chezou/DSFes_enquete_result.ipynb

## DSFes_enquete_result.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import codecs\n",
    "with codecs.open(\"event_34680_participants.csv\", \"r\", \"Shift-JIS\", \"ignore\") as file:\n",
    "    df = pd.read_table(file, delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "参加枠名                              447\n",
      "ユーザー名                             447\n",
      "表示名                               447\n",
      "コメント                              353\n",
      "参加ステータス                           447\n",
      "出欠ステータス                             0\n",
      "本ミートアップの参加目的                      447\n",
      "普段お使いのプログラミング言語 / ツールを教えて下さい      447\n",
      "LT希望者のみ：@発表タイトル A発表内容 をお書きください      4\n",
      "ビックデータをどのように活用したいとお考えですか？         447\n",
      "更新日時                              447\n",
      "受付番号                              447\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>参加枠名</th>\n",
       "      <th>ユーザー名</th>\n",
       "      <th>表示名</th>\n",
       "      <th>コメント</th>\n",
       "      <th>参加ステータス</th>\n",
       "      <th>出欠ステータス</th>\n",
       "      <th>本ミートアップの参加目的</th>\n",
       "      <th>普段お使いのプログラミング言語 / ツールを教えて下さい</th>\n",
       "      <th>LT希望者のみ：@発表タイトル A発表内容 をお書きください</th>\n",
       "      <th>ビックデータをどのように活用したいとお考えですか？</th>\n",
       "      <th>更新日時</th>\n",
       "      <th>受付番号</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>一般参加枠</td>\n",
       "      <td>horihorio</td>\n",
       "      <td>horihorio</td>\n",
       "      <td>夏真っ盛り！Spark + Python + Data Science祭りに参加を申し込みました！</td>\n",
       "      <td>参加</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sparkについて興味がある、学びたい, Pythonについて興味がある、学びたい, Dat...</td>\n",
       "      <td>R, Python, その他</td>\n",
       "      <td>NaN</td>\n",
       "      <td>顧客について総合的かつリアルタイムの把握をしたい, あらゆるデータを大量・高速・低価格で処理...</td>\n",
       "      <td>2016年6月28日 23時00分</td>\n",
       "      <td>360379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>一般参加枠</td>\n",
       "      <td>tomomoto_LV3</td>\n",
       "      <td>tomomoto_factory</td>\n",
       "      <td>夏真っ盛り！Spark + Python + Data Science祭りに参加を申し込みました！</td>\n",
       "      <td>参加</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sparkについて興味がある、学びたい, Pythonについて興味がある、学びたい, Dat...</td>\n",
       "      <td>R, Python, Java, その他</td>\n",
       "      <td>NaN</td>\n",
       "      <td>顧客について総合的かつリアルタイムの把握をしたい, あらゆるデータを大量・高速・低価格で処理...</td>\n",
       "      <td>2016年6月29日 13時18分</td>\n",
       "      <td>361408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>一般参加枠</td>\n",
       "      <td>wwacky</td>\n",
       "      <td>wwacky</td>\n",
       "      <td>夏真っ盛り！Spark + Python + Data Science祭り に参加を申し込み...</td>\n",
       "      <td>参加</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sparkについて興味がある、学びたい, 発表者との交流</td>\n",
       "      <td>Spark, R, Python</td>\n",
       "      <td>NaN</td>\n",
       "      <td>顧客について総合的かつリアルタイムの把握をしたい, あらゆるデータを大量・高速・低価格で処理したい</td>\n",
       "      <td>2016年6月29日 13時19分</td>\n",
       "      <td>361410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>一般参加枠</td>\n",
       "      <td>takenotabi</td>\n",
       "      <td>takenotabi</td>\n",
       "      <td>夏真っ盛り！Spark + Python + Data Science祭りに参加を申し込みました！</td>\n",
       "      <td>参加</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pythonについて興味がある、学びたい, Data Scienceについて興味がある、学び...</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>顧客について総合的かつリアルタイムの把握をしたい, IoTに絡んで、センターからのストリーミ...</td>\n",
       "      <td>2016年6月29日 14時03分</td>\n",
       "      <td>361474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>一般参加枠</td>\n",
       "      <td>msakai</td>\n",
       "      <td>msakai</td>\n",
       "      <td>夏真っ盛り！Spark + Python + Data Science祭りに参加を申し込みました！</td>\n",
       "      <td>参加</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sparkについて興味がある、学びたい, Data Scienceについて興味がある、学びたい</td>\n",
       "      <td>R, Python, Java, その他</td>\n",
       "      <td>NaN</td>\n",
       "      <td>あらゆるデータを大量・高速・低価格で処理したい, 企業内で分散されたデータを一箇所に統合した...</td>\n",
       "      <td>2016年6月30日 07時58分</td>\n",
       "      <td>362417</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    参加枠名         ユーザー名               表示名  \\\n",
       "0  一般参加枠     horihorio         horihorio   \n",
