diff --git a/cartoframes/context.py b/cartoframes/context.py
index b99e4bddf..c3ff23760 100644
--- a/cartoframes/context.py
+++ b/cartoframes/context.py
@@ -12,6 +12,8 @@
 import sys
 import time
 import collections
+import binascii as ba
+from warnings import warn
 
 import requests
 import IPython
@@ -116,7 +118,7 @@ def __init__(self, base_url=None, api_key=None, session=None, verbose=0):
         self._verbose = verbose
 
 
-    def read(self, table_name, limit=None, index='cartodb_id'):
+    def read(self, table_name, limit=None, index='cartodb_id', decode_geom=True):
         """Read tables from CARTO into pandas DataFrames.
 
         Example:
@@ -143,11 +145,11 @@ def read(self, table_name, limit=None, index='cartodb_id'):
             else:
                 raise ValueError("`limit` parameter must an integer >= 0")
 
-        return self.query(query)
+        return self.query(query, decode_geom=decode_geom)
 
 
     def write(self, df, table_name, temp_dir='/tmp', overwrite=False,
-              lnglat=None):
+              lnglat=None, encode_geom=True, geom_col=None):
         """Write a DataFrame to a CARTO table.
 
         Example:
@@ -172,6 +174,27 @@ def write(self, df, table_name, temp_dir='/tmp', overwrite=False,
         Returns:
             None
         """
+        if encode_geom:
+            # None if not a GeoDataFrame
+            is_geopandas = getattr(df, '_geometry_column_name')
+            if is_geopandas is None and geom_col is None:
+                geom_col = df.get('geometry')
+                if geom_col is None:
+                    raise KeyError('Geometries were requested to be encoded '
+                                   'but `{geom_col}` was not found in the '
+                                   'DataFrame and no default geometry column '
+                                   'was set.'.format(geom_col=geom_col))
+            elif is_geopandas is not None and geom_col is not None:
+                warn('Geometry column of the input DataFrame does not '
+                     'match the geometry column supplied. Using user-supplied '
+                     'column...\n'
+                     '\tGeopandas geometry column: {}\n'
+                     '\tSupplied `geom_col`: {}'.format(is_geopandas,
+                                                        geom_col))
+            elif is_geopandas is not None and geom_col is None:
+                geom_col = is_geopandas
+            df['the_geom'] = df[geom_col].apply(_encode_geom)
+
         table_exists = True
         if not overwrite:
             try:
@@ -196,7 +219,8 @@ def remove_tempfile():
             """removes temporary file"""
             os.remove(tempfile)
 
-        df.to_csv(tempfile)
+        # reset DataFrame before sending to CARTO
+        df.drop(geom_col, axis=1, errors='ignore').to_csv(tempfile)
 
         with open(tempfile, 'rb') as f:
             res = self._auth_send('api/v1/imports', 'POST',
@@ -260,7 +284,7 @@ def sync(self, dataframe, table_name):
         pass
 
 
-    def query(self, query, table_name=None):
+    def query(self, query, table_name=None, decode_geom=True):
         """Pull the result from an arbitrary SQL query from a CARTO account
         into a pandas DataFrame. Can also be used to perform database
         operations (creating/dropping tables, adding columns, updates, etc.).
