{
"cells": [
{
"cell_type": "markdown",
"id": "0f88d3ed-cd5e-4a51-800c-9e6bda67c7ea",
"metadata": {},
"source": [
"# Descriptive Analytics:Pasta"
]
},
{
"cell_type": "markdown",
"id": "91264deb-b3ce-4b58-b8bd-c7db9c480f9c",
"metadata": {},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3cba0398-f55e-485c-9b65-4585b75f0ad7",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import scipy as sp\n",
"import seaborn as sns \n",
"import plotnine"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "94174749-41cc-4e1a-b654-660cd8a020d5",
"metadata": {},
"outputs": [],
"source": [
"sns.set()"
]
},
{
"cell_type": "markdown",
"id": "972ca1c4-6733-4bd4-9872-07119e4b26c9",
"metadata": {},
"source": [
"## Read Data"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b45cb377-2df9-4181-8372-dd2791a21136",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
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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>HHID</th>\n",
" <th>TIME</th>\n",
" <th>PASTA</th>\n",
" <th>EXPOS</th>\n",
" <th>AGE</th>\n",
" <th>INCOME</th>\n",
" <th>AREA</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.939444</td>\n",
" <td>1</td>\n",
" <td>61.710758</td>\n",
" <td>25186.798772</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2.560969</td>\n",
" <td>2</td>\n",
" <td>61.710758</td>\n",
" <td>25186.798772</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0.901123</td>\n",
" <td>0</td>\n",
" <td>61.710758</td>\n",
" <td>25186.798772</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1.916530</td>\n",
" <td>1</td>\n",
" <td>61.710758</td>\n",
" <td>25186.798772</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>1.548751</td>\n",
" <td>0</td>\n",
" <td>61.710758</td>\n",
" <td>25186.798772</td>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" HHID TIME PASTA EXPOS AGE INCOME AREA\n",
"0 1 1 0.939444 1 61.710758 25186.798772 3\n",
"1 1 2 2.560969 2 61.710758 25186.798772 3\n",
"2 1 3 0.901123 0 61.710758 25186.798772 3\n",
"3 1 4 1.916530 1 61.710758 25186.798772 3\n",
"4 1 5 1.548751 0 61.710758 25186.798772 3"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv(\"W101_PASTAPURCHASE_EDITED.csv\"); df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8cb6f1ca-a897-438a-8fb0-69ed8c161a0c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 40000 entries, 0 to 39999\n",
"Data columns (total 7 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 HHID 40000 non-null int64 \n",
" 1 TIME 40000 non-null int64 \n",
" 2 PASTA 40000 non-null float64\n",
" 3 EXPOS 40000 non-null int64 \n",
" 4 AGE 40000 non-null float64\n",
" 5 INCOME 40000 non-null float64\n",
" 6 AREA 40000 non-null int64 \n",
"dtypes: float64(3), int64(4)\n",
"memory usage: 2.1 MB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9fa88fc5-22e1-4048-be58-bf135f95c84a",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>HHID</th>\n",
" <th>TIME</th>\n",
" <th>PASTA</th>\n",
" <th>EXPOS</th>\n",
" <th>AGE</th>\n",
" <th>INCOME</th>\n",
" <th>AREA</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>40000.000000</td>\n",
" <td>40000.000000</td>\n",
" <td>40000.000000</td>\n",
" <td>40000.000000</td>\n",
" <td>40000.000000</td>\n",
" <td>40000.000000</td>\n",
" <td>40000.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>1000.500000</td>\n",
" <td>10.500000</td>\n",
" <td>1.841545</td>\n",
" <td>0.499100</td>\n",
" <td>42.251409</td>\n",
" <td>27096.126112</td>\n",
" <td>2.996000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>577.357414</td>\n",
" <td>5.766353</td>\n",
" <td>1.025911</td>\n",
" <td>0.702041</td>\n",
" <td>13.766646</td>\n",
" <td>15928.188291</td>\n",
" <td>1.409622</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>18.007529</td>\n",
" <td>609.107170</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>500.750000</td>\n",
" <td>5.750000</td>\n",
" <td>1.108936</td>\n",
" <td>0.000000</td>\n",
" <td>30.505706</td>\n",
" <td>15379.591862</td>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>1000.500000</td>\n",
" <td>10.500000</td>\n",
" <td>1.811417</td>\n",
" <td>0.000000</td>\n",
" <td>42.436092</td>\n",
" <td>24487.068020</td>\n",
" <td>3.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>1500.250000</td>\n",
" <td>15.250000</td>\n",
" <td>2.534365</td>\n",
" <td>1.000000</td>\n",
" <td>54.362395</td>\n",
" <td>35272.533787</td>\n",
" <td>4.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>2000.000000</td>\n",
" <td>20.000000</td>\n",
" <td>6.592769</td>\n",
" <td>5.000000</td>\n",
" <td>64.984126</td>\n",
" <td>141066.966883</td>\n",
" <td>5.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" HHID TIME PASTA EXPOS AGE \\\n",
"count 40000.000000 40000.000000 40000.000000 40000.000000 40000.000000 \n",
"mean 1000.500000 10.500000 1.841545 0.499100 42.251409 \n",
"std 577.357414 5.766353 1.025911 0.702041 13.766646 \n",
"min 1.000000 1.000000 0.000000 0.000000 18.007529 \n",
"25% 500.750000 5.750000 1.108936 0.000000 30.505706 \n",
"50% 1000.500000 10.500000 1.811417 0.000000 42.436092 \n",
"75% 1500.250000 15.250000 2.534365 1.000000 54.362395 \n",
"max 2000.000000 20.000000 6.592769 5.000000 64.984126 \n",
"\n",
