maecha/seattleWeather_1948-2017.ipynb

## seattleWeather_1948-2017.ipynb
{
  "cells": [
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import sys\nprint(\"Python version: {}\".format(sys.version))\nimport pandas as pd \nprint(\"pandas version: {}\".format(pd.__version__))",
      "execution_count": 1,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "Python version: 3.6.3 |Anaconda custom (64-bit)| (default, Oct  6 2017, 12:04:38) \n[GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)]\npandas version: 0.22.0\n"
        }
      ]
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "from pandas import Series, DataFrame",
      "execution_count": 2,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "seattle_weather_df = pd.read_csv('seattleWeather_1948-2017.csv')",
      "execution_count": 3,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "seattle_weather_df.head(10)",
      "execution_count": 4,
      "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>DATE</th>\n      <th>PRCP</th>\n      <th>TMAX</th>\n      <th>TMIN</th>\n      <th>RAIN</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1948-01-01</td>\n      <td>0.47</td>\n      <td>51</td>\n      <td>42</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1948-01-02</td>\n      <td>0.59</td>\n      <td>45</td>\n      <td>36</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1948-01-03</td>\n      <td>0.42</td>\n      <td>45</td>\n      <td>35</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1948-01-04</td>\n      <td>0.31</td>\n      <td>45</td>\n      <td>34</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1948-01-05</td>\n      <td>0.17</td>\n      <td>45</td>\n      <td>32</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>1948-01-06</td>\n      <td>0.44</td>\n      <td>48</td>\n      <td>39</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>1948-01-07</td>\n      <td>0.41</td>\n      <td>50</td>\n      <td>40</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>1948-01-08</td>\n      <td>0.04</td>\n      <td>48</td>\n      <td>35</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>1948-01-09</td>\n      <td>0.12</td>\n      <td>50</td>\n      <td>31</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>1948-01-10</td>\n      <td>0.74</td>\n      <td>43</td>\n      <td>34</td>\n      <td>True</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "         DATE  PRCP  TMAX  TMIN  RAIN\n0  1948-01-01  0.47    51    42  True\n1  1948-01-02  0.59    45    36  True\n2  1948-01-03  0.42    45    35  True\n3  1948-01-04  0.31    45    34  True\n4  1948-01-05  0.17    45    32  True\n5  1948-01-06  0.44    48    39  True\n6  1948-01-07  0.41    50    40  True\n7  1948-01-08  0.04    48    35  True\n8  1948-01-09  0.12    50    31  True\n9  1948-01-10  0.74    43    34  True"
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "seattle_weather_df.info()",
      "execution_count": 5,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 25551 entries, 0 to 25550\nData columns (total 5 columns):\nDATE    25551 non-null object\nPRCP    25548 non-null float64\nTMAX    25551 non-null int64\nTMIN    25551 non-null int64\nRAIN    25548 non-null object\ndtypes: float64(1), int64(2), object(2)\nmemory usage: 998.2+ KB\n"
        }
      ]
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "import numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n%matplotlib inline",
      "execution_count": 6,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "sns.countplot('RAIN',  data=seattle_weather_df)",
      "execution_count": 7,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": "/Users/seiyamaeda/anaconda3/lib/python3.6/site-packages/seaborn/categorical.py:1460: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n  stat_data = remove_na(group_data)\n"
        },
        {
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x111dfb160>"
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<matplotlib.figure.Figure at 0x111c51550>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "def add_exceed_20_degrees(tmax):\n    # https://www.wikihow.jp/%E8%8F%AF%E6%B0%8F%E3%81%A8%E6%91%82%E6%B0%8F%E3%81%AE%E6%B8%A9%E5%BA%A6%E3%82%92%E6%8F%9B%E7%AE%97%E3%81%99%E3%82%8B\n    c = 5/9 * (int(tmax) - 32)\n    return True if c >= 20 else False",
      "execution_count": 9,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "seattle_weather_df['exceed_20_degrees'] = seattle_weather_df[['TMAX']].apply(add_exceed_20_degrees, axis=1)",
      "execution_count": 10,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "seattle_weather_df.head(10)",
      "execution_count": 11,
      "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>DATE</th>\n      <th>PRCP</th>\n      <th>TMAX</th>\n      <th>TMIN</th>\n      <th>RAIN</th>\n      <th>exceed_20_degrees</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1948-01-01</td>\n      <td>0.47</td>\n      <td>51</td>\n      <td>42</td>\n      <td>True</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1948-01-02</td>\n      <td>0.59</td>\n      <td>45</td>\n      <td>36</td>\n      <td>True</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1948-01-03</td>\n      <td>0.42</td>\n      <td>45</td>\n      <td>35</td>\n      <td>True</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1948-01-04</td>\n      <td>0.31</td>\n      <td>45</td>\n      <td>34</td>\n      <td>True</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1948-01-05</td>\n      <td>0.17</td>\n      <td>45</td>\n      <td>32</td>\n      <td>True</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>1948-01-06</td>\n      <td>0.44</td>\n      <td>48</td>\n      <td>39</td>\n      <td>True</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>1948-01-07</td>\n      <td>0.41</td>\n      <td>50</td>\n      <td>40</td>\n      <td>True</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>1948-01-08</td>\n      <td>0.04</td>\n      <td>48</td>\n      <td>35</td>\n      <td>True</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>1948-01-09</td>\n      <td>0.12</td>\n      <td>50</td>\n      <td>31</td>\n      <td>True</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>1948-01-10</td>\n      <td>0.74</td>\n      <td>43</td>\n      <td>34</td>\n      <td>True</td>\n      <td>False</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "         DATE  PRCP  TMAX  TMIN  RAIN  exceed_20_degrees\n0  1948-01-01  0.47    51    42  True              False\n1  1948-01-02  0.59    45    36  True              False\n2  1948-01-03  0.42    45    35  True              False\n3  1948-01-04  0.31    45    34  True              False\n4  1948-01-05  0.17    45    32  True              False\n5  1948-01-06  0.44    48    39  True              False\n6  1948-01-07  0.41    50    40  True              False\n7  1948-01-08  0.04    48    35  True              False\n8  1948-01-09  0.12    50    31  True              False\n9  1948-01-10  0.74    43    34  True              False"
