New notebooks

datashader-work/datashader-tryout.ipynb

@@ -9,6 +9,874 @@
    ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-03-26T00:34:45.649960Z",
+     "start_time": "2019-03-26T00:34:44.141982Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>indicator_name</th>\n",
+       "      <th>Access to electricity (% of population)</th>\n",
+       "      <th>Access to electricity, rural (% of rural population)</th>\n",
+       "      <th>Access to electricity, urban (% of urban population)</th>\n",
+       "      <th>Adjusted net enrollment rate, primary (% of primary school age children)</th>\n",
+       "      <th>Adjusted net enrollment rate, primary, female (% of primary school age children)</th>\n",
+       "      <th>Adjusted net enrollment rate, primary, male (% of primary school age children)</th>\n",
+       "      <th>Adjusted net national income (annual % growth)</th>\n",
+       "      <th>Adjusted net national income (constant 2010 US$)</th>\n",
+       "      <th>Adjusted net national income (current US$)</th>\n",
+       "      <th>Adjusted net national income per capita (annual % growth)</th>\n",
+       "      <th>Adjusted net national income per capita (constant 2010 US$)</th>\n",
+       "      <th>Adjusted net national income per capita (current US$)</th>\n",
+       "      <th>Adjusted net savings, excluding particulate emission damage (% of GNI)</th>\n",
+       "      <th>Adjusted savings: carbon dioxide damage (% of GNI)</th>\n",
+       "      <th>Adjusted savings: carbon dioxide damage (current US$)</th>\n",
+       "      <th>Adjusted savings: consumption of fixed capital (% of GNI)</th>\n",
+       "      <th>Adjusted savings: consumption of fixed capital (current US$)</th>\n",
+       "      <th>Adjusted savings: education expenditure (% of GNI)</th>\n",
+       "      <th>Adjusted savings: education expenditure (current US$)</th>\n",
+       "      <th>Adjusted savings: energy depletion (% of GNI)</th>\n",
+       "      <th>Adjusted savings: energy depletion (current US$)</th>\n",
+       "      <th>Adjusted savings: gross savings (% of GNI)</th>\n",
+       "      <th>Adjusted savings: mineral depletion (% of GNI)</th>\n",
+       "      <th>Adjusted savings: mineral depletion (current US$)</th>\n",
+       "      <th>Adjusted savings: natural resources depletion (% of GNI)</th>\n",
+       "      <th>Adjusted savings: net forest depletion (% of GNI)</th>\n",
+       "      <th>Adjusted savings: net forest depletion (current US$)</th>\n",
+       "      <th>Adjusted savings: net national savings (% of GNI)</th>\n",
+       "      <th>Adjusted savings: net national savings (current US$)</th>\n",
+       "      <th>Adolescent fertility rate (births per 1,000 women ages 15-19)</th>\n",
+       "      <th>Age dependency ratio (% of working-age population)</th>\n",
+       "      <th>Age dependency ratio, old (% of working-age population)</th>\n",
+       "      <th>Age dependency ratio, young (% of working-age population)</th>\n",
+       "      <th>Agricultural land (% of land area)</th>\n",
+       "      <th>Agricultural land (sq. km)</th>\n",
+       "      <th>Agricultural machinery, tractors</th>\n",
+       "      <th>Agricultural machinery, tractors per 100 sq. km of arable land</th>\n",
+       "      <th>Agricultural methane emissions (% of total)</th>\n",
+       "      <th>Agricultural methane emissions (thousand metric tons of CO2 equivalent)</th>\n",
+       "      <th>Agricultural nitrous oxide emissions (% of total)</th>\n",
+       "      <th>Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)</th>\n",
+       "      <th>Agricultural raw materials exports (% of merchandise exports)</th>\n",
+       "      <th>Agricultural raw materials imports (% of merchandise imports)</th>\n",
+       "      <th>Agriculture, forestry, and fishing, value added (% of GDP)</th>\n",
+       "      <th>Agriculture, forestry, and fishing, value added (annual % growth)</th>\n",
+       "      <th>Agriculture, forestry, and fishing, value added (constant 2010 US$)</th>\n",
+       "      <th>Agriculture, forestry, and fishing, value added (constant LCU)</th>\n",
+       "      <th>Agriculture, forestry, and fishing, value added (current LCU)</th>\n",
+       "      <th>Agriculture, forestry, and fishing, value added (current US$)</th>\n",
+       "      <th>Agriculture, forestry, and fishing, value added per worker (constant 2010 US$)</th>\n",
+       "      <th>Air transport, freight (million ton-km)</th>\n",
+       "      <th>Air transport, passengers carried</th>\n",
+       "      <th>Air transport, registered carrier departures worldwide</th>\n",
+       "      <th>Alternative and nuclear energy (% of total energy use)</th>\n",
+       "      <th>Aquaculture production (metric tons)</th>\n",
+       "      <th>Arable land (% of land area)</th>\n",
+       "      <th>Arable land (hectares per person)</th>\n",
+       "      <th>Arable land (hectares)</th>\n",
+       "      <th>Armed forces personnel (% of total labor force)</th>\n",
+       "      <th>Armed forces personnel, total</th>\n",
+       "      <th>Arms imports (SIPRI trend indicator values)</th>\n",
+       "      <th>Average grace period on new external debt commitments (years)</th>\n",
+       "      <th>Average grace period on new external debt commitments, official (years)</th>\n",
+       "      <th>Average grace period on new external debt commitments, private (years)</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Self-employed, female (% of female employment) (modeled ILO estimate)</th>\n",
+       "      <th>Self-employed, male (% of male employment) (modeled ILO estimate)</th>\n",
+       "      <th>Self-employed, total (% of total employment) (modeled ILO estimate)</th>\n",
+       "      <th>Service exports (BoP, current US$)</th>\n",
