1.jupyter.ipynb

{
 "cells": [
  {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data analysis with Jupyter Notebook, Pandas and Plotly\n",
    "## A workshop by PyLadiesBcn for PyDay 2018\n",
    "\n",
    "![600_336991142.jpeg](attachment:600_336991142.jpeg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Helping more women become active participants in the Python community\n",
    "> [PyLadiesBcn](http://pybcn.org/pyladies-bcn/) is an international mentorship group with a focus on helping more women become active participants and leaders in the Python open-source community. Our mission is to promote, educate and advance a diverse Python community through outreach, education, conferences, events and social gatherings.\n",
    "\n",
    "> PyLadies also aims to provide a friendly support network for women and a bridge to the larger Python world. **Anyone** with an interest in Python is encouraged to participate!\n",
    "\n",
    "## Follow us :)\n",
    "Check out all our activities on [Twitter](https://twitter.com/PyLadiesBCN) and [Meetup](http://www.meetup.com/PyLadies-BCN/). All our workshops are available at our [GitHub](https://github.com/pyladies-bcn) account!\n",
    "\n",
    "## Volunteer!\n",
    "PyLadiesBcn organizers rock, but their knowledge and time are limited ;) We need volunteers to prepare workshops and talks, from beginners to advanced levels.\n",
    "\n",
    "## Talk to us! \n",
    "You are the community, so talk to us! What do you wanna learn? What would you like to organize?\n",
    "\n",
    "## Sponsor us!\n",
    "Would your company like to host a PyLadiesBcn meetup? Would you prefer to support our next event? Contact us through [Meetup](http://www.meetup.com/PyLadies-BCN/) or by [email](mailto:pyladies-bcn@googlegroups.com)! \n",
    "\n",
    "## Send us your job offers\n",
    "Would your company like to hire more women? Send us your job offers by [email](mailto:pyladies-bcn@googlegroups.com) and we will forward them to our comunity. Check out the [rules](http://pybcn.org/job-offers/) first!\n",
    "\n",
    "## Remember the CoC\n",
    "Check out PyBCN/PyLadiesBcn Code of Conduct [here](http://www.pyladies.com/CodeOfConduct/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Jupyter Notebook: create and share documents!\n",
    "\n",
    "Open source web application to create and share documents containing:\n",
    "- **live code** (you can run it!)\n",
    "- **equations**\n",
    "- **visualizations**\n",
    "- **narrative text** (markdown)\n",
    "\n",
    "## notebook: .ipynb file\n",
    "\n",
    "Code, text and output are stored in an editable document called a **notebook**. When you save it, this is sent from your browser to the notebook server, which saves it on disk as a **JSON file with a .ipynb extension**.\n",
    "\n",
    "![notebook components](images/notebook_components.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Why is it cool?\n",
    "\n",
    "### edit rich text in the browser\n",
    "Using the **Markdown** markup language\n",
    "\n",
    "### edit the code in the browser\n",
    "With automatic **syntax highlighting**, **indentation**, and **tab completion**\n",
    "\n",
    "### execute code from the browser\n",
    "With the **results of computations** attached to the code which generated them\n",
    "\n",
    "### rich media respresentations\n",
