Created using Colab

Author: kmoscoe

notebooks/intro_data_science/Feature_Engineering.ipynb

@@ -3793,13 +3793,13 @@
           "height": 447
         },
         "id": "GBJ4rOt73pI1",
-        "outputId": "b5685ff6-6977-43c6-bf68-c5a7171494d1"
+        "outputId": "3bbcfe2b-fbc5-4afd-d042-f6ae26ced695"
       },
       "source": [
         "# Plot histograms to get an idea of data spread\n",
-        "display(filtered_df[\"Median_Income_Person\"].plot.hist(bins=100))\n"
+        "display(df[\"Median_Income_Person\"].plot.hist(bins=100))\n"
       ],
-      "execution_count": 22,
+      "execution_count": 24,
       "outputs": [
         {
           "output_type": "display_data",
@@ -3827,39 +3827,34 @@
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 295
+          "height": 465
         },
         "id": "3j9THBI03tQA",
-        "outputId": "a5523ad7-1cb6-44c6-c769-657c910cf9ca"
+        "outputId": "12d8acbe-8321-4154-ccf0-7ffa2b9cf2cd"
       },
       "source": [
         "display(df[\"Count_Household_With4OrMorePerson\"].plot.hist(bins=100))\n"
       ],
-      "execution_count": null,
+      "execution_count": 23,
       "outputs": [
         {
           "output_type": "display_data",
           "data": {
             "text/plain": [
-              "<matplotlib.axes._subplots.AxesSubplot at 0x7fca31901690>"
+              "<Axes: ylabel='Frequency'>"
             ]
           },
-          "metadata": {
-            "tags": []
-          }
+          "metadata": {}
         },
         {
           "output_type": "display_data",
           "data": {
-            "image/png": 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\n",
             "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
           },
-          "metadata": {
-            "tags": [],
-            "needs_background": "light"
-          }
+          "metadata": {}
         }
       ]
     },