14_Introduction to Importing Data in Python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Importing Data in Python\n",
    "\n",
    "The 14th course in the career track **Data Scientist with Python**.\n",
    "\n",
    "In this course, you'll learn the many ways to import data into Python: from flat files such as .txt and .csv; from files native to other software such as Excel spreadsheets, Stata, SAS, and MATLAB files; and from relational databases such as SQLite and PostgreSQL.\n",
    "\n",
    "1. Read flat files: [use NumPy](#1.2.1-Using-NumPy), or [use Pandas](#1.2.2-Using-Pandas) method `read_csv()`.\n",
    "3. [Read pickle files](#2.1-Pickled-file): use the package `pickle`.\n",
    "4. [Read Excel files](#2.2-Excel-file): use Pandas method `read_excel()`, or use Pandas class `ExcelFile`.\n",
    "5. [Read SAS files](#2.3-SAS-file): use the package `sas7bdat`.\n",
    "6. [Read Stata files](#2.4-Stata-file): use Pandas method `read_stata()`.\n",
    "7. [Read HDF5 files](#2.5-HDF5-file): use the package `h5py`.\n",
    "8. [Read MATLAB files](#2.6-MATLAB-file): use the module `scipy.io`.\n",
    "9. Relational databases: query data [using a connection](#3.1-Query-data-using-a-connection), or [using Pandas](#3.2-Query-data-using-Pandas-method) method `read_sql_query()`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Flat files\n",
    "\n",
    "Flat files are text files containing records. That is table data.\n",
    "- file extension: `.csv` (comma separated values), `.txt`\n",
    "- record: row of fields (attributes)\n",
    "- column: feature (attribute)\n",
    "- delimiters: commas, tabs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 Read text files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "filepath = \"data/moby_dick.txt\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Read entire file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CHAPTER 1. Loomings.\n",
      "\n",
      "Call me Ishmael. Some years ago--never mind how long precisely--having\n",
      "little or no money in my purse, and nothing particular to interest me on\n",
      "shore, I thought I would sail about a little and see the watery part of\n",
      "the world. It is a way I have of driving off the spleen and regulating\n",
      "the circulation. Whenever I find myself growing grim about the mouth;\n",
      "whenever it is a damp, drizzly November in my soul; whenever I find\n",
      "myself involuntarily pausing before coffin warehouses, and bringing up\n",
      "the rear of every funeral I meet; and especially whenever my hypos get\n",
      "such an upper hand of me, that it requires a strong moral principle to\n",
      "prevent me from deliberately stepping into the street, and methodically\n",
      "knocking people's hats off--then, I account it high time to get to sea\n",
      "as soon as I can. This is my substitute for pistol and ball. With a\n",
      "philosophical flourish Cato throws himself upon his sword; I quietly\n",
      "take to the ship. There is nothing surprising in this. If they but knew\n",
      "it, almost all men in their degree, some time or other, cherish very\n",
      "nearly the same feelings towards the ocean with me.\n"
     ]
    }
   ],
   "source": [
    "with open(filepath, \"r\") as fp:\n",
    "    data = fp.read()\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Read file line by line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CHAPTER 1. Loomings.\n",
      "\n",
      "\n",
      "\n",
      "Call me Ishmael. Some years ago--never mind how long precisely--having\n",
      "\n",
      "little or no money in my purse, and nothing particular to interest me on\n",
      "\n"
     ]
    }
   ],
   "source": [
    "nb_lines = 4  # read the first 4 lines\n",
    "with open(filepath, \"r\") as fp:\n",
    "    for i in range(nb_lines):\n",
    "        line = fp.readline()\n",
    "        print(line)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Read flat files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "digits_file = \"data/digits.csv\"\n",
    "digits_header_file = \"data/digits_header.txt\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`digits.csv` is a part of [MNIST dataset](http://yann.lecun.com/exdb/mnist/) in CSV format. It contains 100 rows. Each row consists of 785 values:\n",
    "- the first value is the label (a number from 0 to 9)\n",
    "- the remaining 784 values are the pixel values (a number fro 0 to 255) of an image of size 28x28.\n",
    "\n",
    "More information about the MNIST dataset provided in a CSV format is at [kaggle](https://www.kaggle.com/oddrationale/mnist-in-csv).\n",
    "\n",
    "`digits_header.txt` is similar to `digits.csv` except that:\n",
    "- it has headers at the first line.\n",
    "- the delimiter is a tab."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "sea_slug_file = \"data/seaslug.txt\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`seaslug.txt` consists of percentage of a sea slug larvae that had metamorphosed in a given time period.\n",
    "- it has a text header consisting of strings\n",
    "- the remaing rows consisting numbers\n",
    "- it is tab delimited\n",
    "\n",
