climate_starter.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import style\n",
    "style.use('fivethirtyeight')\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime as dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reflect Tables into SQLAlchemy ORM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Python SQL toolkit and Object Relational Mapper\n",
    "import sqlalchemy\n",
    "from sqlalchemy.ext.automap import automap_base\n",
    "from sqlalchemy.orm import Session\n",
    "from sqlalchemy import create_engine, func, inspect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "engine = create_engine(\"sqlite:///Resources/hawaii.sqlite\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reflect an existing database into a new model\n",
    "Base = automap_base()\n",
    "# reflect the tables\n",
    "Base.prepare(engine, reflect=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['measurement', 'station']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can view all of the classes that automap found\n",
    "Base.classes.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save references to each table\n",
    "Measurement = Base.classes.measurement\n",
    "Station = Base.classes.station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create our session (link) from Python to the DB\n",
    "session = Session(engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reflect Tables into SQLAlchemy ORM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "measurement table:\n",
      "id INTEGER\n",
      "station TEXT\n",
      "date TEXT\n",
      "prcp FLOAT\n",
      "tobs FLOAT\n",
      "\n",
      "station table:\n",
      "id INTEGER\n",
      "station TEXT\n",
      "name TEXT\n",
      "latitude FLOAT\n",
      "longitude FLOAT\n",
      "elevation FLOAT\n"
     ]
    }
   ],
   "source": [
    "# Checking column names:\n",
    "inspector = inspect(engine)\n",
    "cols = inspector.get_columns('measurement')\n",
    "print('measurement table:')\n",
    "for col in cols:\n",
    "    print(col['name'], col['type'])\n",
    "print(\"\")\n",
    "cols = inspector.get_columns('station')\n",
    "print(\"station table:\")\n",
    "for col in cols:\n",
    "    print(col['name'], col['type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1, 'USC00519397', '2010-01-01', 0.08, 65.0),\n",
       " (2, 'USC00519397', '2010-01-02', 0.0, 63.0),\n",
       " (3, 'USC00519397', '2010-01-03', 0.0, 74.0),\n",
       " (4, 'USC00519397', '2010-01-04', 0.0, 76.0),\n",
       " (5, 'USC00519397', '2010-01-06', None, 73.0)]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking first few rows of measurement table:\n",
    "engine.execute('SELECT * FROM measurement limit 5').fetchall()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1, 'USC00519397', 'WAIKIKI 717.2, HI US', 21.2716, -157.8168, 3.0),\n",
       " (2, 'USC00513117', 'KANEOHE 838.1, HI US', 21.4234, -157.8015, 14.6),\n",
       " (3, 'USC00514830', 'KUALOA RANCH HEADQUARTERS 886.9, HI US', 21.5213, -157.8374, 7.0),\n",
       " (4, 'USC00517948', 'PEARL CITY, HI US', 21.3934, -157.9751, 11.9),\n",
       " (5, 'USC00518838', 'UPPER WAHIAWA 874.3, HI US', 21.4992, -158.0111, 306.6)]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking first few rows of station table:\n",
    "engine.execute('SELECT * FROM station limit 5').fetchall()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "### It looks like the 2 tables share the \"station\" column in common; ie. \"station\" is the foreign key for \"measurement\" table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Climate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Design a query to retrieve the last 12 months of precipitation data and plot the results\n",
    "latest_date = session.query(Measurement.date).order_by(Measurement.date.desc()).first()\n",
    "#latest_date[0]\n",
    "# Calculate the date 1 year ago from the last data point in the database\n",
    "year_prior = dt.datetime.strptime(latest_date[0], '%Y-%m-%d') - dt.timedelta(days=365)\n",
    "\n",
    "# Perform a query to retrieve the data and precipitation scores\n",
    "sel = [Measurement.date,\n",
    "       Measurement.prcp]\n",