@@ -321,6 +345,9 @@ def query(self, query, table_name=None):
             columns=[k for k in fields]).astype(schema)
         if 'cartodb_id' in fields:
             df.set_index('cartodb_id', inplace=True)
+
+        if decode_geom:
+            df['geometry'] = df.the_geom.apply(_decode_geom)
         return df
 
 
@@ -708,15 +735,17 @@ def _get_bounds(self, layers):
              if not layer.is_basemap])
 
         extent = self.query('''
-               SELECT
-                 ST_XMIN(ext) AS west,
-                 ST_YMIN(ext) AS south,
-                 ST_XMAX(ext) AS east,
-                 ST_YMAX(ext) AS north
-               FROM (
-                   SELECT st_extent(the_geom) AS ext
-                   FROM ({union_query}) AS wrap1
-               ) AS wrap2'''.format(union_query=union_query))
+                       SELECT
+                         ST_XMIN(ext) AS west,
+                         ST_YMIN(ext) AS south,
+                         ST_XMAX(ext) AS east,
+                         ST_YMAX(ext) AS north
+                       FROM (
+                           SELECT st_extent(the_geom) AS ext
+                           FROM ({union_query}) AS _wrap1
+                       ) AS _wrap2
+                            '''.format(union_query=union_query),
+                            decode_geom=False)
 
         west, south, east, north = extent.values[0]
 
@@ -742,3 +771,18 @@ def _debug_print(self, **kwargs):
                 str_value = '{}\n\n...\n\n{}'.format(str_value[:250], str_value[-50:])
             print('{key}: {value}'.format(key=key,
                                           value=str_value))
+
+
+def _encode_geom(geom):
+    """
+    Encode geometries into hex-encoded wkb
+    """
+    from shapely import wkb
+    return ba.hexlify(wkb.dumps(geom)).decode()
+
+def _decode_geom(ewkb):
+    """
+    Decode encoded wkb into a shapely geometry
+    """
+    from shapely import wkb
+    return wkb.loads(ba.unhexlify(ewkb))
diff --git a/examples/Shapely, Geopandas, and Cartoframes.ipynb b/examples/Shapely, Geopandas, and Cartoframes.ipynb
new file mode 100644
index 000000000..f8f2d6986
--- /dev/null
+++ b/examples/Shapely, Geopandas, and Cartoframes.ipynb	
@@ -0,0 +1,913 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Shapely, Geopandas, and CartoFrames\n",
+    "\n",
+    "Here, you can see the relationship between cartoframes and geopandas."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cartoframes as cf\n",
+    "import geopandas as gpd\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cxn = cf.CartoContext() # remember, store your credentials \n",
+    "                        # using cf.credentials.set_credentials!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "I have an example dataset in my CARTO account which contains some data on criminology from 1960 onwards at the county level. I'll load that now using the standard read function from cartoframes:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = cxn.read('nat')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>the_geom</th>\n",
+       "      <th>the_geom_webmercator</th>\n",
+       "      <th>name</th>\n",
+       "      <th>state_name</th>\n",
+       "      <th>state_fips</th>\n",
+       "      <th>cnty_fips</th>\n",
+       "      <th>fips</th>\n",
+       "      <th>stfips</th>\n",
+       "      <th>cofips</th>\n",
+       "      <th>fipsno</th>\n",
+       "      <th>...</th>\n",
+       "      <th>blk90</th>\n",
+       "      <th>gi59</th>\n",
+       "      <th>gi69</th>\n",
+       "      <th>gi79</th>\n",
+       "      <th>gi89</th>\n",
+       "      <th>fh60</th>\n",
+       "      <th>fh70</th>\n",
+       "      <th>fh80</th>\n",
+       "      <th>fh90</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>cartodb_id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>364</th>\n",
+       "      <td>0106000020E61000000100000001030000000100000007...</td>\n",
+       "      <td>0106000020110F00000100000001030000000100000007...</td>\n",
+       "      <td>Murray</td>\n",
+       "      <td>Minnesota</td>\n",
+       "      <td>27</td>\n",
+       "      <td>101</td>\n",
+       "      <td>27101</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>101.0</td>\n",
+       "      <td>27101.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.340082</td>\n",
+       "      <td>0.414127</td>\n",
+       "      <td>0.382432</td>\n",
+       "      <td>0.364256</td>\n",
+       "      <td>9.146341</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>5.566097</td>\n",
+       "      <td>4.782767</td>\n",
+       "      <td>(POLYGON ((-95.46772003173828 44.1970138549804...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1660</th>\n",
+       "      <td>0106000020E61000000100000001030000000100000006...</td>\n",