    "More information about seaslug at [Sea Slug Data](http://www.stat.ucla.edu/~rgould/datasets/aboutseaslugs.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_file = \"data/titanic.csv\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`titanic.csv` is a part of the dataset at [Titanic - Machine Learning from Disaster](https://www.kaggle.com/c/titanic/data) at kaggle."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.2.1 Using NumPy\n",
    "\n",
    "- all columns have the same data type: use the method `loadtxt()`.\n",
    "- columns have different data types: use one of methods: `genfromtxt()`, `recfromcsv()`, or `recfromtxt()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Using the method `loadtxt()`\n",
    "\n",
    "This method reads data in a file then returns a numpy array. It **requires all elements having the same data type**.\n",
    "\n",
    "By default, the data type of the output array is `float`. We can change it by giving value to the parameter `dtype` of the method."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Data doesn't have headers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.loadtxt(digits_file, delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [1. 0. 0. ... 0. 0. 0.]\n",
      " [4. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]]\n",
      "(100, 785)\n",
      "float64\n"
     ]
    }
   ],
   "source": [
    "print(data[:5,:])\n",
    "print(data.shape)\n",
    "print(data.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SZkl6uYQ+vsPMjs1OnMjMjpU0Vc03FfXLkm7K7t8k6Q8l9nKIZpnGO2+acZW87Uqf/tzdG/4n6Wr1npH/SNKiMnrI6esMSe9kf++W3ZukF9R7WPf/6j238TNJJ0haJ+lDSWsljWyi3v5DvVN7d6s3WGNK6m2yeg/RuyW9nf1dXfa2S/TVkO3G12WBIDhBBwRB2IEgCDsQBGEHgiDsQBCEHQiCsANB/B2NuKA2vYtOzgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label: 6\n"
     ]
    }
   ],
   "source": [
    "# cast type of entire elements to int\n",
    "data = data.astype(\"int\")\n",
    "\n",
    "# show an image with its label\n",
    "label, im_flat = data[21,0], data[21,1:]\n",
    "im = np.reshape(im_flat, (28, 28))\n",
    "plt.imshow(im, cmap=\"gray\")\n",
    "plt.show()\n",
    "print(\"label: %s\" % label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Data has a row of headers and we skip it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.loadtxt(digits_header_file, delimiter=\"\\t\", skiprows=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parameters:\n",
    "- `delimiter`: delimiter in the reading file.\n",
    "- `skiprows`: a number specifies **how many rows to skip**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [1. 0. 0. ... 0. 0. 0.]\n",
      " [4. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]]\n",
      "(100, 785)\n",
      "float64\n"
     ]
    }
   ],
   "source": [
    "print(data[:5,:])\n",
    "print(data.shape)\n",
    "print(data.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another useful parameter of this method is `usecols`. It receives a list of the **indices of columns to keep**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.loadtxt(digits_file, delimiter=\",\", usecols=[0, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 0.]\n",
      " [0. 0.]\n",
      " [1. 0.]\n",
      " [4. 0.]\n",
      " [0. 0.]]\n",
      "(100, 2)\n",
      "float64\n"
     ]
    }
   ],
   "source": [
    "print(data[:5,:])\n",
    "print(data.shape)\n",
    "print(data.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Data has a row of headers and we keep it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_header = np.loadtxt(sea_slug_file, dtype='str', delimiter=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(48, 2)\n",
      "[['Time' 'Percent']\n",
      " ['99' '0.067']\n",
      " ['99' '0.133']\n",
      " ['99' '0.067']\n",
      " ['99' '0']]\n"
     ]
    }
   ],
   "source": [
    "print(data_header.shape)\n",
    "print(data_header[:5,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "header, data = data_header[0,:], data_header[1:,:].astype(\"float\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data[:,0], data[:,1])\n",
    "plt.xlabel(header[0])\n",
    "plt.ylabel(header[1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Using the method `genfromtxt()`\n",
    "\n",
    "This method can read flat files in which **columns have different data types** by indicating the value of the parameter `dtype` is `None`.\n",
    "\n",
    "The resulting returned is a structured array object where each element of the array is a row of the flat file imported.\n",
    "\n",
    "In the data returned, we can:\n",
    "- access row by the index, for example, `data[2]`\n",
    "- access column by the column name, for example, `data[\"Pclass\"]`\n",
    "- access an element by row index & column name/column index, for example, `data[2][\"Pclass\"]`, `data[2][2]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.genfromtxt(titanic_file, delimiter=\",\", names=True, dtype=None, encoding=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parameters:\n",
    "- `names`: True, specifying the file has headers\n",
    "- `dtype`: None, data types will be determined by the contents of each column individually\n",
    "- `encoding`: None, use the system default encoding to decode the file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['PassengerId', 'Survived', 'Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked']\n"
     ]
    }
   ],
   "source": [
    "headers = list(data.dtype.fields.keys())\n",
    "print(headers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(891,)\n",
