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jaymody/shopify-ds-intern-challenge.ipynb

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Shopify DS Intern Challenge
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shopify-ds-intern-challenge.ipynb
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{
"source": [
"# Question 1\n",
"To start, let's load the csv file into a pandas dataframe and take a look at the data."
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# !pip install pandas seaborn\n",
"import pandas as pd\n",
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv(\"2019 Winter Data Science Intern Challenge Data Set - Sheet1.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"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 "
],
"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>"
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"source": [
"df.head()"
]
},
{
"source": [
"The `order_amount` is our column of interest, so let's naively take the mean as our metric for computing the AOV."
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"3145.128"
]
},
"metadata": {},
"execution_count": 4
}
],
"source": [
"df[\"order_amount\"].mean()"
]
},
{
"source": [
"This reproduces the number given in the initial analysis, `3145.13`. This seems quite high, given that our customers are purchasing sneakers. The problem is that we have outliers which are skewing the mean. To verify this, we'll look at the distribution plot of `order_amount`."
],
"cell_type": "markdown",
"metadata": {}
},
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"cell_type": "code",
"execution_count": 5,
"metadata": {},
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{
"output_type": "execute_result",
"data": {
"text/plain": [
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]
},
"metadata": {},
"execution_count": 5
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"output_type": "display_data",
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\n"
},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"sns.displot(df[\"order_amount\"], log_scale=True)"
]
},
{
"source": [
"Note, the x-axis is a log-scale, so the outliers are much \"farther\" away than they look. I would guess that the outliers represent bulk orders made by companies/organizations. We probably don't want to include those orders when considering the \"average\" order value of your \"typical\" customer (who is a person and not a company with lots of money). Here are some possible alternatives for quantifyin the \"average\" order value:"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"## Method 1: Median\n",
"The median gives us the \"middlemost\" value (ie, half our dataset is less than this value, the other half is greater than this value, which in a sense gives us an \"average\" value). The median is highly resistant to outliers, so it gives us a much more reasonable result for the AOV:"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"284.0"
]
},
"metadata": {},
"execution_count": 6
}
],
"source": [
"df[\"order_amount\"].median()"
]
},
{
"source": [
"## Method 2: Remove Outliers via Standard Deviations\n",
"We can filter out outliers by only keeping values that are within 2 standard deviations of the mean (a common trick in math/data analysis). Then we can take the mean. However, as you'll see, this method is not perfect since the mean and standard devaition themselves are influenced by outliers."
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"702.7484440875327"
]
},
"metadata": {},
"execution_count": 7
}
],
"source": [
"def std_outliers(data, m=2):\n",
" \"\"\"Filter out datapoints that are more than m standard devations away from the mean\"\"\"\n",
" return data[data - data.mean() < m*data.std()]\n",
"std_outliers(df[\"order_amount\"]).mean()"
]
},
{
"source": [
"## Method 3: Remove Outliers via Median Absolute Deviations\n",
"To counter the pitfalls of the mean/standard deviation method, we can instead use the median and median absolute deviation metric to remove outliers. This gives us an answer that falls more in line with our median calculation:"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"265.1712483100496"
]
},
"metadata": {},
"execution_count": 8
}
],
"source": [
"def mdev_outliers(data, m=2):\n",
" \"\"\"Filter out datapoints that are more than m median absolute devations away from the median\"\"\"\n",
" d = (data - data.median()).abs()\n",
" mdev = d.median()\n",
" s = d/mdev if mdev else 0\n",
" return data[s<m]\n",
"\n",
"mdev_outliers(df[\"order_amount\"]).mean()"
]
},
{
"source": [
"## Method 4: Remove Outliers via Threshold\n",
"Looking at our distribution plot from earlier, it seems that most of the orders are below 1000. This makes sense, since you're typically consumer is unlikely to spend more than \\$1000 on a single order of sneakers. As such, we can just filter out any orders with an amount > \\$1000, then take the mean:"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"301.06431324812337"
]
},
"metadata": {},
"execution_count": 9
}
],
"source": [
"df[df[\"order_amount\"] < 1000][\"order_amount\"].mean()"
]
},
{
"source": [
"# Question 2"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"## Part A"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Output: 54\n"
]
}
],
"source": [
"\"\"\"\n",
"SELECT COUNT(*)\n",
"FROM Orders\n",
"INNER JOIN Shippers\n",
"ON Orders.ShipperID = Shippers.ShipperID\n",
"WHERE Shippers.ShipperName = 'Speedy Express'\n",
"\"\"\"\n",
"\n",
"print(\"Output: 54\")"
]
},
{
"source": [
"## Part B"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Output: Peacock\n"
]
}
],
"source": [
"\"\"\"\n",
"SELECT LastName\n",
"FROM Orders\n",
"INNER JOIN Employees\n",
"ON Orders.EmployeeID = Employees.EmployeeID\n",
"GROUP BY Orders.EmployeeID\n",
"ORDER BY COUNT(Orders.OrderID) DESC\n",
"LIMIT 1\n",
"\"\"\"\n",
"\n",
"print(\"Output: Peacock\")"
]
},
{
"source": [
"## Part C\n",
"I've provided two solutions here and stated my assumptions, since \"most ordered product\" is ambigous. "
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Output: Gorgonzola Telino\n"
]
}
],
"source": [
"# Assumption: Most ordered product means the product that showed up in the most orders (quantity is not taken into account)\n",
"\n",
"\"\"\"\n",
"SELECT Products.ProductName\n",
"FROM Orders\n",
"INNER JOIN OrderDetails\n",
"ON Orders.OrderID = OrderDetails.OrderID\n",
"INNER JOIN Products\n",
"ON OrderDetails.ProductID = Products.ProductID\n",
"INNER JOIN Customers\n",
"ON Orders.CustomerID = Customers.CustomerID\n",
"WHERE Customers.Country = 'Germany'\n",
"GROUP BY Products.ProductID\n",
"ORDER BY Count(OrderDetails.ProductID) DESC\n",
"LIMIT 1\n",
"\"\"\"\n",
"\n",
"print(\"Output: Gorgonzola Telino\")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Output: Boston Crab Meat\n"
]
}
],
"source": [
"# Assumption: Most ordered product means the product that had the largest total quantity across all orders\n",
"\n",
"\"\"\"\n",
"SELECT Products.ProductName\n",
"FROM Orders\n",
"INNER JOIN OrderDetails\n",
"ON Orders.OrderID = OrderDetails.OrderID\n",
"INNER JOIN Products\n",
"ON OrderDetails.ProductID = Products.ProductID\n",
"INNER JOIN Customers\n",
"ON Orders.CustomerID = Customers.CustomerID\n",
"WHERE Customers.Country = 'Germany'\n",
"GROUP BY Products.ProductID\n",
"ORDER BY SUM(OrderDetails.Quantity) DESC\n",
"LIMIT 1\n",
"\"\"\"\n",
"\n",
"print(\"Output: Boston Crab Meat\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
]
}
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