{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 2. Active journals using OJS by country and World Bank income group"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Notebook objectives:\n",
"1. [Determine the number of active OJS journals by country.](#country)\n",
"2. [Group journals by country income level using World Bank GNI data*.](#wb)\n",
"
\n",
"*GNI data are updated as of FY22 but reflect 2020 data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import packages and read the cleaned beacon dataset into pandas:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from collections import defaultdict\n",
"from collections import Counter\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import pandas as pd\n",
"import numpy as np\n",
"import json\n",
"import time\n",
"import os\n",
"import re"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"beaconActive = pd.read_csv(os.path.join('data', 'beacon_active.csv'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Rename the beaconActive DataFrame's 'country_consolidated' variable as 'tld' (top-level domain):"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"beaconActive.rename(columns={'country_consolidated':'tld'}, inplace=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Preprocess the World Bank country and lending group dataset:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"wbGroups = pd.read_excel(os.path.join('data','wb_tlds.xlsx'))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"#Select only the country, income_group, and tld variables, and rename them accordingly:\n",
"wbGroups = wbGroups[['Economy',\n",
" 'Income group',\n",
" 'Domain']]\n",
"wbGroups.rename(columns={'Economy':'country','Income group':'income_group','Domain':'tld'}, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"country 0\n",
"income_group 0\n",
"tld 1\n",
"dtype: int64"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wbGroups.isnull().sum()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" country | \n",
" income_group | \n",
" tld | \n",
"
\n",
" \n",
" \n",
" \n",
" | 141 | \n",
" Namibia | \n",
" Upper middle income | \n",
" NaN | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" index | \n",
" country | \n",
" income_group | \n",
" tld | \n",
"
\n",
" \n",
" \n",
" \n",
" | 134 | \n",
" 216 | \n",
" Kosovo | \n",
" Upper middle income | \n",
" AL | \n",
"
\n",
" \n",
"
\n",
"