       "1  一般参加枠  tomomoto_LV3  tomomoto_factory   \n",
       "2  一般参加枠        wwacky            wwacky   \n",
       "3  一般参加枠    takenotabi        takenotabi   \n",
       "4  一般参加枠        msakai            msakai   \n",
       "\n",
       "                                                コメント 参加ステータス  出欠ステータス  \\\n",
       "0  夏真っ盛り！Spark + Python + Data Science祭りに参加を申し込みました！      参加      NaN   \n",
       "1  夏真っ盛り！Spark + Python + Data Science祭りに参加を申し込みました！      参加      NaN   \n",
       "2  夏真っ盛り！Spark + Python + Data Science祭り に参加を申し込み...      参加      NaN   \n",
       "3  夏真っ盛り！Spark + Python + Data Science祭りに参加を申し込みました！      参加      NaN   \n",
       "4  夏真っ盛り！Spark + Python + Data Science祭りに参加を申し込みました！      参加      NaN   \n",
       "\n",
       "                                        本ミートアップの参加目的  \\\n",
       "0  Sparkについて興味がある、学びたい, Pythonについて興味がある、学びたい, Dat...   \n",
       "1  Sparkについて興味がある、学びたい, Pythonについて興味がある、学びたい, Dat...   \n",
       "2                       Sparkについて興味がある、学びたい, 発表者との交流   \n",
       "3  Pythonについて興味がある、学びたい, Data Scienceについて興味がある、学び...   \n",
       "4    Sparkについて興味がある、学びたい, Data Scienceについて興味がある、学びたい   \n",
       "\n",
       "  普段お使いのプログラミング言語 / ツールを教えて下さい LT希望者のみ：@発表タイトル A発表内容 をお書きください  \\\n",
       "0               R, Python, その他                            NaN   \n",
       "1         R, Python, Java, その他                            NaN   \n",
       "2             Spark, R, Python                            NaN   \n",
       "3                            R                            NaN   \n",
       "4         R, Python, Java, その他                            NaN   \n",
       "\n",
       "                           ビックデータをどのように活用したいとお考えですか？               更新日時  \\\n",
       "0  顧客について総合的かつリアルタイムの把握をしたい, あらゆるデータを大量・高速・低価格で処理...  2016年6月28日 23時00分   \n",
       "1  顧客について総合的かつリアルタイムの把握をしたい, あらゆるデータを大量・高速・低価格で処理...  2016年6月29日 13時18分   \n",
       "2  顧客について総合的かつリアルタイムの把握をしたい, あらゆるデータを大量・高速・低価格で処理したい  2016年6月29日 13時19分   \n",
       "3  顧客について総合的かつリアルタイムの把握をしたい, IoTに絡んで、センターからのストリーミ...  2016年6月29日 14時03分   \n",
       "4  あらゆるデータを大量・高速・低価格で処理したい, 企業内で分散されたデータを一箇所に統合した...  2016年6月30日 07時58分   \n",
       "\n",
       "     受付番号  \n",
       "0  360379  \n",
       "1  361408  \n",
       "2  361410  \n",
       "3  361474  \n",
       "4  362417  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df.count())\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0              Sparkについて興味がある、学びたい\n",
       "1             Pythonについて興味がある、学びたい\n",
       "2       Data Scienceについて興味がある、学びたい\n",
       "3                          発表者との交流\n",
       "4              Sparkについて興味がある、学びたい\n",
       "5             Pythonについて興味がある、学びたい\n",
       "6       Data Scienceについて興味がある、学びたい\n",
       "7              Sparkについて興味がある、学びたい\n",
       "8                          発表者との交流\n",
       "9             Pythonについて興味がある、学びたい\n",
       "10      Data Scienceについて興味がある、学びたい\n",
       "11                         発表者との交流\n",
       "12             Sparkについて興味がある、学びたい\n",
       "13      Data Scienceについて興味がある、学びたい\n",
       "14             Sparkについて興味がある、学びたい\n",
       "15             Sparkについて興味がある、学びたい\n",
       "16            Pythonについて興味がある、学びたい\n",
       "17      Data Scienceについて興味がある、学びたい\n",
       "18                         発表者との交流\n",
       "19             Sparkについて興味がある、学びたい\n",
       "20            Pythonについて興味がある、学びたい\n",
       "21                         発表者との交流\n",
       "22             Sparkについて興味がある、学びたい\n",
       "23            Pythonについて興味がある、学びたい\n",
       "24      Data Scienceについて興味がある、学びたい\n",
       "25             Sparkについて興味がある、学びたい\n",
       "26            Pythonについて興味がある、学びたい\n",
       "27      Data Scienceについて興味がある、学びたい\n",
       "28                         発表者との交流\n",
       "29             Sparkについて興味がある、学びたい\n",
       "                   ...            \n",
       "1045          Pythonについて興味がある、学びたい\n",
       "1046    Data Scienceについて興味がある、学びたい\n",
       "1047          Pythonについて興味がある、学びたい\n",
       "1048    Data Scienceについて興味がある、学びたい\n",
       "1049           Sparkについて興味がある、学びたい\n",
       "1050          Pythonについて興味がある、学びたい\n",
       "1051    Data Scienceについて興味がある、学びたい\n",
       "1052                       発表者との交流\n",
       "1053           Sparkについて興味がある、学びたい\n",