    "last_12 = session.query(*sel).\\\n",
    "            filter(Measurement.date >= year_prior.date()).all()\n",
    "# Save the query results as a Pandas DataFrame and set the index to the date column\n",
    "last_12_df = pd.DataFrame(last_12)\n",
    "\n",
    "# Sort the dataframe by date\n",
    "last_12_df['date'] = pd.to_datetime(last_12_df['date']) # converting to datetime type\n",
    "sorted_last_12_df = last_12_df.sort_values(by=['date'])\n",
    "# Use Pandas Plotting with Matplotlib to plot the data\n",
    "## Will assume the None/NaN entries are missing rather than zero precipitation, so drop these entries:\n",
    "df_to_plot = sorted_last_12_df[['date','prcp']].dropna()\n",
    "df_to_plot = df_to_plot.groupby('date').max()  # let's just plot the max precipitation values per date\n",
    "df_to_plot = df_to_plot.rename(columns = {'prcp': 'precipitation'}) # LEts rename the column prcp to precipitation\n",
    "ax = df_to_plot.plot.bar(width=3.0,rot=45, figsize=(20,10),fontsize = 30, grid = True)\n",
    "\n",
    "plt.xticks([], [])\n",
    "offset = 10\n",
    "plt.xlim(-offset,df_to_plot.shape[0]+offset)\n",
    "plt.xlabel(\"Date\",fontsize= 30)\n",
    "plt.ylabel(\"Precipitation (mm)\",fontsize= 30)\n",
    "plt.title(\"Precipitation Analysis (8/23/16 to 8/23/17)\", fontsize= 30)\n",
    "plt.legend(loc='upper center',fontsize= 30)\n",
    "#plt.show()\n",
    "plt.savefig('Data\\DataPrecipitation Analysis.png', bbox_inches='tight')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![precipitation](Images/precipitation.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>precipitation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2021.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.177279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.461190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.130000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.700000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       precipitation\n",
       "count    2021.000000\n",
       "mean        0.177279\n",
       "std         0.461190\n",
       "min         0.000000\n",
       "25%         0.000000\n",
       "50%         0.020000\n",
       "75%         0.130000\n",
       "max         6.700000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use Pandas to calcualte the summary statistics for the precipitation data\n",
    "last_12_df[['date','prcp']].rename(columns={'prcp':'precipitation'}).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![describe](Images/describe.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of stations from station table: 9\n",
      "Number of stations from measurement table: 9\n"
     ]
    }
   ],
   "source": [
    "# Design a query to show how many stations are available in this dataset?\n",
    "numStations = session.query(Station.station).group_by(Station.station).count() # group_by is unnecessary here as it's the station table...\n",
    "print(\"Number of stations from station table:\", numStations)\n",
    "numStations2 = session.query(Measurement.station).group_by(Measurement.station).count()\n",
    "print(\"Number of stations from measurement table:\", numStations2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('USC00519281', 2772),\n",
       " ('USC00519397', 2724),\n",
       " ('USC00513117', 2709),\n",
       " ('USC00519523', 2669),\n",
       " ('USC00516128', 2612),\n",
       " ('USC00514830', 2202),\n",
       " ('USC00511918', 1979),\n",
       " ('USC00517948', 1372),\n",
       " ('USC00518838', 511)]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What are the most active stations? (i.e. what stations have the most rows)?\n",
    "# List the stations and the counts in descending order.\n",
    "sel = [Measurement.station, func.count(Measurement.station)]\n",
    "query = session.query(*sel).\\\n",
    "        group_by(Measurement.station).\\\n",
    "        order_by(func.count(Measurement.station).desc()).all()\n",
    "query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 3 active stations are:\n",
      "USC00519281 with 2772 precipitation measurements\n",
      "USC00519397 with 2724 precipitation measurements\n",
      "USC00513117 with 2709 precipitation measurements\n"
