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fenago/proj_2-2_avocado.ipynb

Created October 21, 2024 13:15
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proj_2-2_avocado.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/fenago/2414091ac2d02390aaad811a6b4ee52f/proj_2-2_avocado.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-1whm10EM7Av"
},
"source": [
"# Project 2-2: Analyze the avocado data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "38DanodqM7A1"
},
"outputs": [],
"source": [
"# https://www.kaggle.com/neuromusic/avocado-prices\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "tfHQep4uM7A4"
},
"source": [
"## Tasks"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "mL-B26AlM7A4"
},
"outputs": [],
"source": [
"# 1\n",
"data = pd.read_csv('avocado.csv')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "l5LBERAlM7A5",
"outputId": "df8976da-ae6f-431f-95f5-23b069fae2ff"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 18249 entries, 0 to 18248\n",
"Data columns (total 14 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Unnamed: 0 18249 non-null int64 \n",
" 1 Date 18249 non-null object \n",
" 2 AveragePrice 18249 non-null float64\n",
" 3 Total Volume 18249 non-null float64\n",
" 4 4046 18249 non-null float64\n",
" 5 4225 18249 non-null float64\n",
" 6 4770 18249 non-null float64\n",
" 7 Total Bags 18249 non-null float64\n",
" 8 Small Bags 18249 non-null float64\n",
" 9 Large Bags 18249 non-null float64\n",
" 10 XLarge Bags 18249 non-null float64\n",
" 11 type 18249 non-null object \n",
" 12 year 18249 non-null int64 \n",
" 13 region 18249 non-null object \n",
"dtypes: float64(9), int64(2), object(3)\n",
"memory usage: 1.9+ MB\n"
]
}
],
"source": [
"# 2\n",
"data.info()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "b-FZwvJKM7A7",
"outputId": "6575a82a-1fe7-4b06-91d7-e545afbdb8df"
},
"outputs": [
{
"data": {
"text/plain": [
"Unnamed: 0 53\n",
"Date 169\n",
"AveragePrice 259\n",
"Total Volume 18237\n",
"4046 17702\n",
"4225 18103\n",
"4770 12071\n",
"Total Bags 18097\n",
"Small Bags 17321\n",
"Large Bags 15082\n",
"XLarge Bags 5588\n",
"type 2\n",
"year 4\n",
"region 54\n",
"dtype: int64"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 3\n",
"data.nunique()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "XjpY9MN8M7A8",
"outputId": "efae6fcc-6b53-447c-eeff-4cf260a7c37c"
},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>Date</th>\n",
" <th>AveragePrice</th>\n",
" <th>Total Volume</th>\n",
" <th>4046</th>\n",
" <th>4225</th>\n",
" <th>4770</th>\n",
" <th>Total Bags</th>\n",
" <th>Small Bags</th>\n",
" <th>Large Bags</th>\n",
" <th>XLarge Bags</th>\n",
" <th>type</th>\n",
" <th>year</th>\n",
" <th>region</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>2015-12-27</td>\n",
" <td>1.33</td>\n",
" <td>64236.62</td>\n",
" <td>1036.74</td>\n",
" <td>54454.85</td>\n",
" <td>48.16</td>\n",
" <td>8696.87</td>\n",
" <td>8603.62</td>\n",
" <td>93.25</td>\n",
" <td>0.0</td>\n",
" <td>conventional</td>\n",
" <td>2015</td>\n",
" <td>Albany</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>2015-12-20</td>\n",
" <td>1.35</td>\n",
" <td>54876.98</td>\n",
" <td>674.28</td>\n",
" <td>44638.81</td>\n",
" <td>58.33</td>\n",
" <td>9505.56</td>\n",
" <td>9408.07</td>\n",
" <td>97.49</td>\n",
" <td>0.0</td>\n",
" <td>conventional</td>\n",
" <td>2015</td>\n",
" <td>Albany</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>2015-12-13</td>\n",
" <td>0.93</td>\n",
" <td>118220.22</td>\n",
" <td>794.70</td>\n",
" <td>109149.67</td>\n",
" <td>130.50</td>\n",
" <td>8145.35</td>\n",
" <td>8042.21</td>\n",
" <td>103.14</td>\n",
