Merge pull request #58 from GWC-DCMB/fix_stats_percent_key

Fix stats percent key (lesson 22)

Lessons/Lesson16_Pandas-Subsetting-I.ipynb

@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -491,7 +491,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -505,7 +505,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.9.15"
   }
  },
  "nbformat": 4,

Lessons/Lesson22_Basic_Stats_II_Percents.ipynb

@@ -128,7 +128,8 @@
    "source": [
     "# Load the dataset of house prices in Ames, and convert to\n",
     "# a data frame format so it's easier to view and process\n",
-    "ames_df = pd.DataFrame(housing['data'])\n",
+    "ames_df = pd.DataFrame(housing['data'], columns = housing['feature_names'])\n",
+    "ames_df['SalePrice'] = housing.target\n",
     "ames_df"
    ]
   },
@@ -175,7 +176,7 @@
    },
    "outputs": [],
    "source": [
-    "# Determine number of tracts that bound the Charles River two ways:\n",
+    "# Determine number of homes sold normally two ways:\n",
     "# (1) with the query function\n"
    ]
   },
@@ -218,19 +219,6 @@
     "# Now calculate the percentage of houses sold normally.\n"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "AJZKng3Bs7Vd"
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -264,7 +252,7 @@
     "id": "RLZ-k3L7s7Vq"
    },
    "source": [
-    "What percentage of tracts have a median price **between** $200,000 and $500,000?"
+    "What percentage of houses have a sale price **between** $200,000 and $500,000?"
    ]
   },
   {