     ]
    }
   ],
   "source": [
    "print(\"Top 3 active stations are:\")\n",
    "for i in range(3):\n",
    "    print(f\"{query[i][0]} with {query[i][1]} precipitation measurements\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('USC00519281', 54.0, 85.0, 71.66378066378067)]\n"
     ]
    }
   ],
   "source": [
    "# Using the station id from the previous query, calculate the lowest temperature recorded, \n",
    "# highest temperature recorded, and average temperature most active station?\n",
    "\n",
    "sel = [Measurement.station,\n",
    "       func.min(Measurement.tobs),\n",
    "       func.max(Measurement.tobs),\n",
    "       func.avg(Measurement.tobs)]\n",
    "new_query = session.query(*sel).\\\n",
    "    filter(Measurement.station == query[0][0]).\\\n",
    "    group_by(Measurement.station).all()\n",
    "print(new_query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The most active station USC00519281 recorded:\n",
      "Lowest temperature: 54.0\n",
      "Highest temperature: 85.0\n",
      "Average temperature: 71.7\n"
     ]
    }
   ],
   "source": [
    "print(f\"The most active station {new_query[0][0]} recorded:\")\n",
    "print(f\"Lowest temperature: {new_query[0][1]:.1f}\")\n",
    "print(f\"Highest temperature: {new_query[0][2]:.1f}\")\n",
    "print(f\"Average temperature: {new_query[0][3]:.1f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Choose the station with the highest number of temperature observations.\n",
    "# Query the last 12 months of temperature observation data for this station and plot the results as a histogram\n",
    "data = session.query(Measurement.tobs).\\\n",
    "    filter(Measurement.station == query[0][0]).\\\n",
    "    filter(Measurement.date >= year_prior).all()\n",
    "data = pd.DataFrame(data).dropna()\n",
    "data.plot(kind='hist', grid=True, bins=12)\n",
    "plt.savefig('Data\\Highest number of temperature observations.png', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![precipitation](Images/station-histogram.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(62.0, 69.57142857142857, 74.0)]\n"
     ]
    }
   ],
   "source": [
    "# This function called `calc_temps` will accept start date and end date in the format '%Y-%m-%d' \n",
    "# and return the minimum, average, and maximum temperatures for that range of dates\n",
    "def calc_temps(start_date, end_date):\n",
    "    \"\"\"TMIN, TAVG, and TMAX for a list of dates.\n",
    "    \n",
    "    Args:\n",
    "        start_date (string): A date string in the format %Y-%m-%d\n",
    "        end_date (string): A date string in the format %Y-%m-%d\n",
    "        \n",
    "    Returns:\n",
    "        TMIN, TAVE, and TMAX\n",
    "    \"\"\"\n",
    "    \n",
    "    return session.query(func.min(Measurement.tobs), func.avg(Measurement.tobs), func.max(Measurement.tobs)).\\\n",
    "        filter(Measurement.date >= start_date).filter(Measurement.date <= end_date).all()\n",
    "\n",
    "# function usage example\n",
    "print(calc_temps('2012-02-28', '2012-03-05'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(71.0, 78.93478260869566, 83.0)]\n"
     ]
    }
   ],
   "source": [
    "# Use your previous function `calc_temps` to calculate the tmin, tavg, and tmax \n",
    "# for your trip using the previous year's data for those same dates.\n",
    "trip_start = dt.date(2018, 8, 1)\n",
    "trip_end = dt.date(2018, 8, 12)\n",
    "last_year = dt.timedelta(days=365)\n",
    "temp_avg_lst_year = (calc_temps((trip_start-last_year), (trip_end-last_year)))\n",
    "print(temp_avg_lst_year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the results from your previous query as a bar chart. \n",
    "# Use \"Trip Avg Temp\" as your Title\n",
    "# Use the average temperature for the y value\n",
    "# Use the peak-to-peak (tmax-tmin) value as the y error bar (yerr)\n",
    "plt.figure(figsize=(6,10))\n",
    "plt.bar(0.4, temp_avg_lst_year[0][1], yerr=temp_avg_lst_year[0][2] - temp_avg_lst_year[0][0], \n",
    "        width=0.8, color='red', alpha=0.2, align='center')\n",
    "plt.xlim(-0.2,1)\n",
    "plt.ylim(0,105)\n",
    "plt.title('Trip Avg Temp', fontsize=30)\n",
    "plt.ylabel('Temp (F)', fontsize=24)\n",