+       "      <td>0106000020110F00000100000001030000000100000006...</td>\n",
+       "      <td>Chautauqua</td>\n",
+       "      <td>Kansas</td>\n",
+       "      <td>20</td>\n",
+       "      <td>019</td>\n",
+       "      <td>20019</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>20019.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.521897</td>\n",
+       "      <td>0.368538</td>\n",
+       "      <td>0.437940</td>\n",
+       "      <td>0.401030</td>\n",
+       "      <td>0.395566</td>\n",
+       "      <td>9.501188</td>\n",
+       "      <td>6.8</td>\n",
+       "      <td>7.728495</td>\n",
+       "      <td>8.073541</td>\n",
+       "      <td>(POLYGON ((-96.00579071044922 36.9982643127441...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115</th>\n",
+       "      <td>0106000020E6100000010000000103000000010000000A...</td>\n",
+       "      <td>0106000020110F0000010000000103000000010000000A...</td>\n",
+       "      <td>Becker</td>\n",
+       "      <td>Minnesota</td>\n",
+       "      <td>27</td>\n",
+       "      <td>005</td>\n",
+       "      <td>27005</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>27005.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.071733</td>\n",
+       "      <td>0.328578</td>\n",
+       "      <td>0.390693</td>\n",
+       "      <td>0.374987</td>\n",
+       "      <td>0.375178</td>\n",
+       "      <td>12.385242</td>\n",
+       "      <td>7.2</td>\n",
+       "      <td>8.790072</td>\n",
+       "      <td>11.961039</td>\n",
+       "      <td>(POLYGON ((-95.16522216796874 46.7163505554199...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55</th>\n",
+       "      <td>0106000020E6100000010000000103000000010000000A...</td>\n",
+       "      <td>0106000020110F0000010000000103000000010000000A...</td>\n",
+       "      <td>Pennington</td>\n",
+       "      <td>Minnesota</td>\n",
+       "      <td>27</td>\n",
+       "      <td>113</td>\n",
+       "      <td>27113</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>113.0</td>\n",
+       "      <td>27113.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.082669</td>\n",
+       "      <td>0.264746</td>\n",
+       "      <td>0.361857</td>\n",
+       "      <td>0.357659</td>\n",
+       "      <td>0.370818</td>\n",
+       "      <td>11.554969</td>\n",
+       "      <td>7.1</td>\n",
+       "      <td>10.775000</td>\n",
+       "      <td>10.980841</td>\n",
+       "      <td>(POLYGON ((-96.48169708251953 47.9642181396484...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>0106000020E61000000100000001030000000100000035...</td>\n",
+       "      <td>0106000020110F00000100000001030000000100000035...</td>\n",
+       "      <td>Shoshone</td>\n",
+       "      <td>Idaho</td>\n",
+       "      <td>16</td>\n",
+       "      <td>079</td>\n",
+       "      <td>16079</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>79.0</td>\n",
+       "      <td>16079.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.114852</td>\n",
+       "      <td>0.185961</td>\n",
+       "      <td>0.296188</td>\n",
+       "      <td>0.328998</td>\n",
+       "      <td>0.364126</td>\n",
+       "      <td>7.810136</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>8.762984</td>\n",
+       "      <td>12.869565</td>\n",
+       "      <td>(POLYGON ((-114.9637680053711 46.92529296875, ...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 72 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                     the_geom  \\\n",
+       "cartodb_id                                                      \n",
+       "364         0106000020E61000000100000001030000000100000007...   \n",
+       "1660        0106000020E61000000100000001030000000100000006...   \n",
+       "115         0106000020E6100000010000000103000000010000000A...   \n",
+       "55          0106000020E6100000010000000103000000010000000A...   \n",
+       "62          0106000020E61000000100000001030000000100000035...   \n",
+       "\n",
+       "                                         the_geom_webmercator        name  \\\n",
+       "cartodb_id                                                                  \n",
+       "364         0106000020110F00000100000001030000000100000007...      Murray   \n",
+       "1660        0106000020110F00000100000001030000000100000006...  Chautauqua   \n",
+       "115         0106000020110F0000010000000103000000010000000A...      Becker   \n",
+       "55          0106000020110F0000010000000103000000010000000A...  Pennington   \n",
+       "62          0106000020110F00000100000001030000000100000035...    Shoshone   \n",
+       "\n",
+       "           state_name state_fips cnty_fips   fips  stfips  cofips   fipsno  \\\n",
+       "cartodb_id                                                                   \n",
