northamerica-peakyears.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# First, Peak, and Last Years\n",
    "For each element type and each type of station in North America, find the earliest year, peak year for number of stations, and last year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the dependencies\n",
    "import os\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "CSV_PATH = os.path.join(\"static\", \"data\", \"csv\")\n",
    "IMG_PATH = os.path.join(\"static\", \"img\")\n",
    "FILE_NAME = \"full_inventory.csv\"\n",
    "FILE_PATH = os.path.join(CSV_PATH, FILE_NAME)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dadra\\anaconda\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3020: DtypeWarning: Columns (10,11,13,18) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "# Read in the inventory file\n",
    "# because 'NA' is the code for North America, we need to change pandas.read_csv default behavior\n",
    "# for reading missing values (na's)...\n",
    "\n",
    "df = pd.read_csv(FILE_PATH, keep_default_na=False, na_values=['', '#N/A', '#N/A N/A', '#NA', '-1.#IND', '-1.#QNAN', '-NaN', '-nan',\n",
    "    '1.#IND', '1.#QNAN', 'N/A', 'NULL', 'NaN', 'n/a', 'nan',\n",
    "    'null'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Examine the Stations in North America"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(571534, 19)\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station_id</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>element</th>\n",
       "      <th>first_year</th>\n",
       "      <th>last_year</th>\n",
       "      <th>years</th>\n",
       "      <th>elevation</th>\n",
       "      <th>state</th>\n",
       "      <th>name</th>\n",
       "      <th>gsn_flag</th>\n",
       "      <th>hcn_crn_flag</th>\n",
       "      <th>wmo_id</th>\n",
       "      <th>state_name</th>\n",
       "      <th>country_code</th>\n",
       "      <th>country</th>\n",
       "      <th>continent_code</th>\n",
       "      <th>continent</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACW00011604</td>\n",
       "      <td>17.1167</td>\n",
       "      <td>-61.7833</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>1949</td>\n",
       "      <td>1949</td>\n",
       "      <td>1</td>\n",
       "      <td>10.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ST JOHNS COOLIDGE FLD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AC</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>NA</td>\n",
       "      <td>North America</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ACW00011604</td>\n",
       "      <td>17.1167</td>\n",
       "      <td>-61.7833</td>\n",
       "      <td>TMIN</td>\n",
       "      <td>1949</td>\n",
       "      <td>1949</td>\n",
       "      <td>1</td>\n",
       "      <td>10.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ST JOHNS COOLIDGE FLD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AC</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>NA</td>\n",
       "      <td>North America</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ACW00011604</td>\n",
       "      <td>17.1167</td>\n",
       "      <td>-61.7833</td>\n",
       "      <td>PRCP</td>\n",
       "      <td>1949</td>\n",
       "      <td>1949</td>\n",
       "      <td>1</td>\n",
       "      <td>10.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ST JOHNS COOLIDGE FLD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AC</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>NA</td>\n",
       "      <td>North America</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ACW00011604</td>\n",
       "      <td>17.1167</td>\n",
       "      <td>-61.7833</td>\n",
       "      <td>SNOW</td>\n",
       "      <td>1949</td>\n",
       "      <td>1949</td>\n",
       "      <td>1</td>\n",
       "      <td>10.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ST JOHNS COOLIDGE FLD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AC</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>NA</td>\n",
       "      <td>North America</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ACW00011604</td>\n",
       "      <td>17.1167</td>\n",
       "      <td>-61.7833</td>\n",
       "      <td>SNWD</td>\n",
       "      <td>1949</td>\n",
       "      <td>1949</td>\n",
       "      <td>1</td>\n",
       "      <td>10.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ST JOHNS COOLIDGE FLD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AC</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>NA</td>\n",
       "      <td>North America</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    station_id  latitude  longitude element  first_year  last_year  years  \\\n",
       "0  ACW00011604   17.1167   -61.7833    TMAX        1949       1949      1   \n",
       "1  ACW00011604   17.1167   -61.7833    TMIN        1949       1949      1   \n",
       "2  ACW00011604   17.1167   -61.7833    PRCP        1949       1949      1   \n",
       "3  ACW00011604   17.1167   -61.7833    SNOW        1949       1949      1   \n",
       "4  ACW00011604   17.1167   -61.7833    SNWD        1949       1949      1   \n",
       "\n",
       "   elevation state                   name gsn_flag hcn_crn_flag  wmo_id  \\\n",
       "0       10.1   NaN  ST JOHNS COOLIDGE FLD      NaN          NaN     NaN   \n",
