{
"cells": [
{
"cell_type": "markdown",
"id": "fleet-turkish",
"metadata": {},
"source": [
"### Question one (a)\n",
"\n",
"Think about what could be going wrong with our calculation. Think about a better way to evaluate this data. "
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "occasional-strand",
"metadata": {},
"outputs": [],
"source": [
"import warnings\n",
"warnings.simplefilter(action='ignore', category=FutureWarning)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "vulnerable-usage",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd \n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "choice-routine",
"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>order_id</th>\n",
" <th>shop_id</th>\n",
" <th>user_id</th>\n",
" <th>order_amount</th>\n",
" <th>total_items</th>\n",
" <th>payment_method</th>\n",
" <th>created_at</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>53</td>\n",
" <td>746</td>\n",
" <td>224</td>\n",
" <td>2</td>\n",
" <td>cash</td>\n",
" <td>2017-03-13 12:36:56</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>92</td>\n",
" <td>925</td>\n",
" <td>90</td>\n",
" <td>1</td>\n",
" <td>cash</td>\n",
" <td>2017-03-03 17:38:52</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>44</td>\n",
" <td>861</td>\n",
" <td>144</td>\n",
" <td>1</td>\n",
" <td>cash</td>\n",
" <td>2017-03-14 4:23:56</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>18</td>\n",
" <td>935</td>\n",
" <td>156</td>\n",
" <td>1</td>\n",
" <td>credit_card</td>\n",
" <td>2017-03-26 12:43:37</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>18</td>\n",
" <td>883</td>\n",
" <td>156</td>\n",
" <td>1</td>\n",
" <td>credit_card</td>\n",
" <td>2017-03-01 4:35:11</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" order_id shop_id user_id order_amount total_items payment_method \\\n",
"0 1 53 746 224 2 cash \n",
"1 2 92 925 90 1 cash \n",
"2 3 44 861 144 1 cash \n",
"3 4 18 935 156 1 credit_card \n",
"4 5 18 883 156 1 credit_card \n",
"\n",
" created_at \n",
"0 2017-03-13 12:36:56 \n",
"1 2017-03-03 17:38:52 \n",
"2 2017-03-14 4:23:56 \n",
"3 2017-03-26 12:43:37 \n",
"4 2017-03-01 4:35:11 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv(\"./2019 Winter Data Science Intern Challenge Data Set - Sheet1.csv\")\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "crazy-maple",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 5000 entries, 0 to 4999\n",
"Data columns (total 7 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 order_id 5000 non-null int64 \n",
" 1 shop_id 5000 non-null int64 \n",
" 2 user_id 5000 non-null int64 \n",
" 3 order_amount 5000 non-null int64 \n",
" 4 total_items 5000 non-null int64 \n",
" 5 payment_method 5000 non-null object\n",
" 6 created_at 5000 non-null object\n",
"dtypes: int64(5), object(2)\n",
"memory usage: 273.6+ KB\n"
]
}
],
"source": [
"#check for missing values, there are no missimg values :)\n",
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "metropolitan-smoke",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['cash', 'credit_card', 'debit'], dtype=object)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#check payment methods\n",
"df[\"payment_method\"].unique()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "solid-chorus",
"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>order_amount</th>\n",
" <th>total_items</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>5000.000000</td>\n",
" <td>5000.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>3145.128000</td>\n",
" <td>8.78720</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>41282.539349</td>\n",
" <td>116.32032</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>90.000000</td>\n",
" <td>1.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>163.000000</td>\n",
" <td>1.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>284.000000</td>\n",
" <td>2.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>390.000000</td>\n",
" <td>3.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>704000.000000</td>\n",
" <td>2000.00000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" order_amount total_items\n",
"count 5000.000000 5000.00000\n",
"mean 3145.128000 8.78720\n",
"std 41282.539349 116.32032\n",
"min 90.000000 1.00000\n",
"25% 163.000000 1.00000\n",
"50% 284.000000 2.00000\n",
"75% 390.000000 3.00000\n",
"max 704000.000000 2000.00000"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#check statistics of the data\n",
"df[[\"order_amount\", \"total_items\"]].describe()"
]
