Add crosswalk examples to notebook

Author: dshemetov

_delphi_utils_python/data_proc/geomap/geo_util_test_design.ipynb

@@ -316,6 +316,38 @@
     "df"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's do ZIP to MSA. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "         msa    zip    weight\n0      10180  76437  0.009598\n1      10180  76443  0.130316\n2      10180  76464  0.002363\n3      10180  76469  0.007088\n4      10180  79501  0.174636\n...      ...    ...       ...\n20893  49740  85352  0.002355\n20894  49740  85356  0.023188\n20895  49740  85364  0.382649\n20896  49740  85365  0.237843\n20897  49740  85367  0.104362\n\n[20898 rows x 3 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>msa</th>\n      <th>zip</th>\n      <th>weight</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>10180</td>\n      <td>76437</td>\n      <td>0.009598</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>10180</td>\n      <td>76443</td>\n      <td>0.130316</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>10180</td>\n      <td>76464</td>\n      <td>0.002363</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>10180</td>\n      <td>76469</td>\n      <td>0.007088</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>10180</td>\n      <td>79501</td>\n      <td>0.174636</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>20893</th>\n      <td>49740</td>\n      <td>85352</td>\n      <td>0.002355</td>\n    </tr>\n    <tr>\n      <th>20894</th>\n      <td>49740</td>\n      <td>85356</td>\n      <td>0.023188</td>\n    </tr>\n    <tr>\n      <th>20895</th>\n      <td>49740</td>\n      <td>85364</td>\n      <td>0.382649</td>\n    </tr>\n    <tr>\n      <th>20896</th>\n      <td>49740</td>\n      <td>85365</td>\n      <td>0.237843</td>\n    </tr>\n    <tr>\n      <th>20897</th>\n      <td>49740</td>\n      <td>85367</td>\n      <td>0.104362</td>\n    </tr>\n  </tbody>\n</table>\n<p>20898 rows × 3 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 68
+    }
+   ],
+   "source": [
+    "FIPS_MSA_OUT_FILENAME = \"fips_msa_table.csv\"\n",
+    "ZIP_FIPS_OUT_FILENAME = \"zip_fips_table.csv\"\n",
+    "\n",
+    "zf_df = pd.read_csv(join(OUTPUT_DIR, ZIP_FIPS_OUT_FILENAME), dtype={\"zip\":str, \"fips\":str, \"weight\": float})\n",
+    "fm_df = pd.read_csv(join(OUTPUT_DIR, FIPS_MSA_OUT_FILENAME), dtype={\"fips\":str, \"msa\":str})\n",
+    "\n",
+    "zf_df.merge(fm_df, on=\"fips\").drop(columns=\"fips\").groupby([\"msa\", \"zip\"]).sum().reset_index()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},