+       "364         Minnesota         27       101  27101    27.0   101.0  27101.0   \n",
+       "1660           Kansas         20       019  20019    20.0    19.0  20019.0   \n",
+       "115         Minnesota         27       005  27005    27.0     5.0  27005.0   \n",
+       "55          Minnesota         27       113  27113    27.0   113.0  27113.0   \n",
+       "62              Idaho         16       079  16079    16.0    79.0  16079.0   \n",
+       "\n",
+       "                                  ...                             blk90  \\\n",
+       "cartodb_id                        ...                                     \n",
+       "364                               ...                          0.000000   \n",
+       "1660                              ...                          0.521897   \n",
+       "115                               ...                          0.071733   \n",
+       "55                                ...                          0.082669   \n",
+       "62                                ...                          0.114852   \n",
+       "\n",
+       "                gi59      gi69      gi79      gi89       fh60  fh70  \\\n",
+       "cartodb_id                                                            \n",
+       "364         0.340082  0.414127  0.382432  0.364256   9.146341   5.1   \n",
+       "1660        0.368538  0.437940  0.401030  0.395566   9.501188   6.8   \n",
+       "115         0.328578  0.390693  0.374987  0.375178  12.385242   7.2   \n",
+       "55          0.264746  0.361857  0.357659  0.370818  11.554969   7.1   \n",
+       "62          0.185961  0.296188  0.328998  0.364126   7.810136   5.4   \n",
+       "\n",
+       "                 fh80       fh90  \\\n",
+       "cartodb_id                         \n",
+       "364          5.566097   4.782767   \n",
+       "1660         7.728495   8.073541   \n",
+       "115          8.790072  11.961039   \n",
+       "55          10.775000  10.980841   \n",
+       "62           8.762984  12.869565   \n",
+       "\n",
+       "                                                     geometry  \n",
+       "cartodb_id                                                     \n",
+       "364         (POLYGON ((-95.46772003173828 44.1970138549804...  \n",
+       "1660        (POLYGON ((-96.00579071044922 36.9982643127441...  \n",
+       "115         (POLYGON ((-95.16522216796874 46.7163505554199...  \n",
+       "55          (POLYGON ((-96.48169708251953 47.9642181396484...  \n",
+       "62          (POLYGON ((-114.9637680053711 46.92529296875, ...  \n",
+       "\n",
+       "[5 rows x 72 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, by default, CARTO uses the typical Well-known-binary (WKB) serialization for geometries that come out of PostGIS. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>fipsno</th>\n",
+       "      <th>the_geom</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>cartodb_id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>364</th>\n",
+       "      <td>27101.0</td>\n",
+       "      <td>0106000020E61000000100000001030000000100000007...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1660</th>\n",
+       "      <td>20019.0</td>\n",
+       "      <td>0106000020E61000000100000001030000000100000006...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             fipsno                                           the_geom\n",
+       "cartodb_id                                                            \n",
+       "364         27101.0  0106000020E61000000100000001030000000100000007...\n",
+       "1660        20019.0  0106000020E61000000100000001030000000100000006..."
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(2)[['fipsno', 'the_geom']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With [PR#86](https://github.com/CartoDB/cartoframes/pull/86), these strings are now deserialized into `shapely` objects! This means a dataframe from `cartoframes` is almost directly mapped to `Geopandas` dataframes. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>fipsno</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>cartodb_id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>364</th>\n",
+       "      <td>27101.0</td>\n",
+       "      <td>(POLYGON ((-95.46772003173828 44.1970138549804...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1660</th>\n",
+       "      <td>20019.0</td>\n",
+       "      <td>(POLYGON ((-96.00579071044922 36.9982643127441...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             fipsno                                           geometry\n",
+       "cartodb_id                                                            \n",
+       "364         27101.0  (POLYGON ((-95.46772003173828 44.1970138549804...\n",
+       "1660        20019.0  (POLYGON ((-96.00579071044922 36.9982643127441..."