       "1054           Sparkについて興味がある、学びたい\n",
       "1055          Pythonについて興味がある、学びたい\n",
       "1056    Data Scienceについて興味がある、学びたい\n",
       "1057           Sparkについて興味がある、学びたい\n",
       "1058          Pythonについて興味がある、学びたい\n",
       "1059    Data Scienceについて興味がある、学びたい\n",
       "1060                       発表者との交流\n",
       "1061           Sparkについて興味がある、学びたい\n",
       "1062          Pythonについて興味がある、学びたい\n",
       "1063           Sparkについて興味がある、学びたい\n",
       "1064          Pythonについて興味がある、学びたい\n",
       "1065    Data Scienceについて興味がある、学びたい\n",
       "1066           Sparkについて興味がある、学びたい\n",
       "1067          Pythonについて興味がある、学びたい\n",
       "1068    Data Scienceについて興味がある、学びたい\n",
       "1069          Pythonについて興味がある、学びたい\n",
       "1070    Data Scienceについて興味がある、学びたい\n",
       "1071           Sparkについて興味がある、学びたい\n",
       "1072          Pythonについて興味がある、学びたい\n",
       "1073    Data Scienceについて興味がある、学びたい\n",
       "1074                       発表者との交流\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import functools\n",
    "purposes = df['本ミートアップの参加目的'].dropna()\n",
    "purposes = functools.reduce(lambda x, y: x + y, purposes.str.split(','))\n",
    "purposes = pd.Series(purposes)\n",
    "purposes = purposes.str.strip()\n",
    "purposes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ミートアップの参加目的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data Scienceについて興味がある、学びたい    341\n",
      "Sparkについて興味がある、学びたい           321\n",
      "Pythonについて興味がある、学びたい          288\n",
      "発表者との交流                       114\n",
      "発表者からのお勧めにより                   11\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x108088a58>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DBw4we/ZsgoODue222+jfv78qduwKCgri5ZdfZvbs2c5PqQBhYWHY7XZyc3MJ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\no7Jrw7rklwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108088080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "sum_purpose = purposes.value_counts()\n",
    "print(sum_purpose)\n",
    "sum_purpose.plot(kind='bar', figsize=(6,3), fontsize=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# プログラミング言語 / ツール"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python    280\n",
      "Java      159\n",
      "R         122\n",
      "Spark     120\n",
      "その他       114\n",
      "SPSS       23\n",
      "SAS        17\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1081329b0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jxgw/HQIAAEBw61Lgqqio0E033aRLL71UhYWFp42HhIRo2LBh7X528+bNSk1NVWZmpiQp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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108184668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from itertools import chain\n",
    "\n",
    "def flatten_comma_series(series):\n",
    "    _series = series.dropna()\n",
    "    _series = functools.reduce(lambda x, y: x + y, _series.str.split(','))\n",
    "    _series = pd.Series(_series).str.strip()\n",
    "    return _series\n",
    "\n",
    "languages = flatten_comma_series(df['普段お使いのプログラミング言語 / ツールを教えて下さい'])\n",
    "sum_languages = languages.value_counts()\n",
    "print(sum_languages)\n",
    "sum_languages.plot(kind=\"bar\", figsize=(10,6), fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ビックデータの活用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "あらゆるデータを大量・高速・低価格で処理したい                      271\n",
      "顧客について総合的かつリアルタイムの把握をしたい                     253\n",
      "IoTに絡んで、センターからのストリーミングデータを分析したい              150\n",
      "企業内で分散されたデータを一箇所に統合したい                       147\n",
      "セキュリティへの対応のため、高度な統計・機械学習力により精度の高い脅威を検出したい     82\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1081fd4e0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ag1GnwHXHHXec8QZ27typvn37SpJcLpfy8/O1Y8eOM/73AAAA/M05uWjebrdr3LhxGjVq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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108188390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bigdata = flatten_comma_series(df['ビックデータをどのように活用したいとお考えですか？'])\n",
    "print(bigdata.value_counts())\n",
    "bigdata.value_counts().plot(kind=\"bar\", figsize=(10,6), fontsize=16)"
   ]
  }
 ],
 "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": "3.5.1"
  },
  "widgets": {
   "state": {},
   "version": "1.1.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}