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "sns.countplot('RAIN',  data=seattle_weather_df, hue='exceed_20_degrees')",
      "execution_count": 12,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": "/Users/seiyamaeda/anaconda3/lib/python3.6/site-packages/seaborn/categorical.py:1508: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n  stat_data = remove_na(group_data[hue_mask])\n"
        },
        {
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x1142672b0>"
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<matplotlib.figure.Figure at 0x114316240>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "def add_season(date):\n    month = str(date).split('-')[1]\n    if month[:1] == '0': month = month[1:]\n    month = int(month)\n    \n    # Spring: 4~6\n    # Summer: 7~9\n    # Autom: 10~12\n    # Winter: 1~3\n    if 4 <= month <= 6: \n        return 'Spring'\n    elif 7 <= month <= 9:\n        return 'Summer'\n    elif 10 <= month <= 12:\n        return 'Autom'\n    elif 1 <= month <= 3:\n        return 'Winter'\n    else:\n        return 'N/A'",
      "execution_count": 13,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "seattle_weather_df['season'] = seattle_weather_df[['DATE']].apply(add_season, axis=1)",
      "execution_count": 14,
      "outputs": []
    },
    {
      "metadata": {
        "scrolled": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "seattle_weather_df.head(10)",
      "execution_count": 15,
      "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>DATE</th>\n      <th>PRCP</th>\n      <th>TMAX</th>\n      <th>TMIN</th>\n      <th>RAIN</th>\n      <th>exceed_20_degrees</th>\n      <th>season</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1948-01-01</td>\n      <td>0.47</td>\n      <td>51</td>\n      <td>42</td>\n      <td>True</td>\n      <td>False</td>\n      <td>Winter</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1948-01-02</td>\n      <td>0.59</td>\n      <td>45</td>\n      <td>36</td>\n      <td>True</td>\n      <td>False</td>\n      <td>Winter</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1948-01-03</td>\n      <td>0.42</td>\n      <td>45</td>\n      <td>35</td>\n      <td>True</td>\n      <td>False</td>\n      <td>Winter</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1948-01-04</td>\n      <td>0.31</td>\n      <td>45</td>\n      <td>34</td>\n      <td>True</td>\n      <td>False</td>\n      <td>Winter</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1948-01-05</td>\n      <td>0.17</td>\n      <td>45</td>\n      <td>32</td>\n      <td>True</td>\n      <td>False</td>\n      <td>Winter</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>1948-01-06</td>\n      <td>0.44</td>\n      <td>48</td>\n      <td>39</td>\n      <td>True</td>\n      <td>False</td>\n      <td>Winter</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>1948-01-07</td>\n      <td>0.41</td>\n      <td>50</td>\n      <td>40</td>\n      <td>True</td>\n      <td>False</td>\n      <td>Winter</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>1948-01-08</td>\n      <td>0.04</td>\n      <td>48</td>\n      <td>35</td>\n      <td>True</td>\n      <td>False</td>\n      <td>Winter</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>1948-01-09</td>\n      <td>0.12</td>\n      <td>50</td>\n      <td>31</td>\n      <td>True</td>\n      <td>False</td>\n      <td>Winter</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>1948-01-10</td>\n      <td>0.74</td>\n      <td>43</td>\n      <td>34</td>\n      <td>True</td>\n      <td>False</td>\n      <td>Winter</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "         DATE  PRCP  TMAX  TMIN  RAIN  exceed_20_degrees  season\n0  1948-01-01  0.47    51    42  True              False  Winter\n1  1948-01-02  0.59    45    36  True              False  Winter\n2  1948-01-03  0.42    45    35  True              False  Winter\n3  1948-01-04  0.31    45    34  True              False  Winter\n4  1948-01-05  0.17    45    32  True              False  Winter\n5  1948-01-06  0.44    48    39  True              False  Winter\n6  1948-01-07  0.41    50    40  True              False  Winter\n7  1948-01-08  0.04    48    35  True              False  Winter\n8  1948-01-09  0.12    50    31  True              False  Winter\n9  1948-01-10  0.74    43    34  True              False  Winter"
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "sns.countplot('RAIN',  data=seattle_weather_df, hue=\"season\")",
      "execution_count": 16,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": "/Users/seiyamaeda/anaconda3/lib/python3.6/site-packages/seaborn/categorical.py:1508: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n  stat_data = remove_na(group_data[hue_mask])\n"
        },
        {
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x11432b978>"
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<matplotlib.figure.Figure at 0x11447b780>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": ""
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.6.3",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "gist": {
      "id": "cac314ef555b3cd68ad3b3f7aed1c23b",
      "data": {
        "description": "anaconda3/py_learning/seattleWeather_1948-2017.ipynb",
        "public": true
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