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(2)[['fipsno', 'geometry']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This allows you to do GIS operations locally in Geopandas. To send a dataframe with shapely geometries into a Geopandas DataFrame, you only need to call the constructor directly on the DataFrame:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x10efd0c88>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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UxlxaPUe712S//BjZKTPWxljHyrke5h8yHY0o5bfndu9xWRkCtNsVVkJZqs0y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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10efd0668>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "gdf = gpd.GeoDataFrame(df)\n",
+    "gdf.plot('hr90', linewidth=.1) # to prove we're in geopandas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The nice thing with having the code to serialize/deserialize `shapely` objects is that you can publish directly to CARTO (and make CARTO maps) directly from (geo)pandas:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<iframe srcdoc=\"<!DOCTYPE html>\n",
+       "<html>\n",
+       "  <head>\n",
+       "    <title>Carto</title>\n",
+       "    <meta name='viewport' content='initial-scale=1.0, user-scalable=no' />\n",
+       "    <meta http-equiv='content-type' content='text/html; charset=UTF-8' />\n",
+       "    <link rel='shortcut icon' href='http://cartodb.com/assets/favicon.ico' />\n",
+       "\n",
+       "    <style>\n",
+       "     html, body, #map {\n",
+       "       height: 100%;\n",
+       "       padding: 0;\n",
+       "       margin: 0;\n",
+       "     }\n",
+       "     #zoom-center {\n",
+       "       position: absolute;\n",
+       "       right: 0;\n",
+       "       top: 0;\n",
+       "       background-color: rgba(255, 255, 255, 0.7);\n",
+       "       width: 240px;\n",
+       "       z-index: 100;\n",
+       "       padding: 4px;\n",
+       "     }\n",
+       "    </style>\n",
+       "\n",
+       "    <link rel='stylesheet' href='https://cartodb-libs.global.ssl.fastly.net/cartodb.js/v3/3.15/themes/css/cartodb.css' />\n",
+       "  </head>\n",
+       "  <body>\n",
+       "    <div id='zoom-center'>\n",
+       "      zoom=<span id='zoom'>4</span>,\n",
+       "      lng=<span id='lon'>No data</span>, lat=<span id='lat'>No data</span></div>\n",
+       "    <div id='map'></div>\n",
+       "    <script src='https://cartodb-libs.global.ssl.fastly.net/cartodb.js/v3/3.15/cartodb.js'></script>\n",
+       "\n",
+       "    <script>\n",
+       "     const config  = {'user_name': 'eschbacher', 'maps_api_template': 'https://eschbacher.carto.com', 'sql_api_template': 'https://eschbacher.carto.com', 'tiler_protocol': 'https', 'tiler_domain': 'carto.com', 'tiler_port': '80', 'type': 'namedmap', 'named_map': {'name': 'cartoframes_ver20170406_layers1_time0_baseid1_labels0_zoom0', 'params': {'basemap_url': 'https://cartodb-basemaps-{s}.global.ssl.fastly.net/dark_all/{z}/{x}/{y}.png', 'cartocss_0': &quot;#layer[&#92;'mapnik::geometry_type&#92;'=1] {  marker-width: 10; marker-fill: ramp([hr90], cartocolor(Sunset), quantiles(7)); marker-fill-opacity: 1; marker-allow-overlap: true; marker-line-width: 0.5; marker-line-color: #000; marker-line-opacity: 1;} #layer[&#92;'mapnik::geometry_type&#92;'=2] {  line-width: 1.5; line-color: ramp([hr90], cartocolor(Sunset), quantiles(7));} #layer[&#92;'mapnik::geometry_type&#92;'=3] {  polygon-fill: ramp([hr90], cartocolor(Sunset), quantiles(7)); polygon-opacity: 0.9; polygon-gamma: 0.5; line-color: #FFF; line-width: 0.5; line-opacity: 0.25; line-comp-op: hard-light;} &quot;, 'sql_0': 'SELECT * FROM nat', 'west': -124.73142242431599, 'south': 24.955966949462901, 'east': -66.9698486328125, 'north': 49.371734619140597}}};\n",
+       "     const bounds  = [[49.371734619140597, -66.9698486328125], [24.955966949462901, -124.73142242431599]];\n",
+       "     const options = {'filter': ['http', 'mapnik', 'torque'], 'https': true};\n",
+       "\n",
+       "     const adjustLongitude = (lng) => (\n",
+       "       lng - ((Math.ceil((lng + 180) / 360) - 1) * 360)\n",
+       "     );\n",
+       "     const map = L.map('map', {\n",
