chapter01_basic/04_magic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.4. Creating an IPython extension with custom magic commands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.core.magic import (register_line_magic,\n",
    "                                register_cell_magic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "@register_line_magic\n",
    "def hello(line):\n",
    "    if line == 'french':\n",
    "        print(\"Salut tout le monde!\")\n",
    "    else:\n",
    "        print(\"Hello world!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello world!\n"
     ]
    }
   ],
   "source": [
    "%hello"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Salut tout le monde!\n"
     ]
    }
   ],
   "source": [
    "%hello french"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from io import StringIO\n",
    "\n",
    "@register_cell_magic\n",
    "def csv(line, cell):\n",
    "    # We create a string buffer containing the\n",
    "    # contents of the cell.\n",
    "    sio = StringIO(cell)\n",
    "    # We use Pandas' read_csv function to parse\n",
    "    # the CSV string.\n",
    "    return pd.read_csv(sio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "podoc": {
     "output_text": "Output"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "Output"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%csv\n",
    "col1,col2,col3\n",
    "0,1,2\n",
    "3,4,5\n",
    "7,8,9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "podoc": {
     "output_text": "Output"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AyAxsDQAyA1sDgMzA1gAgM7A1AMgMbA0AMqO5rf3X77PM//efdy/BI/4P1VCt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\nW22r1XYr4dHN61THY1zm+IPFxZ6htLK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98J15qP3P5KU/5OIv5MdnxK8apT/9cus89IiF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\nIJ4weMUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "Output"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = _\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing csvmagic.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile csvmagic.py\n",
    "import pandas as pd\n",
    "from io import StringIO\n",
    "\n",
    "def csv(line, cell):\n",
    "    sio = StringIO(cell)\n",
    "    return pd.read_csv(sio)\n",
    "\n",
    "def load_ipython_extension(ipython):\n",
    "    \"\"\"This function is called when the extension is\n",
    "    loaded. It accepts an IPython InteractiveShell\n",
    "    instance. We can register the magic with the\n",
    "    `register_magic_function` method of the shell\n",
    "    instance.\"\"\"\n",
    "    ipython.register_magic_function(csv, 'cell')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext csvmagic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "podoc": {
     "output_text": "Output"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "Output"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%csv\n",
    "col1,col2,col3\n",
    "0,1,2\n",
    "3,4,5\n",
    "7,8,9"
   ]
  }
 ],
 "metadata": {},
 "nbformat": 4,
 "nbformat_minor": 2
}