add fri materials

spreadsheets.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8a4f78b6-0e92-4965-88c3-23110651b371",
+   "metadata": {},
+   "source": [
+    "# Spreadsheets\n",
+    "\n",
+    "By the end of this lesson, students will be able to:\n",
+    "\n",
+    "- Design spreadsheet data models that enable reproducible data analysis.\n",
+    "- Convert a pivot table operation to `pandas` `groupby` and vice versa.\n",
+    "- Write spreadsheet formulas that apply a function over many cells.\n",
+    "\n",
+    "For this lesson, we'll spend most of our time in the preceding notebook on [groupby-and-indexing.ipynb](groupby-and-indexing.ipynb).\n",
+    "\n",
+    "Later, we'll download the `earthquakes.csv` file and use it to create a spreadsheet. In lecture, we will visit [sheets.new](https://sheets.new) to create a new Google Sheet."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "494a3641",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "\n",
+    "sns.set_theme()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "37a0cdc1",
+   "metadata": {},
+   "source": [
+    "### What is pivot table?\n",
+    "\n",
+    "Let's first revisit the life expectancy dataset and use this as an example of showing what it is in pandas."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a344f81a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "life_expectancy = sns.load_dataset(\"healthexp\", index_col=[\"Year\", \"Country\"])\n",
+    "life_expectancy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b36c314e",
+   "metadata": {},
+   "source": [
+    "Let's try pivoting the table about the \"Country\" column. We can read the documentation of `pivot_table` [here](https://pandas.pydata.org/docs/reference/api/pandas.pivot_table.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "672500b8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pivoted_table = life_expectancy.pivot_table(index=\"Year\", columns=\"Country\", values=\"Life_Expectancy\")\n",
+    "pivoted_table.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1d994006",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "life_expectancy.columns, pivoted_table.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1eafc202",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.relplot(pivoted_table, kind=\"line\")\n",
+    "# pretty much the same as\n",
+    "# sns.relplot(life_expectancy, x=\"Year\", y=\"Life_Expectancy\", hue=\"Country\", kind=\"line\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d382e27c",
+   "metadata": {},
+   "source": [
+    "### Check the pandas version of earthquakes pivot table"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a32efdf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "earthquakes = pd.read_csv(\"earthquakes.csv\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": 5
+}