From ffc64e5dcb0fee51e9e85937b8d7d9f38297396a Mon Sep 17 00:00:00 2001
From: Shohel4921 <114329105+Shohel4921@users.noreply.github.com>
Date: Sun, 25 Sep 2022 12:39:47 +0600
Subject: [PATCH] Create blank.yml
---
.github/workflows/blank.yml | 1321 +++++++++++++++++++++++++++++++++++
1 file changed, 1321 insertions(+)
create mode 100644 .github/workflows/blank.yml
diff --git a/.github/workflows/blank.yml b/.github/workflows/blank.yml
new file mode 100644
index 0000000..4a656d2
--- /dev/null
+++ b/.github/workflows/blank.yml
@@ -0,0 +1,1321 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "
\n",
+ "\n",
+ "![](\"https://user-images.githubusercontent.com/7065401/58563302-42466a80-8201-11e9-9948-b3e9f88a5662.jpg\"\n",)
\n",
+ "\n",
+ "# Exercises\n",
+ "## Bike store sales"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "## Hands on! "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sales = pd.read_csv(\n",
+ " 'data/sales_data.csv',\n",
+ " parse_dates=['Date'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sales.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### What's the mean of `Customers_Age`?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# your code goes here\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "cell_type": "hint"
+ },
+ "source": [
+ "Why don't you try with `.mean()`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cell_type": "solution"
+ },
+ "outputs": [],
+ "source": [
+ "sales['Customer_Age'].mean()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Go ahead and show a density (KDE) and a box plot with the `Customer_Age` data:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# your code goes here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cell_type": "solution"
+ },
+ "outputs": [],
+ "source": [
+ "sales['Customer_Age'].plot(kind='kde', figsize=(14,6))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cell_type": "solution"
+ },
+ "outputs": [],
+ "source": [
+ "sales['Customer_Age'].plot(kind='box', vert=False, figsize=(14,6))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### What's the mean of `Order_Quantity`?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# your code goes here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cell_type": "solution"
+ },
+ "outputs": [],
+ "source": [
+ "sales['Order_Quantity'].mean()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Go ahead and show a histogram and a box plot with the `Order_Quantity` data:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# your code goes here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cell_type": "solution"
+ },
+ "outputs": [],
+ "source": [
+ "sales['Order_Quantity'].plot(kind='hist', bins=30, figsize=(14,6))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cell_type": "solution"
+ },
+ "outputs": [],
+ "source": [
+ "sales['Order_Quantity'].plot(kind='box', vert=False, figsize=(14,6))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### How many sales per year do we have?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# your code goes here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cell_type": "solution"
+ },
+ "outputs": [],
+ "source": [
+ "sales['Year'].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Go ahead and show a pie plot with the previous data:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# your code goes here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cell_type": "solution"
+ },
+ "outputs": [],
+ "source": [
+ "sales['Year'].value_counts().plot(kind='pie', figsize=(6,6))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### How many sales per month do we have?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# your code goes here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cell_type": "solution"
+ },
+ "outputs": [],
+ "source": [
+ "sales['Month'].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Go ahead and show a bar plot with the previous data:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# your code goes here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cell_type": "solution"
+ },
+ "outputs": [],
+ "source": [
+ "sales['Month'].value_counts().plot(kind='bar', figsize=(14,6))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### Which country has the most sales `quantity of sales`?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# your code goes here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cell_type": "solution"
+ },
+ "outputs": [],
+ "source": [
+ "sales['Country'].value_counts().head(1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cell_type": "solution"
+ },
+ "outputs": [],
+ "source": [
+ "sales['Country'].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Go ahead and show a bar plot of the sales per country:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# your code goes here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cell_type": "solution"
+ },
+ "outputs": [],
+ "source": [
+ "sales['Country'].value_counts().plot(kind='bar', figsize=(14,6))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### Create a list of every product sold"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# your code goes here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cell_type": "solution"
+ },
+ "outputs": [],
+ "source": [
+ "#sales.loc[:, 'Product'].unique()\n",
+ "\n",
+ "sales['Product'].unique()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Create a **bar plot** showing the 10 most sold products (best sellers):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# your code goes here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cell_type": "solution"
+ },
+ "outputs": [],
+ "source": [
+ "sales['Product'].value_counts().head(10).plot(kind='bar', figsize=(14,6))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### Can you see any relationship between `Unit_Cost` and `Unit_Price`?\n",