+       "      <th>Service imports (BoP, current US$)</th>\n",
+       "      <th>Services, value added (% of GDP)</th>\n",
+       "      <th>Services, value added (annual % growth)</th>\n",
+       "      <th>Services, value added (constant 2010 US$)</th>\n",
+       "      <th>Services, value added (constant LCU)</th>\n",
+       "      <th>Services, value added (current LCU)</th>\n",
+       "      <th>Services, value added (current US$)</th>\n",
+       "      <th>Services, value added per worker (constant 2010 US$)</th>\n",
+       "      <th>Short-term debt (% of exports of goods, services and primary income)</th>\n",
+       "      <th>Short-term debt (% of total external debt)</th>\n",
+       "      <th>Short-term debt (% of total reserves)</th>\n",
+       "      <th>Surface area (sq. km)</th>\n",
+       "      <th>Survival to age 65, female (% of cohort)</th>\n",
+       "      <th>Survival to age 65, male (% of cohort)</th>\n",
+       "      <th>Taxes less subsidies on products (constant LCU)</th>\n",
+       "      <th>Taxes less subsidies on products (current LCU)</th>\n",
+       "      <th>Taxes less subsidies on products (current US$)</th>\n",
+       "      <th>Technical cooperation grants (BoP, current US$)</th>\n",
+       "      <th>Terms of trade adjustment (constant LCU)</th>\n",
+       "      <th>Total amount of debt rescheduled (current US$)</th>\n",
+       "      <th>Total change in external debt stocks (current US$)</th>\n",
+       "      <th>Total debt service (% of GNI)</th>\n",
+       "      <th>Total fisheries production (metric tons)</th>\n",
+       "      <th>Total greenhouse gas emissions (kt of CO2 equivalent)</th>\n",
+       "      <th>Total natural resources rents (% of GDP)</th>\n",
+       "      <th>Total reserves (% of total external debt)</th>\n",
+       "      <th>Total reserves (includes gold, current US$)</th>\n",
+       "      <th>Total reserves in months of imports</th>\n",
+       "      <th>Total reserves minus gold (current US$)</th>\n",
+       "      <th>Trade (% of GDP)</th>\n",
+       "      <th>Trade in services (% of GDP)</th>\n",
+       "      <th>Trademark applications, direct nonresident</th>\n",
+       "      <th>Trademark applications, direct resident</th>\n",
+       "      <th>Trademark applications, total</th>\n",
+       "      <th>Transport services (% of commercial service exports)</th>\n",
+       "      <th>Transport services (% of commercial service imports)</th>\n",
+       "      <th>Transport services (% of service exports, BoP)</th>\n",
+       "      <th>Transport services (% of service imports, BoP)</th>\n",
+       "      <th>Travel services (% of commercial service exports)</th>\n",
+       "      <th>Travel services (% of commercial service imports)</th>\n",
+       "      <th>Travel services (% of service exports, BoP)</th>\n",
+       "      <th>Travel services (% of service imports, BoP)</th>\n",
+       "      <th>Undisbursed external debt, official creditors (UND, current US$)</th>\n",
+       "      <th>Undisbursed external debt, private creditors (UND, current US$)</th>\n",
+       "      <th>Undisbursed external debt, total (UND, current US$)</th>\n",
+       "      <th>Unemployment, female (% of female labor force) (modeled ILO estimate)</th>\n",
+       "      <th>Unemployment, male (% of male labor force) (modeled ILO estimate)</th>\n",
+       "      <th>Unemployment, total (% of total labor force) (modeled ILO estimate)</th>\n",
+       "      <th>Unemployment, youth female (% of female labor force ages 15-24) (modeled ILO estimate)</th>\n",
+       "      <th>Unemployment, youth male (% of male labor force ages 15-24) (modeled ILO estimate)</th>\n",
+       "      <th>Unemployment, youth total (% of total labor force ages 15-24) (modeled ILO estimate)</th>\n",
+       "      <th>Urban population</th>\n",
+       "      <th>Urban population (% of total)</th>\n",
+       "      <th>Urban population growth (annual %)</th>\n",
+       "      <th>Use of IMF credit (DOD, current US$)</th>\n",
+       "      <th>Vulnerable employment, female (% of female employment) (modeled ILO estimate)</th>\n",
+       "      <th>Vulnerable employment, male (% of male employment) (modeled ILO estimate)</th>\n",
+       "      <th>Vulnerable employment, total (% of total employment) (modeled ILO estimate)</th>\n",
+       "      <th>Wage and salaried workers, female (% of female employment) (modeled ILO estimate)</th>\n",
+       "      <th>Wage and salaried workers, male (% of male employment) (modeled ILO estimate)</th>\n",
+       "      <th>Wage and salaried workers, total (% of total employment) (modeled ILO estimate)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Access to electricity (% of population)</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.967760</td>\n",
+       "      <td>0.900338</td>\n",
+       "      <td>0.778743</td>\n",
+       "      <td>0.788319</td>\n",
+       "      <td>0.778785</td>\n",
+       "      <td>-0.040982</td>\n",
+       "      <td>0.164313</td>\n",
+       "      <td>0.080233</td>\n",
+       "      <td>-0.008385</td>\n",
+       "      <td>0.467992</td>\n",
+       "      <td>0.452831</td>\n",
+       "      <td>0.300685</td>\n",
+       "      <td>0.213132</td>\n",
+       "      <td>0.117452</td>\n",
+       "      <td>0.273084</td>\n",
+       "      <td>0.149927</td>\n",
+       "      <td>0.111795</td>\n",
+       "      <td>0.162033</td>\n",
+       "      <td>-0.026653</td>\n",
+       "      <td>0.134945</td>\n",
+       "      <td>0.273615</td>\n",
+       "      <td>-0.142684</td>\n",
+       "      <td>0.061323</td>\n",
+       "      <td>-0.277651</td>\n",
+       "      <td>-0.516037</td>\n",
+       "      <td>-0.155885</td>\n",
+       "      <td>0.135280</td>\n",