    "plt.xticks([],[])\n",
    "plt.yticks(fontsize=18)\n",
    "plt.savefig('Data\\Trip Avg Temp.png', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('2017-08-08',\n",
       "  'USC00516128',\n",
       "  'WAIKIKI 717.2, HI US',\n",
       "  0.34,\n",
       "  21.2716,\n",
       "  -157.8168,\n",
       "  3.0),\n",
       " ('2017-08-08',\n",
       "  'USC00516128',\n",
       "  'KANEOHE 838.1, HI US',\n",
       "  0.34,\n",
       "  21.4234,\n",
       "  -157.8015,\n",
       "  14.6),\n",
       " ('2017-08-08',\n",
       "  'USC00516128',\n",
       "  'KUALOA RANCH HEADQUARTERS 886.9, HI US',\n",
       "  0.34,\n",
       "  21.5213,\n",
       "  -157.8374,\n",
       "  7.0),\n",
       " ('2017-08-08',\n",
       "  'USC00516128',\n",
       "  'PEARL CITY, HI US',\n",
       "  0.34,\n",
       "  21.3934,\n",
       "  -157.9751,\n",
       "  11.9),\n",
       " ('2017-08-08',\n",
       "  'USC00516128',\n",
       "  'UPPER WAHIAWA 874.3, HI US',\n",
       "  0.34,\n",
       "  21.4992,\n",
       "  -158.0111,\n",
       "  306.6),\n",
       " ('2017-08-08',\n",
       "  'USC00516128',\n",
       "  'WAIMANALO EXPERIMENTAL FARM, HI US',\n",
       "  0.34,\n",
       "  21.33556,\n",
       "  -157.71139,\n",
       "  19.5),\n",
       " ('2017-08-08',\n",
       "  'USC00516128',\n",
       "  'WAIHEE 837.5, HI US',\n",
       "  0.34,\n",
       "  21.45167,\n",
       "  -157.84888999999998,\n",
       "  32.9),\n",
       " ('2017-08-08',\n",
       "  'USC00516128',\n",
       "  'HONOLULU OBSERVATORY 702.2, HI US',\n",
       "  0.34,\n",
       "  21.3152,\n",
       "  -157.9992,\n",
       "  0.9),\n",
       " ('2017-08-08',\n",
       "  'USC00516128',\n",
       "  'MANOA LYON ARBO 785.2, HI US',\n",
       "  0.34,\n",
       "  21.3331,\n",
       "  -157.8025,\n",
       "  152.4),\n",
       " ('2017-08-02',\n",
       "  'USC00516128',\n",
       "  'WAIKIKI 717.2, HI US',\n",
       "  0.25,\n",
       "  21.2716,\n",
       "  -157.8168,\n",
       "  3.0),\n",
       " ('2017-08-02',\n",
       "  'USC00516128',\n",
       "  'KANEOHE 838.1, HI US',\n",
       "  0.25,\n",
       "  21.4234,\n",
       "  -157.8015,\n",
       "  14.6),\n",
       " ('2017-08-02',\n",
       "  'USC00516128',\n",
       "  'KUALOA RANCH HEADQUARTERS 886.9, HI US',\n",
       "  0.25,\n",
       "  21.5213,\n",
       "  -157.8374,\n",
       "  7.0),\n",
       " ('2017-08-02',\n",
       "  'USC00516128',\n",
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       "  -157.8168,\n",
       "  3.0),\n",
       " ('2017-08-12',\n",
       "  'USC00519523',\n",
       "  'KANEOHE 838.1, HI US',\n",
       "  0.0,\n",
       "  21.4234,\n",
       "  -157.8015,\n",
       "  14.6),\n",
       " ('2017-08-12',\n",
       "  'USC00519523',\n",
       "  'KUALOA RANCH HEADQUARTERS 886.9, HI US',\n",
       "  0.0,\n",
       "  21.5213,\n",
       "  -157.8374,\n",
       "  7.0),\n",
       " ('2017-08-12',\n",
       "  'USC00519523',\n",
       "  'PEARL CITY, HI US',\n",
       "  0.0,\n",
       "  21.3934,\n",
       "  -157.9751,\n",
       "  11.9),\n",
       " ('2017-08-12',\n",
       "  'USC00519523',\n",
       "  'UPPER WAHIAWA 874.3, HI US',\n",
       "  0.0,\n",
       "  21.4992,\n",
       "  -158.0111,\n",
       "  306.6),\n",
       " ('2017-08-12',\n",
       "  'USC00519523',\n",
       "  'WAIMANALO EXPERIMENTAL FARM, HI US',\n",
       "  0.0,\n",
       "  21.33556,\n",
       "  -157.71139,\n",
       "  19.5),\n",
       " ('2017-08-12',\n",
       "  'USC00519523',\n",
       "  'WAIHEE 837.5, HI US',\n",
       "  0.0,\n",
       "  21.45167,\n",
       "  -157.84888999999998,\n",
       "  32.9),\n",
       " ('2017-08-12',\n",
       "  'USC00519523',\n",
       "  'HONOLULU OBSERVATORY 702.2, HI US',\n",
       "  0.0,\n",
       "  21.3152,\n",
       "  -157.9992,\n",
       "  0.9),\n",
       " ('2017-08-12',\n",
       "  'USC00519523',\n",
       "  'MANOA LYON ARBO 785.2, HI US',\n",
       "  0.0,\n",
       "  21.3331,\n",
       "  -157.8025,\n",
       "  152.4),\n",
       " ('2017-08-04',\n",
       "  'USC00519281',\n",
       "  'WAIKIKI 717.2, HI US',\n",
