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Practice 21 #60

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Jul 24, 2024
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Practice 21 #60

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502aefc
Replaced Boston dataset with California
Feb 29, 2024
bba9f9c
Edited Practice 21 to match key
Feb 29, 2024
dbae4fb
Merge bba9f9c949d2d298751d7f47c266318ac3c3fb45 into eca27789d2eb83539…
echou89 Feb 29, 2024
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76 changes: 30 additions & 46 deletions Practices/Practice21_Basic_Stats_I_Averages.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -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."
]
},
{
Expand All @@ -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"
]
},
{
Expand All @@ -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"
]
},
Expand All @@ -51,8 +58,8 @@
},
"outputs": [],
"source": [
"# Load the Boston data\n",
"boston = load_boston()"
"# Load the California data\n",
"california = fetch_california_housing()"
]
},
{
Expand All @@ -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)"
]
},
{
Expand All @@ -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"
]
},
{
Expand Down Expand Up @@ -123,9 +120,7 @@
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
"source": []
},
{
"cell_type": "markdown",
Expand All @@ -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."
]
},
{
Expand All @@ -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",
Expand All @@ -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."
]
},
{
Expand All @@ -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"
]
},
{
Expand All @@ -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": {
Expand All @@ -228,7 +212,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
"version": "3.1.0"
}
},
"nbformat": 4,
Expand Down
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