+       "      <td>0.122615</td>\n",
+       "      <td>-0.753078</td>\n",
+       "      <td>-0.815717</td>\n",
+       "      <td>0.568068</td>\n",
+       "      <td>-0.823038</td>\n",
+       "      <td>-0.151445</td>\n",
+       "      <td>0.016380</td>\n",
+       "      <td>0.150554</td>\n",
+       "      <td>0.339583</td>\n",
+       "      <td>-0.373394</td>\n",
+       "      <td>0.019435</td>\n",
+       "      <td>-0.345924</td>\n",
+       "      <td>0.018509</td>\n",
+       "      <td>-0.395171</td>\n",
+       "      <td>-0.068828</td>\n",
+       "      <td>-0.740938</td>\n",
+       "      <td>-0.059554</td>\n",
+       "      <td>0.082937</td>\n",
+       "      <td>0.052887</td>\n",
+       "      <td>0.052405</td>\n",
+       "      <td>0.091760</td>\n",
+       "      <td>0.325666</td>\n",
+       "      <td>0.151190</td>\n",
+       "      <td>0.154554</td>\n",
+       "      <td>0.152831</td>\n",
+       "      <td>0.308758</td>\n",
+       "      <td>0.087754</td>\n",
+       "      <td>0.059715</td>\n",
+       "      <td>0.011458</td>\n",
+       "      <td>0.076666</td>\n",
+       "      <td>0.134433</td>\n",
+       "      <td>0.077643</td>\n",
+       "      <td>0.084582</td>\n",
+       "      <td>-0.061842</td>\n",
+       "      <td>-0.229851</td>\n",
+       "      <td>0.342986</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.791727</td>\n",
+       "      <td>-0.762249</td>\n",
+       "      <td>-0.785969</td>\n",
+       "      <td>0.148503</td>\n",
+       "      <td>0.148101</td>\n",
+       "      <td>0.489558</td>\n",
+       "      <td>-0.095213</td>\n",
+       "      <td>0.162039</td>\n",
+       "      <td>0.083086</td>\n",
+       "      <td>0.068215</td>\n",
+       "      <td>0.153152</td>\n",
+       "      <td>0.449570</td>\n",
+       "      <td>-0.067991</td>\n",
+       "      <td>0.261496</td>\n",
+       "      <td>-0.094819</td>\n",
+       "      <td>0.020513</td>\n",
+       "      <td>0.884138</td>\n",
+       "      <td>0.794181</td>\n",
+       "      <td>0.078323</td>\n",
+       "      <td>0.064857</td>\n",
+       "      <td>0.214238</td>\n",
+       "      <td>0.016230</td>\n",
+       "      <td>-0.040191</td>\n",
+       "      <td>0.058914</td>\n",
+       "      <td>0.096962</td>\n",
+       "      <td>0.201450</td>\n",
+       "      <td>0.084539</td>\n",
+       "      <td>0.097263</td>\n",
+       "      <td>-0.312598</td>\n",
+       "      <td>0.069057</td>\n",
+       "      <td>0.128002</td>\n",
+       "      <td>0.086818</td>\n",
+       "      <td>0.115966</td>\n",
+       "      <td>0.200763</td>\n",
+       "      <td>0.126568</td>\n",
+       "      <td>0.094728</td>\n",
+       "      <td>0.072757</td>\n",
+       "      <td>0.090422</td>\n",
+       "      <td>0.038891</td>\n",
+       "      <td>-0.373567</td>\n",
+       "      <td>0.131826</td>\n",
+       "      <td>-0.314060</td>\n",
+       "      <td>-0.065887</td>\n",
+       "      <td>0.238993</td>\n",
+       "      <td>-0.001109</td>\n",
+       "      <td>0.273434</td>\n",
+       "      <td>0.097864</td>\n",
+       "      <td>0.140708</td>\n",
+       "      <td>0.107847</td>\n",
+       "      <td>0.101278</td>\n",
+       "      <td>0.117717</td>\n",
+       "      <td>0.100356</td>\n",
+       "      <td>0.219540</td>\n",
+       "      <td>0.220289</td>\n",
+       "      <td>0.218698</td>\n",
+       "      <td>0.069052</td>\n",
+       "      <td>0.680238</td>\n",
+       "      <td>-0.570946</td>\n",
+       "      <td>0.130843</td>\n",
+       "      <td>-0.792334</td>\n",
+       "      <td>-0.774618</td>\n",
+       "      <td>-0.793262</td>\n",
+       "      <td>0.791727</td>\n",
+       "      <td>0.762249</td>\n",
+       "      <td>0.785969</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Access to electricity, rural (% of rural popul...</td>\n",
+       "      <td>0.967760</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.815735</td>\n",
+       "      <td>0.710961</td>\n",
+       "      <td>0.727187</td>\n",
+       "      <td>0.714469</td>\n",
+       "      <td>-0.041588</td>\n",
+       "      <td>0.157529</td>\n",
+       "      <td>0.054557</td>\n",
+       "      <td>-0.007615</td>\n",
+       "      <td>0.497899</td>\n",
+       "      <td>0.476476</td>\n",
+       "      <td>0.246278</td>\n",
+       "      <td>0.178954</td>\n",
+       "      <td>0.126030</td>\n",
+       "      <td>0.284193</td>\n",
+       "      <td>0.161879</td>\n",
+       "      <td>0.082516</td>\n",
+       "      <td>0.173460</td>\n",
+       "      <td>-0.070043</td>\n",
+       "      <td>0.138128</td>\n",
+       "      <td>0.206564</td>\n",
+       "      <td>-0.177359</td>\n",
+       "      <td>0.060857</td>\n",
+       "      <td>-0.280963</td>\n",
+       "      <td>-0.457900</td>\n",
+       "      <td>-0.170948</td>\n",
+       "      <td>0.061166</td>\n",
+       "      <td>0.126798</td>\n",
+       "      <td>-0.764386</td>\n",
+       "      <td>-0.804982</td>\n",
+       "      <td>0.593037</td>\n",
+       "      <td>-0.823623</td>\n",
+       "      <td>-0.137236</td>\n",
+       "      <td>-0.007659</td>\n",
+       "      <td>0.113987</td>\n",
+       "      <td>0.350716</td>\n",
+       "      <td>-0.322877</td>\n",
+       "      <td>-0.014108</td>\n",
+       "      <td>-0.310214</td>\n",
+       "      <td>-0.006246</td>\n",
+       "      <td>-0.375548</td>\n",
+       "      <td>-0.035783</td>\n",
+       "      <td>-0.663840</td>\n",
+       "      <td>-0.073352</td>\n",
+       "      <td>0.053009</td>\n",
+       "      <td>0.046368</td>\n",
+       "      <td>0.051956</td>\n",
+       "      <td>0.064805</td>\n",
+       "      <td>0.347843</td>\n",
+       "      <td>0.145052</td>\n",
+       "      <td>0.146155</td>\n",
+       "      <td>0.144586</td>\n",