" <td>0.0</td>\n",
" <td>conventional</td>\n",
" <td>2015</td>\n",
" <td>Albany</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>2015-12-06</td>\n",
" <td>1.08</td>\n",
" <td>78992.15</td>\n",
" <td>1132.00</td>\n",
" <td>71976.41</td>\n",
" <td>72.58</td>\n",
" <td>5811.16</td>\n",
" <td>5677.40</td>\n",
" <td>133.76</td>\n",
" <td>0.0</td>\n",
" <td>conventional</td>\n",
" <td>2015</td>\n",
" <td>Albany</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>2015-11-29</td>\n",
" <td>1.28</td>\n",
" <td>51039.60</td>\n",
" <td>941.48</td>\n",
" <td>43838.39</td>\n",
" <td>75.78</td>\n",
" <td>6183.95</td>\n",
" <td>5986.26</td>\n",
" <td>197.69</td>\n",
" <td>0.0</td>\n",
" <td>conventional</td>\n",
" <td>2015</td>\n",
" <td>Albany</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18244</th>\n",
" <td>7</td>\n",
" <td>2018-02-04</td>\n",
" <td>1.63</td>\n",
" <td>17074.83</td>\n",
" <td>2046.96</td>\n",
" <td>1529.20</td>\n",
" <td>0.00</td>\n",
" <td>13498.67</td>\n",
" <td>13066.82</td>\n",
" <td>431.85</td>\n",
" <td>0.0</td>\n",
" <td>organic</td>\n",
" <td>2018</td>\n",
" <td>WestTexNewMexico</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18245</th>\n",
" <td>8</td>\n",
" <td>2018-01-28</td>\n",
" <td>1.71</td>\n",
" <td>13888.04</td>\n",
" <td>1191.70</td>\n",
" <td>3431.50</td>\n",
" <td>0.00</td>\n",
" <td>9264.84</td>\n",
" <td>8940.04</td>\n",
" <td>324.80</td>\n",
" <td>0.0</td>\n",
" <td>organic</td>\n",
" <td>2018</td>\n",
" <td>WestTexNewMexico</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18246</th>\n",
" <td>9</td>\n",
" <td>2018-01-21</td>\n",
" <td>1.87</td>\n",
" <td>13766.76</td>\n",
" <td>1191.92</td>\n",
" <td>2452.79</td>\n",
" <td>727.94</td>\n",
" <td>9394.11</td>\n",
" <td>9351.80</td>\n",
" <td>42.31</td>\n",
" <td>0.0</td>\n",
" <td>organic</td>\n",
" <td>2018</td>\n",
" <td>WestTexNewMexico</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18247</th>\n",
" <td>10</td>\n",
" <td>2018-01-14</td>\n",
" <td>1.93</td>\n",
" <td>16205.22</td>\n",
" <td>1527.63</td>\n",
" <td>2981.04</td>\n",
" <td>727.01</td>\n",
" <td>10969.54</td>\n",
" <td>10919.54</td>\n",
" <td>50.00</td>\n",
" <td>0.0</td>\n",
" <td>organic</td>\n",
" <td>2018</td>\n",
" <td>WestTexNewMexico</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18248</th>\n",
" <td>11</td>\n",
" <td>2018-01-07</td>\n",
" <td>1.62</td>\n",
" <td>17489.58</td>\n",
" <td>2894.77</td>\n",
" <td>2356.13</td>\n",
" <td>224.53</td>\n",
" <td>12014.15</td>\n",
" <td>11988.14</td>\n",
" <td>26.01</td>\n",
" <td>0.0</td>\n",
" <td>organic</td>\n",
" <td>2018</td>\n",
" <td>WestTexNewMexico</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>18249 rows × 14 columns</p>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 Date AveragePrice Total Volume 4046 4225 \\\n",
"0 0 2015-12-27 1.33 64236.62 1036.74 54454.85 \n",
"1 1 2015-12-20 1.35 54876.98 674.28 44638.81 \n",
"2 2 2015-12-13 0.93 118220.22 794.70 109149.67 \n",
"3 3 2015-12-06 1.08 78992.15 1132.00 71976.41 \n",
"4 4 2015-11-29 1.28 51039.60 941.48 43838.39 \n",
"... ... ... ... ... ... ... \n",
"18244 7 2018-02-04 1.63 17074.83 2046.96 1529.20 \n",
"18245 8 2018-01-28 1.71 13888.04 1191.70 3431.50 \n",
"18246 9 2018-01-21 1.87 13766.76 1191.92 2452.79 \n",
"18247 10 2018-01-14 1.93 16205.22 1527.63 2981.04 \n",
"18248 11 2018-01-07 1.62 17489.58 2894.77 2356.13 \n",
"\n",
" 4770 Total Bags Small Bags Large Bags XLarge Bags type \\\n",
"0 48.16 8696.87 8603.62 93.25 0.0 conventional \n",
"1 58.33 9505.56 9408.07 97.49 0.0 conventional \n",
"2 130.50 8145.35 8042.21 103.14 0.0 conventional \n",
"3 72.58 5811.16 5677.40 133.76 0.0 conventional \n",
