ... ... ... ... \n4295 1 True False True False \n4298 1 True False True False \n4301 1 True False True False \n4302 1 True False True False \n4305 1 True False True False \n\n fightcap_high fightcap_low fightcap_moderate \n31 False True False \n32 False True False \n33 False True False \n34 False True False \n43 False True False \n... ... ... ... \n4295 False False True \n4298 False False True \n4301 False False True \n4302 False False True \n4305 False False True \n\n[848 rows x 26 columns]",
"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>acdid</th>\n <th>location</th>\n <th>sidea</th>\n <th>sideb</th>\n <th>model_weight</th>\n <th>mobcap</th>\n <th>intens</th>\n <th>polity</th>\n <th>is_leftist_group</th>\n <th>trial</th>\n <th>...</th>\n <th>terrcont</th>\n <th>year</th>\n <th>year_of_conflict</th>\n <th>conciliatory</th>\n <th>coercive</th>\n <th>DCJ_used</th>\n <th>cold_war</th>\n <th>fightcap_high</th>\n <th>fightcap_low</th>\n <th>fightcap_moderate</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>31</th>\n <td>6</td>\n <td>Iran</td>\n <td>Iran</td>\n <td>KDPI</td>\n <td>1.0</td>\n <td>False</td>\n <td>True</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>...</td>\n <td>False</td>\n <td>1979</td>\n <td>1</td>\n <td>True</td>\n <td>False</td>\n <td>True</td>\n <td>True</td>\n <td>False</td>\n <td>True</td>\n <td>False</td>\n </tr>\n <tr>\n <th>32</th>\n <td>6</td>\n <td>Iran</td>\n <td>Iran</td>\n <td>KDPI</td>\n <td>1.0</td>\n <td>False</td>\n <td>True</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>...</td>\n <td>True</td>\n <td>1979</td>\n <td>1</td>\n <td>True</td>\n <td>False</td>\n <td>True</td>\n <td>True</td>\n <td>False</td>\n <td>True</td>\n <td>False</td>\n </tr>\n <tr>\n <th>33</th>\n <td>6</td>\n <td>Iran</td>\n <td>Iran</td>\n <td>KDPI</td>\n <td>1.0</td>\n <td>False</td>\n <td>True</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>...</td>\n <td>False</td>\n <td>1979</td>\n <td>1</td>\n <td>True</td>\n <td>False</td>\n <td>True</td>\n <td>True</td>\n <td>False</td>\n <td>True</td>\n <td>False</td>\n </tr>\n <tr>\n <th>34</th>\n <td>6</td>\n <td>Iran</td>\n <td>Iran</td>\n <td>KDPI</td>\n <td>1.0</td>\n <td>False</td>\n <td>True</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>...</td>\n <td>True</td>\n <td>1979</td>\n <td>1</td>\n <td>True</td>\n <td>False</td>\n <td>True</td>\n <td>True</td>\n <td>False</td>\n <td>True</td>\n <td>False</td>\n </tr>\n <tr>\n <th>43</th>\n <td>6</td>\n <td>Iran</td>\n <td>Iran</td>\n <td>KDPI</td>\n <td>1.0</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>...</td>\n <td>False</td>\n <td>1984</td>\n <td>6</td>\n <td>True</td>\n <td>False</td>\n <td>True</td>\n <td>True</td>\n <td>False</td>\n <td>True</td>\n <td>False</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>4295</th>\n <td>271</td>\n <td>Libya</td>\n <td>Libya</td>\n <td>NTC</td>\n <td>0.5</td>\n <td>True</td>\n <td>True</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>...</td>\n <td>True</td>\n <td>2011</td>\n <td>1</td>\n <td>True</td>\n <td>False</td>\n <td>True</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>True</td>\n </tr>\n <tr>\n <th>4298</th>\n <td>271</td>\n <td>Libya</td>\n <td>Libya</td>\n <td>NTC</td>\n <td>0.5</td>\n <td>True</td>\n <td>True</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>...</td>\n <td>True</td>\n <td>2011</td>\n <td>1</td>\n <td>True</td>\n <td>False</td>\n <td>True</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>True</td>\n </tr>\n <tr>\n <th>4301</th>\n <td>271</td>\n <td>Libya</td>\n <td>Libya</td>\n <td>Forces of Muammar Gaddafi</td>\n <td>0.5</td>\n <td>False</td>\n <td>True</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>...