+       "      <td>0.327551</td>\n",
+       "      <td>0.067831</td>\n",
+       "      <td>0.080188</td>\n",
+       "      <td>0.049162</td>\n",
+       "      <td>0.070840</td>\n",
+       "      <td>0.142566</td>\n",
+       "      <td>0.042607</td>\n",
+       "      <td>0.051362</td>\n",
+       "      <td>-0.047910</td>\n",
+       "      <td>-0.214207</td>\n",
+       "      <td>0.299903</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.790674</td>\n",
+       "      <td>-0.750135</td>\n",
+       "      <td>-0.779741</td>\n",
+       "      <td>0.147003</td>\n",
+       "      <td>0.144662</td>\n",
+       "      <td>0.440017</td>\n",
+       "      <td>-0.128833</td>\n",
+       "      <td>0.170720</td>\n",
+       "      <td>0.079684</td>\n",
+       "      <td>0.069943</td>\n",
+       "      <td>0.148404</td>\n",
+       "      <td>0.465658</td>\n",
+       "      <td>-0.053242</td>\n",
+       "      <td>0.254408</td>\n",
+       "      <td>-0.084619</td>\n",
+       "      <td>0.003619</td>\n",
+       "      <td>0.872854</td>\n",
+       "      <td>0.769268</td>\n",
+       "      <td>0.079490</td>\n",
+       "      <td>0.066764</td>\n",
+       "      <td>0.223254</td>\n",
+       "      <td>0.004896</td>\n",
+       "      <td>-0.040225</td>\n",
+       "      <td>0.040867</td>\n",
+       "      <td>0.087680</td>\n",
+       "      <td>0.217893</td>\n",
+       "      <td>0.059640</td>\n",
+       "      <td>0.073193</td>\n",
+       "      <td>-0.278328</td>\n",
+       "      <td>0.053087</td>\n",
+       "      <td>0.130154</td>\n",
+       "      <td>0.047759</td>\n",
+       "      <td>0.117078</td>\n",
+       "      <td>0.185101</td>\n",
+       "      <td>0.135614</td>\n",
+       "      <td>0.069746</td>\n",
+       "      <td>0.051621</td>\n",
+       "      <td>0.069562</td>\n",
+       "      <td>0.047384</td>\n",
+       "      <td>-0.397744</td>\n",
+       "      <td>0.116939</td>\n",
+       "      <td>-0.355709</td>\n",
+       "      <td>-0.093570</td>\n",
+       "      <td>0.250591</td>\n",
+       "      <td>-0.052648</td>\n",
+       "      <td>0.282557</td>\n",
+       "      <td>0.071293</td>\n",
+       "      <td>0.111057</td>\n",
+       "      <td>0.080088</td>\n",
+       "      <td>0.086720</td>\n",
+       "      <td>0.123460</td>\n",
+       "      <td>0.098469</td>\n",
+       "      <td>0.195224</td>\n",
+       "      <td>0.224138</td>\n",
+       "      <td>0.210252</td>\n",
+       "      <td>0.039789</td>\n",
+       "      <td>0.601805</td>\n",
+       "      <td>-0.565753</td>\n",
+       "      <td>0.112623</td>\n",
+       "      <td>-0.790501</td>\n",
+       "      <td>-0.763866</td>\n",
+       "      <td>-0.786949</td>\n",
+       "      <td>0.790674</td>\n",
+       "      <td>0.750135</td>\n",
+       "      <td>0.779741</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Access to electricity, urban (% of urban popul...</td>\n",
+       "      <td>0.900338</td>\n",
+       "      <td>0.815735</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.733129</td>\n",
+       "      <td>0.724329</td>\n",
+       "      <td>0.737436</td>\n",
+       "      <td>-0.031816</td>\n",
+       "      <td>0.154175</td>\n",
+       "      <td>0.089289</td>\n",
+       "      <td>-0.003022</td>\n",
+       "      <td>0.368258</td>\n",
+       "      <td>0.349837</td>\n",
+       "      <td>0.331598</td>\n",
+       "      <td>0.206007</td>\n",
+       "      <td>0.097781</td>\n",
+       "      <td>0.166410</td>\n",
+       "      <td>0.118767</td>\n",
+       "      <td>0.108439</td>\n",
+       "      <td>0.128157</td>\n",
+       "      <td>-0.027611</td>\n",
+       "      <td>0.112921</td>\n",
+       "      <td>0.276175</td>\n",
+       "      <td>-0.081406</td>\n",
+       "      <td>0.052398</td>\n",
+       "      <td>-0.241416</td>\n",
+       "      <td>-0.462255</td>\n",
+       "      <td>-0.068212</td>\n",
+       "      <td>0.184181</td>\n",
+       "      <td>0.104703</td>\n",
+       "      <td>-0.677207</td>\n",
+       "      <td>-0.690376</td>\n",
+       "      <td>0.443348</td>\n",
+       "      <td>-0.686689</td>\n",
+       "      <td>-0.142623</td>\n",
+       "      <td>0.057905</td>\n",
+       "      <td>0.155303</td>\n",
+       "      <td>0.263925</td>\n",
+       "      <td>-0.274161</td>\n",
+       "      <td>0.073052</td>\n",
+       "      <td>-0.291382</td>\n",
+       "      <td>0.064858</td>\n",
+       "      <td>-0.428305</td>\n",
+       "      <td>-0.066750</td>\n",
+       "      <td>-0.657957</td>\n",
+       "      <td>-0.044881</td>\n",
+       "      <td>0.114699</td>\n",
+       "      <td>0.060677</td>\n",
+       "      <td>0.047973</td>\n",
+       "      <td>0.109878</td>\n",
+       "      <td>0.248796</td>\n",
+       "      <td>0.132771</td>\n",
+       "      <td>0.138262</td>\n",
+       "      <td>0.137614</td>\n",
+       "      <td>0.235944</td>\n",
+       "      <td>0.097383</td>\n",
+       "      <td>0.075579</td>\n",
+       "      <td>-0.014718</td>\n",
+       "      <td>0.088693</td>\n",
+       "      <td>0.094327</td>\n",
+       "      <td>0.113816</td>\n",
+       "      <td>0.123744</td>\n",
+       "      <td>-0.041577</td>\n",
+       "      <td>-0.171477</td>\n",
+       "      <td>0.295788</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.638469</td>\n",
+       "      <td>-0.612671</td>\n",
+       "      <td>-0.634385</td>\n",
+       "      <td>0.131355</td>\n",
+       "      <td>0.133255</td>\n",
+       "      <td>0.412979</td>\n",
+       "      <td>-0.052931</td>\n",
+       "      <td>0.130609</td>\n",
+       "      <td>0.078862</td>\n",
+       "      <td>0.058456</td>\n",
+       "      <td>0.136064</td>\n",
+       "      <td>0.351330</td>\n",
+       "      <td>-0.085136</td>\n",
