{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Starting to use Python (and Pandas)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup plotting\n",
    "\n",
    "To plot data in JupyterLab in a simple fashion use the following code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This will display plots directly in the notebook. Note, that there are more advanced displaying options for JupyterLab. The allow for example to zoom in."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load requiered packages\n",
    "\n",
    "Packages offer a wide range of funtionalities, e.g. plotting, calculations, accessing websites with programms. They are mostly listed at [pypi.org](https://pypi.org/) and can be installed either in a terminal or with the [Anaconda](https://www.anaconda.com/distribution/) distribution. \n",
    "\n",
    "Terminal:\n",
    "```bash\n",
    "pip install packageName\n",
    "```\n",
    "\n",
    "Anaconda:\n",
    "> Have a look at the [Cheat Sheet (PDF)](https://docs.anaconda.com/_downloads/9ee215ff15fde24bf01791d719084950/Anaconda-Starter-Guide.pdf)\n",
    "\n",
    "For this tutorial, we will need [Pandas](https://pandas.pydata.org/)(Statistics package), [Matplotlib](https://matplotlib.org/)(Plotting package) and [Numpy](https://numpy.org/)(package for numerical work). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import package\n",
    "\n",
    "After loading the packages, you can access their functionality with the TAB key. If you import the packages like below, the Pandas package e.g. will be available as `pd`. If you add a DOT `pd.`and press the TAB key, you will see a list of possible functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test the package access. Uncomment and run the cell. You should see a list of methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pd."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a time series\n",
    "\n",
    "Using the Numpy package, we can create a random time series of 1000 entries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = pd.Series(\n",
    "  np.random.randn(1000),\n",
    "  index=pd.date_range('1/1/2020',\n",
    "  periods=1000)\n",
    "  )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then create a dataset of random numbers with the time series as an index. Possible columns in the dataset are simply A,B,C and D."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(\n",
    "  np.random.randn(1000, 4),\n",
    "  index=ts.index,\n",
    "  columns=['A', 'B', 'C', 'D']\n",
    "  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>1.333624</td>\n",
       "      <td>-0.707484</td>\n",
       "      <td>-0.789363</td>\n",
       "      <td>0.152281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>-1.912371</td>\n",
       "      <td>2.753899</td>\n",
       "      <td>-1.315583</td>\n",
       "      <td>0.065484</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2020-01-01  1.333624 -0.707484 -0.789363  0.152281\n",
       "2020-01-02 -1.912371  2.753899 -1.315583  0.065484"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate cumulative sum "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can calculate the cumulative sum of all columns by running "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.cumsum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Have a look at the beginning of the dataset by simply writing `df.head(5)` and press enter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>1.333624</td>\n",
       "      <td>-0.707484</td>\n",
       "      <td>-0.789363</td>\n",
       "      <td>0.152281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>-0.578747</td>\n",
       "      <td>2.046415</td>\n",
       "      <td>-2.104946</td>\n",
       "      <td>0.217766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>-0.173813</td>\n",
       "      <td>1.611432</td>\n",
       "      <td>-2.508927</td>\n",
       "      <td>0.953219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>0.883644</td>\n",
       "      <td>2.065965</td>\n",
       "      <td>-3.386507</td>\n",
       "      <td>1.226043</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2020-01-01  1.333624 -0.707484 -0.789363  0.152281\n",
       "2020-01-02 -0.578747  2.046415 -2.104946  0.217766\n",
       "2020-01-03 -0.173813  1.611432 -2.508927  0.953219\n",
       "2020-01-04  0.883644  2.065965 -3.386507  1.226043"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the dataset\n",
    "\n",
    "Since the data is numerical and we have times as index, we can easily create a plot of the dataset by using the `.plot()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is just to give you a small hint on what can be done with Jupyter Notebooks and Phython. In the course of the book, we will introduce many different useful programms and routines to work with text. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}