{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load Excel File\n",
    "filename = 'data/car_financing.xlsx'\n",
    "df = pd.read_excel(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Filtering \n",
    "car_filter = df['car_type']=='Toyota Sienna'\n",
    "interest_filter = df['interest_rate']==0.0702\n",
    "df = df.loc[car_filter & interest_filter, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Approach 1 dictionary substitution using rename method\n",
    "df = df.rename(columns={'Starting Balance': 'starting_balance',\n",
    "                        'Interest Paid': 'interest_paid', \n",
    "                        'Principal Paid': 'principal_paid',\n",
    "                        'New Balance': 'new_balance'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Approach 2 list replacement\n",
    "# Only changing Month -> month, but we need to list the rest of the columns\n",
    "df.columns = ['month',\n",
    "              'starting_balance',\n",
    "              'Repayment',\n",
    "              'interest_paid',\n",
    "              'principal_paid',\n",
    "              'new_balance',\n",
    "              'term',\n",
    "              'interest_rate',\n",
    "              'car_type']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Approach 1\n",
    "# This approach allows you to drop multiple columns at a time \n",
    "df = df.drop(columns=['term'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Approach 2 use the del command\n",
    "del df['Repayment']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# missing values can be excluded in calculations by default. \n",
    "# excludes missing values in the calculation \n",
    "interest_missing = df['interest_paid'].isna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill in with the actual value\n",
    "df.loc[interest_missing,'interest_paid'] = 93.24"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convert Pandas DataFrames to NumPy arrays or Dictionaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert Pandas DataFrames to NumPy Arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Approach 1\n",
    "df.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Approach 2\n",
    "df.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert Pandas DataFrames to Dictionaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_dict()"
   ]
  }
 ],
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