       "1       10.1   NaN  ST JOHNS COOLIDGE FLD      NaN          NaN     NaN   \n",
       "2       10.1   NaN  ST JOHNS COOLIDGE FLD      NaN          NaN     NaN   \n",
       "3       10.1   NaN  ST JOHNS COOLIDGE FLD      NaN          NaN     NaN   \n",
       "4       10.1   NaN  ST JOHNS COOLIDGE FLD      NaN          NaN     NaN   \n",
       "\n",
       "  state_name country_code              country continent_code      continent  \\\n",
       "0        NaN           AC  Antigua and Barbuda             NA  North America   \n",
       "1        NaN           AC  Antigua and Barbuda             NA  North America   \n",
       "2        NaN           AC  Antigua and Barbuda             NA  North America   \n",
       "3        NaN           AC  Antigua and Barbuda             NA  North America   \n",
       "4        NaN           AC  Antigua and Barbuda             NA  North America   \n",
       "\n",
       "  type  \n",
       "0    W  \n",
       "1    W  \n",
       "2    W  \n",
       "3    W  \n",
       "4    W  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "na_df = df.query(\"continent == 'North America'\")\n",
    "print(na_df.shape)\n",
    "na_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "na_prcp = na_df.query('element == \"PRCP\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(74440, 19)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "na_prcp.shape  # Number of stations in North America reporting PRCP (at some point in time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(74440, 19)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "na_prcp.drop_duplicates('station_id').shape  # sanity check!  no duplicate stations after filtering on elements"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Consider the Different Types of Stations in North America"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    35986\n",
       "C    22667\n",
       "0     7931\n",
       "N     5170\n",
       "W     1794\n",
       "R     1509\n",
       "S      859\n",
       "M      107\n",
       "E       47\n",
       "P        3\n",
       "Name: ctype, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ctype'] = df.type.map(lambda x: str(x)) # type type column had issues with 1 and '1' being different - fix here\n",
    "na_df = df.query(\"continent == 'North America'\")\n",
    "na_stations = na_df.drop_duplicates('station_id')\n",
    "na_stations.ctype.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_station_types_by_year(inventory, element):\n",
    "    \n",
    "    def initialize_dict_from_array(arr):\n",
    "        return {item : 0 for item in arr}\n",
    "    \n",
    "    # range of years\n",
    "    min_year = inventory[inventory['element'] == element].first_year.min()\n",
    "    max_year = inventory[inventory['element'] == element].last_year.max()\n",
    "    sub_inventory = inventory[inventory['element'] == element]\n",
    "    \n",
    "    types = inventory.station_id.apply(lambda x: x[2]).unique()\n",
    "    \n",
    "    results_list = []\n",
    "    # Not the most efficient - but need to figure out why the above failed...\n",
    "    for year in range(min_year, max_year + 1):\n",
    "        results_dict = initialize_dict_from_array(types)\n",
    "        results_dict['year'] = year\n",
    "        \n",
    "        for row in sub_inventory.itertuples():\n",
    "            if ((row.first_year <= year) and (year <= row.last_year)):\n",
    "                # station was active in the given year:\n",
    "                station = row.station_id\n",
    "                \n",
    "                # get the station type:\n",
    "                station_type = row.station_id[2]\n",
    "                results_dict[station_type] += 1\n",
    "                \n",
    "        results_list.append(results_dict)\n",
    "        \n",
    "    return pd.DataFrame(results_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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       "      <th>year</th>\n",
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       "      <th>179</th>\n",
       "      <td>1291</td>\n",
       "      <td>19259</td>\n",
       "      <td>6968</td>\n",
       "      <td>3</td>\n",
       "      <td>94</td>\n",
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       "      <td>0</td>\n",
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       "      <td>853</td>\n",
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       "      <td>2015</td>\n",
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       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>1227</td>\n",
       "      <td>19623</td>\n",
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       "      <td>94</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>850</td>\n",
       "      <td>1164</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>1168</td>\n",
       "      <td>19798</td>\n",
       "      <td>6697</td>\n",
       "      <td>3</td>\n",
       "      <td>89</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>832</td>\n",
       "      <td>1159</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>1125</td>\n",
       "      <td>19748</td>\n",
       "      <td>6498</td>\n",
       "      <td>3</td>\n",
       "      <td>89</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>770</td>\n",