+       "      <td>0.171845</td>\n",
+       "      <td>-0.163707</td>\n",
+       "      <td>0.057812</td>\n",
+       "      <td>0.789128</td>\n",
+       "      <td>0.719375</td>\n",
+       "      <td>0.066413</td>\n",
+       "      <td>0.054298</td>\n",
+       "      <td>0.173104</td>\n",
+       "      <td>0.036701</td>\n",
+       "      <td>-0.033509</td>\n",
+       "      <td>0.072280</td>\n",
+       "      <td>0.089999</td>\n",
+       "      <td>0.139069</td>\n",
+       "      <td>0.105722</td>\n",
+       "      <td>0.117620</td>\n",
+       "      <td>-0.314668</td>\n",
+       "      <td>0.037412</td>\n",
+       "      <td>0.101960</td>\n",
+       "      <td>0.086292</td>\n",
+       "      <td>0.092608</td>\n",
+       "      <td>0.160571</td>\n",
+       "      <td>0.078418</td>\n",
+       "      <td>0.111946</td>\n",
+       "      <td>0.088856</td>\n",
+       "      <td>0.105551</td>\n",
+       "      <td>0.039999</td>\n",
+       "      <td>-0.272625</td>\n",
+       "      <td>0.141242</td>\n",
+       "      <td>-0.213146</td>\n",
+       "      <td>-0.104067</td>\n",
+       "      <td>0.150610</td>\n",
+       "      <td>-0.035177</td>\n",
+       "      <td>0.180917</td>\n",
+       "      <td>0.116864</td>\n",
+       "      <td>0.145090</td>\n",
+       "      <td>0.124721</td>\n",
+       "      <td>0.026533</td>\n",
+       "      <td>0.016846</td>\n",
+       "      <td>0.004535</td>\n",
+       "      <td>0.140961</td>\n",
+       "      <td>0.117002</td>\n",
+       "      <td>0.122609</td>\n",
+       "      <td>0.101500</td>\n",
+       "      <td>0.523818</td>\n",
+       "      <td>-0.447963</td>\n",
+       "      <td>0.129015</td>\n",
+       "      <td>-0.637635</td>\n",
+       "      <td>-0.620773</td>\n",
+       "      <td>-0.639211</td>\n",
+       "      <td>0.638469</td>\n",
+       "      <td>0.612671</td>\n",
+       "      <td>0.634385</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Adjusted net enrollment rate, primary (% of pr...</td>\n",
+       "      <td>0.778743</td>\n",
+       "      <td>0.710961</td>\n",
+       "      <td>0.733129</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.986786</td>\n",
+       "      <td>0.979660</td>\n",
+       "      <td>-0.043278</td>\n",
+       "      <td>0.146440</td>\n",
+       "      <td>0.054514</td>\n",
+       "      <td>-0.008870</td>\n",
+       "      <td>0.424795</td>\n",
+       "      <td>0.383135</td>\n",
+       "      <td>0.181438</td>\n",
+       "      <td>0.171375</td>\n",
+       "      <td>0.133369</td>\n",
+       "      <td>0.341817</td>\n",
+       "      <td>0.139552</td>\n",
+       "      <td>0.281205</td>\n",
+       "      <td>0.136543</td>\n",
+       "      <td>-0.040812</td>\n",
+       "      <td>0.122070</td>\n",
+       "      <td>0.194621</td>\n",
+       "      <td>-0.010766</td>\n",
+       "      <td>0.099075</td>\n",
+       "      <td>-0.219820</td>\n",
+       "      <td>-0.371080</td>\n",
+       "      <td>-0.062407</td>\n",
+       "      <td>0.039560</td>\n",
+       "      <td>0.187416</td>\n",
+       "      <td>-0.723223</td>\n",
+       "      <td>-0.697960</td>\n",
+       "      <td>0.486711</td>\n",
+       "      <td>-0.703345</td>\n",
+       "      <td>-0.075241</td>\n",
+       "      <td>-0.024204</td>\n",
+       "      <td>0.194741</td>\n",
+       "      <td>0.326738</td>\n",
+       "      <td>-0.264018</td>\n",
+       "      <td>-0.000853</td>\n",
+       "      <td>-0.304035</td>\n",
+       "      <td>0.008118</td>\n",
+       "      <td>-0.378115</td>\n",
+       "      <td>-0.068011</td>\n",
+       "      <td>-0.661277</td>\n",
+       "      <td>-0.056453</td>\n",
+       "      <td>0.056657</td>\n",
+       "      <td>0.060361</td>\n",
+       "      <td>0.053565</td>\n",
+       "      <td>0.092389</td>\n",
+       "      <td>0.296955</td>\n",
+       "      <td>0.152870</td>\n",
+       "      <td>0.159765</td>\n",
+       "      <td>0.153873</td>\n",
+       "      <td>0.326562</td>\n",
+       "      <td>0.105604</td>\n",
+       "      <td>0.151689</td>\n",
+       "      <td>-0.038088</td>\n",
+       "      <td>0.090317</td>\n",
+       "      <td>0.007147</td>\n",
+       "      <td>0.040852</td>\n",
+       "      <td>-0.038999</td>\n",
+       "      <td>-0.082783</td>\n",
+       "      <td>-0.306119</td>\n",
+       "      <td>0.267094</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.612831</td>\n",
+       "      <td>-0.603168</td>\n",
+       "      <td>-0.612722</td>\n",
+       "      <td>0.145752</td>\n",
+       "      <td>0.143817</td>\n",
+       "      <td>0.467512</td>\n",
+       "      <td>-0.062671</td>\n",
+       "      <td>0.140381</td>\n",
+       "      <td>0.079662</td>\n",
+       "      <td>0.068434</td>\n",
+       "      <td>0.155935</td>\n",
+       "      <td>0.387721</td>\n",
+       "      <td>-0.104080</td>\n",
+       "      <td>0.222250</td>\n",
+       "      <td>-0.049695</td>\n",
+       "      <td>-0.028384</td>\n",
+       "      <td>0.807704</td>\n",
+       "      <td>0.742608</td>\n",
+       "      <td>0.069702</td>\n",
+       "      <td>0.064036</td>\n",
+       "      <td>0.196931</td>\n",
+       "      <td>0.013903</td>\n",
+       "      <td>0.013865</td>\n",
+       "      <td>0.034723</td>\n",
+       "      <td>0.094920</td>\n",
+       "      <td>0.218278</td>\n",
+       "      <td>0.105137</td>\n",
+       "      <td>0.079983</td>\n",
+       "      <td>-0.256604</td>\n",
+       "      <td>0.104301</td>\n",
+       "      <td>0.164289</td>\n",
+       "      <td>0.047826</td>\n",
+       "      <td>0.149158</td>\n",
+       "      <td>0.185993</td>\n",
+       "      <td>0.113935</td>\n",
+       "      <td>0.098824</td>\n",
+       "      <td>0.094421</td>\n",
+       "      <td>0.103538</td>\n",