},
{
"cell_type": "markdown",
"id": "european-salem",
"metadata": {},
"source": [
"from the output, it can be easily noticed that mean value is extremely high compared to others values. The minimum value is 90 while the 25th, 50th, and 75th percentile are 163, 284 and 390 respectively. with 704000 been the maximum value we can assume that extremely large values (outliers) like this are what is overstating the value of the mean.\n",
"\n",
"A boxplot will show a more detailed view of the outliers in the data."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "deadly-support",
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df[\"order_amount\"].plot(kind=\"box\",) \n",
"plt.title(\"A boxplot showing the distribution of order amount\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "modified-president",
"metadata": {},
"source": [
"The boxplot shows there are alot of outliers, the line seems to be on 0."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "hydraulic-upset",
"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>order_amount</th>\n",
" <th>total_items</th>\n",
" <th>count</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>275</th>\n",
" <td>704000</td>\n",
" <td>2000</td>\n",
" <td>17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>274</th>\n",
" <td>154350</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>273</th>\n",
" <td>102900</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>272</th>\n",
" <td>77175</td>\n",
" <td>3</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>271</th>\n",
" <td>51450</td>\n",
" <td>2</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>270</th>\n",
" <td>25725</td>\n",
" <td>1</td>\n",
" <td>19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>269</th>\n",
" <td>1760</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>268</th>\n",
" <td>1408</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>267</th>\n",
" <td>1086</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>266</th>\n",
" <td>1064</td>\n",
" <td>8</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" order_amount total_items count\n",
"275 704000 2000 17\n",
"274 154350 6 1\n",
"273 102900 4 1\n",
"272 77175 3 9\n",
"271 51450 2 16\n",
"270 25725 1 19\n",
"269 1760 5 1\n",
"268 1408 4 2\n",
"267 1086 6 1\n",
"266 1064 8 1"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"orders = df.groupby([\"order_amount\", \"total_items\"]).size(\n",
" ).reset_index(name=\"count\").sort_values(by=\"order_amount\", ascending=False)\n",
"orders.head(10)"
]
},
{
"cell_type": "markdown",
"id": "handled-reservoir",
"metadata": {},
"source": [
"Digging deep into the data by grouping shows that a number of values are extremely high when compared to the rest of the values. Take a look at 704000 we can see from the table that the items ordered made the amount large because if the amount is divided by the total items it shows that each sneaker cost 352. "
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "established-royalty",
"metadata": {},
"outputs": [],
"source": [
"max_order_per_item = df[\"order_amount\"] / df['total_items']"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "dated-opportunity",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" 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>order_id</th>\n",
" <th>shop_id</th>\n",
" <th>user_id</th>\n",
" <th>order_amount</th>\n",
" <th>total_items</th>\n",
" <th>payment_method</th>\n",
" <th>created_at</th>\n",
" <th>max_order_per_item</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>53</td>\n",
" <td>746</td>\n",
" <td>224</td>\n",
" <td>2</td>\n",
" <td>cash</td>\n",
" <td>2017-03-13 12:36:56</td>\n",
" <td>112.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>92</td>\n",
" <td>925</td>\n",
" <td>90</td>\n",
" <td>1</td>\n",
" <td>cash</td>\n",
" <td>2017-03-03 17:38:52</td>\n",
" <td>90.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>44</td>\n",
" <td>861</td>\n",
" <td>144</td>\n",
" <td>1</td>\n",
" <td>cash</td>\n",
" <td>2017-03-14 4:23:56</td>\n",
" <td>144.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>18</td>\n",
" <td>935</td>\n",
" <td>156</td>\n",
" <td>1</td>\n",
" <td>credit_card</td>\n",
" <td>2017-03-26 12:43:37</td>\n",
" <td>156.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>18</td>\n",
" <td>883</td>\n",
" <td>156</td>\n",
" <td>1</td>\n",
" <td>credit_card</td>\n",
" <td>2017-03-01 4:35:11</td>\n",
" <td>156.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" order_id shop_id user_id order_amount total_items payment_method \\\n",