       "      <td>1155</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>1066</td>\n",
       "      <td>18134</td>\n",
       "      <td>6219</td>\n",
       "      <td>0</td>\n",
       "      <td>88</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>764</td>\n",
       "      <td>1150</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0      1     C  E   M  N  P  R    S     W  year\n",
       "179  1291  19259  6968  3  94  0  0  0  853  1165  2015\n",
       "180  1227  19623  6816  3  94  0  0  0  850  1164  2016\n",
       "181  1168  19798  6697  3  89  0  0  0  832  1159  2017\n",
       "182  1125  19748  6498  3  89  0  0  0  770  1155  2018\n",
       "183  1066  18134  6219  0  88  0  0  0  764  1150  2019"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "na_prcp_by_type = count_station_types_by_year(na_df, 'PRCP')\n",
    "na_prcp_by_type.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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       "      <th>1</th>\n",
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       "      <td>1</td>\n",
       "      <td>1839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1840</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0  1  C  E  M  N  P  R  S  W  year\n",
       "0  0  0  0  0  0  0  0  0  0  1  1836\n",
       "1  0  0  0  0  0  0  0  0  0  1  1837\n",
       "2  0  0  0  0  0  0  0  0  0  1  1838\n",
       "3  0  0  0  0  0  0  0  0  0  1  1839\n",
       "4  1  0  1  0  0  0  0  0  0  1  1840"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "na_prcp_by_type.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The following are helper functions:\n",
    "\n",
    "def nonzero_indices(ser):\n",
    "    \"\"\"Return the nonzero indices of a pandas series.\"\"\"\n",
    "    return [i for i, element in enumerate(ser) if element != 0]\n",
    "\n",
    "def first_nonzero_index(ser):\n",
    "    \"\"\"Returns the first nonzero index of a pandas series.\"\"\"\n",
    "    nonzero = nonzero_indices(ser)\n",
    "    if nonzero:\n",
    "        return min(nonzero)\n",
    "    else:\n",
    "        return None\n",
    "    \n",
    "def last_nonzero_index(ser):\n",
    "    \"\"\"Returns the last nonzero index of a pandas series.\"\"\"\n",
    "    nonzero = nonzero_indices(ser)\n",
    "    if nonzero:\n",
    "        return max(nonzero)\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def max_nonzero_index(ser):\n",
    "    \"\"\"Returns the (first) index of the largest, nonzero element of a pandas series.\"\"\"\n",
    "    nonzero = nonzero_indices(ser)\n",
    "    if nonzero:\n",
    "        return ser.idxmax()\n",
    "    else:\n",
    "        return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code to find the last nonzero index of each column in a pandas DataFrame\n",
    "def last_nonzero(df):\n",
    "    \"\"\"\n",
    "    Returns the position of the last nonzero element in each column of the DataFrame df.\n",
    "    \"\"\"\n",
    "    results = {}\n",
    "    \n",
    "    for column in df.columns:\n",
    "        results[column] = last_nonzero_index(df[column])\n",
    "\n",
    "    return results\n",
    "\n",
    "def first_nonzero(df):\n",
    "    \"\"\"\n",
    "    Returns the position of the first nonzero element in each column of the DataFrame df.\n",
    "    \"\"\"\n",
    "    results = {}\n",
    "    \n",
    "    for column in df.columns:\n",
    "        results[column] = first_nonzero_index(df[column])\n",
    "        \n",
    "    return results\n",
    "\n",
    "def max_nonzero(df):\n",
    "    \"\"\"\n",
    "    Returns the position of the largest nonzero element in each column of the DataFrame df.\n",
    "    \"\"\"\n",
    "    results = {}\n",
    "    \n",
    "    for column in df.columns:\n",
    "        results[column] = max_nonzero_index(df[column])\n",
    "        \n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'0': 183,\n",
       " '1': 183,\n",
       " 'C': 183,\n",
       " 'E': 182,\n",
       " 'M': 183,\n",
       " 'N': 178,\n",
       " 'P': 56,\n",
       " 'R': None,\n",
       " 'S': 183,\n",
       " 'W': 183,\n",
       " 'year': 183}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "last_nonzero(na_prcp_by_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'0': 4,\n",
       " '1': 162,\n",
       " 'C': 4,\n",
       " 'E': 37,\n",
       " 'M': 102,\n",
       " 'N': 66,\n",
       " 'P': 20,\n",
       " 'R': None,\n",
       " 'S': 142,\n",
       " 'W': 0,\n",
       " 'year': 0}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_nonzero(na_prcp_by_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'0': 138,\n",
       " '1': 181,\n",
       " 'C': 115,\n",
       " 'E': 130,\n",
       " 'M': 160,\n",
       " 'N': 147,\n",
       " 'P': 27,\n",
       " 'R': None,\n",
       " 'S': 178,\n",
       " 'W': 176,\n",
       " 'year': 183}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_nonzero(na_prcp_by_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def first_peak_last_years(df):\n",
    "    \"\"\"\n",
    "    Return the first nonzero year, largest year, and last nonzero year of each column of a dataframe\n",
    "    \"\"\"\n",
    "    def lookup_if_exists(ser, index):\n",
    "        if index or (index == 0):\n",
    "            return ser[index]\n",
    "        else:\n",
    "            return np.nan\n",