+       "       zoom: 3,\n",
+       "       center: [0, 0],\n",
+       "     });\n",
+       "     const updateMapInfo = () => {\n",
+       "       $('#zoom').text(map.getZoom());\n",
+       "       $('#lat').text(map.getCenter().lat.toFixed(4));\n",
+       "       $('#lon').text(adjustLongitude(map.getCenter().lng).toFixed(4));\n",
+       "     };\n",
+       "\n",
+       "     cartodb.createLayer(map, config, options)\n",
+       "            .addTo(map)\n",
+       "            .done((layer) => {\n",
+       "              if (bounds.length) {\n",
+       "                map.fitBounds(bounds);\n",
+       "              }\n",
+       "              updateMapInfo();\n",
+       "              map.on('move', () => {\n",
+       "                updateMapInfo();\n",
+       "              });\n",
+       "            })\n",
+       "            .error((err) => {\n",
+       "              console.log('ERROR: ', err);\n",
+       "            });\n",
+       "    </script>\n",
+       "\n",
+       "  </body>\n",
+       "</html>\n",
+       "\" width=800 height=400>  Preview image: <img src=\"https://eschbacher.carto.com/api/v1/map/static/named/cartoframes_ver20170406_layers1_time0_baseid1_labels0_zoom0/800/400.png?config=%7B%22basemap_url%22%3A+%22https%3A%2F%2Fcartodb-basemaps-%7Bs%7D.global.ssl.fastly.net%2Fdark_all%2F%7Bz%7D%2F%7Bx%7D%2F%7By%7D.png%22%2C+%22cartocss_0%22%3A+%22%23layer%5B%27mapnik%3A%3Ageometry_type%27%3D1%5D+%7B++marker-width%3A+10%3B+marker-fill%3A+ramp%28%5Bhr90%5D%2C+cartocolor%28Sunset%29%2C+quantiles%287%29%29%3B+marker-fill-opacity%3A+1%3B+marker-allow-overlap%3A+true%3B+marker-line-width%3A+0.5%3B+marker-line-color%3A+%23000%3B+marker-line-opacity%3A+1%3B%7D+%23layer%5B%27mapnik%3A%3Ageometry_type%27%3D2%5D+%7B++line-width%3A+1.5%3B+line-color%3A+ramp%28%5Bhr90%5D%2C+cartocolor%28Sunset%29%2C+quantiles%287%29%29%3B%7D+%23layer%5B%27mapnik%3A%3Ageometry_type%27%3D3%5D+%7B++polygon-fill%3A+ramp%28%5Bhr90%5D%2C+cartocolor%28Sunset%29%2C+quantiles%287%29%29%3B+polygon-opacity%3A+0.9%3B+polygon-gamma%3A+0.5%3B+line-color%3A+%23FFF%3B+line-width%3A+0.5%3B+line-opacity%3A+0.25%3B+line-comp-op%3A+hard-light%3B%7D+%22%2C+%22sql_0%22%3A+%22SELECT+%2A+FROM+nat%22%7D&anti_cache=0.03286307778211395\" /></iframe>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from cartoframes import Layer, styling\n",
+    "cxn.map(layers=Layer('nat', color={'column': 'hr90', 'scheme': styling.sunset(7)}))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cxn.write(gdf,\n",
+    "          encode_geom=True,\n",
+    "          table_name='cartoframes_geopandas',\n",
+    "          overwrite=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you change the geometries locally, the changes propagate back to CARTO:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gdf['geometry'] = gdf.geometry.apply(lambda x: x.buffer(2))\n",
+    "df['geometry'] = df.geometry.apply(lambda x: x.buffer(2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "cxn.write(gdf, encode_geom=True,\n",
+    "          table_name='cartoframes_geopandas_buffered',\n",
+    "          overwrite=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>the_geom</th>\n",
+       "      <th>the_geom_webmercator</th>\n",
+       "      <th>name</th>\n",
+       "      <th>state_name</th>\n",
+       "      <th>state_fips</th>\n",
+       "      <th>cnty_fips</th>\n",
+       "      <th>fips</th>\n",
+       "      <th>stfips</th>\n",
+       "      <th>cofips</th>\n",
+       "      <th>fipsno</th>\n",
+       "      <th>...</th>\n",
+       "      <th>blk90</th>\n",
+       "      <th>gi59</th>\n",
+       "      <th>gi69</th>\n",
+       "      <th>gi79</th>\n",
+       "      <th>gi89</th>\n",
+       "      <th>fh60</th>\n",
+       "      <th>fh70</th>\n",
+       "      <th>fh80</th>\n",
+       "      <th>fh90</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>cartodb_id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>364</th>\n",
+       "      <td>0103000000010000004900000013d72ae150dd57c02a78...</td>\n",
+       "      <td>0106000020110F00000100000001030000000100000007...</td>\n",
+       "      <td>Murray</td>\n",
+       "      <td>Minnesota</td>\n",