       "  0.0,\n",
       "  21.2716,\n",
       "  -157.8168,\n",
       "  3.0),\n",
       " ('2017-08-04',\n",
       "  'USC00519281',\n",
       "  'KANEOHE 838.1, HI US',\n",
       "  0.0,\n",
       "  21.4234,\n",
       "  -157.8015,\n",
       "  14.6),\n",
       " ('2017-08-04',\n",
       "  'USC00519281',\n",
       "  'KUALOA RANCH HEADQUARTERS 886.9, HI US',\n",
       "  0.0,\n",
       "  21.5213,\n",
       "  -157.8374,\n",
       "  7.0),\n",
       " ('2017-08-04',\n",
       "  'USC00519281',\n",
       "  'PEARL CITY, HI US',\n",
       "  0.0,\n",
       "  21.3934,\n",
       "  -157.9751,\n",
       "  11.9),\n",
       " ('2017-08-04',\n",
       "  'USC00519281',\n",
       "  'UPPER WAHIAWA 874.3, HI US',\n",
       "  0.0,\n",
       "  21.4992,\n",
       "  -158.0111,\n",
       "  306.6),\n",
       " ('2017-08-04',\n",
       "  'USC00519281',\n",
       "  'WAIMANALO EXPERIMENTAL FARM, HI US',\n",
       "  0.0,\n",
       "  21.33556,\n",
       "  -157.71139,\n",
       "  19.5),\n",
       " ('2017-08-04',\n",
       "  'USC00519281',\n",
       "  'WAIHEE 837.5, HI US',\n",
       "  0.0,\n",
       "  21.45167,\n",
       "  -157.84888999999998,\n",
       "  32.9),\n",
       " ('2017-08-04',\n",
       "  'USC00519281',\n",
       "  'HONOLULU OBSERVATORY 702.2, HI US',\n",
       "  0.0,\n",
       "  21.3152,\n",
       "  -157.9992,\n",
       "  0.9),\n",
       " ('2017-08-04',\n",
       "  'USC00519281',\n",
       "  'MANOA LYON ARBO 785.2, HI US',\n",
       "  0.0,\n",
       "  21.3331,\n",
       "  -157.8025,\n",
       "  152.4),\n",
       " ('2017-08-06',\n",
       "  'USC00519281',\n",
       "  'WAIKIKI 717.2, HI US',\n",
       "  0.0,\n",
       "  21.2716,\n",
       "  -157.8168,\n",
       "  3.0),\n",
       " ('2017-08-06',\n",
       "  'USC00519281',\n",
       "  'KANEOHE 838.1, HI US',\n",
       "  0.0,\n",
       "  21.4234,\n",
       "  -157.8015,\n",
       "  14.6),\n",
       " ('2017-08-06',\n",
       "  'USC00519281',\n",
       "  'KUALOA RANCH HEADQUARTERS 886.9, HI US',\n",
       "  0.0,\n",
       "  21.5213,\n",
       "  -157.8374,\n",
       "  7.0),\n",
       " ('2017-08-06',\n",
       "  'USC00519281',\n",
       "  'PEARL CITY, HI US',\n",
       "  0.0,\n",
       "  21.3934,\n",
       "  -157.9751,\n",
       "  11.9),\n",
       " ('2017-08-06',\n",
       "  'USC00519281',\n",
       "  'UPPER WAHIAWA 874.3, HI US',\n",
       "  0.0,\n",
       "  21.4992,\n",
       "  -158.0111,\n",
       "  306.6),\n",
       " ('2017-08-06',\n",
       "  'USC00519281',\n",
       "  'WAIMANALO EXPERIMENTAL FARM, HI US',\n",
       "  0.0,\n",
       "  21.33556,\n",
       "  -157.71139,\n",
       "  19.5),\n",
       " ('2017-08-06',\n",
       "  'USC00519281',\n",
       "  'WAIHEE 837.5, HI US',\n",
       "  0.0,\n",
       "  21.45167,\n",
       "  -157.84888999999998,\n",
       "  32.9),\n",
       " ('2017-08-06',\n",
       "  'USC00519281',\n",
       "  'HONOLULU OBSERVATORY 702.2, HI US',\n",
       "  0.0,\n",
       "  21.3152,\n",
       "  -157.9992,\n",
       "  0.9),\n",
       " ('2017-08-06',\n",
       "  'USC00519281',\n",
       "  'MANOA LYON ARBO 785.2, HI US',\n",
       "  0.0,\n",
       "  21.3331,\n",
       "  -157.8025,\n",
       "  152.4),\n",
       " ('2017-08-01',\n",
       "  'USC00516128',\n",
       "  'WAIKIKI 717.2, HI US',\n",
       "  None,\n",
       "  21.2716,\n",
       "  -157.8168,\n",
       "  3.0),\n",
       " ('2017-08-01',\n",
       "  'USC00516128',\n",
       "  'KANEOHE 838.1, HI US',\n",
       "  None,\n",
       "  21.4234,\n",
       "  -157.8015,\n",
       "  14.6),\n",
       " ('2017-08-01',\n",
       "  'USC00516128',\n",
       "  'KUALOA RANCH HEADQUARTERS 886.9, HI US',\n",
       "  None,\n",
       "  21.5213,\n",
       "  -157.8374,\n",
       "  7.0),\n",
       " ('2017-08-01',\n",
       "  'USC00516128',\n",
       "  'PEARL CITY, HI US',\n",
       "  None,\n",
       "  21.3934,\n",
       "  -157.9751,\n",
       "  11.9),\n",
       " ('2017-08-01',\n",
       "  'USC00516128',\n",
       "  'UPPER WAHIAWA 874.3, HI US',\n",
       "  None,\n",
       "  21.4992,\n",
       "  -158.0111,\n",