    "        \n",
    "    # first_nonzero_index = df.ne(0).idxmax().to_dict()\n",
    "    first_nonzero_index = first_nonzero(df)\n",
    "    #max_index = df.idxmax().to_dict()\n",
    "    max_index = max_nonzero(df)\n",
    "    last_nonzero_index = last_nonzero(df)\n",
    "    \n",
    "    results = pd.DataFrame()\n",
    "    results['station_type'] = first_nonzero_index.keys()\n",
    "    results['first_year'] = np.nan\n",
    "    results['last_year'] = np.nan\n",
    "    results['peak_year'] = np.nan\n",
    "    results.set_index('station_type', inplace=True)\n",
    "    \n",
    "    for key in first_nonzero_index.keys():\n",
    "        results.loc[key, 'first_year'] = lookup_if_exists(df.year, first_nonzero_index[key])\n",
    "        results.loc[key, 'peak_year'] = lookup_if_exists(df.year, max_index[key])\n",
    "        results.loc[key, 'last_year'] = lookup_if_exists(df.year, last_nonzero_index[key])\n",
    "        \n",
    "    return results\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>first_year</th>\n",
       "      <th>last_year</th>\n",
       "      <th>peak_year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>station_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1840.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1974.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1998.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>2017.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>1840.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1951.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>1873.0</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>1966.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>1938.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1996.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N</th>\n",
       "      <td>1902.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>1983.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P</th>\n",
       "      <td>1856.0</td>\n",
       "      <td>1892.0</td>\n",
       "      <td>1863.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>1978.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>W</th>\n",
       "      <td>1836.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>2012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>1836.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              first_year  last_year  peak_year\n",
       "station_type                                  \n",
       "0                 1840.0     2019.0     1974.0\n",
       "1                 1998.0     2019.0     2017.0\n",
       "C                 1840.0     2019.0     1951.0\n",
       "E                 1873.0     2018.0     1966.0\n",
       "M                 1938.0     2019.0     1996.0\n",
       "N                 1902.0     2014.0     1983.0\n",
       "P                 1856.0     1892.0     1863.0\n",
       "R                    NaN        NaN        NaN\n",
       "S                 1978.0     2019.0     2014.0\n",
       "W                 1836.0     2019.0     2012.0\n",
       "year              1836.0     2019.0     2019.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_peak_last_years(na_prcp_by_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>C</th>\n",
       "      <th>E</th>\n",
       "      <th>M</th>\n",
       "      <th>N</th>\n",
       "      <th>P</th>\n",
       "      <th>R</th>\n",
       "      <th>S</th>\n",
       "      <th>W</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>1287</td>\n",
       "      <td>0</td>\n",
       "      <td>5231</td>\n",
       "      <td>17</td>\n",
       "      <td>96</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1292</td>\n",
       "      <td>853</td>\n",
       "      <td>1156</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>1237</td>\n",
       "      <td>0</td>\n",
       "      <td>5107</td>\n",
       "      <td>17</td>\n",
       "      <td>95</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1281</td>\n",
       "      <td>849</td>\n",
       "      <td>1156</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>1185</td>\n",
       "      <td>0</td>\n",
       "      <td>5011</td>\n",
       "      <td>17</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1272</td>\n",
       "      <td>832</td>\n",
       "      <td>1152</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>1145</td>\n",
       "      <td>0</td>\n",
       "      <td>4873</td>\n",
       "      <td>17</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1259</td>\n",
       "      <td>770</td>\n",
       "      <td>1149</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>1091</td>\n",
       "      <td>0</td>\n",
       "      <td>4706</td>\n",
       "      <td>2</td>\n",
       "      <td>88</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1234</td>\n",
       "      <td>764</td>\n",
       "      <td>1144</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0  1     C   E   M  N  P     R    S     W  year\n",
       "175  1287  0  5231  17  96  0  0  1292  853  1156  2015\n",
       "176  1237  0  5107  17  95  0  0  1281  849  1156  2016\n",
       "177  1185  0  5011  17  90  0  0  1272  832  1152  2017\n",
       "178  1145  0  4873  17  90  0  0  1259  770  1149  2018\n",