"4 75.78 6183.95 5986.26 197.69 0.0 conventional \n",
"... ... ... ... ... ... ... \n",
"18244 0.00 13498.67 13066.82 431.85 0.0 organic \n",
"18245 0.00 9264.84 8940.04 324.80 0.0 organic \n",
"18246 727.94 9394.11 9351.80 42.31 0.0 organic \n",
"18247 727.01 10969.54 10919.54 50.00 0.0 organic \n",
"18248 224.53 12014.15 11988.14 26.01 0.0 organic \n",
"\n",
" year region \n",
"0 2015 Albany \n",
"1 2015 Albany \n",
"2 2015 Albany \n",
"3 2015 Albany \n",
"4 2015 Albany \n",
"... ... ... \n",
"18244 2018 WestTexNewMexico \n",
"18245 2018 WestTexNewMexico \n",
"18246 2018 WestTexNewMexico \n",
"18247 2018 WestTexNewMexico \n",
"18248 2018 WestTexNewMexico \n",
"\n",
"[18249 rows x 14 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 4\n",
"data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Fg3-A6ijM7A9",
"outputId": "23c20a00-705e-4702-acb7-2904c407bff0"
},
"outputs": [
{
"data": {
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" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>2015-12-27</td>\n",
" <td>1.33</td>\n",
" <td>64236.62</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>conventional</td>\n",
" <td>2015</td>\n",
" <td>Albany</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>2015-12-20</td>\n",
" <td>1.35</td>\n",
" <td>54876.98</td>\n",
" <td>...</td>\n",
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" <td>conventional</td>\n",
" <td>2015</td>\n",
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" <th>2</th>\n",
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" <td>0.93</td>\n",
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" <td>0.0</td>\n",
" <td>conventional</td>\n",
" <td>2015</td>\n",
" <td>Albany</td>\n",
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" <th>3</th>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
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" <td>...</td>\n",
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" <td>Albany</td>\n",
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" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>organic</td>\n",
" <td>2018</td>\n",
" <td>WestTexNewMexico</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18245</th>\n",
" <td>8</td>\n",
" <td>2018-01-28</td>\n",
" <td>1.71</td>\n",
" <td>13888.04</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>organic</td>\n",
" <td>2018</td>\n",
" <td>WestTexNewMexico</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18246</th>\n",
" <td>9</td>\n",
" <td>2018-01-21</td>\n",
" <td>1.87</td>\n",
" <td>13766.76</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>organic</td>\n",
" <td>2018</td>\n",
" <td>WestTexNewMexico</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18247</th>\n",
" <td>10</td>\n",
" <td>2018-01-14</td>\n",
" <td>1.93</td>\n",
" <td>16205.22</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>organic</td>\n",
" <td>2018</td>\n",
" <td>WestTexNewMexico</td>\n",
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" <tr>\n",
" <th>18248</th>\n",
" <td>11</td>\n",
" <td>2018-01-07</td>\n",
" <td>1.62</td>\n",
" <td>17489.58</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>organic</td>\n",
" <td>2018</td>\n",
" <td>WestTexNewMexico</td>\n",
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"</table>\n",
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"</div>"
],
"text/plain": [
" Unnamed: 0 Date AveragePrice Total Volume ... XLarge Bags \\\n",
"0 0 2015-12-27 1.33 64236.62 ... 0.0 \n",
"1 1 2015-12-20 1.35 54876.98 ... 0.0 \n",
"2 2 2015-12-13 0.93 118220.22 ... 0.0 \n",
"3 3 2015-12-06 1.08 78992.15 ... 0.0 \n",