" INCOME AREA \n",
"count 40000.000000 40000.000000 \n",
"mean 27096.126112 2.996000 \n",
"std 15928.188291 1.409622 \n",
"min 609.107170 1.000000 \n",
"25% 15379.591862 2.000000 \n",
"50% 24487.068020 3.000000 \n",
"75% 35272.533787 4.000000 \n",
"max 141066.966883 5.000000 "
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "beaea334-5950-4f81-9e80-5db8b2a0a761",
"metadata": {},
"outputs": [],
"source": [
"df_agg = df.groupby(\"AREA\")['INCOME'].agg(['min', 'max', 'mean'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ba1f3d27-69c2-4452-a191-f9ad325892b1",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" <th>min</th>\n",
" <th>max</th>\n",
" <th>mean</th>\n",
" </tr>\n",
" <tr>\n",
" <th>AREA</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2353.176959</td>\n",
" <td>111295.840727</td>\n",
" <td>25879.485698</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1331.260222</td>\n",
" <td>141066.966883</td>\n",
" <td>26030.910606</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>609.107170</td>\n",
" <td>106319.106367</td>\n",
" <td>26218.857948</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2740.776521</td>\n",
" <td>85241.329860</td>\n",
" <td>28076.599968</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1241.960729</td>\n",
" <td>112983.969631</td>\n",
" <td>29260.133137</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" min max mean\n",
"AREA \n",
"3 2353.176959 111295.840727 25879.485698\n",
"5 1331.260222 141066.966883 26030.910606\n",
"2 609.107170 106319.106367 26218.857948\n",
"1 2740.776521 85241.329860 28076.599968\n",
"4 1241.960729 112983.969631 29260.133137"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_agg.sort_values(by='mean')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3dad83ac-5bff-46a5-bdac-87eeaea55f1d",
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
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" <th></th>\n",
" <th>sum</th>\n",
" </tr>\n",
" <tr>\n",
" <th>HHID</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1493</th>\n",
" <td>55.361931</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1511</th>\n",
" <td>51.030304</td>\n",
" </tr>\n",
" <tr>\n",
" <th>151</th>\n",
" <td>50.816654</td>\n",
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" <tr>\n",
" <th>647</th>\n",
" <td>50.771865</td>\n",
" </tr>\n",
" <tr>\n",
" <th>916</th>\n",
" <td>50.502666</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>92</th>\n",
" <td>23.926629</td>\n",
" </tr>\n",
" <tr>\n",
" <th>267</th>\n",
" <td>23.380097</td>\n",
" </tr>\n",
" <tr>\n",
" <th>572</th>\n",
" <td>23.225390</td>\n",
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" <tr>\n",
" <th>1921</th>\n",
" <td>21.738494</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1917</th>\n",
" <td>19.256710</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2000 rows × 1 columns</p>\n",
"</div>"
],
"text/plain": [
" sum\n",
"HHID \n",
"1493 55.361931\n",
"1511 51.030304\n",
"151 50.816654\n",
"647 50.771865\n",
"916 50.502666\n",
"... ...\n",
"92 23.926629\n",
"267 23.380097\n",
"572 23.225390\n",
"1921 21.738494\n",
"1917 19.256710\n",
"\n",
"[2000 rows x 1 columns]"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.groupby('HHID')['PASTA'].agg(['sum']).sort_values(by='sum', ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f434e14e-7c37-456e-a642-01ca94343f72",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"TIME 218\n",
"PASTA 218\n",
"EXPOS 218\n",
"AGE 218\n",
"INCOME 218\n",
"AREA 218\n",
"dtype: int64"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[(df['AREA']==2) & (df['INCOME']> 20000)].groupby('HHID').agg('sum').query(\"PASTA>30\").count()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4757d0b4-50ff-441b-8887-f60aa6bc981d",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" <th>PASTA</th>\n",
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" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>PASTA</th>\n",
" <td>1.000000</td>\n",
" <td>0.326617</td>\n",
" </tr>\n",
" <tr>\n",
" <th>EXPOS</th>\n",
" <td>0.326617</td>\n",
" <td>1.000000</td>\n",
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" </tbody>\n",
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"</div>"
],
"text/plain": [
" PASTA EXPOS\n",
"PASTA 1.000000 0.326617\n",
"EXPOS 0.326617 1.000000"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[['PASTA', 'EXPOS']].corr()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fbe42523-1bc0-4b8a-a8f6-e8f4b66a187a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:ylabel='Frequency'>"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.groupby('HHID')['PASTA'].sum().plot(kind='hist')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2462a350-72fd-4e7a-aa2a-fb7db4ff3397",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
]
},
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='TIME', ylabel='PASTA'>"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.groupby('TIME')['PASTA'].sum().reset_index().plot.scatter(y='PASTA',x='TIME')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f96da096-2161-4afa-8eea-c1bc89d97cba",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}