</td>\n <td>True</td>\n <td>2011</td>\n <td>1</td>\n <td>True</td>\n <td>False</td>\n <td>True</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>True</td>\n </tr>\n <tr>\n <th>4302</th>\n <td>271</td>\n <td>Libya</td>\n <td>Libya</td>\n <td>Forces of Muammar Gaddafi</td>\n <td>0.5</td>\n <td>False</td>\n <td>True</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>...</td>\n <td>True</td>\n <td>2011</td>\n <td>1</td>\n <td>True</td>\n <td>False</td>\n <td>True</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>True</td>\n </tr>\n <tr>\n <th>4305</th>\n <td>271</td>\n <td>Libya</td>\n <td>Libya</td>\n <td>Forces of Muammar Gaddafi</td>\n <td>0.5</td>\n <td>False</td>\n <td>True</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>...</td>\n <td>True</td>\n <td>2011</td>\n <td>1</td>\n <td>True</td>\n <td>False</td>\n <td>True</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>True</td>\n </tr>\n </tbody>\n</table>\n<p>848 rows × 26 columns</p>\n</div>"
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset3"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-06T12:40:04.065696900Z",
"start_time": "2024-08-06T12:40:03.969752500Z"
}
},
"id": "baba14b6bc7a6f41"
},
{
"cell_type": "code",
"execution_count": 61,
"outputs": [],
"source": [
"coefs6['log_likelihood'] = -log_loss(y_pred=logreg6.predict(dataset3[['mobcap', 'fightcap_high', 'fightcap_moderate', 'fightcap_low', 'intens', 'polity', 'is_leftist_group', 'incomp', 'terrcont', 'year_of_conflict', 'cold_war']]), y_true=dataset3['truth'], labels=[True, False])\n",
"coefs7['log_likelihood'] = -log_loss(logreg7.predict(dataset3[['mobcap', 'fightcap_high', 'fightcap_moderate', 'fightcap_low', 'intens', 'polity', 'is_leftist_group', 'incomp', 'terrcont', 'year_of_conflict', 'cold_war']]), dataset3['rep'])\n",
"coefs8['log_likelihood'] = -log_loss(logreg8.predict(dataset3[['mobcap', 'fightcap_high', 'fightcap_moderate', 'fightcap_low', 'intens', 'polity', 'is_leftist_group', 'incomp', 'terrcont', 'year_of_conflict', 'cold_war']]), dataset3['amnesty'])\n",
"coefs9['log_likelihood'] = -log_loss(logreg9.predict(dataset4[['mobcap', 'fightcap_high', 'fightcap_moderate', 'fightcap_low', 'intens', 'polity', 'is_leftist_group', 'incomp', 'terrcont', 'year_of_conflict', 'cold_war']]), dataset4['trial'], labels=[True, False])\n",
"coefs10['log_likelihood'] = -log_loss(logreg10.predict(dataset4[['mobcap', 'fightcap_high', 'fightcap_moderate', 'fightcap_low', 'intens', 'polity', 'is_leftist_group', 'incomp', 'terrcont', 'year_of_conflict', 'cold_war']]), dataset4['exile'], labels=[True, False])\n",
"coefs11['log_likelihood'] = -log_loss(logreg11.predict(dataset4[['mobcap', 'fightcap_high', 'fightcap_moderate', 'fightcap_low', 'intens', 'polity', 'is_leftist_group', 'incomp', 'terrcont', 'year_of_conflict', 'cold_war']]), dataset4['purge'], labels=[True, False])"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-06T12:48:20.663890200Z",
"start_time": "2024-08-06T12:48:20.623128200Z"
}
},
"id": "9a01cbd01b32fc4b"
},
{
"cell_type": "code",
"execution_count": 62,
"outputs": [],
"source": [
"results = pd.concat([coefs, coefs2, coefs3, coefs4, coefs5, coefs6, coefs7, coefs8, coefs9, coefs10, coefs11]).T"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-06T12:48:23.897784500Z",
"start_time": "2024-08-06T12:48:23.874977Z"
}
},
"id": "19ab6857807e8d6f"
},
{
"cell_type": "code",
"execution_count": 63,
"outputs": [
{
"data": {