"4 4 2015-11-29 1.28 51039.60 ... 0.0 \n",
"... ... ... ... ... ... ... \n",
"18244 7 2018-02-04 1.63 17074.83 ... 0.0 \n",
"18245 8 2018-01-28 1.71 13888.04 ... 0.0 \n",
"18246 9 2018-01-21 1.87 13766.76 ... 0.0 \n",
"18247 10 2018-01-14 1.93 16205.22 ... 0.0 \n",
"18248 11 2018-01-07 1.62 17489.58 ... 0.0 \n",
"\n",
" type year region \n",
"0 conventional 2015 Albany \n",
"1 conventional 2015 Albany \n",
"2 conventional 2015 Albany \n",
"3 conventional 2015 Albany \n",
"4 conventional 2015 Albany \n",
"... ... ... ... \n",
"18244 organic 2018 WestTexNewMexico \n",
"18245 organic 2018 WestTexNewMexico \n",
"18246 organic 2018 WestTexNewMexico \n",
"18247 organic 2018 WestTexNewMexico \n",
"18248 organic 2018 WestTexNewMexico \n",
"\n",
"[18249 rows x 14 columns]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# 5\n",
"with pd.option_context(\n",
" 'display.max_rows', 10,\n",
" 'display.max_columns', 8):\n",
" display(data)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "aivJth4EM7A-",
"outputId": "91592f17-0dce-4f50-edfa-a088558249b4"
},
"outputs": [
{
"data": {
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>AveragePrice</th>\n",
" <th>Total Volume</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2015-12-27</td>\n",
" <td>1.33</td>\n",
" <td>64236.62</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2015-12-20</td>\n",
" <td>1.35</td>\n",
" <td>54876.98</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2015-12-13</td>\n",
" <td>0.93</td>\n",
" <td>118220.22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2015-12-06</td>\n",
" <td>1.08</td>\n",
" <td>78992.15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2015-11-29</td>\n",
" <td>1.28</td>\n",
" <td>51039.60</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date AveragePrice Total Volume\n",
"0 2015-12-27 1.33 64236.62\n",
"1 2015-12-20 1.35 54876.98\n",
"2 2015-12-13 0.93 118220.22\n",
"3 2015-12-06 1.08 78992.15\n",
"4 2015-11-29 1.28 51039.60"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 6\n",
"data[['Date','AveragePrice','Total Volume']].head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "zF93kr0BM7BD",
"outputId": "bcab8e58-a099-4c85-b94f-1aa123cb5340"
},
"outputs": [
{
"data": {
"text/plain": [
"0 1.33\n",
"1 1.35\n",
"2 0.93\n",
"3 1.08\n",
"4 1.28\n",
" ... \n",
"18244 1.63\n",
"18245 1.71\n",
"18246 1.87\n",
"18247 1.93\n",
"18248 1.62\n",
"Name: AveragePrice, Length: 18249, dtype: float64"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 7\n",
"data.AveragePrice"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "FXK-F0OLM7BF",
"outputId": "ba06f9bf-f365-4fea-8318-c08ef6d7c90f"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" .dataframe thead th {\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>Date</th>\n",
" <th>AveragePrice</th>\n",
" <th>Total Volume</th>\n",
" <th>4046</th>\n",
" <th>4225</th>\n",
" <th>4770</th>\n",
" <th>Total Bags</th>\n",
" <th>Small Bags</th>\n",
" <th>Large Bags</th>\n",
" <th>XLarge Bags</th>\n",
" <th>type</th>\n",
" <th>year</th>\n",
" <th>region</th>\n",
" <th>EstimatedRevenue</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>2015-12-27</td>\n",
" <td>1.33</td>\n",
" <td>64236.62</td>\n",
" <td>1036.74</td>\n",
" <td>54454.85</td>\n",
" <td>48.16</td>\n",
" <td>8696.87</td>\n",
" <td>8603.62</td>\n",
" <td>93.25</td>\n",
" <td>0.0</td>\n",
" <td>conventional</td>\n",
" <td>2015</td>\n",
" <td>Albany</td>\n",
" <td>85434.7046</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>2015-12-20</td>\n",
" <td>1.35</td>\n",
" <td>54876.98</td>\n",
" <td>674.28</td>\n",
" <td>44638.81</td>\n",
" <td>58.33</td>\n",
" <td>9505.56</td>\n",
