Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting altair>3\n",
      "  Using cached https://files.pythonhosted.org/packages/47/ff/965f48420a6403c88d15706e780b77741f925bec227407132697b7b49ce0/altair-3.1.0-py2.py3-none-any.whl\n",
      "Requirement already satisfied: jsonschema in /usr/local/miniconda3/envs/jupyterlab/lib/python3.6/site-packages (from altair>3) (3.0.1)\n",
      "Requirement already satisfied: jinja2 in /usr/local/miniconda3/envs/jupyterlab/lib/python3.6/site-packages (from altair>3) (2.10)\n",
      "Requirement already satisfied: toolz in /usr/local/miniconda3/envs/jupyterlab/lib/python3.6/site-packages (from altair>3) (0.9.0)\n",
      "Requirement already satisfied: pandas in /usr/local/miniconda3/envs/jupyterlab/lib/python3.6/site-packages (from altair>3) (0.24.1)\n",
      "Requirement already satisfied: six in /usr/local/miniconda3/envs/jupyterlab/lib/python3.6/site-packages (from altair>3) (1.11.0)\n",
      "Requirement already satisfied: entrypoints in /usr/local/miniconda3/envs/jupyterlab/lib/python3.6/site-packages (from altair>3) (0.2.3)\n",
      "Requirement already satisfied: numpy in /usr/local/miniconda3/envs/jupyterlab/lib/python3.6/site-packages (from altair>3) (1.16.2)\n",
      "Requirement already satisfied: setuptools in /usr/local/miniconda3/envs/jupyterlab/lib/python3.6/site-packages (from jsonschema->altair>3) (40.5.0)\n",
      "Requirement already satisfied: pyrsistent>=0.14.0 in /usr/local/miniconda3/envs/jupyterlab/lib/python3.6/site-packages (from jsonschema->altair>3) (0.14.5)\n",
      "Requirement already satisfied: attrs>=17.4.0 in /usr/local/miniconda3/envs/jupyterlab/lib/python3.6/site-packages (from jsonschema->altair>3) (18.2.0)\n",
      "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/miniconda3/envs/jupyterlab/lib/python3.6/site-packages (from jinja2->altair>3) (1.1.0)\n",
      "Requirement already satisfied: pytz>=2011k in /usr/local/miniconda3/envs/jupyterlab/lib/python3.6/site-packages (from pandas->altair>3) (2018.7)\n",
      "Requirement already satisfied: python-dateutil>=2.5.0 in /usr/local/miniconda3/envs/jupyterlab/lib/python3.6/site-packages (from pandas->altair>3) (2.7.5)\n",
      "Installing collected packages: altair\n",
      "  Found existing installation: altair 2.4.1\n",
      "    Uninstalling altair-2.4.1:\n",
      "      Successfully uninstalled altair-2.4.1\n",
      "Successfully installed altair-3.1.0\n"
     ]
    }
   ],
   "source": [
    "!pip install \"altair>3\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v3+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v3.3.0.json",
       "config": {
        "mark": {
         "tooltip": null
        },
        "view": {
         "height": 300,
         "width": 400
        }
       },
       "data": {
        "name": "data-c2a3e89ba9d5d1687d5e8c28d630a033"
       },
       "datasets": {
        "data-c2a3e89ba9d5d1687d5e8c28d630a033": [
         {
          "a": "A",
          "b": 28
         },
         {
          "a": "B",
          "b": 55
         },
         {
          "a": "C",
          "b": 43
         },
         {
          "a": "D",
          "b": 91
         },
         {
          "a": "E",
          "b": 81
         },
         {
          "a": "F",
          "b": 53
         },
         {
          "a": "G",
          "b": 19
         },
         {
          "a": "H",
          "b": 87
         },
         {
          "a": "I",
          "b": 52
         }
        ]
       },
       "encoding": {
        "x": {
         "field": "a",
         "type": "nominal"
        },
        "y": {
         "field": "b",
         "type": "quantitative"
        }
       },
       "mark": "bar"
      },
      "image/png": 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",
      "text/plain": [
       "<VegaLite 3 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import altair.vegalite.v3 as alt\n",
    "import pandas as pd\n",
    "\n",
    "source = pd.DataFrame({\n",
    "    'a': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I'],\n",
    "    'b': [28, 55, 43, 91, 81, 53, 19, 87, 52]\n",
    "})\n",
    "\n",
    "alt.Chart(source).mark_bar().encode(\n",
    "    x='a',\n",
    "    y='b'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v2+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v2.6.0.json",
       "config": {
        "view": {
         "height": 300,
         "width": 400
        }
       },
       "data": {
        "name": "data-c2a3e89ba9d5d1687d5e8c28d630a033"
       },
       "datasets": {
        "data-c2a3e89ba9d5d1687d5e8c28d630a033": [
         {
          "a": "A",
          "b": 28
         },
         {
          "a": "B",
          "b": 55
         },
         {
          "a": "C",
          "b": 43
         },
         {
          "a": "D",
          "b": 91
         },
         {
          "a": "E",
          "b": 81
         },
         {
          "a": "F",
          "b": 53
         },
         {
          "a": "G",
          "b": 19
         },
         {
          "a": "H",
          "b": 87
         },
         {
          "a": "I",
          "b": 52
         }
        ]
       },
       "encoding": {
        "x": {
         "field": "a",
         "type": "nominal"
        },
        "y": {
         "field": "b",
         "type": "quantitative"
        }
       },
       "mark": "bar"
      },
      "image/png": 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",
      "text/plain": [
       "<VegaLite 2 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import altair.vegalite.v2 as alt\n",
    "import pandas as pd\n",
    "\n",
    "source = pd.DataFrame({\n",
    "    'a': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I'],\n",
    "    'b': [28, 55, 43, 91, 81, 53, 19, 87, 52]\n",
    "})\n",
    "\n",
    "alt.Chart(source).mark_bar().encode(\n",
    "    x='a',\n",
    "    y='b'\n",
    ")"
   ]
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