"text/plain": " 1 2 3 4 5 \\\nmobcap 0.661976 0.388605 0.241040 0.240883 -0.240883 \nfightcap_high -0.449099 0.457280 -0.829236 0.958880 -0.958879 \nfightcap_moderate 0.103389 -0.053578 0.222631 -0.200837 0.200837 \nfightcap_low 0.167188 -0.516068 0.515424 -0.650171 0.650172 \nintens 0.634839 0.191754 0.336639 -0.070971 0.070971 \npolity 1.551596 -0.125052 1.304639 -0.743485 0.743485 \nis_leftist_group -0.472057 -0.310735 -0.119309 -0.307852 0.307852 \nincomp -0.976216 -0.347354 -0.527997 -0.059748 0.059748 \nterrcont -0.769757 0.134406 -0.792893 0.599034 -0.599034 \nyear_of_conflict 0.079885 0.024635 0.030991 0.014480 -0.014480 \ncold_war -1.734773 -0.812434 -0.942777 0.049247 -0.049247 \nconstant 0.788109 -0.868502 -0.627620 -0.231353 0.231352 \nlog_likelihood -7.054748 -7.121697 -10.276667 -10.238452 -10.238452 \n\n Truth Commissions Reparations Amnesty Trials \\\nmobcap 0.388710 0.115131 -0.258683 0.490600 \nfightcap_high 9.692238 -1.852436 0.296176 -1.269715 \nfightcap_moderate 8.170877 -0.838711 0.501842 -0.671203 \nfightcap_low 8.116678 -1.293090 0.898837 -0.252559 \nintens 0.289315 0.222522 -0.324709 -0.351910 \npolity 0.703958 0.497264 -0.732421 0.362496 \nis_leftist_group -1.536770 0.749356 -0.194584 0.710211 \nincomp 0.043450 0.155515 -0.156774 -0.032337 \nterrcont -0.333139 -0.730111 0.747777 -0.112496 \nyear_of_conflict 0.018288 0.026035 -0.029845 -0.003418 \ncold_war -0.255991 -0.913336 0.816814 -0.473723 \nconstant -10.741586 -0.221894 0.217306 2.356162 \nlog_likelihood -3.910396 -8.500862 -11.986215 -3.190586 \n\n Exiles Purges \nmobcap -0.475132 -0.460048 \nfightcap_high -4.782811 7.576780 \nfightcap_moderate -0.996337 6.767420 \nfightcap_low -0.554519 5.739381 \nintens 0.299519 0.431241 \npolity -0.065839 -0.673826 \nis_leftist_group -1.167977 -0.176958 \nincomp 0.021043 -0.007348 \nterrcont 0.529108 -0.322486 \nyear_of_conflict 0.015373 -0.016281 \ncold_war 0.546352 0.361043 \nconstant -2.356438 -8.609149 \nlog_likelihood -1.678381 -1.512205 ",
"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>1</th>\n <th>2</th>\n <th>3</th>\n <th>4</th>\n <th>5</th>\n <th>Truth Commissions</th>\n <th>Reparations</th>\n <th>Amnesty</th>\n <th>Trials</th>\n <th>Exiles</th>\n <th>Purges</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>mobcap</th>\n <td>0.661976</td>\n <td>0.388605</td>\n <td>0.241040</td>\n <td>0.240883</td>\n <td>-0.240883</td>\n <td>0.388710</td>\n <td>0.115131</td>\n <td>-0.258683</td>\n <td>0.490600</td>\n <td>-0.475132</td>\n <td>-0.460048</td>\n </tr>\n <tr>\n <th>fightcap_high</th>\n <td>-0.449099</td>\n <td>0.457280</td>\n <td>-0.829236</td>\n <td>0.958880</td>\n <td>-0.958879</td>\n <td>9.692238</td>\n <td>-1.852436</td>\n <td>0.296176</td>\n <td>-1.269715</td>\n <td>-4.782811</td>\n <td>7.576780</td>\n </tr>\n <tr>\n <th>fightcap_moderate</th>\n <td>0.103389</td>\n <td>-0.053578</td>\n <td>0.222631</td>\n <td>-0.200837</td>\n <td>0.200837</td>\n <td>8.170877</td>\n <td>-0.838711</td>\n <td>0.501842</td>\n <td>-0.671203</td>\n <td>-0.996337</td>\n <td>6.767420</td>\n </tr>\n <tr>\n <th>fightcap_low</th>\n <td>0.167188</td>\n <td>-0.516068</td>\n <td>0.515424</td>\n <td>-0.650171</td>\n <td>0.650172</td>\n <td>8.116678</td>\n <td>-1.293090</td>\n <td>0.898837</td>\n <td>-0.252559</td>\n <td>-0.554519</td>\n <td>5.739381</td>\n </tr>\n <tr>\n <th>intens</th>\n <td>0.634839</td>\n <td>0.191754</td>\n <td>0.336639</td>\n <td>-0.070971</td>\n <td>0.070971</td>\n <td>0.289315</td>\n <td>0.222522</td>\n <td>-0.324709</td>\n <td>-0.351910</td>\n <td>0.299519</td>\n <td>0.431241</td>\n </tr>\n <tr>\n <th>polity</th>\n <td>1.551596</td>\n <td>-0.125052</td>\n <td>1.304639</td>\n <td>-0.743485</td>\n <td>0.743485</td>\n <td>0.703958</td>\n <td>0.497264</td>\n <td>-0.732421</td>\n <td>0.362496</td>\n <td>-0.065839</td>\n <td>-0.673826</td>\n </tr>\n <tr>\n <th>is_leftist_group</th>\n <td>-0.472057</td>\n <td>-0.310735</td>\n <td>-0.119309</td>\n <td>-0.307852</td>\n <td>0.307852</td>\n <td>-1.536770</td>\n <td>0.749356</td>\n <td>-0.194584</td>\n <td>0.710211</td>\n <td>-1.167977</td>\n <td>-0.176958</td>\n </tr>\n <tr>\n <th>incomp</th>\n <td>-0.976216</td>\n <td>-0.347354</td>\n <td>-0.527997</td>\n <td>-0.059748</td>\n <td>0.059748</td>\n <td>0.043450</td>\n <td>0.155515</td>\n <td>-0.156774</td>\n <td>-0.032337</td>\n <td>0.021043</td>\n <td>-0.007348</td>\n </tr>\n <tr>\n <th>terrcont</th>\n <td>-0.769757</td>\n <td>0.134406</td>\n <td>-0.792893</td>\n <td>0.599034</td>\n <td>-0.599034</td>\n <td>-0.333139</td>\n <td>-0.730111</td>\n <td>0.747777</td>\n <td>-0.112496</td>\n <td>0.529108</td>\n <td>-0.322486</td>\n </tr>\n <tr>\n <th>year_of_conflict</th>\n <td>0.079885</td>\n <td>0.024635</td>\n <td>0.030991</td>\n <td>0.014480</td>\n <td>-0.014480</td>\n <td>0.018288</td>\n <td>0.026035</td>\n <td>-0.029845</td>\n <td>-0.003418</td>\n <td>0.015373</td>\n <td>-0.016281</td>\n </tr>\n <tr>\n <th>cold_war</th>\n <td>-1.734773</td>\n <td>-0.812434</td>\n <td>-0.942777</td>\n <td>0.049247</td>\n <td>-0.049247</td>\n <td>-0.255991</td>\n <td>-0.913336</td>\n <td>0.816814</td>\n <td>-0.473723</td>\n <td>0.546352</td>\n <td>0.361043</td>\n </tr>\n <tr>\n <th>constant</th>\n <td>0.788109</td>\n <td>-0.868502</td>\n <td>-0.627620</td>\n <td>-0.231353</td>\n <td>0.231352</td>\n <td>-10.741586</td>\n <td>-0.221894</td>\n <td>0.217306</td>\n <td>2.356162</td>\n <td>-2.356438</td>\n <td>-8.609149</td>\n </tr>\n <tr>\n <th>log_likelihood</th>\n <td>-7.054748</td>\n <td>-7.121697</td>\n <td>-10.276667</td>\n <td>-10.238452</td>\n <td>-10.238452</td>\n <td>-3.910396</td>\n <td>-8.500862</td>\n <td>-11.986215</td>\n <td>-3.190586</td>\n <td>-1.678381</td>\n <td>-1.512205</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"results"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-06T12:48:26.917670100Z",
"start_time": "2024-08-06T12:48:26.885380800Z"
}
},
"id": "bc72adf2a0fa1c0a"
},
{
"cell_type": "markdown",
"source": [
"Note: Truth, exile and purge each contain less than 100 \"true\" rows"
],
"metadata": {
"collapsed": false
},
"id": "fd3e5c6fde7ae8e"
},
{
"cell_type": "code",
"execution_count": 71,
"outputs": [],
"source": [
"import statsmodels.api as sm"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-06T12:56:25.538250400Z",
"start_time": "2024-08-06T12:56:17.761043400Z"
}
},
"id": "781dd8cbb11efbdd"
},
{
"cell_type": "code",
"execution_count": 75,
"outputs": [
{
"ename": "TypeError",
"evalue": "ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''",
"output_type": "error",
"traceback": [
"\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
"\u001B[1;31mTypeError\u001B[0m Traceback (most recent call last)",
"Cell \u001B[1;32mIn[75], line 5\u001B[0m\n\u001B[0;32m 2\u001B[0m X_with_intercept \u001B[38;5;241m=\u001B[39m sm\u001B[38;5;241m.\u001B[39madd_constant(np\u001B[38;5;241m.\u001B[39marray(dataset3[[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mmobcap\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mfightcap_high\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mfightcap_moderate\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mfightcap_low\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mintens\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mpolity\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mis_leftist_group\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mincomp\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mterrcont\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124myear_of_conflict\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mcold_war\u001B[39m\u001B[38;5;124m'\u001B[39m]]))\n\u001B[0;32m 4\u001B[0m \u001B[38;5;66;03m# Fit the model using statsmodels\u001B[39;00m\n\u001B[1;32m----> 5\u001B[0m logit_model \u001B[38;5;241m=\u001B[39m sm\u001B[38;5;241m.\u001B[39mLogit(np\u001B[38;5;241m.\u001B[39marray(dataset3[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mtruth\u001B[39m\u001B[38;5;124m'\u001B[39m]), X_with_intercept)\u001B[38;5;241m.\u001B[39mfit()\n\u001B[0;32m 7\u001B[0m \u001B[38;5;66;03m# Get the p-values\u001B[39;00m\n\u001B[0;32m 8\u001B[0m p_values \u001B[38;5;241m=\u001B[39m logit_model\u001B[38;5;241m.\u001B[39mpvalues\n",
