msb-public
/
ML-For-Beginners
mirror of https://github.com/microsoft/ML-For-Beginners.git
1
0
Code Issues Projects Releases Wiki Activity
ML-For-Beginners/2-Regression/2-Data/solution/notebook.ipynb

427 lines
28 KiB
Raw Blame History Escape

This file contains ambiguous Unicode characters!

This file contains ambiguous Unicode characters that may be confused with others in your current locale. If your use case is intentional and legitimate, you can safely ignore this warning. Use the Escape button to highlight these characters.

{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Linear Regression for Pumpkins - Lesson 2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>City Name</th>\n",
" <th>Type</th>\n",
" <th>Package</th>\n",
" <th>Variety</th>\n",
" <th>Sub Variety</th>\n",
" <th>Grade</th>\n",
" <th>Date</th>\n",
" <th>Low Price</th>\n",
" <th>High Price</th>\n",
" <th>Mostly Low</th>\n",
" <th>...</th>\n",
" <th>Unit of Sale</th>\n",
" <th>Quality</th>\n",
" <th>Condition</th>\n",
" <th>Appearance</th>\n",
" <th>Storage</th>\n",
" <th>Crop</th>\n",
" <th>Repack</th>\n",
" <th>Trans Mode</th>\n",
" <th>Unnamed: 24</th>\n",
" <th>Unnamed: 25</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>70</th>\n",
" <td>BALTIMORE</td>\n",
" <td>NaN</td>\n",
" <td>1 1/9 bushel cartons</td>\n",
" <td>PIE TYPE</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>9/24/16</td>\n",
" <td>15.0</td>\n",
" <td>15.0</td>\n",
" <td>15.0</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>N</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>71</th>\n",
" <td>BALTIMORE</td>\n",
" <td>NaN</td>\n",
" <td>1 1/9 bushel cartons</td>\n",
" <td>PIE TYPE</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>9/24/16</td>\n",
" <td>18.0</td>\n",
" <td>18.0</td>\n",
" <td>18.0</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>N</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>72</th>\n",
" <td>BALTIMORE</td>\n",
" <td>NaN</td>\n",
" <td>1 1/9 bushel cartons</td>\n",
" <td>PIE TYPE</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>10/1/16</td>\n",
" <td>18.0</td>\n",
" <td>18.0</td>\n",
" <td>18.0</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>N</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>73</th>\n",
" <td>BALTIMORE</td>\n",
" <td>NaN</td>\n",
" <td>1 1/9 bushel cartons</td>\n",
" <td>PIE TYPE</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>10/1/16</td>\n",
" <td>17.0</td>\n",
" <td>17.0</td>\n",
" <td>17.0</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>N</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>74</th>\n",
" <td>BALTIMORE</td>\n",
" <td>NaN</td>\n",
" <td>1 1/9 bushel cartons</td>\n",
" <td>PIE TYPE</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>10/8/16</td>\n",
" <td>15.0</td>\n",
" <td>15.0</td>\n",
" <td>15.0</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>N</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 26 columns</p>\n",
"</div>"
],
"text/plain": [
" City Name Type Package Variety Sub Variety Grade \\\n",
"70 BALTIMORE NaN 1 1/9 bushel cartons PIE TYPE NaN NaN \n",
"71 BALTIMORE NaN 1 1/9 bushel cartons PIE TYPE NaN NaN \n",
"72 BALTIMORE NaN 1 1/9 bushel cartons PIE TYPE NaN NaN \n",
"73 BALTIMORE NaN 1 1/9 bushel cartons PIE TYPE NaN NaN \n",
"74 BALTIMORE NaN 1 1/9 bushel cartons PIE TYPE NaN NaN \n",
"\n",
" Date Low Price High Price Mostly Low ... Unit of Sale Quality \\\n",
"70 9/24/16 15.0 15.0 15.0 ... NaN NaN \n",
"71 9/24/16 18.0 18.0 18.0 ... NaN NaN \n",
"72 10/1/16 18.0 18.0 18.0 ... NaN NaN \n",
"73 10/1/16 17.0 17.0 17.0 ... NaN NaN \n",
"74 10/8/16 15.0 15.0 15.0 ... NaN NaN \n",