       "179  1091  0  4706   2  88  0  0  1234  764  1144  2019"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "na_tmin = count_station_types_by_year(na_df, \"TMIN\")\n",
    "na_tmin.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>C</th>\n",
       "      <th>E</th>\n",
       "      <th>M</th>\n",
       "      <th>N</th>\n",
       "      <th>P</th>\n",
       "      <th>R</th>\n",
       "      <th>S</th>\n",
       "      <th>W</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1844</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0  1  C  E  M  N  P  R  S  W  year\n",
       "0  1  0  0  0  0  0  0  0  0  0  1840\n",
       "1  1  0  0  0  0  0  0  0  0  0  1841\n",
       "2  1  0  0  0  0  0  0  0  0  0  1842\n",
       "3  1  0  0  0  0  0  0  0  0  0  1843\n",
       "4  1  0  0  0  0  0  0  0  0  0  1844"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "na_tmin.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>first_year</th>\n",
       "      <th>last_year</th>\n",
       "      <th>peak_year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>station_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1840.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1974.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>1853.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1970.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>1873.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1962.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>1938.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1996.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N</th>\n",
       "      <td>1902.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>1983.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P</th>\n",
       "      <td>1877.0</td>\n",
       "      <td>1892.0</td>\n",
       "      <td>1877.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R</th>\n",
       "      <td>1983.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>2005.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>1979.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>W</th>\n",
       "      <td>1851.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>2011.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>1840.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              first_year  last_year  peak_year\n",
       "station_type                                  \n",
       "0                 1840.0     2019.0     1974.0\n",
       "1                    NaN        NaN        NaN\n",
       "C                 1853.0     2019.0     1970.0\n",
       "E                 1873.0     2019.0     1962.0\n",
       "M                 1938.0     2019.0     1996.0\n",
       "N                 1902.0     2014.0     1983.0\n",
       "P                 1877.0     1892.0     1877.0\n",
       "R                 1983.0     2019.0     2005.0\n",
       "S                 1979.0     2019.0     2014.0\n",
       "W                 1851.0     2019.0     2011.0\n",
       "year              1840.0     2019.0     2019.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_peak_last_years(na_tmin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>R</th>\n",
       "      <th>S</th>\n",
       "      <th>W</th>\n",
       "      <th>year</th>\n",
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       "      <td>0</td>\n",
       "      <td>1840</td>\n",
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       "      <th>1</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1841</td>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1842</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1844</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0  1  C  E  M  N  P  R  S  W  year\n",
       "0  1  0  0  0  0  0  0  0  0  0  1840\n",
       "1  1  0  0  0  0  0  0  0  0  0  1841\n",
       "2  1  0  0  0  0  0  0  0  0  0  1842\n",
       "3  1  0  0  0  0  0  0  0  0  0  1843\n",
       "4  1  0  0  0  0  0  0  0  0  0  1844"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "na_tmax = count_station_types_by_year(na_df, \"TMAX\")\n",
    "na_tmax.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>P</th>\n",
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       "      <th>S</th>\n",
       "      <th>W</th>\n",
       "      <th>year</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>1288</td>\n",
       "      <td>0</td>\n",
       "      <td>5231</td>\n",
       "      <td>17</td>\n",
       "      <td>96</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1292</td>\n",
       "      <td>853</td>\n",
       "      <td>1156</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>1236</td>\n",
       "      <td>0</td>\n",
       "      <td>5108</td>\n",
       "      <td>17</td>\n",
       "      <td>95</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1281</td>\n",
       "      <td>849</td>\n",
       "      <td>1156</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>1186</td>\n",
       "      <td>0</td>\n",
       "      <td>5012</td>\n",
       "      <td>17</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1272</td>\n",
       "      <td>832</td>\n",
       "      <td>1152</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>1145</td>\n",
       "      <td>0</td>\n",
       "      <td>4875</td>\n",
       "      <td>17</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1259</td>\n",
       "      <td>769</td>\n",