+       "      <td>27</td>\n",
+       "      <td>101</td>\n",
+       "      <td>27101</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>101.0</td>\n",
+       "      <td>27101.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.340082</td>\n",
+       "      <td>0.414127</td>\n",
+       "      <td>0.382432</td>\n",
+       "      <td>0.364256</td>\n",
+       "      <td>9.146341</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>5.566097</td>\n",
+       "      <td>4.782767</td>\n",
+       "      <td>POLYGON ((-95.45806149657828 46.19699053301913...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1660</th>\n",
+       "      <td>01030000000100000047000000c828d722b9fb57c02ced...</td>\n",
+       "      <td>0106000020110F00000100000001030000000100000006...</td>\n",
+       "      <td>Chautauqua</td>\n",
+       "      <td>Kansas</td>\n",
+       "      <td>20</td>\n",
+       "      <td>019</td>\n",
+       "      <td>20019</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>20019.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.521897</td>\n",
+       "      <td>0.368538</td>\n",
+       "      <td>0.437940</td>\n",
+       "      <td>0.401030</td>\n",
+       "      <td>0.395566</td>\n",
+       "      <td>9.501188</td>\n",
+       "      <td>6.8</td>\n",
+       "      <td>7.728495</td>\n",
+       "      <td>8.073541</td>\n",
+       "      <td>POLYGON ((-95.93317481052065 34.99958301471847...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115</th>\n",
+       "      <td>0103000000010000004c000000d1b43b8a3e8b58c02e9d...</td>\n",
+       "      <td>0106000020110F0000010000000103000000010000000A...</td>\n",
+       "      <td>Becker</td>\n",
+       "      <td>Minnesota</td>\n",
+       "      <td>27</td>\n",
+       "      <td>005</td>\n",
+       "      <td>27005</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>27005.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.071733</td>\n",
+       "      <td>0.328578</td>\n",
+       "      <td>0.390693</td>\n",
+       "      <td>0.374987</td>\n",
+       "      <td>0.375178</td>\n",
+       "      <td>12.385242</td>\n",
+       "      <td>7.2</td>\n",
+       "      <td>8.790072</td>\n",
+       "      <td>11.961039</td>\n",
+       "      <td>POLYGON ((-98.17569213705725 46.72406149358208...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55</th>\n",
+       "      <td>0103000000010000004a000000c0248c918d9a58c08a4b...</td>\n",
+       "      <td>0106000020110F0000010000000103000000010000000A...</td>\n",
+       "      <td>Pennington</td>\n",
+       "      <td>Minnesota</td>\n",
+       "      <td>27</td>\n",
+       "      <td>113</td>\n",
+       "      <td>27113</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>113.0</td>\n",
+       "      <td>27113.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.082669</td>\n",
+       "      <td>0.264746</td>\n",
+       "      <td>0.361857</td>\n",
+       "      <td>0.357659</td>\n",
+       "      <td>0.370818</td>\n",
+       "      <td>11.554969</td>\n",
+       "      <td>7.1</td>\n",
+       "      <td>10.775000</td>\n",
+       "      <td>10.980841</td>\n",
+       "      <td>POLYGON ((-98.41489065824408 47.46063804640205...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>0103000000010000005b000000644f2b866ee75cc03ff8...</td>\n",
+       "      <td>0106000020110F00000100000001030000000100000035...</td>\n",
+       "      <td>Shoshone</td>\n",
+       "      <td>Idaho</td>\n",
+       "      <td>16</td>\n",
+       "      <td>079</td>\n",
+       "      <td>16079</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>79.0</td>\n",
+       "      <td>16079.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.114852</td>\n",
+       "      <td>0.185961</td>\n",
+       "      <td>0.296188</td>\n",
+       "      <td>0.328998</td>\n",
+       "      <td>0.364126</td>\n",
+       "      <td>7.810136</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>8.762984</td>\n",
+       "      <td>12.869565</td>\n",
+       "      <td>POLYGON ((-115.6161208556128 49.95441929622074...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 72 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                     the_geom  \\\n",