+ "\n",
+ "Show a scatter plot between both columns."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# your code goes here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cell_type": "solution"
+ },
+ "outputs": [],
+ "source": [
+ "sales.plot(kind='scatter', x='Unit_Cost', y='Unit_Price', figsize=(6,6))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### Can you see any relationship between `Order_Quantity` and `Profit`?\n",
+ "\n",
+ "Show a scatter plot between both columns."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# your code goes here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cell_type": "solution"
+ },
+ "outputs": [],
+ "source": [
+ "sales.plot(kind='scatter', x='Order_Quantity', y='Profit', figsize=(6,6))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### Can you see any relationship between `Profit` per `Country`?\n",
+ "\n",
+ "Show a grouped box plot per country with the profit values."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# your code goes here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cell_type": "solution"
+ },
+ "outputs": [],
+ "source": [
+ "sales[['Profit', 'Country']].boxplot(by='Country', figsize=(10,6))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### Can you see any relationship between the `Customer_Age` per `Country`?\n",
+ "\n",
+ "Show a grouped box plot per country with the customer age values."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# your code goes here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cell_type": "solution"
+ },
+ "outputs": [],
+ "source": [
+ "sales[['Customer_Age', 'Country']].boxplot(by='Country', figsize=(10,6))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### Add and calculate a new `Calculated_Date` column\n",
+ "\n",
+ "Use `Day`, `Month`, `Year` to create a `Date` column (`YYYY-MM-DD`)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# your code goes here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cell_type": "solution"
+ },
+ "outputs": [],
+ "source": [
+ "sales['Calculated_Date'] = sales[['Year', 'Month', 'Day']].apply(lambda x: '{}-{}-{}'.format(x[0], x[1], x[2]), axis=1)\n",
+ "\n",
+ "sales['Calculated_Date'].head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### Parse your `Calculated_Date` column into a datetime object"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# your code goes here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cell_type": "solution"
+ },
+ "outputs": [],
+ "source": [
+ "sales['Calculated_Date'] = pd.to_datetime(sales['Calculated_Date'])\n",
+ "\n",
+ "sales['Calculated_Date'].head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### How did sales evolve through the years?\n",
+ "\n",
+ "Show a line plot using `Calculated_Date` column as the x-axis and the count of sales as the y-axis."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# your code goes here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cell_type": "solution"
+ },
+ "outputs": [],
+ "source": [
+ "sales['Calculated_Date'].value_counts().plot(kind='line', figsize=(14,6))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### Increase 50 U$S revenue to every sale"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# your code goes here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cell_type": "solution"
+ },
+ "outputs": [],
+ "source": [
+ "#sales['Revenue'] = sales['Revenue'] + 50\n",
+ "\n",
+ "sales['Revenue'] += 50"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### How many orders were made in `Canada` or `France`?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# your code goes here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cell_type": "solution"
+ },
+ "outputs": [],
+ "source": [
+ "sales.loc[(sales['Country'] == 'Canada') | (sales['Country'] == 'France')].shape[0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### How many `Bike Racks` orders were made from Canada?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# your code goes here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cell_type": "solution"
+ },
+ "outputs": [],
+ "source": [
+ "sales.loc[(sales['Country'] == 'Canada') & (sales['Sub_Category'] == 'Bike Racks')].shape[0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### How many orders were made in each region (state) of France?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# your code goes here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cell_type": "solution"
+ },
+ "outputs": [],
+ "source": [
+ "france_states = sales.loc[sales['Country'] == 'France', 'State'].value_counts()\n",
+ "\n",
+ "france_states"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Go ahead and show a bar plot with the results:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# your code goes here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cell_type": "solution"
+ },
+ "outputs": [],
+ "source": [
+ "france_states.plot(kind='bar', figsize=(14,6))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### How many sales were made per category?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# your code goes here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cell_type": "solution"
+ },
+ "outputs": [],
+ "source": [
+ "sales['Product_Category'].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Go ahead and show a pie plot with the results:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# your code goes here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cell_type": "solution"
+ },
+ "outputs": [],
+ "source": [
