Merge pull request #60 from GWC-DCMB/elysia_lesson_practice_review

Practice 21
GWC-DCMB · Jul 24, 2024 · 9b264fd · 9b264fd
2 parents cee3fe7 + dbae4fb
commit 9b264fd
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diff --git a/Practices/Practice21_Basic_Stats_I_Averages.ipynb b/Practices/Practice21_Basic_Stats_I_Averages.ipynb
@@ -4,7 +4,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For this practice, let's use the Boston dataset."
+    "# Practice: Basic Statistics I: Averages"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For this practice, let's use the California dataset."
    ]
   },
   {
@@ -27,8 +34,8 @@
    },
    "outputs": [],
    "source": [
-    "# Import the load_boston method \n",
-    "from sklearn.datasets import load_boston"
+    "# Import the fetch_california_housing method to load the California data later on\n",
+    "from sklearn.datasets import fetch_california_housing"
    ]
   },
   {
@@ -39,7 +46,7 @@
    },
    "outputs": [],
    "source": [
-    "# Import pandas, so that we can work with the data frame version of the Boston data\n",
+    "# Import pandas, so that we can work with the data frame version of the California data\n",
     "import pandas as pd"
    ]
   },
@@ -51,8 +58,8 @@
    },
    "outputs": [],
    "source": [
-    "# Load the Boston data\n",
-    "boston = load_boston()"
+    "# Load the California data\n",
+    "california = fetch_california_housing()"
    ]
   },
   {
@@ -61,21 +68,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# This will provide the characteristics for the Boston dataset\n",
-    "print(boston.DESCR)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Here, I'm including the prices of Boston's houses, which is boston['target'], as a column with the other \n",
-    "# features in the Boston dataset.\n",
-    "boston_data = np.concatenate((boston['data'], pd.DataFrame(boston['target'])), axis = 1)"
+    "# This will provide the characteristics for the California dataset\n",
+    "print(california.DESCR)"
    ]
   },
   {
@@ -86,9 +80,12 @@
    },
    "outputs": [],
    "source": [
-    "# Convert the Boston data to a data frame format, so that it's easier to view and process\n",
-    "boston_df = pd.DataFrame(boston_data, columns = np.concatenate((boston['feature_names'], 'MEDV'), axis = None))\n",
-    "boston_df"
+    "# Convert the housing object to a data frame format, so that it's easier to view and process\n",
+    "california_df = pd.DataFrame(california['data'], columns = california['feature_names'])\n",
+    "# Here, I'm including the prices of California's houses, which is california['target'], \n",
+    "# as a column with the other features in the California dataset.\n",
+    "california_df['HouseValue'] = california['target']\n",
+    "california_df"
    ]
   },
   {
@@ -123,9 +120,7 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": [
-    ""
-   ]
+   "source": []
   },
   {
    "cell_type": "markdown",
@@ -138,7 +133,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We will determine the average price for houses along the Charles River and that for houses NOT along the river."
+    "We will determine the average price for houses less than 20 years old and that for houses 20 years old or more."
    ]
   },
   {
@@ -149,22 +144,20 @@
    },
    "outputs": [],
    "source": [
-    "# Use the query method to define a subset of boston_df that only include houses are along the river (CHAS = 1). "
+    "# Use the query method to define a subset of california_df that only include houses less than 20 years old. "
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "What do you notice about the CHAS column? "
+    "What do you notice about the HouseAge column? "
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": [
-    ""
-   ]
+   "source": []
   },
   {
    "cell_type": "code",
@@ -174,14 +167,14 @@
    },
    "outputs": [],
    "source": [
-    "# Now determine the average price for these houses. 'MEDV' is the column name for the prices. "
+    "# Now determine the average price for these houses. 'HouseValue' is the column name for the prices. "
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now try determining the average for houses NOT along the River."
+    "Now try determining the average for houses 20 years or older."
    ]
   },
   {
@@ -192,7 +185,7 @@
    },
    "outputs": [],
    "source": [
-    "# Determine the average price for houses that are NOT along the Charles River (when CHAS = 0). "
+    "# Determine the average price for houses that are 20 years or older. \n"
    ]
   },
   {
@@ -201,15 +194,6 @@
    "source": [
     "Good work! You're becoming an expert in subsetting and determining averages on subsetted data. This will be integral for your capstone projects and future careers as data scientists! "
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -228,7 +212,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.1.0"
   }
  },
  "nbformat": 4,