      "[(1, 0, 3, 'male', 22., 1, 0, 'A/5 21171',  7.25  , '', 'S')\n",
      " (2, 1, 1, 'female', 38., 1, 0, 'PC 17599', 71.2833, 'C85', 'C')\n",
      " (3, 1, 3, 'female', 26., 0, 0, 'STON/O2. 3101282',  7.925 , '', 'S')\n",
      " (4, 1, 1, 'female', 35., 1, 0, '113803', 53.1   , 'C123', 'S')\n",
      " (5, 0, 3, 'male', 35., 0, 0, '373450',  8.05  , '', 'S')]\n",
      "[3 1 3 1 3 3 1 3 3 2 3 1 3 3 3 2 3 2 3 3 2 2 3 1 3 3 3 1 3 3 1 1 3 2 1 1 3\n",
      " 3 3 3 3 2 3 2 3 3 3 3 3 3 3 3 1 2 1 1 2 3 2 3 3 1 1 3 1 3 2 3 3 3 2 3 2 3\n",
      " 3 3 3 3 2 3 3 3 3 1 2 3 3 3 1 3 3 3 1 3 3 3 1 1 2 2 3 3 1 3 3 3 3 3 3 3 1\n",
      " 3 3 3 3 3 3 2 1 3 2 3 2 2 1 3 3 3 3 3 3 3 3 2 2 2 1 1 3 1 3 3 3 3 2 2 3 3\n",
      " 2 2 2 1 3 3 3 1 3 3 3 3 3 2 3 3 3 3 1 3 1 3 1 3 3 3 1 3 3 1 2 3 3 2 3 2 3\n",
      " 1 3 1 3 3 2 2 3 2 1 1 3 3 3 2 3 3 3 3 3 3 3 3 3 1 3 2 3 2 3 1 3 2 1 2 3 2\n",
      " 3 3 1 3 2 3 2 3 1 3 2 3 2 3 2 2 2 2 3 3 2 3 3 1 3 2 1 2 3 3 1 3 3 3 1 1 1\n",
      " 2 3 3 1 1 3 2 3 3 1 1 1 3 2 1 3 1 3 2 3 3 3 3 3 3 1 3 3 3 2 3 1 1 2 3 3 1\n",
      " 3 1 1 1 3 3 3 2 3 1 1 1 2 1 1 1 2 3 2 3 2 2 1 1 3 3 2 2 3 1 3 2 3 1 3 1 1\n",
      " 3 1 3 1 1 3 1 2 1 2 2 2 2 2 3 3 3 3 1 3 3 3 3 1 2 3 3 3 2 3 3 3 3 1 3 3 1\n",
      " 1 3 3 1 3 1 3 1 3 3 1 3 3 1 3 2 3 2 3 2 1 3 3 1 3 3 3 2 2 2 3 3 3 3 3 2 3\n",
      " 2 3 3 3 3 1 2 3 3 2 2 2 3 3 3 3 3 3 3 2 2 3 3 1 3 2 3 1 1 3 2 1 2 2 3 3 2\n",
      " 3 1 2 1 3 1 2 3 1 1 3 3 1 1 2 3 1 3 1 2 3 3 2 1 3 3 3 3 2 2 3 1 2 3 3 3 3\n",
      " 2 3 3 1 3 1 1 3 3 3 3 1 1 3 3 1 3 1 3 3 3 3 3 1 1 2 1 3 3 3 3 1 1 3 1 2 3\n",
      " 2 3 1 3 3 1 3 3 2 1 3 2 2 3 3 3 3 2 1 1 3 1 1 3 3 2 1 1 2 2 3 2 1 2 3 3 3\n",
      " 1 1 1 1 3 3 3 2 3 3 3 3 3 3 3 2 1 1 3 3 3 2 1 3 3 2 1 2 1 3 1 2 1 3 3 3 1\n",
      " 3 3 2 3 2 3 3 1 2 3 1 3 1 3 3 1 2 1 3 3 3 3 3 2 3 3 2 2 3 1 3 3 3 1 2 1 3\n",
      " 3 1 3 1 1 3 2 3 2 3 3 3 1 3 3 3 1 3 1 3 3 3 2 3 3 3 2 3 3 2 1 1 3 1 3 3 2\n",
      " 2 3 3 1 2 1 2 2 2 3 3 3 3 1 3 1 3 3 2 2 3 3 3 1 1 3 3 3 1 2 3 3 1 3 1 1 3\n",
      " 3 3 2 2 1 1 3 1 1 1 3 2 3 1 2 3 3 2 3 2 2 1 3 2 3 2 3 1 3 2 2 2 3 3 1 3 3\n",
      " 1 1 1 3 3 1 3 2 1 3 2 3 3 3 2 2 3 2 3 1 3 3 3 1 3 1 1 3 3 3 3 3 2 3 2 3 3\n",
      " 3 3 1 3 1 1 3 3 3 3 3 3 1 3 2 3 1 3 2 1 3 3 3 2 2 1 3 3 3 1 3 2 1 3 3 2 3\n",
      " 3 1 3 2 3 3 1 3 1 3 3 3 3 2 3 1 3 2 3 3 3 1 3 3 3 1 3 2 1 3 3 3 3 3 2 1 3\n",
      " 3 3 1 2 3 1 1 3 3 3 2 1 3 2 2 2 1 3 3 3 1 1 3 2 3 3 3 3 1 2 3 3 2 3 3 2 1\n",
      " 3 1 3]\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "print(data.shape)\n",
    "print(data[:5])\n",
    "print(data[\"Pclass\"])\n",
    "print(data[2][\"Pclass\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Using the method `recfromcsv()`\n",
    "\n",
    "This method is similar to the method `genfromtxt()` except that:\n",
    "- the `delimiter` is set to comma `,` by default.\n",
    "- the `dtype` is set to `None` by default.\n",
    "- the `names` is set to `True` by default.\n",
    "- headers are in lower case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.recfromcsv(titanic_file, encoding=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['passengerid', 'survived', 'pclass', 'sex', 'age', 'sibsp', 'parch', 'ticket', 'fare', 'cabin', 'embarked']\n"
     ]
    }
   ],
   "source": [
    "headers = list(data.dtype.fields.keys())\n",
    "print(headers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(891,)\n",
      "[(1, 0, 3, 'male', 22., 1, 0, 'A/5 21171',  7.25  , '', 'S')\n",
      " (2, 1, 1, 'female', 38., 1, 0, 'PC 17599', 71.2833, 'C85', 'C')\n",
      " (3, 1, 3, 'female', 26., 0, 0, 'STON/O2. 3101282',  7.925 , '', 'S')\n",
      " (4, 1, 1, 'female', 35., 1, 0, '113803', 53.1   , 'C123', 'S')\n",
      " (5, 0, 3, 'male', 35., 0, 0, '373450',  8.05  , '', 'S')]\n",
      "[3 1 3 1 3 3 1 3 3 2 3 1 3 3 3 2 3 2 3 3 2 2 3 1 3 3 3 1 3 3 1 1 3 2 1 1 3\n",
      " 3 3 3 3 2 3 2 3 3 3 3 3 3 3 3 1 2 1 1 2 3 2 3 3 1 1 3 1 3 2 3 3 3 2 3 2 3\n",
      " 3 3 3 3 2 3 3 3 3 1 2 3 3 3 1 3 3 3 1 3 3 3 1 1 2 2 3 3 1 3 3 3 3 3 3 3 1\n",
      " 3 3 3 3 3 3 2 1 3 2 3 2 2 1 3 3 3 3 3 3 3 3 2 2 2 1 1 3 1 3 3 3 3 2 2 3 3\n",
      " 2 2 2 1 3 3 3 1 3 3 3 3 3 2 3 3 3 3 1 3 1 3 1 3 3 3 1 3 3 1 2 3 3 2 3 2 3\n",
      " 1 3 1 3 3 2 2 3 2 1 1 3 3 3 2 3 3 3 3 3 3 3 3 3 1 3 2 3 2 3 1 3 2 1 2 3 2\n",
      " 3 3 1 3 2 3 2 3 1 3 2 3 2 3 2 2 2 2 3 3 2 3 3 1 3 2 1 2 3 3 1 3 3 3 1 1 1\n",
      " 2 3 3 1 1 3 2 3 3 1 1 1 3 2 1 3 1 3 2 3 3 3 3 3 3 1 3 3 3 2 3 1 1 2 3 3 1\n",
      " 3 1 1 1 3 3 3 2 3 1 1 1 2 1 1 1 2 3 2 3 2 2 1 1 3 3 2 2 3 1 3 2 3 1 3 1 1\n",
      " 3 1 3 1 1 3 1 2 1 2 2 2 2 2 3 3 3 3 1 3 3 3 3 1 2 3 3 3 2 3 3 3 3 1 3 3 1\n",
      " 1 3 3 1 3 1 3 1 3 3 1 3 3 1 3 2 3 2 3 2 1 3 3 1 3 3 3 2 2 2 3 3 3 3 3 2 3\n",
      " 2 3 3 3 3 1 2 3 3 2 2 2 3 3 3 3 3 3 3 2 2 3 3 1 3 2 3 1 1 3 2 1 2 2 3 3 2\n",
      " 3 1 2 1 3 1 2 3 1 1 3 3 1 1 2 3 1 3 1 2 3 3 2 1 3 3 3 3 2 2 3 1 2 3 3 3 3\n",
      " 2 3 3 1 3 1 1 3 3 3 3 1 1 3 3 1 3 1 3 3 3 3 3 1 1 2 1 3 3 3 3 1 1 3 1 2 3\n",
      " 2 3 1 3 3 1 3 3 2 1 3 2 2 3 3 3 3 2 1 1 3 1 1 3 3 2 1 1 2 2 3 2 1 2 3 3 3\n",
      " 1 1 1 1 3 3 3 2 3 3 3 3 3 3 3 2 1 1 3 3 3 2 1 3 3 2 1 2 1 3 1 2 1 3 3 3 1\n",
      " 3 3 2 3 2 3 3 1 2 3 1 3 1 3 3 1 2 1 3 3 3 3 3 2 3 3 2 2 3 1 3 3 3 1 2 1 3\n",
      " 3 1 3 1 1 3 2 3 2 3 3 3 1 3 3 3 1 3 1 3 3 3 2 3 3 3 2 3 3 2 1 1 3 1 3 3 2\n",
      " 2 3 3 1 2 1 2 2 2 3 3 3 3 1 3 1 3 3 2 2 3 3 3 1 1 3 3 3 1 2 3 3 1 3 1 1 3\n",