       "      <td>1149</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>1089</td>\n",
       "      <td>0</td>\n",
       "      <td>4708</td>\n",
       "      <td>2</td>\n",
       "      <td>88</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1234</td>\n",
       "      <td>763</td>\n",
       "      <td>1144</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0  1     C   E   M  N  P     R    S     W  year\n",
       "175  1288  0  5231  17  96  0  0  1292  853  1156  2015\n",
       "176  1236  0  5108  17  95  0  0  1281  849  1156  2016\n",
       "177  1186  0  5012  17  90  0  0  1272  832  1152  2017\n",
       "178  1145  0  4875  17  90  0  0  1259  769  1149  2018\n",
       "179  1089  0  4708   2  88  0  0  1234  763  1144  2019"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "na_tmax.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>first_year</th>\n",
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       "      <th>C</th>\n",
       "      <td>1853.0</td>\n",
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       "      <td>1970.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>1876.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1962.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>1938.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1996.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N</th>\n",
       "      <td>1916.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>1983.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P</th>\n",
       "      <td>1877.0</td>\n",
       "      <td>1892.0</td>\n",
       "      <td>1877.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R</th>\n",
       "      <td>1983.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>2005.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>1978.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>W</th>\n",
       "      <td>1851.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>2011.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>1840.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              first_year  last_year  peak_year\n",
       "station_type                                  \n",
       "0                 1840.0     2019.0     1974.0\n",
       "1                    NaN        NaN        NaN\n",
       "C                 1853.0     2019.0     1970.0\n",
       "E                 1876.0     2019.0     1962.0\n",
       "M                 1938.0     2019.0     1996.0\n",
       "N                 1916.0     2014.0     1983.0\n",
       "P                 1877.0     1892.0     1877.0\n",
       "R                 1983.0     2019.0     2005.0\n",
       "S                 1978.0     2019.0     2014.0\n",
       "W                 1851.0     2019.0     2011.0\n",
       "year              1840.0     2019.0     2019.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_peak_last_years(na_tmax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "   0  1  C  E  M  N  P  R  S  W  year\n",
       "0  1  0  0  0  0  0  0  0  0  0  1840\n",
       "1  1  0  0  0  0  0  0  0  0  0  1841\n",
       "2  1  0  0  0  0  0  0  0  0  0  1842\n",
       "3  1  0  0  0  0  0  0  0  0  0  1843\n",
       "4  1  0  0  0  0  0  0  0  0  0  1844"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "na_snow = count_station_types_by_year(na_df, \"SNOW\")\n",
    "na_snow.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
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       "      <th>C</th>\n",
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       "      <th>P</th>\n",
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       "      <th>year</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>175</th>\n",
       "      <td>604</td>\n",
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       "      <td>701</td>\n",
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       "      <td>0</td>\n",
       "      <td>703</td>\n",
       "      <td>2016</td>\n",
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       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>478</td>\n",
       "      <td>17695</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>708</td>\n",
       "      <td>2017</td>\n",
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       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>438</td>\n",
       "      <td>17102</td>\n",
       "      <td>5438</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>708</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>398</td>\n",
       "      <td>14749</td>\n",
       "      <td>5076</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>657</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       0      1     C  E  M  N  P  R  S    W  year\n",
       "175  604  17914  5957  0  0  0  0  0  0  701  2015\n",
       "176  539  17800  5817  0  0  0  0  0  0  703  2016\n",
       "177  478  17695  5660  0  0  0  0  0  0  708  2017\n",
       "178  438  17102  5438  0  0  0  0  0  0  708  2018\n",
       "179  398  14749  5076  0  0  0  0  0  0  657  2019"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "na_snow.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>first_year</th>\n",
       "      <th>last_year</th>\n",
       "      <th>peak_year</th>\n",