"0 1 53 746 224 2 cash \n",
"1 2 92 925 90 1 cash \n",
"2 3 44 861 144 1 cash \n",
"3 4 18 935 156 1 credit_card \n",
"4 5 18 883 156 1 credit_card \n",
"\n",
" created_at max_order_per_item \n",
"0 2017-03-13 12:36:56 112.0 \n",
"1 2017-03-03 17:38:52 90.0 \n",
"2 2017-03-14 4:23:56 144.0 \n",
"3 2017-03-26 12:43:37 156.0 \n",
"4 2017-03-01 4:35:11 156.0 "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[\"max_order_per_item\"] = max_order_per_item\n",
"df.head()\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "governmental-venture",
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
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" <th></th>\n",
" <th>max_order_per_item</th>\n",
" <th>order_amount</th>\n",
" <th>total_items</th>\n",
" <th>count</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>275</th>\n",
" <td>25725.0</td>\n",
" <td>154350</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>274</th>\n",
" <td>25725.0</td>\n",
" <td>102900</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>273</th>\n",
" <td>25725.0</td>\n",
" <td>77175</td>\n",
" <td>3</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>272</th>\n",
" <td>25725.0</td>\n",
" <td>51450</td>\n",
" <td>2</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>271</th>\n",
" <td>25725.0</td>\n",
" <td>25725</td>\n",
" <td>1</td>\n",
" <td>19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>270</th>\n",
" <td>352.0</td>\n",
" <td>704000</td>\n",
" <td>2000</td>\n",
" <td>17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>269</th>\n",
" <td>352.0</td>\n",
" <td>1760</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>268</th>\n",
" <td>352.0</td>\n",
" <td>1408</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>267</th>\n",
" <td>352.0</td>\n",
" <td>1056</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>266</th>\n",
" <td>352.0</td>\n",
" <td>704</td>\n",
" <td>2</td>\n",
" <td>13</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" max_order_per_item order_amount total_items count\n",
"275 25725.0 154350 6 1\n",
"274 25725.0 102900 4 1\n",
"273 25725.0 77175 3 9\n",
"272 25725.0 51450 2 16\n",
"271 25725.0 25725 1 19\n",
"270 352.0 704000 2000 17\n",
"269 352.0 1760 5 1\n",
"268 352.0 1408 4 2\n",
"267 352.0 1056 3 3\n",
"266 352.0 704 2 13"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"order_per_item = df.groupby([\"max_order_per_item\", \"order_amount\", \"total_items\"]).size(\n",
" ).reset_index(name=\"count\").sort_values(by=\"max_order_per_item\", ascending=False)\n",
"order_per_item.head(10)"
]
},
{
"cell_type": "markdown",
"id": "intelligent-missouri",
"metadata": {},
"source": [
"From the table above it is obvious that the first row is extremely large for a pair of sneakers, therefore we can attribute that to be an exception(luxury) or a mistake during data entry.\n",
"\n",
"Next, I'll remove the outliers using the interquarter range method to see the effects"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "italian-tractor",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"order_id 2499.5\n",
"shop_id 51.0\n",
"user_id 150.0\n",
"order_amount 227.0\n",
"total_items 2.0\n",
"max_order_per_item 36.0\n",
"dtype: float64\n"
]
}
],
"source": [
"q1 = df.quantile(0.25)\n",
"q3 = df.quantile(0.75)\n",
"iqr = q3-q1\n",
"print(iqr)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "colonial-edmonton",
"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>order_id</th>\n",
" <th>shop_id</th>\n",
" <th>user_id</th>\n",
" <th>order_amount</th>\n",
" <th>total_items</th>\n",
" <th>payment_method</th>\n",
" <th>created_at</th>\n",
" <th>max_order_per_item</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>53</td>\n",
" <td>746</td>\n",
" <td>224</td>\n",
" <td>2</td>\n",
" <td>cash</td>\n",
" <td>2017-03-13 12:36:56</td>\n",
" <td>112.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>92</td>\n",
" <td>925</td>\n",
" <td>90</td>\n",
" <td>1</td>\n",
" <td>cash</td>\n",
" <td>2017-03-03 17:38:52</td>\n",
" <td>90.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>44</td>\n",
" <td>861</td>\n",
" <td>144</td>\n",
" <td>1</td>\n",
" <td>cash</td>\n",
" <td>2017-03-14 4:23:56</td>\n",
" <td>144.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>18</td>\n",
" <td>935</td>\n",
" <td>156</td>\n",
" <td>1</td>\n",
" <td>credit_card</td>\n",
" <td>2017-03-26 12:43:37</td>\n",
" <td>156.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>18</td>\n",