"File \u001B[1;32m~\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:475\u001B[0m, in \u001B[0;36mBinaryModel.__init__\u001B[1;34m(self, endog, exog, offset, check_rank, **kwargs)\u001B[0m\n\u001B[0;32m 472\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m__init__\u001B[39m(\u001B[38;5;28mself\u001B[39m, endog, exog, offset\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, check_rank\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[0;32m 473\u001B[0m \u001B[38;5;66;03m# unconditional check, requires no extra kwargs added by subclasses\u001B[39;00m\n\u001B[0;32m 474\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_check_kwargs(kwargs)\n\u001B[1;32m--> 475\u001B[0m \u001B[38;5;28msuper\u001B[39m()\u001B[38;5;241m.\u001B[39m\u001B[38;5;21m__init__\u001B[39m(endog, exog, offset\u001B[38;5;241m=\u001B[39moffset, check_rank\u001B[38;5;241m=\u001B[39mcheck_rank,\n\u001B[0;32m 476\u001B[0m \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m 477\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28missubclass\u001B[39m(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__class__\u001B[39m, MultinomialModel):\n\u001B[0;32m 478\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m np\u001B[38;5;241m.\u001B[39mall((\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mendog \u001B[38;5;241m>\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;241m0\u001B[39m) \u001B[38;5;241m&\u001B[39m (\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mendog \u001B[38;5;241m<\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;241m1\u001B[39m)):\n",
"File \u001B[1;32m~\\anaconda3\\Lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:185\u001B[0m, in \u001B[0;36mDiscreteModel.__init__\u001B[1;34m(self, endog, exog, check_rank, **kwargs)\u001B[0m\n\u001B[0;32m 183\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m__init__\u001B[39m(\u001B[38;5;28mself\u001B[39m, endog, exog, check_rank\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[0;32m 184\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_check_rank \u001B[38;5;241m=\u001B[39m check_rank\n\u001B[1;32m--> 185\u001B[0m \u001B[38;5;28msuper\u001B[39m()\u001B[38;5;241m.\u001B[39m\u001B[38;5;21m__init__\u001B[39m(endog, exog, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m 186\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mraise_on_perfect_prediction \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mFalse\u001B[39;00m \u001B[38;5;66;03m# keep for backwards compat\u001B[39;00m\n\u001B[0;32m 187\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mk_extra \u001B[38;5;241m=\u001B[39m \u001B[38;5;241m0\u001B[39m\n",
"File \u001B[1;32m~\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:270\u001B[0m, in \u001B[0;36mLikelihoodModel.__init__\u001B[1;34m(self, endog, exog, **kwargs)\u001B[0m\n\u001B[0;32m 269\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m__init__\u001B[39m(\u001B[38;5;28mself\u001B[39m, endog, exog\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[1;32m--> 270\u001B[0m \u001B[38;5;28msuper\u001B[39m()\u001B[38;5;241m.\u001B[39m\u001B[38;5;21m__init__\u001B[39m(endog, exog, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m 271\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39minitialize()\n",