      " 3 3 2 2 1 1 3 1 1 1 3 2 3 1 2 3 3 2 3 2 2 1 3 2 3 2 3 1 3 2 2 2 3 3 1 3 3\n",
      " 1 1 1 3 3 1 3 2 1 3 2 3 3 3 2 2 3 2 3 1 3 3 3 1 3 1 1 3 3 3 3 3 2 3 2 3 3\n",
      " 3 3 1 3 1 1 3 3 3 3 3 3 1 3 2 3 1 3 2 1 3 3 3 2 2 1 3 3 3 1 3 2 1 3 3 2 3\n",
      " 3 1 3 2 3 3 1 3 1 3 3 3 3 2 3 1 3 2 3 3 3 1 3 3 3 1 3 2 1 3 3 3 3 3 2 1 3\n",
      " 3 3 1 2 3 1 1 3 3 3 2 1 3 2 2 2 1 3 3 3 1 1 3 2 3 3 3 3 1 2 3 3 2 3 3 2 1\n",
      " 3 1 3]\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "print(data.shape)\n",
    "print(data[:5])\n",
    "print(data[\"pclass\"])\n",
    "print(data[2][\"pclass\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.2.2 Using Pandas\n",
    "\n",
    "The method `read_csv()` is used to read flat files. By default, it considers:\n",
    "- the file has headers.\n",
    "- the delimiter is comma.\n",
    "- data types of columns will be determined by the contents individually."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Normal CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(titanic_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass     Sex   Age  SibSp  Parch  \\\n",
       "0            1         0       3    male  22.0      1      0   \n",
       "1            2         1       1  female  38.0      1      0   \n",
       "2            3         1       3  female  26.0      0      0   \n",
       "3            4         1       1  female  35.0      1      0   \n",
       "4            5         0       3    male  35.0      0      0   \n",
       "\n",
       "             Ticket     Fare Cabin Embarked  \n",
       "0         A/5 21171   7.2500   NaN        S  \n",
       "1          PC 17599  71.2833   C85        C  \n",
       "2  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3            113803  53.1000  C123        S  \n",
       "4            373450   8.0500   NaN        S  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PassengerId      int64\n",
      "Survived         int64\n",
      "Pclass           int64\n",
      "Sex             object\n",
      "Age            float64\n",
      "SibSp            int64\n",
      "Parch            int64\n",
      "Ticket          object\n",
      "Fare           float64\n",
      "Cabin           object\n",
      "Embarked        object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# explorer data\n",
    "display(df.head())\n",
    "print(df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['PassengerId', 'Survived', 'Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked']\n"
     ]
    }
   ],
   "source": [
    "# get headers\n",
    "headers = list(df.columns)\n",
    "print(headers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert to numpy array\n",
    "data = df.values\n",
    "type(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['Age'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Non-header file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(digits_file, header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
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       "      <th>6</th>\n",
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       "      <th>9</th>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 785 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   0    1    2    3    4    5    6    7    8    9    ...  775  776  777  778  \\\n",
       "0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0   \n",
       "1  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0   \n",
       "2  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0   \n",
       "3  4.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0   \n",
       "4  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0   \n",
       "\n",
       "   779  780  781  782  783  784  \n",
       "0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "1  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "3  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "4  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "\n",
       "[5 rows x 785 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 785)\n",
      "0      float64\n",
      "1      float64\n",
      "2      float64\n",
      "3      float64\n",
      "4      float64\n",
      "        ...   \n",
      "780    float64\n",
      "781    float64\n",
      "782    float64\n",
      "783    float64\n",
      "784    float64\n",
      "Length: 785, dtype: object\n"
     ]
    }
   ],
   "source": [
    "display(df.head())\n",
    "print(df.shape)\n",
    "print(df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label: 0\n"
     ]
    }
   ],
   "source": [
    "# show an image with its label\n",
    "data = df.values[4].astype(\"int\")\n",
    "label, im_flat = data[0], data[1:]\n",
    "im = np.reshape(im_flat, (28, 28))\n",
    "plt.imshow(im, cmap=\"gray\")\n",
    "plt.show()\n",
    "print(\"label: %s\" % label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Tab delimiter file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(sea_slug_file, sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>99</td>\n",
       "      <td>0.067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>99</td>\n",
       "      <td>0.133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>99</td>\n",