"\n",
" Condition Appearance Storage Crop Repack Trans Mode Unnamed: 24 \\\n",
"70 NaN NaN NaN NaN N NaN NaN \n",
"71 NaN NaN NaN NaN N NaN NaN \n",
"72 NaN NaN NaN NaN N NaN NaN \n",
"73 NaN NaN NaN NaN N NaN NaN \n",
"74 NaN NaN NaN NaN N NaN NaN \n",
"\n",
" Unnamed: 25 \n",
"70 NaN \n",
"71 NaN \n",
"72 NaN \n",
"73 NaN \n",
"74 NaN \n",
"\n",
"[5 rows x 26 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"pumpkins = pd.read_csv('../../data/US-pumpkins.csv')\n",
"\n",
"pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]\n",
"\n",
"pumpkins.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"City Name 0\n",
"Type 406\n",
"Package 0\n",
"Variety 0\n",
"Sub Variety 167\n",
"Grade 415\n",
"Date 0\n",
"Low Price 0\n",
"High Price 0\n",
"Mostly Low 24\n",
"Mostly High 24\n",
"Origin 0\n",
"Origin District 396\n",
"Item Size 114\n",
"Color 145\n",
"Environment 415\n",
"Unit of Sale 404\n",
"Quality 415\n",
"Condition 415\n",
"Appearance 415\n",
"Storage 415\n",
"Crop 415\n",
"Repack 0\n",
"Trans Mode 415\n",
"Unnamed: 24 415\n",
"Unnamed: 25 391\n",
"dtype: int64"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pumpkins.isnull().sum()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Month Package Low Price High Price Price\n",
"70 9 1 1/9 bushel cartons 15.00 15.0 13.50\n",
"71 9 1 1/9 bushel cartons 18.00 18.0 16.20\n",
"72 10 1 1/9 bushel cartons 18.00 18.0 16.20\n",
"73 10 1 1/9 bushel cartons 17.00 17.0 15.30\n",
"74 10 1 1/9 bushel cartons 15.00 15.0 13.50\n",
"... ... ... ... ... ...\n",
"1738 9 1/2 bushel cartons 15.00 15.0 30.00\n",
"1739 9 1/2 bushel cartons 13.75 15.0 28.75\n",
"1740 9 1/2 bushel cartons 10.75 15.0 25.75\n",
"1741 9 1/2 bushel cartons 12.00 12.0 24.00\n",
"1742 9 1/2 bushel cartons 12.00 12.0 24.00\n",
"\n",
"[415 rows x 5 columns]\n"
]
}
],
"source": [
"\n",
"# A set of new columns for a new dataframe. Filter out nonmatching columns\n",
"columns_to_select = ['Package', 'Low Price', 'High Price', 'Date']\n",
"pumpkins = pumpkins.loc[:, columns_to_select]\n",
"\n",
"# Get an average between low and high price for the base pumpkin price\n",
"price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2\n",
"\n",
"# Convert the date to its month only\n",
"month = pd.DatetimeIndex(pumpkins['Date']).month\n",
"\n",
"# Create a new dataframe with this basic data\n",
"new_pumpkins = pd.DataFrame({'Month': month, 'Package': pumpkins['Package'], 'Low Price': pumpkins['Low Price'],'High Price': pumpkins['High Price'], 'Price': price})\n",
"\n",
"# Convert the price if the Package contains fractional bushel values\n",
"new_pumpkins.loc[new_pumpkins['Package'].str.contains('1 1/9'), 'Price'] = price/(1 + 1/9)\n",
"\n",
"new_pumpkins.loc[new_pumpkins['Package'].str.contains('1/2'), 'Price'] = price/(1/2)\n",
"\n",
"print(new_pumpkins)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"\n",
"price = new_pumpkins.Price\n",
"month = new_pumpkins.Month\n",
"plt.scatter(price, month)\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Pumpkin Price')"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"\n",
"new_pumpkins.groupby(['Month'])['Price'].mean().plot(kind='bar')\n",
"plt.ylabel(\"Pumpkin Price\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"interpreter": {
"hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
},
"kernelspec": {
"display_name": "Python 3.7.0 64-bit ('3.7')",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.1"
},
"metadata": {
"interpreter": {
"hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
}
},
"orig_nbformat": 2
},
"nbformat": 4,
"nbformat_minor": 2
}
Reference in new issue View Git Blame Copy Permalink