Create notebook for the fifth application of tidying

5_one_type_in_multiple_tables.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# One Type in multiple Tables\n",
+    "\n",
+    "The repository with the original R code does not provide code for this case but only refers to other projects that cannot be replicated any more (source website not available any more)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Messy Data\n",
+    "\n",
+    "> It’s also common to find data values about a single type of observational unit spread out over multiple tables or files. These tables and files are often split up by another variable, so that each represents a single year, person, or location."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Tidy Data\n",
+    "\n",
+    "> As long as the format for individual records is consistent, this is an easy problem to fix:\n",
+    "1. Read the files into a list of tables.\n",
+    "2. For each table, add a new column that records the original file name (because the file name is often the value of an important variable).\n",
+    "3. Combine all tables into a single table"
+   ]
+  }
+ ],
+ "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": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}