" <td>9408.07</td>\n",
" <td>97.49</td>\n",
" <td>0.0</td>\n",
" <td>conventional</td>\n",
" <td>2015</td>\n",
" <td>Albany</td>\n",
" <td>74083.9230</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>2015-12-13</td>\n",
" <td>0.93</td>\n",
" <td>118220.22</td>\n",
" <td>794.70</td>\n",
" <td>109149.67</td>\n",
" <td>130.50</td>\n",
" <td>8145.35</td>\n",
" <td>8042.21</td>\n",
" <td>103.14</td>\n",
" <td>0.0</td>\n",
" <td>conventional</td>\n",
" <td>2015</td>\n",
" <td>Albany</td>\n",
" <td>109944.8046</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>2015-12-06</td>\n",
" <td>1.08</td>\n",
" <td>78992.15</td>\n",
" <td>1132.00</td>\n",
" <td>71976.41</td>\n",
" <td>72.58</td>\n",
" <td>5811.16</td>\n",
" <td>5677.40</td>\n",
" <td>133.76</td>\n",
" <td>0.0</td>\n",
" <td>conventional</td>\n",
" <td>2015</td>\n",
" <td>Albany</td>\n",
" <td>85311.5220</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>2015-11-29</td>\n",
" <td>1.28</td>\n",
" <td>51039.60</td>\n",
" <td>941.48</td>\n",
" <td>43838.39</td>\n",
" <td>75.78</td>\n",
" <td>6183.95</td>\n",
" <td>5986.26</td>\n",
" <td>197.69</td>\n",
" <td>0.0</td>\n",
" <td>conventional</td>\n",
" <td>2015</td>\n",
" <td>Albany</td>\n",
" <td>65330.6880</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 Date AveragePrice Total Volume 4046 4225 \\\n",
"0 0 2015-12-27 1.33 64236.62 1036.74 54454.85 \n",
"1 1 2015-12-20 1.35 54876.98 674.28 44638.81 \n",
"2 2 2015-12-13 0.93 118220.22 794.70 109149.67 \n",
"3 3 2015-12-06 1.08 78992.15 1132.00 71976.41 \n",
"4 4 2015-11-29 1.28 51039.60 941.48 43838.39 \n",
"\n",
" 4770 Total Bags Small Bags Large Bags XLarge Bags type \\\n",
"0 48.16 8696.87 8603.62 93.25 0.0 conventional \n",
"1 58.33 9505.56 9408.07 97.49 0.0 conventional \n",
"2 130.50 8145.35 8042.21 103.14 0.0 conventional \n",
"3 72.58 5811.16 5677.40 133.76 0.0 conventional \n",
"4 75.78 6183.95 5986.26 197.69 0.0 conventional \n",
"\n",
" year region EstimatedRevenue \n",
"0 2015 Albany 85434.7046 \n",
"1 2015 Albany 74083.9230 \n",
"2 2015 Albany 109944.8046 \n",
"3 2015 Albany 85311.5220 \n",
"4 2015 Albany 65330.6880 "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 8\n",
"data['EstimatedRevenue'] = data['Total Volume'] * data['AveragePrice']\n",
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [],
"id": "Upb2DOU7M7BG",
"outputId": "dca6a057-b654-4f98-8bec-2d0f8ba00de3"
},
"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>region</th>\n",
" <th>type</th>\n",
" <th>AveragePrice</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Albany</td>\n",
" <td>conventional</td>\n",
" <td>1.348757</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Albany</td>\n",
" <td>organic</td>\n",
" <td>1.773314</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Atlanta</td>\n",
" <td>conventional</td>\n",
" <td>1.068817</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Atlanta</td>\n",
" <td>organic</td>\n",
" <td>1.607101</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>BaltimoreWashington</td>\n",
" <td>conventional</td>\n",
" <td>1.344201</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" region type AveragePrice\n",
"0 Albany conventional 1.348757\n",
"1 Albany organic 1.773314\n",
"2 Atlanta conventional 1.068817\n",
"3 Atlanta organic 1.607101\n",
"4 BaltimoreWashington conventional 1.344201"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 9\n",
"data_grouped = data.groupby(['region','type'])[['AveragePrice']].mean()\n",
"data_grouped.reset_index(inplace=True)\n",
"data_grouped.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "rurGUNB9M7BG",
"outputId": "03e93cfc-d68b-4fff-d350-27b4c99a46c2"
