week-11-activities.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using Pandas to work with, and manipulate data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_customers = pd.read_csv('files/customers.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_customers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Can we create a mask to search for customers living in Virginia?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hint, we can use the fancy mask feature discussed in lecture..."
   ]
  },
  {
   "cell_type": "code",
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   "source": []
  },
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  {
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  {
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  {
   "cell_type": "code",
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  {
   "cell_type": "code",
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   "source": [
    "# create a query mask for state\n",
    "query_mask = df_customers['state'] == \"VA\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_customers[query_mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
  },
  {
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Can we search for customers that live on a \"parkway\""
   ]
  },
  {
   "cell_type": "code",
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  },
  {
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  {
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  {
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  {
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  {
   "cell_type": "code",
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  {
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  {
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   "source": [
    "query_mask_pkwy = df_customers['address'].str.contains(\"Pkwy\")\n",
    "query_mask_parkway = df_customers['address'].str.contains(\"Parkway\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_customers[query_mask_pkwy | query_mask_parkway]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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  },
  {
   "cell_type": "code",
   "execution_count": null,
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  {
   "cell_type": "code",
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Can we serarch for a customer that lives on a Parkway and has first name Thomas??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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  },
  {
   "cell_type": "code",
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  {
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  {
   "cell_type": "code",
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  {
   "cell_type": "code",
   "execution_count": null,
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  },
  {
   "cell_type": "code",
   "execution_count": null,
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  },
  {
   "cell_type": "code",
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  {
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   "cell_type": "code",
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   "source": [
    "query_mask_pkwy = df_customers['address'].str.contains(\"Pkwy\")\n",
    "query_mask_parkway = df_customers['address'].str.contains(\"Parkway\")\n",
    "query_mask_thomas = df_customers['first_name'] == \"Thomas\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_customers[(query_mask_pkwy | query_mask_parkway) & query_mask_thomas]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
  },
  {
   "cell_type": "code",
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  },
  {
   "cell_type": "code",
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  },
  {
   "cell_type": "code",
   "execution_count": null,
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  },
  {
   "cell_type": "code",
   "execution_count": null,
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  },
  {
   "cell_type": "code",
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's bring in another data source to work with...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_orders = pd.read_csv('files/orders.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_orders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_customers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How do we combine these together to get a table of orders with the customer infomration?"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "\n",
    "pd.merge(df_customers, df_orders, on='customer_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.merge(df_customers, df_orders, left_on='customer_id', right_on='customer_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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  },
  {
   "cell_type": "code",
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's bring in a third dataframe for some fun..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "df_returns = pd.read_csv('files/returns.csv', index_col=0)\n",
    "df_returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's start simple... how do we get a list of returns with customer infomration?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
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  {
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  {
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   "source": [
    "\n",
    "pd.merge(df_customers, df_returns, left_index=True, right_on='return_customer_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now wht if we want to see a \"full merge\" where get all the orders and returns even when there is no overlap?"
   ]
  },
  {
   "cell_type": "code",
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  {
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  {
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  {
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  {
   "cell_type": "code",
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  {
   "cell_type": "code",
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   "outputs": [],
   "source": [
    "pd.merge(df_orders, df_returns, on='order_id', how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_combined = pd.merge(df_orders, df_returns, on='order_id', how='outer', suffixes=('_orders', '_returns'))\n",
    "df_combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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  },
  {
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## But wait a minute!  What happened to values!  We have a lot of garbage and weird data types now!\n",
    "\n",
    "### How can we change back the customer ID to an integer?"
   ]
  },
  {
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   "source": [
    "df_combined['customer_id_clean'] = (df_combined['customer_id'] > 0).astype('int32')\n",
    "df_combined"
   ]
  },
  {
   "cell_type": "code",
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  {
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  {
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  {
   "cell_type": "code",
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  {
   "cell_type": "code",
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We can also use regular expressions to clean up dataframes!!!  Here we are going to change the string values for he dollar amounts to floating numbers!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_combined['amount_returns'] = df_combined['amount_returns'].replace({'\\$': '', ',': ''}, regex=True).astype(float)\n",
    "df_combined['amount_orders'] = df_combined['amount_orders'].replace({'\\$': '', ',': ''}, regex=True).astype(float)\n",
    "df_combined"
   ]
  },
  {
   "cell_type": "code",
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  },
  {
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We can also change the string version of the data time to actual date time values!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_combined['order_date_orders'] = df_combined['order_date_orders'].astype('datetime64[ns]')\n",
    "df_combined['order_date_returns'] = df_combined['order_date_returns'].astype('datetime64[ns]')\n",
    "df_combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_combined.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now that we have our column types clearn up, can we create a new column that gives us the days between the order date and return date?"
   ]
  },
  {
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df_combined['days_between'] = df_combined['order_date_returns'] - df_combined['order_date_orders']\n",
    "df_combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's say for the days between order and return the store is earning interest on the money from the sale (before it is returned).  Let's say the store earns a daily periodic rate of 0.0043%. How much money would they earn holding on to customer's money?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "hint!  Just like with the str dataframe methods we need to use the [datatime methods](https://pandas.pydata.org/pandas-docs/version/1.0.1/reference/api/pandas.Series.dt.html?highlight=dt#pandas.Series.dt)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "periodic_rate_daily = 0.000043\n",
    "\n",
    "s_interest = df_combined['amount_orders'] * df_combined['days_between'].dt.days * periodic_rate_daily\n",
    "s_interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s_interest.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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