"File \u001B[1;32m~\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:95\u001B[0m, in \u001B[0;36mModel.__init__\u001B[1;34m(self, endog, exog, **kwargs)\u001B[0m\n\u001B[0;32m 93\u001B[0m missing \u001B[38;5;241m=\u001B[39m kwargs\u001B[38;5;241m.\u001B[39mpop(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mmissing\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mnone\u001B[39m\u001B[38;5;124m'\u001B[39m)\n\u001B[0;32m 94\u001B[0m hasconst \u001B[38;5;241m=\u001B[39m kwargs\u001B[38;5;241m.\u001B[39mpop(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mhasconst\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;28;01mNone\u001B[39;00m)\n\u001B[1;32m---> 95\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mdata \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_handle_data(endog, exog, missing, hasconst,\n\u001B[0;32m 96\u001B[0m \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m 97\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mk_constant \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mdata\u001B[38;5;241m.\u001B[39mk_constant\n\u001B[0;32m 98\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mexog \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mdata\u001B[38;5;241m.\u001B[39mexog\n",
"File \u001B[1;32m~\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:135\u001B[0m, in \u001B[0;36mModel._handle_data\u001B[1;34m(self, endog, exog, missing, hasconst, **kwargs)\u001B[0m\n\u001B[0;32m 134\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m_handle_data\u001B[39m(\u001B[38;5;28mself\u001B[39m, endog, exog, missing, hasconst, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[1;32m--> 135\u001B[0m data \u001B[38;5;241m=\u001B[39m handle_data(endog, exog, missing, hasconst, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m 136\u001B[0m \u001B[38;5;66;03m# kwargs arrays could have changed, easier to just attach here\u001B[39;00m\n\u001B[0;32m 137\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m key \u001B[38;5;129;01min\u001B[39;00m kwargs:\n",
"File \u001B[1;32m~\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\data.py:675\u001B[0m, in \u001B[0;36mhandle_data\u001B[1;34m(endog, exog, missing, hasconst, **kwargs)\u001B[0m\n\u001B[0;32m 672\u001B[0m exog \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39masarray(exog)\n\u001B[0;32m 674\u001B[0m klass \u001B[38;5;241m=\u001B[39m handle_data_class_factory(endog, exog)\n\u001B[1;32m--> 675\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m klass(endog, exog\u001B[38;5;241m=\u001B[39mexog, missing\u001B[38;5;241m=\u001B[39mmissing, hasconst\u001B[38;5;241m=\u001B[39mhasconst,\n\u001B[0;32m 676\u001B[0m \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n",
"File \u001B[1;32m~\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\data.py:88\u001B[0m, in \u001B[0;36mModelData.__init__\u001B[1;34m(self, endog, exog, missing, hasconst, **kwargs)\u001B[0m\n\u001B[0;32m 86\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mconst_idx \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[0;32m 87\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mk_constant \u001B[38;5;241m=\u001B[39m \u001B[38;5;241m0\u001B[39m\n\u001B[1;32m---> 88\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_handle_constant(hasconst)\n\u001B[0;32m 89\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_check_integrity()\n\u001B[0;32m 90\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_cache \u001B[38;5;241m=\u001B[39m {}\n",
"File \u001B[1;32m~\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\data.py:133\u001B[0m, in \u001B[0;36mModelData._handle_constant\u001B[1;34m(self, hasconst)\u001B[0m\n\u001B[0;32m 131\u001B[0m check_implicit \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mFalse\u001B[39;00m\n\u001B[0;32m 132\u001B[0m exog_max \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39mmax(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mexog, axis\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m0\u001B[39m)\n\u001B[1;32m--> 133\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m np\u001B[38;5;241m.\u001B[39misfinite(exog_max)\u001B[38;5;241m.\u001B[39mall():\n\u001B[0;32m 134\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m MissingDataError(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mexog contains inf or nans\u001B[39m\u001B[38;5;124m'\u001B[39m)\n\u001B[0;32m 135\u001B[0m exog_min \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39mmin(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mexog, axis\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m0\u001B[39m)\n",