+       "cartodb_id                                                      \n",
+       "364         0103000000010000004900000013d72ae150dd57c02a78...   \n",
+       "1660        01030000000100000047000000c828d722b9fb57c02ced...   \n",
+       "115         0103000000010000004c000000d1b43b8a3e8b58c02e9d...   \n",
+       "55          0103000000010000004a000000c0248c918d9a58c08a4b...   \n",
+       "62          0103000000010000005b000000644f2b866ee75cc03ff8...   \n",
+       "\n",
+       "                                         the_geom_webmercator        name  \\\n",
+       "cartodb_id                                                                  \n",
+       "364         0106000020110F00000100000001030000000100000007...      Murray   \n",
+       "1660        0106000020110F00000100000001030000000100000006...  Chautauqua   \n",
+       "115         0106000020110F0000010000000103000000010000000A...      Becker   \n",
+       "55          0106000020110F0000010000000103000000010000000A...  Pennington   \n",
+       "62          0106000020110F00000100000001030000000100000035...    Shoshone   \n",
+       "\n",
+       "           state_name state_fips cnty_fips   fips  stfips  cofips   fipsno  \\\n",
+       "cartodb_id                                                                   \n",
+       "364         Minnesota         27       101  27101    27.0   101.0  27101.0   \n",
+       "1660           Kansas         20       019  20019    20.0    19.0  20019.0   \n",
+       "115         Minnesota         27       005  27005    27.0     5.0  27005.0   \n",
+       "55          Minnesota         27       113  27113    27.0   113.0  27113.0   \n",
+       "62              Idaho         16       079  16079    16.0    79.0  16079.0   \n",
+       "\n",
+       "                                  ...                             blk90  \\\n",
+       "cartodb_id                        ...                                     \n",
+       "364                               ...                          0.000000   \n",
+       "1660                              ...                          0.521897   \n",
+       "115                               ...                          0.071733   \n",
+       "55                                ...                          0.082669   \n",
+       "62                                ...                          0.114852   \n",
+       "\n",
+       "                gi59      gi69      gi79      gi89       fh60  fh70  \\\n",
+       "cartodb_id                                                            \n",
+       "364         0.340082  0.414127  0.382432  0.364256   9.146341   5.1   \n",
+       "1660        0.368538  0.437940  0.401030  0.395566   9.501188   6.8   \n",
+       "115         0.328578  0.390693  0.374987  0.375178  12.385242   7.2   \n",
+       "55          0.264746  0.361857  0.357659  0.370818  11.554969   7.1   \n",
+       "62          0.185961  0.296188  0.328998  0.364126   7.810136   5.4   \n",
+       "\n",
+       "                 fh80       fh90  \\\n",
+       "cartodb_id                         \n",
+       "364          5.566097   4.782767   \n",
+       "1660         7.728495   8.073541   \n",
+       "115          8.790072  11.961039   \n",
+       "55          10.775000  10.980841   \n",
+       "62           8.762984  12.869565   \n",
+       "\n",
+       "                                                     geometry  \n",
+       "cartodb_id                                                     \n",
+       "364         POLYGON ((-95.45806149657828 46.19699053301913...  \n",
+       "1660        POLYGON ((-95.93317481052065 34.99958301471847...  \n",
+       "115         POLYGON ((-98.17569213705725 46.72406149358208...  \n",
+       "55          POLYGON ((-98.41489065824408 47.46063804640205...  \n",
+       "62          POLYGON ((-115.6161208556128 49.95441929622074...  \n",
+       "\n",
+       "[5 rows x 72 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gdf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
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+    "cxn.map(layers=[cf.BaseMap('light'),\n",
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