       "  306.6),\n",
       " ('2017-08-01',\n",
       "  'USC00516128',\n",
       "  'WAIMANALO EXPERIMENTAL FARM, HI US',\n",
       "  None,\n",
       "  21.33556,\n",
       "  -157.71139,\n",
       "  19.5),\n",
       " ('2017-08-01',\n",
       "  'USC00516128',\n",
       "  'WAIHEE 837.5, HI US',\n",
       "  None,\n",
       "  21.45167,\n",
       "  -157.84888999999998,\n",
       "  32.9),\n",
       " ('2017-08-01',\n",
       "  'USC00516128',\n",
       "  'HONOLULU OBSERVATORY 702.2, HI US',\n",
       "  None,\n",
       "  21.3152,\n",
       "  -157.9992,\n",
       "  0.9),\n",
       " ('2017-08-01',\n",
       "  'USC00516128',\n",
       "  'MANOA LYON ARBO 785.2, HI US',\n",
       "  None,\n",
       "  21.3331,\n",
       "  -157.8025,\n",
       "  152.4),\n",
       " ('2017-08-05',\n",
       "  'USC00516128',\n",
       "  'WAIKIKI 717.2, HI US',\n",
       "  None,\n",
       "  21.2716,\n",
       "  -157.8168,\n",
       "  3.0),\n",
       " ('2017-08-05',\n",
       "  'USC00516128',\n",
       "  'KANEOHE 838.1, HI US',\n",
       "  None,\n",
       "  21.4234,\n",
       "  -157.8015,\n",
       "  14.6),\n",
       " ('2017-08-05',\n",
       "  'USC00516128',\n",
       "  'KUALOA RANCH HEADQUARTERS 886.9, HI US',\n",
       "  None,\n",
       "  21.5213,\n",
       "  -157.8374,\n",
       "  7.0),\n",
       " ('2017-08-05',\n",
       "  'USC00516128',\n",
       "  'PEARL CITY, HI US',\n",
       "  None,\n",
       "  21.3934,\n",
       "  -157.9751,\n",
       "  11.9),\n",
       " ('2017-08-05',\n",
       "  'USC00516128',\n",
       "  'UPPER WAHIAWA 874.3, HI US',\n",
       "  None,\n",
       "  21.4992,\n",
       "  -158.0111,\n",
       "  306.6),\n",
       " ('2017-08-05',\n",
       "  'USC00516128',\n",
       "  'WAIMANALO EXPERIMENTAL FARM, HI US',\n",
       "  None,\n",
       "  21.33556,\n",
       "  -157.71139,\n",
       "  19.5),\n",
       " ('2017-08-05',\n",
       "  'USC00516128',\n",
       "  'WAIHEE 837.5, HI US',\n",
       "  None,\n",
       "  21.45167,\n",
       "  -157.84888999999998,\n",
       "  32.9),\n",
       " ('2017-08-05',\n",
       "  'USC00516128',\n",
       "  'HONOLULU OBSERVATORY 702.2, HI US',\n",
       "  None,\n",
       "  21.3152,\n",
       "  -157.9992,\n",
       "  0.9),\n",
       " ('2017-08-05',\n",
       "  'USC00516128',\n",
       "  'MANOA LYON ARBO 785.2, HI US',\n",
       "  None,\n",
       "  21.3331,\n",
       "  -157.8025,\n",
       "  152.4),\n",
       " ('2017-08-06',\n",
       "  'USC00516128',\n",
       "  'WAIKIKI 717.2, HI US',\n",
       "  None,\n",
       "  21.2716,\n",
       "  -157.8168,\n",
       "  3.0),\n",
       " ('2017-08-06',\n",
       "  'USC00516128',\n",
       "  'KANEOHE 838.1, HI US',\n",
       "  None,\n",
       "  21.4234,\n",
       "  -157.8015,\n",
       "  14.6),\n",
       " ('2017-08-06',\n",
       "  'USC00516128',\n",
       "  'KUALOA RANCH HEADQUARTERS 886.9, HI US',\n",
       "  None,\n",
       "  21.5213,\n",
       "  -157.8374,\n",
       "  7.0),\n",
       " ('2017-08-06',\n",
       "  'USC00516128',\n",
       "  'PEARL CITY, HI US',\n",
       "  None,\n",
       "  21.3934,\n",
       "  -157.9751,\n",
       "  11.9),\n",
       " ('2017-08-06',\n",
       "  'USC00516128',\n",
       "  'UPPER WAHIAWA 874.3, HI US',\n",
       "  None,\n",
       "  21.4992,\n",
       "  -158.0111,\n",
       "  306.6),\n",
       " ('2017-08-06',\n",
       "  'USC00516128',\n",
       "  'WAIMANALO EXPERIMENTAL FARM, HI US',\n",
       "  None,\n",
       "  21.33556,\n",
       "  -157.71139,\n",
       "  19.5),\n",
       " ('2017-08-06',\n",
       "  'USC00516128',\n",
       "  'WAIHEE 837.5, HI US',\n",
       "  None,\n",
       "  21.45167,\n",
       "  -157.84888999999998,\n",
       "  32.9),\n",
       " ('2017-08-06',\n",
       "  'USC00516128',\n",
       "  'HONOLULU OBSERVATORY 702.2, HI US',\n",
       "  None,\n",
       "  21.3152,\n",
       "  -157.9992,\n",
       "  0.9),\n",
       " ('2017-08-06',\n",
       "  'USC00516128',\n",
       "  'MANOA LYON ARBO 785.2, HI US',\n",
       "  None,\n",
       "  21.3331,\n",
       "  -157.8025,\n",
       "  152.4),\n",