" <td>883</td>\n",
" <td>156</td>\n",
" <td>1</td>\n",
" <td>credit_card</td>\n",
" <td>2017-03-01 4:35:11</td>\n",
" <td>156.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" order_id shop_id user_id order_amount total_items payment_method \\\n",
"0 1 53 746 224 2 cash \n",
"1 2 92 925 90 1 cash \n",
"2 3 44 861 144 1 cash \n",
"3 4 18 935 156 1 credit_card \n",
"4 5 18 883 156 1 credit_card \n",
"\n",
" created_at max_order_per_item \n",
"0 2017-03-13 12:36:56 112.0 \n",
"1 2017-03-03 17:38:52 90.0 \n",
"2 2017-03-14 4:23:56 144.0 \n",
"3 2017-03-26 12:43:37 156.0 \n",
"4 2017-03-01 4:35:11 156.0 "
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_df = df[~((df < (q1 - (1.5 * iqr))) |\n",
" (df > (q3 + (1.5 * iqr)))).any(axis=1)]\n",
"new_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "absolute-surrey",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 4831.000000\n",
"mean 292.430346\n",
"std 143.256683\n",
"min 90.000000\n",
"25% 162.000000\n",
"50% 280.000000\n",
"75% 380.000000\n",
"max 730.000000\n",
"Name: order_amount, dtype: float64"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_df[\"order_amount\"].describe()"
]
},
{
"cell_type": "markdown",
"id": "alert-oliver",
"metadata": {},
"source": [
"After removing the outliers it can be noticed that the value of the mean dropped to a more representative value of the entire dataset with other statistics still within range.\n",
"\n",
"Let's make a boxplot to show it in details"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "russian-burlington",
"metadata": {},
"outputs": [
{
"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": [
"new_df.boxplot(column=\"order_amount\")\n",
"plt.title(\"A boxplot showing the distribution of order amount\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "toxic-madonna",
"metadata": {},
"source": [
"Although there are visible outliers can be fixed but there is improvement in the appearence of the boxplot."
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "equal-beaver",
"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>order_id</th>\n",
" <th>shop_id</th>\n",
" <th>user_id</th>\n",
" <th>order_amount</th>\n",
" <th>total_items</th>\n",
" <th>payment_method</th>\n",
" <th>created_at</th>\n",
" <th>max_order_per_item</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>16</td>\n",
" <td>42</td>\n",
" <td>607</td>\n",
" <td>704000</td>\n",
" <td>2000</td>\n",
" <td>credit_card</td>\n",
" <td>2017-03-07 4:00:00</td>\n",
" <td>352.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>41</td>\n",
" <td>42</td>\n",
" <td>793</td>\n",
" <td>352</td>\n",
" <td>1</td>\n",
" <td>credit_card</td>\n",
" <td>2017-03-24 14:15:41</td>\n",
" <td>352.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60</th>\n",
" <td>61</td>\n",
" <td>42</td>\n",
" <td>607</td>\n",
" <td>704000</td>\n",
" <td>2000</td>\n",
" <td>credit_card</td>\n",
" <td>2017-03-04 4:00:00</td>\n",
" <td>352.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99</th>\n",
" <td>100</td>\n",
" <td>18</td>\n",
" <td>752</td>\n",
" <td>780</td>\n",
" <td>5</td>\n",
" <td>cash</td>\n",
" <td>2017-03-06 23:41:16</td>\n",
" <td>156.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>136</th>\n",
" <td>137</td>\n",
" <td>15</td>\n",
" <td>961</td>\n",
" <td>765</td>\n",
" <td>5</td>\n",
" <td>credit_card</td>\n",
" <td>2017-03-26 5:06:46</td>\n",
" <td>153.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" order_id shop_id user_id order_amount total_items payment_method \\\n",
"15 16 42 607 704000 2000 credit_card \n",
"40 41 42 793 352 1 credit_card \n",
"60 61 42 607 704000 2000 credit_card \n",
"99 100 18 752 780 5 cash \n",
"136 137 15 961 765 5 credit_card \n",
"\n",
" created_at max_order_per_item \n",
"15 2017-03-07 4:00:00 352.0 \n",
"40 2017-03-24 14:15:41 352.0 \n",
"60 2017-03-04 4:00:00 352.0 \n",
"99 2017-03-06 23:41:16 156.0 \n",
"136 2017-03-26 5:06:46 153.0 "
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_out = df[((df < (q1 - 1.5 * iqr)) |(df > (q3 + 1.5 * iqr))).any(axis=1)]\n",
"df_out.head()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "arranged-republican",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The rows left after removing outliers are: 4831\n"
]
}
],
"source": [
"print(\"The rows left after removing outliers are:\", new_df.shape[0])"
]
},
{
"cell_type": "markdown",
"id": "psychological-keeping",
"metadata": {},
"source": [
"### Question one (b)\n",
"\n",
"What metric would you report for this dataset?"