    "Display the results of computation using **HTML**, **LaTeX**, **PNG**, **SVG** ...\n",
    "\n",
    "### include mathematical notation\n",
    "Using **LaTeX**\n",
    "\n",
    "### share your notebooks\n",
    "As **.ipynb** files, allowing others to execute your code\n",
    "\n",
    "### export your notebooks\n",
    "To show your work as **HTML**, **pdf**, **LaTeX** ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# I want to use it! But how?\n",
    "\n",
    "<br/><br/>\n",
    "\n",
    "![jupyter packages](images/which_packages.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# JupyterHub: a multi-user version of the notebook\n",
    "\n",
    "Gives users access to **computational environments and resources**.\n",
    "\n",
    "Useful for **research labs**, **classrooms**, **companies** ...\n",
    "\n",
    "- no installation or maintenance needed (for the end-users)\n",
    "- pluggable authentication (i.e. GitHub authentication)\n",
    "- centralized deployment\n",
    "- container friendly\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Installation with virtualenv and Python 3\n",
    "\n",
    "```shell\n",
    "# If pip3 is not installed, install it\n",
    "sudo apt-get install python3-pip\n",
    "\n",
    "# Upgrade\n",
    "pip3 install --upgrade pip\n",
    "\n",
    "# If virtualenv is not installed, install it\n",
    "# Feel free to use either pip or pip3, doesn't matter\n",
    "pip install virtualenv\n",
    "\n",
    "# Move to the folder and create the virtualenv\n",
    "virtualenv -p python3 <my-virtualenv-name>\n",
    "\n",
    "# Activate the virtualenv\n",
    "source <my-virtualenv-name>/bin/activate\n",
    "\n",
    "# Install Jupyter, Pandas and Plotly\n",
    "pip3 install jupyter pandas plotly matplotlib\n",
    "\n",
    "# Start the notebook server (from withing the folder with the virtualenv folder)\n",
    "jupyter notebook\n",
    "\n",
    "# Open a new notebook from the top-right button \"New\"\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Jupyter-notebook: an overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modal editor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jupyter notebook has a modal user interface. This means that the keyboard does different things depending on which mode the notebook is in. There are two modes:\n",
    "- **Edit mode**: indicated by a green cell border and a prompt showing in the editor area. You can type into the cell, like a normal text editor. Enter to edit mode by pressing **`Enter`** or using the mouse to `click` on a cell’s editor area."
   ]
  },
  {
   "attachments": {
    "edit_mode.png": {
     "image/png": 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HIgQQQAABBBBAAAEEEEAAgdQCBMvURtRAAAEEEEAAAQQQQAABBBBIIpDw4T1J6lOUBQKRSES9vb0aHR3NgtEwBAQQQAABBBBAAAEEELgWAi6XS+Xl5bL+TbURLFMJZWF5IBBQUVGR8vLysnB0DAkBBBBAAAEEEEAAAQTsEBgeHo5OSN1+++0pu+NW2JRE2VfBmrEkVGbfdWVECCCAAAIIIIAAAgjYKWBlhnTvciRY2ilPXwgggAACCCCAAAIIIICAAwUIlg686AwZAQQQQAABBBBAAAEEELBTgGBppyZ9IYAAAggggAACCCCAAAIOFCBYOvCiM2QEEEAAAQQQQAABBBBAwE4BgqWdmvR13Qrk5uZet+fGiSGAAAIIIIAAAgggMN8FeN3INbiCw2dP6rXuES2If91LZEx5Xp9qfEtSHrG/u1OvnA2pJL69xlToXalqb1HK9k6uEP40qIGvjMBNxSq91e1kCsaOAAIIIIAAAggggMC3JuCAYBnSa40H9ez5BKYFS9Xx+v2y+22O/WcDenHfmMoqc+Uxhx22Dm2CZdmSSNxJjOncW0f1852fa+WTa/RSTflk2eilIfm7RvRJNFjmmP0hvdtl2jddcnSw9L/1onxntqvuB91q3WDJxm9htb9So9ovjk/uXHvLGzr0iyoRLydJ+IAAAggggAACCCCAwDURcECwdOnuHZXaH8nR6IWz2rxrSA886dXDy3IVcRXYHiqtq+RyWWEwRy8816DlCS7b6AW/9u46qQPd44XxedPsWr7+Z3ppfXzDc3p83Tu6GO03fr8zPoc/Pa39h+/U9gmnsenjPt0WC5V/uKNbm+8pVfBEq3zdtappO6X2uhXTG7AHAQQQQAABBBBAAAEEbBNwwBrLHJV4vVrl8+hu76Io3EqfTxW+O7TKu9Q2yEQdRSaC0BWF/jYrVC7U/lfv1+990sUryqd9NbOdTt78J6xQuVlv1/5LLWYWd+BKjC/92tNnZioXv6mt6z1y3+SWx1uhtabe8b498n95ZQO+I4AAAggggAACCCCAgJ0CDgiW33CNjn+MTEt8Ef2z+aB2d12QRgLavb1VP1n3N93360PqGbE/1HkerdGpt+u0akmBRi99c36ZfurvPBI9392dfZl2Mb1d+ILaW/doY9Vqra6q0qZt27TtqVYFp9e85ns86z/T4K92a3VZscxS02lb+NMetZm9e32V42VhHfr7OsVuim1TTzA8rQ07EEAAAQQQQAABBBBAwD4BRwXLq7ON6YuukA4c6NTjD3bqQMClLU2LdLH7kh7ZeVITgfTq7WdWsmBhgRbMrEmS2iGd2Pd5tPzAPr895xr2a9vi5aptelruqse1deMGqaNZzc+36/IcZDR3UX7SdZIDH7eb8a9VZVlsNeXl9/er4X9Si++g6kxJe+9cxOEkl4wiBBBAAAEEEEAAAQSyTMABayzTu2ILCkw98yTXD6s96thxV3Tt5Sq16hET2vpNUaK1kun1fK1r5ar6dx59uK9P65p8NgTWsDr+6FOzOe2W7kHVe2JhrdLdo5cbw0kDXvxIw+HLupwkhbrz85XvtuexOmFZabdYbmtp61dBPfPudumWU6q/N1/tZh1rMMEsZ/y58hkBBBBAAAEEEEAAAQRmJ0CwHPeLzkoWFOnIeKi0ducttJJKSMPWWskpr/6wSq+fLa/8Lu16zqbzCQe1/3kz/7fXBLPxUGn1HAtv6R8j2Pp9+Z5IUv+5UwptsfmhOt+R/G2bTCiuU/dDpu9Rf5IToAgBBBBAAAEEEEAAAQTsEiBYTkiatY5lj3pVMvE97l/7bluN6/Q6/RgeiK1XbLnnytd5WLOL6d8H69k4qMGNyQZpz2yldQR3dB41rOD7r+lp8xCf3/jOyWNCZrjXr5dN+S/N/w/E1lsmOx/KEEAAAQQQQAABBBBAIFMBgmW8nMOfvhqlSJgdTWjrsiKatWIxve2yv0OHe4aU+G7XsBbdtkFVK/LT6yxFreLbqqR/N6r2XfMIn5x9evPeJbEWX8VWx1b9sDQaMFN0QzECCCCAAAIIIIAAAghkKODIYOlyXS/3teaMr4m0brmdzTaic4HPdUv5ctvey9nlHzC3wpZGTypoHtxTay26nME28PExNTV1qKIiQaOeHt3T0m1bsHTfWhGNvG2q0BsPbtREXO14r9EcvE4VpfbNjiYYDbsQQAABBBBAAAEEEHC8gAOC5Zj6A371R3I0en4oesHPdJ+RZyRXEdcirSq/tu+yTPQLG74QkP9CRC5XSB+MSMOBc+oJRBQxrzYpKfeoJLq2M1HLRPvGdGJXmzYfNWXVl/TRjpWJKqW9z+2p1GZTu7nhKVW+vVXFHzfrgSZrttLa0g9onvrdCtXHWs327/Anfp0eNA8OyhnSafMgnqH/9uj0WTO1at5Pme9ZodLverR16Vq1mdtgg33mCbBlpQq+86JqB6WKJZujt8XO9hxojwACCCCAAAIIIIAAAlcXcECwjOjEzi49e/4bhMMvnNFh62vBUnW8vvTqs3wuaybR/keK9nee1KbmuH6Pntcj5o+1bfnLMj3mXRj9PPO/4vqceePxFqX6bXeLOnyNalxnvR2yQi2nPtMG9zEt9u2fwSrLjE9gWsNg9xatC8atkhxs1J3HYtX2LhjUpgq3VtQd0sGWe9XwwY/1xAexsorFB/VOw+pp/bEDAQQQQAABBBBAAAEE7BVwQLDMVX3Lo0o+eZa4zvLqBn1UnTn41e649dRt1EfpL1c0T6RNdqtsju7eUacjDw8pb5k9s6/5nnq9N7jBvLPSzBKa14LE5inrFbJrCnKGpJ6H2s2zeVNtbtU0vqfPzOzmgHmHpW4ulqds4qbYVG0pRwABBBBAAAEEEEAAgdkIOCBYzoYns7YR6/UkiuiZXYfkmZh8HImocM1demxN6vDX33VMe98KqXCybUgfmh7LrG4TbgtVsmyicsIKM99pnrpj13smZ37wzFvkl3km11hm3gstEUAAAQQQQAABBBBAYCYCBMuZaKVZN698iTZUj5hbbMc0bNZQRjcTNgvTva3Wejqt+TPZVrl6uNqlsnKbw2Oa46EaAggggAACCCCAAAIIIJBMgGCZTCfDshLfeu30ZdjYNCtZU61dazJvT0sEEEAAAQQQQAABBBBA4NsUuPHbPBjHQmCuBEKh1Ks05+rcOC4CCCCAAAIIIIAAAvNdgGA5368g548AAggggAACCCCAAAIIzLEAwXKOLwCHRwABBBBAAAEEEEAAAQTmuwDBcr5fQc4fAQQQQAABBBBAAAEEEJhjAYLlHF8ADo8AAggggAACCCCAAAIIzHcBguV8v4IZnL/L5dLw8HAGLWmCAAIIIIAAAggggAACThGwMoOVHdLZeN1IOkpZVqe8vFyBQEB9fX1ZNjKGgwACCCCAAAIIIIAAAnYJWKHSyg7pbATLdJSyrI71A/F6vVk2KoaDAAIIIIAAAggggAACcyXArbBzJc9xEUAAAQQQQAABBBBAAIEsESBYZsmFZBgIIIAAAggggAACCCCAwFwJECznSp7jIoAAAggggAACCCCAAAJZIpAwWBbf/L0sGR7DQAABBBBAAAEEEEAAAQQQsFugcEHBlC5v+NpsU/aYL8f7O3Wk96gGRwevLOI7AggggAACCCCAAAIIIICAgwUK3AVq8NTJV7RiUiFhsJws5QMCCCCAAAIIIIAAAggggAACKQQS3gqbog3FCCCAAAIIIIAAAggggAACCEwKECwnKfiAAAIIIIAAAggggAACCCCQiQDBMhM12iCAAAIIIIAAAggggAACCEwK/B/Q13tKbMkgnQAAAABJRU5ErkJggg=="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![edit_mode.png](attachment:edit_mode.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Command mode**: indicated by a grey cell border with a blue left margin. You are able to edit the notebook as a whole, and the keyboard is mapped to a set of shortcuts that let you perform notebook and cell actions efficiently.  Enter command mode by pressing **`Esc`** or using the mouse to `click` outside a cell’s editor area"
