8. 범주형 변수.ipynb

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   "source": [
    "# 라이브러리 로드\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# os에 따라 시각화 폰트 설정하기\n",
    "import os\n",
    "\n",
    "if os.name == 'nt':\n",
    "    font_family = \"Malgun Gothic\"\n",
    "else:\n",
    "    font_family = \"AppleGothic\"\n",
    "    \n",
    "sns.set(font=font_family, rc={\"axes.unicode_minus\" : False })"
   ]
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# retina 를 지원하는 디스플레이에서 폰트가 선명하게 보이기 위함\n",
    "from IPython.display import set_matplotlib_formats\n",
    "\n",
    "set_matplotlib_formats(\"retina\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# csv 파일 로드\n",
    "df = pd.read_csv(\"bd_kma_weather.csv\")\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": [
    "df.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_nunique = df.nunique()\n",
    "df_nunique[df_nunique < 20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"최대 풍속 풍향(16방위)\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"최대 풍속 풍향(16방위)\"].value_counts(normalize=True) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.crosstab(df[\"지점명\"], df[\"최대 풍속 풍향(16방위)\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 범주형 변수의 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.countplot(data=df, x=\"지점명\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_seoul_busan = df[df[\"지점명\"].isin([\"서울\", \"부산\"])].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(15, 4))\n",
    "sns.countplot(data=df_seoul_busan, x=\"최대 풍속 풍향(16방위)\", hue=\"지점명\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.groupby(\"지점명\")[\"평균기온(°C)\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "g = sns.barplot(data=df, x=\"지점명\", y=\"평균기온(°C)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = sns.barplot(data=df, x=\"지점명\", y=\"평균기온(°C)\", ci=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.boxplot(data=df, x=\"지점명\", y=\"평균기온(°C)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.violinplot(data=df, x=\"지점명\", y=\"평균기온(°C)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "cell_type": "code",
   "execution_count": null,
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