]
},
{
"cell_type": "markdown",
"id": "enabling-audience",
"metadata": {},
"source": [
"Looking at the analysis done earlier it is obvious that that the mean was affected by extreme values so it will be preferrable to use a measure of dispersion that isn't affected by extreme values. Therefore, I'll be using the median value."
]
},
{
"cell_type": "markdown",
"id": "breathing-flush",
"metadata": {},
"source": [
"### Question one (c) \n",
"\n",
"What is its value?"
]
},
{
"cell_type": "markdown",
"id": "corrected-judgment",
"metadata": {},
"source": [
"The median value of the original data is 284."
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "rural-atlantic",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 5000.000000\n",
"mean 3145.128000\n",
"std 41282.539349\n",
"min 90.000000\n",
"25% 163.000000\n",
"50% 284.000000\n",
"75% 390.000000\n",
"max 704000.000000\n",
"Name: order_amount, dtype: float64"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[\"order_amount\"].describe()"
]
},
{
"cell_type": "markdown",
"id": "advisory-emerald",
"metadata": {},
"source": [
" while the median value for the truncated data is 280."
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "honest-sunrise",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 4831.000000\n",
"mean 292.430346\n",
"std 143.256683\n",
"min 90.000000\n",
"25% 162.000000\n",
"50% 280.000000\n",
"75% 380.000000\n",
"max 730.000000\n",
"Name: order_amount, dtype: float64"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_df[\"order_amount\"].describe()"
]
},
{
"cell_type": "markdown",
"id": "alike-genesis",
"metadata": {},
"source": [
"### Question two (a) SQL\n",
"\n",
"How many orders were shipped by Speedy Express in total?"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "unknown-services",
"metadata": {},
"outputs": [],
"source": [
"SELECT count(*) \n",
"FROM orders, shippers\n",
"where orders.shipperid = shippers.shipperid \n",
"and shippers.shippername = \"Speedy Express\";"
]
},
{
"cell_type": "markdown",
"id": "colored-ontario",
"metadata": {},
"source": [
"Output: 54"
]
},
{
"cell_type": "markdown",
"id": "reverse-burden",
"metadata": {},
"source": [
"### Question two (b)\n",
"\n",
"What is the last name of the employee with the most orders?"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "apparent-contemporary",
"metadata": {},
"outputs": [],
"source": [
"SELECT employees.lastname, count(*) as count\n",
"from orders, employees\n",
"where orders.employeeid = employees.employeeid\n",
"group by orders.employeeid\n",
"order by count DESC"
]
},
{
"cell_type": "markdown",
"id": "loved-interference",
"metadata": {},
"source": [
"Output: Peacock with 40 orders"
]
},
{
"cell_type": "markdown",
"id": "latin-green",
"metadata": {},
"source": [
"### Question two (c)\n",
"\n",
"What product was ordered the most by customers in Germany?"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fifty-conducting",
"metadata": {},
"outputs": [],
"source": [
"SELECT p.ProductID, p.ProductName, SUM(Quantity) AS TotalQuantity \n",
"FROM Orders AS o, OrderDetails AS od, Customers AS c, Products AS p \n",
"WHERE c.Country = \"Germany\" AND od.OrderID = o.OrderID \n",
"AND od.ProductID = p.ProductID AND c.CustomerID = o.CustomerID \n",
"GROUP BY p.ProductID \n",
"ORDER BY TotalQuantity DESC \n",
"LIMIT 1;"
]
},
{
"cell_type": "markdown",
"id": "norwegian-milwaukee",
"metadata": {},
"source": [
"Output: Boston crab meat with 160 quantity"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "successful-absence",
"metadata": {},
"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": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}