       " ('2017-08-11',\n",
       "  'USC00516128',\n",
       "  'WAIKIKI 717.2, HI US',\n",
       "  None,\n",
       "  21.2716,\n",
       "  -157.8168,\n",
       "  3.0),\n",
       " ('2017-08-11',\n",
       "  'USC00516128',\n",
       "  'KANEOHE 838.1, HI US',\n",
       "  None,\n",
       "  21.4234,\n",
       "  -157.8015,\n",
       "  14.6),\n",
       " ('2017-08-11',\n",
       "  'USC00516128',\n",
       "  'KUALOA RANCH HEADQUARTERS 886.9, HI US',\n",
       "  None,\n",
       "  21.5213,\n",
       "  -157.8374,\n",
       "  7.0),\n",
       " ('2017-08-11',\n",
       "  'USC00516128',\n",
       "  'PEARL CITY, HI US',\n",
       "  None,\n",
       "  21.3934,\n",
       "  -157.9751,\n",
       "  11.9),\n",
       " ('2017-08-11',\n",
       "  'USC00516128',\n",
       "  'UPPER WAHIAWA 874.3, HI US',\n",
       "  None,\n",
       "  21.4992,\n",
       "  -158.0111,\n",
       "  306.6),\n",
       " ('2017-08-11',\n",
       "  'USC00516128',\n",
       "  'WAIMANALO EXPERIMENTAL FARM, HI US',\n",
       "  None,\n",
       "  21.33556,\n",
       "  -157.71139,\n",
       "  19.5),\n",
       " ('2017-08-11',\n",
       "  'USC00516128',\n",
       "  'WAIHEE 837.5, HI US',\n",
       "  None,\n",
       "  21.45167,\n",
       "  -157.84888999999998,\n",
       "  32.9),\n",
       " ('2017-08-11',\n",
       "  'USC00516128',\n",
       "  'HONOLULU OBSERVATORY 702.2, HI US',\n",
       "  None,\n",
       "  21.3152,\n",
       "  -157.9992,\n",
       "  0.9),\n",
       " ('2017-08-11',\n",
       "  'USC00516128',\n",
       "  'MANOA LYON ARBO 785.2, HI US',\n",
       "  None,\n",
       "  21.3331,\n",
       "  -157.8025,\n",
       "  152.4)]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the total amount of rainfall per weather station for your trip dates using the previous year's matching dates.\n",
    "# Sort this in descending order by precipitation amount and list the station, name, latitude, longitude, and elevation\n",
    "start_date= dt.date(2018,8,1)\n",
    "end_date= dt.date(2018,8,12)\n",
    "last_year = dt.timedelta(days=365)\n",
    "start = start_date-last_year\n",
    "end = end_date-last_year\n",
    "trip_data = session.query(Measurement.date, Measurement.station, Station.name, Measurement.prcp, Station.latitude, Station.longitude, Station.elevation).\\\n",
    "                filter(Measurement.date >= start).filter(Measurement.date <= end).order_by(Measurement.prcp.desc()).all()\n",
    "trip_data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optional Challenge Assignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(62.0, 69.15384615384616, 77.0)]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a query that will calculate the daily normals \n",
    "# (i.e. the averages for tmin, tmax, and tavg for all historic data matching a specific month and day)\n",
    "\n",
    "def daily_normals(date):\n",
    "    \"\"\"Daily Normals.\n",
    "    \n",
    "    Args:\n",
    "        date (str): A date string in the format '%m-%d'\n",
    "        \n",
    "    Returns:\n",
    "        A list of tuples containing the daily normals, tmin, tavg, and tmax\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    sel = [func.min(Measurement.tobs), func.avg(Measurement.tobs), func.max(Measurement.tobs)]\n",
    "    return session.query(*sel).filter(func.strftime(\"%m-%d\", Measurement.date) == date).all()\n",
    "    \n",
    "daily_normals(\"01-01\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(67.0, 75.54, 83.0),\n",
       " (68.0, 75.60377358490567, 84.0),\n",
       " (70.0, 76.61111111111111, 85.0),\n",
       " (69.0, 76.71153846153847, 84.0),\n",
       " (69.0, 76.14814814814815, 82.0),\n",
       " (67.0, 76.25, 83.0),\n",
       " (71.0, 77.15686274509804, 83.0),\n",
       " (68.0, 76.5576923076923, 83.0),\n",
       " (69.0, 75.98039215686275, 81.0),\n",
       " (65.0, 76.42, 83.0),\n",
       " (67.0, 75.98113207547169, 82.0),\n",
       " (67.0, 76.52727272727273, 83.0)]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate the daily normals for your trip\n",
    "# push each tuple of calculations into a list called `normals`\n",
    "normals = []\n",