       "      <td>0.067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>99</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>99</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time  Percent\n",
       "0    99    0.067\n",
       "1    99    0.133\n",
       "2    99    0.067\n",
       "3    99    0.000\n",
       "4    99    0.000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(47, 2)\n",
      "Time         int64\n",
      "Percent    float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "display(df.head())\n",
    "print(df.shape)\n",
    "print(df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df.Time, df.Percent)\n",
    "plt.xlabel(df.columns[0])\n",
    "plt.ylabel(df.columns[1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Other file types\n",
    "\n",
    "In the first section, we learn how to import flat files, but there are many other file types we will potentially have to work with as a data scientist. In this section, we'll figure out how to import data into Python from a wide array of important file types. These include pickled files, Excel spreadsheets, SAS and Stata files, HDF5 files, a file type for storing large quantities of numerical data, and MATLAB files."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle_file = \"data/data.pkl\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`data.pkl` contains a dictionary which is serialized in bytes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "battle_file = \"data/battledeath.xlsx\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The spreadsheet `battledeath.xlsx` is modified from the Peace Research Institute Oslo's (PRIO) [dataset](https://www.prio.org/Data/Armed-Conflict/Battle-Deaths/The-Battle-Deaths-Dataset-version-30/). This data contains age-adjusted mortality rates due to war in various countries over several years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "sales_file = \"data/sales.sas7bdat\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data `sales.sas7bdat` are adapted from the website of the undergraduate text book [Principles of Econometrics](http://www.principlesofeconometrics.com/) by Hill, Griffiths and Lim."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "disarea_file = \"data/disarea.dta\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data file `disarea.dta` consists of disease extents for several diseases in various countries (more information can be found [here](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/SPHS5E))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "ligo_file = \"data/LIGO_data.hdf5\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can find the LIGO data plus loads of documentation and tutorials [here](https://losc.ligo.org/events/GW150914/). There is also a great tutorial on Signal Processing with the data [here](https://www.gw-openscience.org/GW150914data/LOSC_Event_tutorial_GW150914.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "albeck_file = \"data/albeck_gene_expression.mat\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`albeck_gene_expression.mat` contains [gene expression data](https://www.mcb.ucdavis.edu/faculty-labs/albeck/workshop.htm) from the Albeck Lab at UC Davis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Pickled files\n",
    "\n",
    "There are a number of datatypes that cannot be saved easily to flat files, such as lists and dictionaries. If you want to be able to import them into Python, you can **serialize** them. All this means is converting the object into a **sequence of bytes, or a bytestream**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'June': 69.4, 'Aug': 85, 'Airline': 8, 'Mar': 84.4}\n",
      "<class 'dict'>\n"
     ]
    }
   ],
   "source": [
    "# load data\n",
    "with open(pickle_file, \"rb\") as fp:\n",
    "    data = pickle.load(fp)\n",
    "print(data)\n",
    "print(type(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# serialize data\n",
    "with open(pickle_file, \"wb\") as fp:\n",
    "    pickle.dump(data, fp, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The advantage of `HIGHEST_PROTOCOL` is that files get smaller. This makes unpickling sometimes much faster.\n",
    "- The **maximum file size** of pickle is about 2GB."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Excel files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using the method `read_excel()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read the first sheet\n",
    "df = pd.read_excel(battle_file, engine=\"openpyxl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The latest version of xlrd (2.0.1) only supports `.xls` files. The reason `xlsx` support was removed is because it had potential security vulnerabilities and no-one was maintaining it.\n",
    "- The engine `openpyxl` is used to read `.xlsx` file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>War, age-adjusted mortality due to</th>\n",
       "      <th>2002</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>36.083990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>0.128908</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>18.314120</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>18.964560</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  War, age-adjusted mortality due to       2002  Unnamed: 2  Unnamed: 3  \\\n",
       "0                        Afghanistan  36.083990         NaN         NaN   \n",
