visualization/count_graph.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "plt.style.use(\"ggplot\")\n",
    "mpl.rcParams[\"axes.unicode_minus\"] = False\n",
    "plt.rcParams[\"font.size\"] = 12\n",
    "plt.rcParams[\"font.family\"] = 'NanumGothic'\n",
    "\n",
    "train = pd.read_csv('/home/yahwang/elice/titanic_train.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  명목(범주형) 변수 Count 시각화"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### Titanic 데이터를 활용\n",
    "> ### Survived 컬럼 - 0 or 1을 가진 이항변수"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 컬럼별 단순 시각화"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 기본 - value_counts( )와 Series.plot 을 활용 ( NA 값은 자동으로 무시된다. )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, ''), Text(0.5, 1.0, 'Survived')]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12,4))\n",
    "# bar plot\n",
    "train.Survived.value_counts().plot(kind='bar', ax=axes[0])\n",
    "# pie plot \n",
    "## autopct : 퍼센트 표시 format, textprops - fontsize : 폰트 크기 설정, ( startangle=90, counterclock=False가 올바른 시각화를 위한 기본 설정)\n",
    "## explode : 특정 부분을 따로 분리 ( unique한 개수만큼 리스트 설정 ) - 0을 기준으로 값이 커질수록 원래 위치에서 멀어짐\n",
    "train.Survived.value_counts().plot(kind='pie', explode=[0.1,0], autopct='%.1f %%', ax=axes[1], shadow=True, startangle=90, counterclock=False, textprops={'fontsize': 16})\n",
    "axes[0].set(title='Survived', ylabel='')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### seaborn - countplot 활용"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, '성별 정보')]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure, (ax1,ax2) = plt.subplots(ncols=2,nrows=1, figsize=(12,4))\n",
    "sns.countplot( x='Survived', data=train, ax=ax1) # x : 세로 bar\n",
    "sns.countplot( y='Sex', data=train, ax=ax2) # y: 가로 bar\n",
    "ax1.set(title=\"생사 정보\")\n",
    "ax2.set(title=\"성별 정보\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 컬럼별 세분 시각화"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### * 기본 - 시각화용 DataFrame을 만들어 DataFrame.plot을 활용한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>233</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dead</th>\n",
       "      <td>81</td>\n",
       "      <td>468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          female  male\n",
       "Survived     233   109\n",
       "Dead          81   468"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 조건 별 컬럼으로 이루어진 DataFrame 형태를 만들어서 plot한다. ( NA 값 무시 )\n",
    "feature='Sex'\n",
    "survived = train.query('Survived ==1')[feature].value_counts()\n",
    "dead = train.query('Survived ==0')[feature].value_counts()\n",
    "df = pd.DataFrame([survived, dead]) # df = pd.concat([survived, dead], axis=1).T \n",
    "df.index = ['Survived', 'Dead'] # DataFrame의 index로 조건에 맞는 String 값을 넣어준다.\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f196ec82978>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# X축이 index, Y축은 컬럼별 value, 다른 컬럼은 색깔로 구분한다.\n",
    "fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(6,4))\n",
    "df.plot(kind='bar', stacked=True, ax=ax)\n",
    "ax.set(title=\"성별 생사 정보\")\n",
    "plt.setp(ax.get_xticklabels(), rotation=0)\n",
    "ax.legend(title='Survived',loc=0,title_fontsize=15, fontsize=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 조건이 여러 개일 경우,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C</th>\n",
       "      <th>B</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>A</th>\n",
       "      <th>T</th>\n",
       "      <th>F</th>\n",
       "      <th>G</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1st class</th>\n",
       "      <td>59.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>25</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2nd class</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3rd class</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              C     B     D   E     A    T    F    G\n",
       "1st class  59.0  47.0  29.0  25  15.0  1.0  NaN  NaN\n",
       "2nd class   NaN   NaN   4.0   4   NaN  NaN  8.0  NaN\n",
       "3rd class   NaN   NaN   NaN   3   NaN  NaN  5.0  4.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pclass_cabin = train[['Pclass','Cabin']].copy()\n",
    "pclass_cabin.Cabin = pclass_cabin.Cabin.map(lambda x: x[0], na_action='ignore') # 데이터 전처리(앞 글자만 가져온다.)\n",
    "df = pd.DataFrame() # 빈 데이터프레임 생성\n",
    "class_index = {1:'1st class', 2:'2nd class', 3:'3rd class'} # index mapping을 위한 dict 생성\n",
    "for class_num in sorted(pclass_cabin.Pclass.unique()): \n",
    "    # 조건별 데이터프레임 생성( 결과 DF의 row가 합치기 전 하나의 DF를 의미 )\n",
    "    add_df = pclass_cabin.query('Pclass==@class_num').Cabin.value_counts().to_frame().T # Cabin의 value가 컬럼이 된다. \n",
    "    add_df.index = [class_index[class_num]] # dict를 활용한 index mapping\n",
    "    df = pd.concat([df,add_df],sort=False) # 데이터프레임 결합 ( concat은 컬럼 개수가 달라도 결합 가능 )]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[None, None, None, None, None, None]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(6,4))\n",
    "df.plot(kind='bar', stacked=True, ax=ax)\n",
    "ax.set(title=\"Class별 Cabin\")\n",
    "plt.setp(ax.get_xticklabels(), rotation=0) # x축 표시값을 가로로 표시"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### * seaborn - countplot, pointplot, catplot(categorical plot)을 활용"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### countplot \n",
    "    hue : 해당 컬럼의 value 별로 자동으로 구분하여 출력 ( stacked 처리는 지원하지 않는다. )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Pclass별 Cabin 정보')]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure, (ax1,ax2) = plt.subplots(ncols=2,nrows=1,figsize=(12,4))\n",
    "sns.countplot(x='Survived', hue='Sex', data=train, ax=ax1) # Sex를 색깔로 구분\n",
    "ax1.set(title=\"성별 생사 정보\")\n",
    "# 데이터 전처리 후 활용\n",
    "pclass_cabin_2 = train[['Pclass','Cabin']].copy()\n",
    "pclass_cabin_2.Cabin = pclass_cabin.Cabin.map(lambda x: x[0], na_action='ignore') # 데이터 전처리\n",
    "sns.countplot(x='Pclass', hue='Cabin', data=pclass_cabin_2, ax=ax2) # Cabin을 색깔로 구분\n",
    "ax2.set(title=\"Pclass별 Cabin 정보\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f196ec36128>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 785.475x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# catplot을 활용한 countplot\n",
    "## col : value 별로 plot을 분리\n",
    "sns.catplot(kind='count', x='Pclass', hue='Survived', data=train, col='Sex')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### pointplot \n",
    "    - 평균값(추정값)과 신뢰구간을 표시한다. \n",
    "    - 신뢰구간 ci  : 95 (default)를 포함한 숫자 ( 99, ... ) | 'sd'  -> +- 표준편차 값 | None\n",
    "    - 이항변수에 적용할 때 유용하다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/yahwang/.pyenv/versions/3.6.6/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f196e95ac50>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1,figsize=(6,4))\n",
    "sns.pointplot(x='Pclass', y='Survived', hue='Sex', data=train, ci=95, ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f196e8abe10>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1160.7x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# col : value 별 새로운 plot 생성\n",
    "sns.catplot(kind='point', x='Pclass', y='Survived', hue='Sex', data=train, col='Embarked')"
   ]
  }
 ],
 "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}