+ "sales['Product_Category'].value_counts().plot(kind='pie', figsize=(6,6))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### How many orders were made per accessory sub-categories?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# your code goes here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cell_type": "solution"
+ },
+ "outputs": [],
+ "source": [
+ "accessories = sales.loc[sales['Product_Category'] == 'Accessories', 'Sub_Category'].value_counts()\n",
+ "\n",
+ "accessories"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Go ahead and show a bar plot with the results:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# your code goes here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cell_type": "solution"
+ },
+ "outputs": [],
+ "source": [
+ "accessories.plot(kind='bar', figsize=(14,6))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### How many orders were made per bike sub-categories?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# your code goes here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cell_type": "solution"
+ },
+ "outputs": [],
+ "source": [
+ "bikes = sales.loc[sales['Product_Category'] == 'Bikes', 'Sub_Category'].value_counts()\n",
+ "\n",
+ "bikes"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Go ahead and show a pie plot with the results:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# your code goes here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cell_type": "solution"
+ },
+ "outputs": [],
+ "source": [
+ "bikes.plot(kind='pie', figsize=(6,6))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### Which gender has the most amount of sales?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# your code goes here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cell_type": "solution"
+ },
+ "outputs": [],
+ "source": [
+ "sales['Customer_Gender'].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cell_type": "solution"
+ },
+ "outputs": [],
+ "source": [
+ "sales['Customer_Gender'].value_counts().plot(kind='bar')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### How many sales with more than 500 in `Revenue` were made by men?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# your code goes here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cell_type": "solution"
+ },
+ "outputs": [],
+ "source": [
+ "sales.loc[(sales['Customer_Gender'] == 'M') & (sales['Revenue'] == 500)].shape[0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### Get the top-5 sales with the highest revenue"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# your code goes here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cell_type": "solution"
+ },
+ "outputs": [],
+ "source": [
+ "sales.sort_values(['Revenue'], ascending=False).head(5)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### Get the sale with the highest revenue"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# your code goes here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cell_type": "solution"
+ },
+ "outputs": [],
+ "source": [
+ "#sales.sort_values(['Revenue'], ascending=False).head(1)\n",
+ "\n",
+ "cond = sales['Revenue'] == sales['Revenue'].max()\n",
+ "\n",
+ "sales.loc[cond]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### What is the mean `Order_Quantity` of orders with more than 10K in revenue?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# your code goes here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cell_type": "solution"
+ },
+ "outputs": [],
+ "source": [
+ "cond = sales['Revenue'] > 10_000\n",
+ "\n",
+ "sales.loc[cond, 'Order_Quantity'].mean()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### What is the mean `Order_Quantity` of orders with less than 10K in revenue?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# your code goes here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cell_type": "solution"
+ },
+ "outputs": [],
+ "source": [
+ "cond = sales['Revenue'] < 10_000\n",
+ "\n",
+ "sales.loc[cond, 'Order_Quantity'].mean()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### How many orders were made in May of 2016?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# your code goes here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cell_type": "solution"
+ },
+ "outputs": [],
+ "source": [
+ "cond = (sales['Year'] == 2016) & (sales['Month'] == 'May')\n",
+ "\n",
+ "sales.loc[cond].shape[0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### How many orders were made between May and July of 2016?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# your code goes here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cell_type": "solution"
+ },
+ "outputs": [],
+ "source": [
+ "cond = (sales['Year'] == 2016) & (sales['Month'].isin(['May', 'June', 'July']))\n",
+ "\n",
+ "sales.loc[cond].shape[0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Show a grouped box plot per month with the profit values."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# your code goes here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cell_type": "solution"
+ },
+ "outputs": [],
+ "source": [
+ "profit_2016 = sales.loc[sales['Year'] == 2016, ['Profit', 'Month']]\n",
+ "\n",
+ "profit_2016.boxplot(by='Month', figsize=(14,6))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "### Add 7.2% TAX on every sale `Unit_Price` within United States"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# your code goes here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "cell_type": "solution"
+ },
+ "outputs": [],
+ "source": [
+ "#sales.loc[sales['Country'] == 'United States', 'Unit_Price'] = sales.loc[sales['Country'] == 'United States', 'Unit_Price'] * 1.072\n",
+ "\n",
+ "sales.loc[sales['Country'] == 'United States', 'Unit_Price'] *= 1.072"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "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": "3.8.1"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}