       "1                            Albania   0.128908         NaN         NaN   \n",
       "2                            Algeria  18.314120         NaN         NaN   \n",
       "3                            Andorra   0.000000         NaN         NaN   \n",
       "4                             Angola  18.964560         NaN         NaN   \n",
       "\n",
       "   Unnamed: 4  \n",
       "0         NaN  \n",
       "1         NaN  \n",
       "2         NaN  \n",
       "3         NaN  \n",
       "4         NaN  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(192, 5)\n"
     ]
    }
   ],
   "source": [
    "display(df.head())\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read all sheets\n",
    "data = pd.read_excel(battle_file, engine=\"openpyxl\", sheet_name=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- `sheet_name=None` to read all sheets in the Excel file.\n",
    "- The returned data is a dictionary which has keys contain sheet names, values are dataframes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['2002', '2004'])\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>War, age-adjusted mortality due to</th>\n",
       "      <th>2002</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>36.083990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>0.128908</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>18.314120</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>18.964560</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  War, age-adjusted mortality due to       2002  Unnamed: 2  Unnamed: 3  \\\n",
       "0                        Afghanistan  36.083990         NaN         NaN   \n",
       "1                            Albania   0.128908         NaN         NaN   \n",
       "2                            Algeria  18.314120         NaN         NaN   \n",
       "3                            Andorra   0.000000         NaN         NaN   \n",
       "4                             Angola  18.964560         NaN         NaN   \n",
       "\n",
       "   Unnamed: 4  \n",
       "0         NaN  \n",
       "1         NaN  \n",
       "2         NaN  \n",
       "3         NaN  \n",
       "4         NaN  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>War(country)</th>\n",
       "      <th>2004</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>9.451028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>0.130354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>3.407277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>2.597931</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  War(country)      2004\n",
       "0  Afghanistan  9.451028\n",
       "1      Albania  0.130354\n",
       "2      Algeria  3.407277\n",
       "3      Andorra  0.000000\n",
       "4       Angola  2.597931"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get sheet names\n",
    "sheet_names = data.keys()\n",
    "print(sheet_names)\n",
    "# explorer data\n",
    "display(data[\"2002\"].head())\n",
    "display(data[\"2004\"].head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using the class `ExcelFile()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "xls = pd.ExcelFile(battle_file, engine=\"openpyxl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Return an ExcelFile object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['2002', '2004']\n"
     ]
    }
   ],
   "source": [
    "# get sheet names\n",
    "sheet_names = xls.sheet_names\n",
    "print(sheet_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>War, age-adjusted mortality due to</th>\n",
       "      <th>2002</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>36.083990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>0.128908</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>18.314120</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>18.964560</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  War, age-adjusted mortality due to       2002  Unnamed: 2  Unnamed: 3  \\\n",
       "0                        Afghanistan  36.083990         NaN         NaN   \n",
       "1                            Albania   0.128908         NaN         NaN   \n",
       "2                            Algeria  18.314120         NaN         NaN   \n",
       "3                            Andorra   0.000000         NaN         NaN   \n",
       "4                             Angola  18.964560         NaN         NaN   \n",
       "\n",
       "   Unnamed: 4  \n",
       "0         NaN  \n",
       "1         NaN  \n",
       "2         NaN  \n",
       "3         NaN  \n",
       "4         NaN  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(192, 5)\n"
     ]
    }
   ],
   "source": [
    "# read a sheet by its index\n",
    "df = xls.parse(0)\n",
    "# explorer data\n",
    "display(df.head())\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>War(country)</th>\n",
       "      <th>2004</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>9.451028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>0.130354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>3.407277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>2.597931</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  War(country)      2004\n",
       "0  Afghanistan  9.451028\n",
       "1      Albania  0.130354\n",
       "2      Algeria  3.407277\n",
       "3      Andorra  0.000000\n",