    "# Set the start and end date of the trip\n",
    "start_date= dt.date(2018,8,1)\n",
    "end_date= dt.date(2018,8,12)\n",
    "last_year = dt.timedelta(days=365)\n",
    "numdays = end_date - start_date\n",
    "# Use the start and end date to create a range of dates\n",
    "dates = [start_date + dt.timedelta(days=i) for i in range(numdays.days+1)]\n",
    "# Stip off the year and save a list of %m-%d strings\n",
    "date_strings = [mydate.strftime('%m-%d') for mydate in dates]\n",
    "# Loop through the list of %m-%d strings and calculate the normals for each date\n",
    "normals = [daily_normals(mydate)[0] for mydate in date_strings]\n",
    "normals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>tmin</th>\n",
       "      <th>tavg</th>\n",
       "      <th>tmax</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-08-01</th>\n",
       "      <td>67.0</td>\n",
       "      <td>75.540000</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-02</th>\n",
       "      <td>68.0</td>\n",
       "      <td>75.603774</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-03</th>\n",
       "      <td>70.0</td>\n",
       "      <td>76.611111</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-04</th>\n",
       "      <td>69.0</td>\n",
       "      <td>76.711538</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-05</th>\n",
       "      <td>69.0</td>\n",
       "      <td>76.148148</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-06</th>\n",
       "      <td>67.0</td>\n",
       "      <td>76.250000</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-07</th>\n",
       "      <td>71.0</td>\n",
       "      <td>77.156863</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-08</th>\n",
       "      <td>68.0</td>\n",
       "      <td>76.557692</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-09</th>\n",
       "      <td>69.0</td>\n",
       "      <td>75.980392</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10</th>\n",
       "      <td>65.0</td>\n",
       "      <td>76.420000</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-11</th>\n",
       "      <td>67.0</td>\n",
       "      <td>75.981132</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-12</th>\n",
       "      <td>67.0</td>\n",
       "      <td>76.527273</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            tmin       tavg  tmax\n",
       "date                             \n",
       "2018-08-01  67.0  75.540000  83.0\n",
       "2018-08-02  68.0  75.603774  84.0\n",
       "2018-08-03  70.0  76.611111  85.0\n",
       "2018-08-04  69.0  76.711538  84.0\n",
       "2018-08-05  69.0  76.148148  82.0\n",
       "2018-08-06  67.0  76.250000  83.0\n",
       "2018-08-07  71.0  77.156863  83.0\n",
       "2018-08-08  68.0  76.557692  83.0\n",
       "2018-08-09  69.0  75.980392  81.0\n",
       "2018-08-10  65.0  76.420000  83.0\n",
       "2018-08-11  67.0  75.981132  82.0\n",
       "2018-08-12  67.0  76.527273  83.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the previous query results into a Pandas DataFrame and add the `trip_dates` range as the `date` index\n",
    "full_date_strings = [mydate.strftime('%Y-%m-%d') for mydate in dates]\n",
    "normals_df = pd.DataFrame(normals, columns=['tmin','tavg','tmax'], index=full_date_strings)\n",
    "normals_df.index.name = 'date'\n",
    "normals_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the daily normals as an area plot with `stacked=False`\n",
    "ax = normals_df.plot.area(stacked=False, xticks=(np.arange(len(full_date_strings))), \n",
    "                          rot=30, figsize=(10,7), alpha=0.2, grid=True)\n",
    "ax.set_ylabel('Temperature (F)')\n",
    "plt.title('Daily normal temperatures for the chosen trip dates')\n",
    "plt.savefig('Data\\Daily normal temp for the chosen trip dates.png', bbox_inches='tight')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernel_info": {
   "name": "python3"
  },
  "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": "3.7.1"
  },
  "nteract": {
   "version": "0.12.3"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
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   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}