   ]
  },
  {
   "attachments": {
    "command_mode.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![command_mode.png](attachment:command_mode.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export the notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "File > Download as\n",
    "\n",
    "- Notebook (.ipynb)\n",
    "- Executable Script (.py)\n",
    "- HTML (.html)\n",
    "- ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Command mode"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Basic navigation: \n",
    "- **`enter`**: edit a cell\n",
    "- **`shift`+`enter`**: run the cell\n",
    "- **`up`** / `k`: move to the previous cell\n",
    "- **`down`** / `j`: move to next cell\n",
    "\n",
    "Saving the notebook: \n",
    "- **`s`**: save the notebook\n",
    "\n",
    "Change cell types: \n",
    "- **`y`**: change to code cell\n",
    "- **`m`**: change to text cell\n",
    "- `1` - `6`: change to text cell title 1 - 6\n",
    "\n",
    "Cell creation: \n",
    "- **`a`**: create a cell above\n",
    "- **`b`**: create a cell below\n",
    "\n",
    "Cell editing: \n",
    "- **`x`**: cut a cell\n",
    "- **`c`**: copy a cell\n",
    "- **`v`**: paste a cell\n",
    "- **`z`**: undo cell deletion\n",
    "\n",
    "Kernel operations: \n",
    "- `i` `i` : interrupt the kernel\n",
    "- `0` `0`: restart the kernel "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Edit mode"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two types of cells:\n",
    "- **Code cells**: to write and run code. Code will be executed when you run the cell\n",
    "- **Text cells**: to write titles, information, show images, ... Text will be formatted in markdown when you run the cell\n",
    "\n",
    "To run a cell press **`shift`+`enter`**\n",
    "\n",
    "Keywoard shortcuts to edit the text / code of a cell:\n",
    "- **`x`**: cut text\n",
    "- **`c`**: copy text\n",
    "- **`v`**: paste text\n",
    "- **`z`**: undo previous action\n",
    "- `multicursor` editing is available"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code cells"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The jupyter notebook is capable of running code in a wide range of languages. However, each notebook is associated with a single kernel. This notebook is associated with the IPython kernel, therefore runs Python code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    }
   ],
   "source": [
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Getting help"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "? print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Managing the Kernel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Code is run in a separate process called the Kernel. The Kernel can be interrupted or restarted with the buttons or the keywoard shortcuts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Stdout and stderr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The stdout and stderr streams are displayed as text in the output area."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hi, stdout\n"
     ]
    }
   ],
   "source": [