       "4       Angola  2.597931"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(192, 2)\n"
     ]
    }
   ],
   "source": [
    "# read a sheet by its name\n",
    "df = xls.parse(\"2004\")\n",
    "# explorer data\n",
    "display(df.head())\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 SAS files\n",
    "\n",
    "SAS file is a Statistical Analysis Software file. It has extension `.sas7bdat` (storing dataset) or `.sas7bcat` (storing SAS catalog).\n",
    "\n",
    "We use the package `sas7bdat` to read SAS files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sas7bdat import SAS7BDAT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "with SAS7BDAT(sales_file) as fp:\n",
    "    df = fp.to_data_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YEAR</th>\n",
       "      <th>P</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1950.0</td>\n",
       "      <td>12.9</td>\n",
       "      <td>181.899994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1951.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>245.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1952.0</td>\n",
       "      <td>10.7</td>\n",
       "      <td>250.199997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1953.0</td>\n",
       "      <td>11.3</td>\n",
       "      <td>265.899994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1954.0</td>\n",
       "      <td>11.2</td>\n",
       "      <td>248.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     YEAR     P           S\n",
       "0  1950.0  12.9  181.899994\n",
       "1  1951.0  11.9  245.000000\n",
       "2  1952.0  10.7  250.199997\n",
       "3  1953.0  11.3  265.899994\n",
       "4  1954.0  11.2  248.500000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(36, 3)\n"
     ]
    }
   ],
   "source": [
    "display(df.head())\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df[\"P\"])\n",
    "plt.ylabel(\"count\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 Stata files\n",
    "\n",
    "Stata file stores statistic and data. It has extension `.dta`.\n",
    "\n",
    "We use the Pandas method `read_stata()` to read Stata files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_stata(disarea_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wbcode</th>\n",
       "      <th>country</th>\n",
       "      <th>disa1</th>\n",
       "      <th>disa2</th>\n",
       "      <th>disa3</th>\n",
       "      <th>disa4</th>\n",
       "      <th>disa5</th>\n",
       "      <th>disa6</th>\n",
       "      <th>disa7</th>\n",
       "      <th>disa8</th>\n",
       "      <th>...</th>\n",
       "      <th>disa16</th>\n",
       "      <th>disa17</th>\n",
       "      <th>disa18</th>\n",
       "      <th>disa19</th>\n",
       "      <th>disa20</th>\n",
       "      <th>disa21</th>\n",
       "      <th>disa22</th>\n",
       "      <th>disa23</th>\n",
       "      <th>disa24</th>\n",
       "      <th>disa25</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AGO</td>\n",
       "      <td>Angola</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ARE</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ARG</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  wbcode               country  disa1  disa2  disa3  disa4  disa5  disa6  \\\n",
       "0    AFG           Afghanistan   0.00   0.00   0.76   0.73    0.0   0.00   \n",
       "1    AGO                Angola   0.32   0.02   0.56   0.00    0.0   0.00   \n",
       "2    ALB               Albania   0.00   0.00   0.02   0.00    0.0   0.00   \n",
       "3    ARE  United Arab Emirates   0.00   0.00   0.00   0.00    0.0   0.00   \n",
       "4    ARG             Argentina   0.00   0.24   0.24   0.00    0.0   0.23   \n",
       "\n",
       "   disa7  disa8  ...  disa16  disa17  disa18  disa19  disa20  disa21  disa22  \\\n",
       "0   0.00    0.0  ...     0.0     0.0     0.0    0.00    0.00     0.0    0.00   \n",
       "1   0.56    0.0  ...     0.0     0.4     0.0    0.61    0.00     0.0    0.99   \n",
       "2   0.00    0.0  ...     0.0     0.0     0.0    0.00    0.00     0.0    0.00   \n",
       "3   0.00    0.0  ...     0.0     0.0     0.0    0.00    0.00     0.0    0.00   \n",
       "4   0.00    0.0  ...     0.0     0.0     0.0    0.00    0.05     0.0    0.00   \n",
       "\n",
       "   disa23  disa24  disa25  \n",
       "0    0.02    0.00    0.00  \n",
       "1    0.98    0.61    0.00  \n",
       "2    0.00    0.00    0.16  \n",
       "3    0.00    0.00    0.00  \n",
       "4    0.01    0.00    0.11  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(164, 27)\n"
     ]
    }
   ],
   "source": [
    "display(df.head())\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df[\"disa10\"])\n",
    "plt.xlabel(\"Extent of disease\")\n",
    "plt.ylabel(\"Number of countries\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5 HDF5 files\n",
    "\n",
    "HDF5 stands for Hierarchical Data Format version 5. It is standard for storing large quatities of numerical data. Datasets can be hundreds of gigabytes or terabytes. HDF5 can scale to exabytes.\n",
    "\n",
    "We use the package `h5py` to read HDF5 files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "import h5py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = h5py.File(ligo_file, \"r\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'h5py._hl.files.File'>\n",