"\u001B[1;31mTypeError\u001B[0m: ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''"
]
}
],
"source": [
"# Add a constant term to the features (intercept)\n",
"X_with_intercept = sm.add_constant(np.array(dataset3[['mobcap', 'fightcap_high', 'fightcap_moderate', 'fightcap_low', 'intens', 'polity', 'is_leftist_group', 'incomp', 'terrcont', 'year_of_conflict', 'cold_war']]))\n",
"\n",
"# Fit the model using statsmodels\n",
"logit_model = sm.Logit(np.array(), X_with_intercept).fit()\n",
"\n",
"# Get the p-values\n",
"p_values = logit_model.pvalues\n",
"\n",
"print(p_values)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-06T12:58:43.724479100Z",
"start_time": "2024-08-06T12:58:43.655034800Z"
}
},
"id": "65171e15688703ef"
},
{
"cell_type": "code",
"execution_count": 74,
"outputs": [],
"source": [
"import numpy as np"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-06T12:58:41.970487800Z",
"start_time": "2024-08-06T12:58:41.937911100Z"
}
},
"id": "69dbc3282e9c7f68"
},
{
"cell_type": "code",
"execution_count": 76,
"outputs": [],
"source": [
"dataset5 = dataset.replace({True: 1, False: 0})"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-06T13:01:41.446777200Z",
"start_time": "2024-08-06T13:01:41.351401200Z"
}
},
"id": "fccc9c0e744e37cd"
},
{
"cell_type": "code",
"execution_count": 78,
"outputs": [],
"source": [
"dataset5.to_csv('CleanData1_0.csv')"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-06T13:02:15.192700700Z",
"start_time": "2024-08-06T13:02:15.106141900Z"
}
},
"id": "f91447312c3f877f"
},
{
"cell_type": "code",
"execution_count": 79,
"outputs": [],
"source": [
"dataset5[dataset5['conciliatory'] == 1].to_csv('CleanData_conc.csv')\n",
"dataset5[dataset5['coercive'] == 1].to_csv('CleanData_coer.csv')"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-08-06T13:09:22.465735900Z",
"start_time": "2024-08-06T13:09:22.403981400Z"
}
},
"id": "58104b9621a63aab"
},
{
"cell_type": "code",
"execution_count": 22,
"outputs": [
{
"data": {
"text/plain": " acdid year location gwno region epid styear \\\n1 100 1966 Nigeria 475.0 4 100_1966 1966 \n2 100 2009 Nigeria 475.0 4 100_2009 2009 \n3 100 2011 Nigeria 475.0 4 100_2011 2011 \n4 101 1966 South Africa 560.0 4 101_1966-1988 1966 \n5 101 1967 South Africa 560.0 4 101_1966-1988 1966 \n... ... ... ... ... ... ... ... \n1933 9 1949 Laos 812.0 3 9_1946-1953 1946 \n1934 9 1950 Laos 812.0 3 9_1946-1953 1946 \n1935 9 1951 Laos 812.0 3 9_1946-1953 1946 \n1936 9 1952 Laos 812.0 3 9_1946-1953 1946 \n1937 9 1953 Laos 812.0 3 9_1946-1953 1946 \n\n endyear epend dcjdummy ... epdum_govdcj epdum_rebdcj polity \\\n1 1966 1 0 ... 0 0 -7.0 \n2 2009 0 3 ... 1 0 4.0 \n3 2011 0 14 ... 1 0 4.0 \n4 1988 0 0 ... 1 0 4.0 \n5 1988 0 1 ... 1 0 4.0 \n... ... ... ... ... ... ... ... \n1933 1953 0 0 ... 0 0 NaN \n1934 1953 0 0 ... 0 0 NaN \n1935 1953 0 0 ... 0 0 NaN \n1936 1953 0 0 ... 0 0 NaN \n1937 1953 1 0 ... 0 0 NaN \n\n regime rebstrength bdeadbes bdeadchgrel chgbdeadrel outcome \\\n1 3.0 4.0 20.0 NaN NaN 4.0 \n2 2.0 1.0 NaN NaN NaN NaN \n3 2.0 1.0 NaN NaN NaN NaN \n4 2.0 2.0 NaN NaN NaN NaN \n5 2.0 2.0 NaN NaN NaN NaN \n... ... ... ... ... ... ... \n1933 NaN 1.0 NaN NaN NaN NaN \n1934 NaN 1.0 NaN NaN NaN NaN \n1935 NaN 1.0 NaN NaN NaN NaN \n1936 NaN 1.0 NaN NaN NaN NaN \n1937 NaN 1.0 NaN NaN NaN 5.0 \n\n conflterm \n1 2.0 \n2 NaN \n3 NaN \n4 NaN \n5 NaN \n... ... \n1933 NaN \n1934 NaN \n1935 NaN \n1936 NaN \n1937 3.0 \n\n[1937 rows x 66 columns]",