},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='year'>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# 10\n",
"data.groupby('year')['Total Volume'].agg(['mean','median','std']).plot.bar()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "V-UAaCs2M7BH"
},
"source": [
"## Questions"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "B1XKckbWM7BI",
"outputId": "d44cd7f0-220b-4cdb-8078-dd0951584f22"
},
"outputs": [
{
"data": {
"text/plain": [
"54"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 1\n",
"data.region.nunique()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "7NCiCiHpM7BI",
"outputId": "955a64c0-8827-48d7-b605-ca9e6c88d2c4"
},
"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>AveragePrice</th>\n",
" </tr>\n",
" <tr>\n",
" <th>type</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>conventional</th>\n",
" <td>1.158040</td>\n",
" </tr>\n",
" <tr>\n",
" <th>organic</th>\n",
" <td>1.653999</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" AveragePrice\n",
"type \n",
"conventional 1.158040\n",
"organic 1.653999"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 2\n",
"data.groupby('type').mean()[['AveragePrice']]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "kYzjtQjgM7BJ",
"outputId": "839c23d9-a96c-40b1-801a-258256ef2667"
},
"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>type</th>\n",
" <th>conventional</th>\n",
" <th>organic</th>\n",
" </tr>\n",
" <tr>\n",
" <th>region</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Houston</th>\n",
" <td>0.825089</td>\n",
" <td>1.270769</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"type conventional organic\n",
"region \n",
"Houston 0.825089 1.270769"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 3\n",
"data_grouped.pivot(index='region', columns='type', values='AveragePrice').sort_values('organic').head(1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "OczMibJLM7BK",
"outputId": "88174a69-58c0-4c04-99d7-4080c9115eea"
},
"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",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>Total Bags</th>\n",
" </tr>\n",
" <tr>\n",
" <th>type</th>\n",
" <th>year</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">conventional</th>\n",
" <th>2015</th>\n",
" <td>7.518535e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016</th>\n",
" <td>1.405738e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017</th>\n",
" <td>1.549660e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018</th>\n",
" <td>2.033493e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">organic</th>\n",
" <th>2015</th>\n",
" <td>2.589149e+04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016</th>\n",
" <td>6.756273e+04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017</th>\n",
" <td>1.058683e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018</th>\n",
" <td>1.407772e+05</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Total Bags\n",
"type year \n",
"conventional 2015 7.518535e+05\n",
" 2016 1.405738e+06\n",
" 2017 1.549660e+06\n",
" 2018 2.033493e+06\n",
"organic 2015 2.589149e+04\n",
" 2016 6.756273e+04\n",
" 2017 1.058683e+05\n",
" 2018 1.407772e+05"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#4\n",
"data.groupby(['type','year']).std()[['Total Bags']]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "sQE7vTn_M7BL"
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.8.8"
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"colab": {
"provenance": [],
"include_colab_link": true
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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