+       "      <td>-0.113182</td>\n",
+       "      <td>-0.378042</td>\n",
+       "      <td>0.004737</td>\n",
+       "      <td>-0.276108</td>\n",
+       "      <td>-0.003964</td>\n",
+       "      <td>0.203914</td>\n",
+       "      <td>0.056891</td>\n",
+       "      <td>0.246905</td>\n",
+       "      <td>0.095328</td>\n",
+       "      <td>0.109466</td>\n",
+       "      <td>0.100722</td>\n",
+       "      <td>0.022853</td>\n",
+       "      <td>0.059954</td>\n",
+       "      <td>0.037299</td>\n",
+       "      <td>0.141466</td>\n",
+       "      <td>0.168559</td>\n",
+       "      <td>0.157653</td>\n",
+       "      <td>0.083084</td>\n",
+       "      <td>0.579354</td>\n",
+       "      <td>-0.582496</td>\n",
+       "      <td>0.120715</td>\n",
+       "      <td>-0.613678</td>\n",
+       "      <td>-0.611295</td>\n",
+       "      <td>-0.617159</td>\n",
+       "      <td>0.612831</td>\n",
+       "      <td>0.603168</td>\n",
+       "      <td>0.612722</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Adjusted net enrollment rate, primary, female ...</td>\n",
+       "      <td>0.788319</td>\n",
+       "      <td>0.727187</td>\n",
+       "      <td>0.724329</td>\n",
+       "      <td>0.986786</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.934241</td>\n",
+       "      <td>-0.048493</td>\n",
+       "      <td>0.157943</td>\n",
+       "      <td>0.052355</td>\n",
+       "      <td>-0.013479</td>\n",
+       "      <td>0.439902</td>\n",
+       "      <td>0.402792</td>\n",
+       "      <td>0.178952</td>\n",
+       "      <td>0.189117</td>\n",
+       "      <td>0.139185</td>\n",
+       "      <td>0.358926</td>\n",
+       "      <td>0.141666</td>\n",
+       "      <td>0.309653</td>\n",
+       "      <td>0.143333</td>\n",
+       "      <td>-0.049903</td>\n",
+       "      <td>0.125167</td>\n",
+       "      <td>0.201943</td>\n",
+       "      <td>0.020420</td>\n",
+       "      <td>0.111724</td>\n",
+       "      <td>-0.210799</td>\n",
+       "      <td>-0.352545</td>\n",
+       "      <td>-0.059250</td>\n",
+       "      <td>0.035615</td>\n",
+       "      <td>0.190948</td>\n",
+       "      <td>-0.731950</td>\n",
+       "      <td>-0.726983</td>\n",
+       "      <td>0.508373</td>\n",
+       "      <td>-0.732711</td>\n",
+       "      <td>-0.065738</td>\n",
+       "      <td>-0.029544</td>\n",
+       "      <td>0.215352</td>\n",
+       "      <td>0.359209</td>\n",
+       "      <td>-0.257617</td>\n",
+       "      <td>-0.015972</td>\n",
+       "      <td>-0.298082</td>\n",
+       "      <td>0.003919</td>\n",
+       "      <td>-0.402923</td>\n",
+       "      <td>-0.118220</td>\n",
+       "      <td>-0.687455</td>\n",
+       "      <td>-0.071067</td>\n",
+       "      <td>0.052911</td>\n",
+       "      <td>0.043272</td>\n",
+       "      <td>0.039616</td>\n",
+       "      <td>0.099436</td>\n",
+       "      <td>0.302401</td>\n",
+       "      <td>0.168348</td>\n",
+       "      <td>0.174634</td>\n",
+       "      <td>0.168330</td>\n",
+       "      <td>0.336953</td>\n",
+       "      <td>0.118123</td>\n",
+       "      <td>0.123931</td>\n",
+       "      <td>-0.039366</td>\n",
+       "      <td>0.077602</td>\n",
+       "      <td>-0.018578</td>\n",
+       "      <td>0.035569</td>\n",
+       "      <td>-0.061950</td>\n",
+       "      <td>-0.046145</td>\n",
+       "      <td>-0.299525</td>\n",
+       "      <td>0.284926</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.649772</td>\n",
+       "      <td>-0.639764</td>\n",
+       "      <td>-0.649406</td>\n",
+       "      <td>0.160246</td>\n",
+       "      <td>0.156929</td>\n",
+       "      <td>0.482209</td>\n",
+       "      <td>-0.048860</td>\n",
+       "      <td>0.145967</td>\n",
+       "      <td>0.062230</td>\n",
+       "      <td>0.057727</td>\n",
+       "      <td>0.166750</td>\n",
+       "      <td>0.396028</td>\n",
+       "      <td>-0.082077</td>\n",
+       "      <td>0.244498</td>\n",
+       "      <td>-0.046021</td>\n",
+       "      <td>-0.038213</td>\n",
+       "      <td>0.819346</td>\n",
+       "      <td>0.748711</td>\n",
+       "      <td>0.066098</td>\n",
+       "      <td>0.058439</td>\n",
+       "      <td>0.202800</td>\n",
+       "      <td>0.009741</td>\n",
+       "      <td>0.014190</td>\n",
+       "      <td>0.032908</td>\n",
+       "      <td>0.107950</td>\n",
+       "      <td>0.237039</td>\n",
+       "      <td>0.112658</td>\n",
+       "      <td>0.083325</td>\n",
+       "      <td>-0.255822</td>\n",
+       "      <td>0.137900</td>\n",
+       "      <td>0.167890</td>\n",
+       "      <td>0.058591</td>\n",
+       "      <td>0.155298</td>\n",
+       "      <td>0.230228</td>\n",
+       "      <td>0.138319</td>\n",
+       "      <td>0.116490</td>\n",
+       "      <td>0.112381</td>\n",
+       "      <td>0.120923</td>\n",
+       "      <td>-0.111071</td>\n",
+       "      <td>-0.436075</td>\n",
+       "      <td>0.001679</td>\n",
+       "      <td>-0.324034</td>\n",
+       "      <td>0.004212</td>\n",
+       "      <td>0.256274</td>\n",
+       "      <td>0.064690</td>\n",
+       "      <td>0.299591</td>\n",
+       "      <td>0.098413</td>\n",
+       "      <td>0.102713</td>\n",
+       "      <td>0.102086</td>\n",
+       "      <td>0.038924</td>\n",
+       "      <td>0.082295</td>\n",
+       "      <td>0.062243</td>\n",
+       "      <td>0.165724</td>\n",
+       "      <td>0.188684</td>\n",
+       "      <td>0.182778</td>\n",
+       "      <td>0.084568</td>\n",
+       "      <td>0.608055</td>\n",
+       "      <td>-0.597672</td>\n",
+       "      <td>0.129998</td>\n",
+       "      <td>-0.651126</td>\n",
+       "      <td>-0.645804</td>\n",
+       "      <td>-0.652258</td>\n",
+       "      <td>0.649772</td>\n",
