Untitled.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "id": "urban-lambda",
   "metadata": {},
   "source": [
    "## Streamz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "sound-booth",
   "metadata": {},
   "outputs": [],
   "source": [
    "import streamz\n",
    "\n",
    "source = streamz.Source()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "green-memorial",
   "metadata": {},
   "outputs": [],
   "source": [
    "def inc(x):\n",
    "    return x + 1\n",
    "\n",
    "def double(x):\n",
    "    return 2 * x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "signed-force",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8d889350f36a4df0b2b15ce27753e31c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = source.map(inc)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "secondary-temple",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "eceb19632b1b477cbce9dcccbd5c3354",
       "version_major": 2,
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      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "b = source.map(double)\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "comprehensive-coral",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "third-channels",
   "metadata": {},
   "outputs": [],
   "source": [
    "source.emit(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "incorrect-triple",
   "metadata": {},
   "source": [
    "## Streaming dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "organized-contribution",
   "metadata": {},
   "outputs": [],
   "source": [
    "import streamz\n",
    "import streamz.dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "certified-voluntary",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "47d0a3d1dbe04c74aff9efb43f3be825",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = streamz.dataframe.Random()\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "aquatic-capture",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8bf8ddd6ad304bbca7910367c0ea4830",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.x.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "royal-penalty",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "004df47cefb04c949176dda0788c463a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(\"y\")[[\"x\", \"y\"]].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "packed-director",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4998123614ae40db8691f39937c5103e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.stream.map(type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "young-mobile",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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