    "print(\"hi, stdout\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "hi, stderr\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "print('hi, stderr', file=sys.stderr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All output is displayed asynchronously as it is generated in the Kernel. If you execute the next cell, you will see the output one piece at a time, not all at the end."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n"
     ]
    }
   ],
   "source": [
    "import time, sys\n",
    "for i in range(8):\n",
    "    print(i)\n",
    "    time.sleep(0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To better handle large outputs, the output area can be collapsed. Run the following cell and then single- or double- click on the active area to the left of the output:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "48\n",
      "49\n"
     ]
    }
   ],
   "source": [
    "for i in range(50):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### IPython Magic Commands"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**%who** lists all variables of global scope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "i\t sys\t \n"
     ]
    }
   ],
   "source": [
    "%who"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**%time cell** will give you information about a single run of the code in your cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 0 ns, sys: 0 ns, total: 0 ns\n",
      "Wall time: 3.58 µs\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
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      "48\n",
      "49\n"
     ]
    }
   ],
   "source": [
    "%time\n",
    "for i in range(50):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**%timeit statement** runs the statement 100,000 times and provides the mean of the fastest three times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "289 ns ± 2.44 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit for _ in range(10): True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An many more:\n",
    "- **%writefile file** magic saves the contents of that cell to an external file\n",
    "- **%pycat file** shows you in a popup the syntax highlighted contents of an external file.\n",
    "- **%run script.py** executes python code from notebook or script\n",
    "- **%python2** runs another kernel (python2, bash, ruby, perl, ...)\n",
    "- **%matplotlib inline** shows matplotlib plots in the notebook (like pandas)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IPython Shell Commands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0m\u001b[01;32m1.jupyter.ipynb\u001b[0m*          \u001b[01;32m4.plotly.ipynb\u001b[0m*             \u001b[01;35mpython-logo.png\u001b[0m  \u001b[01;34mvenv\u001b[0m/\r\n",
      "\u001b[01;32m2.pandas_intro.ipynb\u001b[0m*     cargos_targetas_black.xlsx  README.md\r\n",
      "\u001b[01;32m3.pandas_advanced.ipynb\u001b[0m*  \u001b[01;34mimages\u001b[0m/                     tarjetas.csv\r\n"
     ]
    }
   ],
   "source": [
    "ls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Text cells"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Text can be added to Jupyter Notebooks using **Markdown** cells. You can change the cell type to Markdown by using the `Cell` menu, the toolbar, or the key shortcut `m`. Markdown is a popular markup language that is a superset of HTML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can make text *italic* or **bold**.\n",