+       "      <td>0.639764</td>\n",
+       "      <td>0.649406</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                      indicator_name                                       ...                                         Wage and salaried workers, total (% of total employment) (modeled ILO estimate)\n",
+       "0            Access to electricity (% of population)                                       ...                                                                                  0.785969                              \n",
+       "1  Access to electricity, rural (% of rural popul...                                       ...                                                                                  0.779741                              \n",
+       "2  Access to electricity, urban (% of urban popul...                                       ...                                                                                  0.634385                              \n",
+       "3  Adjusted net enrollment rate, primary (% of pr...                                       ...                                                                                  0.612722                              \n",
+       "4  Adjusted net enrollment rate, primary, female ...                                       ...                                                                                  0.649406                              \n",
+       "\n",
+       "[5 rows x 838 columns]"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "df = pd.read_csv('C:/Users/willk/Downloads/corrs.csv')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
    "cell_type": "markdown",
    "metadata": {},
    "source": [

plotly/military_data.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c30ba6d1467c293683f3f6c64da3f064cb75b6ae6c98d69cd1fa9e4e55999e07
+size 9134

testing-exercises.ipynb

@@ -1,670 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2019-02-23T16:38:38.465675Z",
-     "start_time": "2019-02-23T16:38:38.422180Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
-      ],
-      "text/vnd.plotly.v1+html": [
-       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
-      ],
-      "text/vnd.plotly.v1+html": [
-       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "\n",
-    "%load_ext autoreload\n",
-    "%autoreload 2\n",
-    "\n",
-    "import sys\n",
-    "sys.path.append('../..')\n",
-    "\n",
-    "# Options for pandas\n",
-    "pd.options.display.max_columns = 20\n",
-    "pd.options.display.max_rows = 10\n",
-    "\n",
-    "# Display all cell outputs\n",
-    "from IPython.core.interactiveshell import InteractiveShell\n",
-    "InteractiveShell.ast_node_interactivity = 'all'\n",
-    "\n",
-    "import plotly.plotly as py\n",
-    "import plotly.graph_objs as go\n",
-    "from plotly.offline import iplot, init_notebook_mode\n",
-    "init_notebook_mode(connected=True)\n",
-    "\n",
-    "import cufflinks\n",
-    "cf.go_offline(connected=True)\n",
-    "cf.set_config_file(theme='pearl')\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2019-02-23T16:39:00.607978Z",
-     "start_time": "2019-02-23T16:39:00.567876Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1000, 100)"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = pd.DataFrame(np.random.randn(1000, 100))\n",
-    "df.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2019-02-23T16:39:16.845392Z",
-     "start_time": "2019-02-23T16:39:16.774748Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(100, 100)"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "corrs = df.corr()\n",
-    "corrs.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2019-02-23T16:42:26.032480Z",
-     "start_time": "2019-02-23T16:42:25.998079Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([ 3,  8, 44, 45, 54, 96]), array([54, 96, 45, 44,  3,  8]))"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "threshold = -0.1\n",
-    "direction = 'less'\n",
-    "\n",
-    "if direction == 'greater':\n",
-    "    values_index = np.where(corrs > threshold)\n",
-    "elif direction == 'less':\n",
-    "    values_index = np.where(corrs < threshold)\n",
-    "    \n",
-    "values_index"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2019-02-23T16:47:56.940313Z",
-     "start_time": "2019-02-23T16:47:56.909882Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "rows_index = values_index[0]\n",
-    "columns_index = values_index[1]\n",
-    "\n",
-    "pairs = list(map(tuple, set([frozenset((x, y)) for x, y in zip(rows_index, columns_index)])))\n",
-    "\n",
-    "from collections import Counter\n",
-    "\n",
-    "# Counter(pairs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2019-02-23T16:47:57.429941Z",
-     "start_time": "2019-02-23T16:47:57.397928Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(3, 54), (8, 96), (44, 45)]"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pairs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2019-02-23T17:04:56.074717Z",
-     "start_time": "2019-02-23T17:04:56.041811Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "subset_df = pd.DataFrame(dict(value=corrs.values[values_index], var1=corrs.index[values_index[0]],\n",
-    "                         var2=corrs.columns[values_index[1]]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2019-02-24T20:36:52.221603Z",
-     "start_time": "2019-02-24T20:36:52.182531Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\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",
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-       "      <th>0</th>\n",
-       "      <td>-0.111172</td>\n",
-       "      <td>3</td>\n",
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-       "      <th>1</th>\n",
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-       "      <td>8</td>\n",