      "<KeysViewHDF5 ['meta', 'quality', 'strain']>\n",
      "<KeysViewHDF5 ['Strain']>\n"
     ]
    }
   ],
   "source": [
    "print(type(data))\n",
    "print(data.keys())\n",
    "print(data[\"strain\"].keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "strain = data[\"strain\"][\"Strain\"]\n",
    "nb_samples = int(1e4)\n",
    "gps_time = np.arange(0, 1, 1/nb_samples)\n",
    "\n",
    "plt.plot(gps_time, strain[:nb_samples])\n",
    "plt.xlabel(\"GPS Time (s)\")\n",
    "plt.ylabel(\"Strain\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.6 MATLAB files\n",
    "\n",
    "MATLAB data is saved in files with extension `.mat`.\n",
    "\n",
    "We use the module `scipy.io` to read MATLAB files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.io"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "mat = scipy.io.loadmat(albeck_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'dict'>\n",
      "dict_keys(['__header__', '__version__', '__globals__', 'rfpCyt', 'rfpNuc', 'cfpNuc', 'cfpCyt', 'yfpNuc', 'yfpCyt', 'CYratioCyt'])\n"
     ]
    }
   ],
   "source": [
    "print(type(mat))\n",
    "print(mat.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "(200, 137)\n"
     ]
    }
   ],
   "source": [
    "print(type(mat[\"CYratioCyt\"]))\n",
    "data = mat[\"CYratioCyt\"]\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[6, 6])\n",
    "plt.plot(data[25,5:])\n",
    "plt.xlabel(\"time (min.)\")\n",
    "plt.ylabel(\"normalized fluorescence (measure of expression)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Relational databases\n",
    "\n",
    "In this section, we'll learn how to extract meaningful data from relational databases, an essential skill for any data scientist.\n",
    "\n",
    "We use the package `sqlalchemy` as it works with many Relational Database Management Systems (RDMS)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sqlalchemy import create_engine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a database engine\n",
    "\n",
    "The Engine is the starting point for any SQLAlchemy application."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "db_url = \"sqlite:///data/Chinook.sqlite\"\n",
    "engine = create_engine(db_url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Album', 'Artist', 'Customer', 'Employee', 'Genre', 'Invoice', 'InvoiceLine', 'MediaType', 'Playlist', 'PlaylistTrack', 'Track']\n"
     ]
    }
   ],
   "source": [
    "# get table names\n",
    "table_names = engine.table_names()\n",
    "print(table_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Query data using a connection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AlbumId</th>\n",
       "      <th>Title</th>\n",
       "      <th>ArtistId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>For Those About To Rock We Salute You</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Balls to the Wall</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Restless and Wild</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Let There Be Rock</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Big Ones</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AlbumId                                  Title  ArtistId\n",
       "0        1  For Those About To Rock We Salute You         1\n",
       "1        2                      Balls to the Wall         2\n",
       "2        3                      Restless and Wild         2\n",
       "3        4                      Let There Be Rock         1\n",
       "4        5                               Big Ones         3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "347\n"
     ]
    }
   ],
   "source": [
    "query = \"SELECT * FROM Album\"\n",
    "with engine.connect() as conn:\n",
    "    rs = conn.execute(query)\n",
    "    df = pd.DataFrame(rs.fetchall())\n",
    "    df.columns = rs.keys()\n",
    "\n",
    "display(df.head())\n",
    "print(len(df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Query data using Pandas method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AlbumId</th>\n",
       "      <th>Title</th>\n",
       "      <th>ArtistId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>For Those About To Rock We Salute You</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Balls to the Wall</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Restless and Wild</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Let There Be Rock</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Big Ones</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AlbumId                                  Title  ArtistId\n",
       "0        1  For Those About To Rock We Salute You         1\n",
       "1        2                      Balls to the Wall         2\n",
       "2        3                      Restless and Wild         2\n",
       "3        4                      Let There Be Rock         1\n",
       "4        5                               Big Ones         3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "347\n"
     ]
    }
   ],
   "source": [
    "query = \"SELECT * FROM Album\"\n",
    "df = pd.read_sql_query(query, engine)\n",
    "display(df.head())\n",
    "print(len(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
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 },
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