    "\n",
    "You can build nested itemized or enumerated **lists**:\n",
    "\n",
    "- One\n",
    "    - Sublist\n",
    "        - This\n",
    "               \n",
    "Now another list:\n",
    "1. Here we go\n",
    "    1. Sublist\n",
    "    2. Sublist\n",
    "2. There we go\n",
    "3. Now this\n",
    "\n",
    "You can add **horizontal rules**:\n",
    "\n",
    "-----\n",
    "\n",
    "\n",
    "Here is a **blockquote**:\n",
    "\n",
    "> Beautiful is better than ugly. Explicit is better than implicit. Simple is better than complex. Complex is better than complicated. Flat is better than nested. Sparse is better than dense. Readability counts. Special cases aren’t special enough to break the rules. Although practicality beats purity. Errors should never pass silently. Unless explicitly silenced. In the face of ambiguity, refuse the temptation to guess. There should be one– and preferably only one –obvious way to do it. Although that way may not be obvious at first unless you’re Dutch. Now is better than never. Although never is often better than right now. If the implementation is hard to explain, it’s a bad idea. If the implementation is easy to explain, it may be a good idea. Namespaces are one honking great idea – let’s do more of those!\n",
    "\n",
    "And shorthand for **links**: [Jupyter's website](http://jupyter.org/)\n",
    "\n",
    "**Headings**:\n",
    "\n",
    "# H1\n",
    "## H2\n",
    "### H3\n",
    "#### H4\n",
    "##### H5\n",
    "###### H6\n",
    "\n",
    "You can **embed code** meant for illustration instead of execution in Python:\n",
    "\n",
    "```python\n",
    "def square(x):\n",
    "    return x**2\n",
    "```\n",
    "\n",
    "**Inline expressions** can be added by surrounding the **LaTeX** code with $:\n",
    "\n",
    "$-b \\pm \\sqrt{b^2 - 4ac} \\over 2a$\n",
    "\n",
    "And a **table** like this:\n",
    "\n",
    "| This | is   |\n",
    "|------|------|\n",
    "|   a  | table|\n",
    "\n",
    "If you have **local files** in your Notebook directory, you can refer to these files in Markdown cells directly:\n",
    "\n",
    "![python-logo](python-logo.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clone the repository and start Jupyter Notebook\n",
    "\n",
    "```shell\n",
    "~$ cd code/\n",
    "~/code$ git clone https://github.com/pyladies-bcn/jupyter_pandas_plotly\n",
    "~/code$ cd jupyter_pandas_plotly/\n",
    "~/code/jupyter_pandas_plotly$ jupyter-notebook \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please, take a couple of minutes to familiarize yourself with the interface by completing the user interface tour that you will find under `Help > User Interface Tour` and take a look at the shortcuts in `Help > Keyboard Shortcuts`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Learn more\n",
    "[Jupyter Notebook documentation](https://jupyter-notebook.readthedocs.io/en/stable/notebook.html)<br/>\n",
    "[Markdown for Jupyter notebooks cheatsheet\n",
    "](https://medium.com/ibm-data-science-experience/markdown-for-jupyter-notebooks-cheatsheet-386c05aeebed)<br/>\n",
    "[JupyterHub documentation](https://jupyterhub.readthedocs.io/en/stable/)<br/>\n",
    "[nbgrader documentation](https://nbgrader.readthedocs.io/en/latest)<br/>\n"
   ]
  }
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