-       "      <td>96</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>-0.104640</td>\n",
-       "      <td>44</td>\n",
-       "      <td>45</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      value  var1  var2\n",
-       "0 -0.111172     3    54\n",
-       "1 -0.117402     8    96\n",
-       "2 -0.104640    44    45"
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "subset_df.iloc[:int(len(subset_df)/2)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2019-02-23T17:05:53.369263Z",
-     "start_time": "2019-02-23T17:05:53.337720Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "var1  var2\n",
-       "3     54      1\n",
-       "8     96      1\n",
-       "44    45      1\n",
-       "45    44      1\n",
-       "54    3       1\n",
-       "96    8       1\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "subset_df.groupby(['var1', 'var2']).size()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2019-02-24T14:57:30.059725Z",
-     "start_time": "2019-02-24T14:57:30.027029Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['value', 'variable1', 'var2'], dtype='object')"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "subset_df.columns.str.replace('var1', 'variable1')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2019-02-23T16:50:24.548345Z",
-     "start_time": "2019-02-23T16:50:24.513301Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "values = []; indices = []; columns = []\n",
-    "\n",
-    "for pair in pairs:\n",
-    "    indices.append(corrs.index[pair[0]])\n",
-    "    columns.append(corrs.columns[pair[1]])\n",
-    "    values.append(corrs.values[pair])\n",
-    "    \n",
-    "subset_df = pd.DataFrame(dict(value=values, var1=indices, var2=columns))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2019-02-23T16:50:25.388032Z",
-     "start_time": "2019-02-23T16:50:25.352969Z"
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
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-       "      <th></th>\n",
-       "      <th>value</th>\n",
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-       "      <th>0</th>\n",
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-       "      <td>3</td>\n",
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-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>-0.117402</td>\n",
-       "      <td>8</td>\n",
-       "      <td>96</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>-0.104640</td>\n",
-       "      <td>44</td>\n",
-       "      <td>45</td>\n",
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-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      value  var1  var2\n",
-       "0 -0.111172     3    54\n",
-       "1 -0.117402     8    96\n",
-       "2 -0.104640    44    45"
-      ]
-     },
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-     "metadata": {},
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-    "subset_df"
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-  },
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-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2019-02-23T16:50:35.252569Z",
-     "start_time": "2019-02-23T16:50:35.217497Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-0.11117190190235929"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
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-    "corrs.loc[3, 54]"
-   ]
-  },
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-   "cell_type": "code",
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-     "start_time": "2019-02-23T16:50:55.096675Z"
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-     "data": {
-      "text/plain": [
-       "-0.11117190190235929"
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-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "corrs.loc[54, 3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2019-02-23T16:51:03.603309Z",
-     "start_time": "2019-02-23T16:51:03.569575Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-0.11740191658722447"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
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-    }
-   ],
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-    "corrs.loc[96, 8]"
-   ]
-  },
-  {
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-   "execution_count": 45,
-   "metadata": {
-    "ExecuteTime": {
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-     "start_time": "2019-02-23T16:51:12.472562Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-0.10463995106844964"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
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-    }
-   ],
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-    "corrs.loc[44, 45]"
-   ]
-  },
-  {
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-   "outputs": [
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-     "data": {
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-       "-0.11117190190235929"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
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-   "source": [
-    "corrs.values[(3, 54)]"
-   ]
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-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2019-02-23T16:43:58.225875Z",
-     "start_time": "2019-02-23T16:43:58.190963Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(3, 54), (8, 96), (44, 45)]"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
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-    }
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-  "kernelspec": {
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-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.5"
-  },
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-   "title_cell": "Table of Contents",
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