"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>acdid</th>\n <th>year</th>\n <th>location</th>\n <th>gwno</th>\n <th>region</th>\n <th>epid</th>\n <th>styear</th>\n <th>endyear</th>\n <th>epend</th>\n <th>dcjdummy</th>\n <th>...</th>\n <th>epdum_govdcj</th>\n <th>epdum_rebdcj</th>\n <th>polity</th>\n <th>regime</th>\n <th>rebstrength</th>\n <th>bdeadbes</th>\n <th>bdeadchgrel</th>\n <th>chgbdeadrel</th>\n <th>outcome</th>\n <th>conflterm</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>1</th>\n <td>100</td>\n <td>1966</td>\n <td>Nigeria</td>\n <td>475.0</td>\n <td>4</td>\n <td>100_1966</td>\n <td>1966</td>\n <td>1966</td>\n <td>1</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>-7.0</td>\n <td>3.0</td>\n <td>4.0</td>\n <td>20.0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>4.0</td>\n <td>2.0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>100</td>\n <td>2009</td>\n <td>Nigeria</td>\n <td>475.0</td>\n <td>4</td>\n <td>100_2009</td>\n <td>2009</td>\n <td>2009</td>\n <td>0</td>\n <td>3</td>\n <td>...</td>\n <td>1</td>\n <td>0</td>\n <td>4.0</td>\n <td>2.0</td>\n <td>1.0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>3</th>\n <td>100</td>\n <td>2011</td>\n <td>Nigeria</td>\n <td>475.0</td>\n <td>4</td>\n <td>100_2011</td>\n <td>2011</td>\n <td>2011</td>\n <td>0</td>\n <td>14</td>\n <td>...</td>\n <td>1</td>\n <td>0</td>\n <td>4.0</td>\n <td>2.0</td>\n <td>1.0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>4</th>\n <td>101</td>\n <td>1966</td>\n <td>South Africa</td>\n <td>560.0</td>\n <td>4</td>\n <td>101_1966-1988</td>\n <td>1966</td>\n <td>1988</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>1</td>\n <td>0</td>\n <td>4.0</td>\n <td>2.0</td>\n <td>2.0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>5</th>\n <td>101</td>\n <td>1967</td>\n <td>South Africa</td>\n <td>560.0</td>\n <td>4</td>\n <td>101_1966-1988</td>\n <td>1966</td>\n <td>1988</td>\n <td>0</td>\n <td>1</td>\n <td>...</td>\n <td>1</td>\n <td>0</td>\n <td>4.0</td>\n <td>2.0</td>\n <td>2.0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>1933</th>\n <td>9</td>\n <td>1949</td>\n <td>Laos</td>\n <td>812.0</td>\n <td>3</td>\n <td>9_1946-1953</td>\n <td>1946</td>\n <td>1953</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>1.0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>1934</th>\n <td>9</td>\n <td>1950</td>\n <td>Laos</td>\n <td>812.0</td>\n <td>3</td>\n <td>9_1946-1953</td>\n <td>1946</td>\n <td>1953</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>1.0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>1935</th>\n <td>9</td>\n <td>1951</td>\n <td>Laos</td>\n <td>812.0</td>\n <td>3</td>\n <td>9_1946-1953</td>\n <td>1946</td>\n <td>1953</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>1.0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>1936</th>\n <td>9</td>\n <td>1952</td>\n <td>Laos</td>\n <td>812.0</td>\n <td>3</td>\n <td>9_1946-1953</td>\n <td>1946</td>\n <td>1953</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>1.0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>1937</th>\n <td>9</td>\n <td>1953</td>\n <td>Laos</td>\n <td>812.0</td>\n <td>3</td>\n <td>9_1946-1953</td>\n <td>1946</td>\n <td>1953</td>\n <td>1</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>1.0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>5.0</td>\n <td>3.0</td>\n </tr>\n </tbody>\n</table>\n<p>1937 rows × 66 columns</p>\n</div>"
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_3"
],
"metadata": {
"collapsed": false,
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