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       "      <th>station_type</th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1840.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1974.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1998.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>1846.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>1949.0</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>1949.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P</th>\n",
       "      <td>1862.0</td>\n",
       "      <td>1892.0</td>\n",
       "      <td>1862.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>W</th>\n",
       "      <td>1857.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1961.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>1840.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              first_year  last_year  peak_year\n",
       "station_type                                  \n",
       "0                 1840.0     2019.0     1974.0\n",
       "1                 1998.0     2019.0     2014.0\n",
       "C                 1846.0     2019.0     1950.0\n",
       "E                    NaN        NaN        NaN\n",
       "M                 1949.0     1995.0     1949.0\n",
       "N                    NaN        NaN        NaN\n",
       "P                 1862.0     1892.0     1862.0\n",
       "R                    NaN        NaN        NaN\n",
       "S                    NaN        NaN        NaN\n",
       "W                 1857.0     2019.0     1961.0\n",
       "year              1840.0     2019.0     2019.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_peak_last_years(na_snow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1859</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1860</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1861</td>\n",
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      ],
      "text/plain": [
       "   0  1  C  E  M  N  P  R  S  W  year\n",
       "0  0  0  1  0  0  0  0  0  0  0  1857\n",
       "1  0  0  1  0  0  0  0  0  0  0  1858\n",
       "2  0  0  1  0  0  0  0  0  0  0  1859\n",
       "3  0  0  1  0  0  0  0  0  0  0  1860\n",
       "4  0  0  1  0  0  0  0  0  0  0  1861"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "na_snwd = count_station_types_by_year(na_df, \"SNWD\")\n",
    "na_snwd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2017</td>\n",
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       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>825</td>\n",
       "      <td>9239</td>\n",
       "      <td>5405</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>773</td>\n",
       "      <td>719</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>757</td>\n",
       "      <td>6981</td>\n",
       "      <td>5041</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>768</td>\n",
       "      <td>661</td>\n",
       "      <td>2019</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "       0     1     C  E  M  N  P  R    S    W  year\n",
       "158  994  9388  5938  0  6  0  0  0  850  730  2015\n",
       "159  937  9194  5797  0  6  0  0  0  849  728  2016\n",
       "160  870  9519  5646  0  6  0  0  0  834  730  2017\n",
       "161  825  9239  5405  0  2  0  0  0  773  719  2018\n",
       "162  757  6981  5041  0  1  0  0  0  768  661  2019"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "na_snwd.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
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       "      <th>E</th>\n",
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       "      <td>1964.0</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>P</th>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>R</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>1978.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>W</th>\n",
       "      <td>1873.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>1857.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              first_year  last_year  peak_year\n",
       "station_type                                  \n",
       "0                 1933.0     2019.0     1991.0\n",
       "1                 1998.0     2019.0     2013.0\n",
       "C                 1857.0     2019.0     1950.0\n",
       "E                 1958.0     2008.0     1964.0\n",
       "M                 1949.0     2019.0     1993.0\n",
       "N                    NaN        NaN        NaN\n",
       "P                    NaN        NaN        NaN\n",
       "R                    NaN        NaN        NaN\n",
       "S                 1978.0     2019.0     2015.0\n",
       "W                 1873.0     2019.0     1999.0\n",
       "year              1857.0     2019.0     2019.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_peak_last_years(na_snwd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "United States    1.0\n",
       "Name: country, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Where are the type 'P' stations coming from?\n",
    "na_df.drop_duplicates('station_id').query('ctype == \"P\"').country.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Maximum Number of Active Stations in a Given Year\n",
    "By element type and type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        2998\n",
       "1       19798\n",
       "C       10999\n",
       "E          30\n",
       "M         103\n",
       "N        4093\n",
       "P           2\n",
       "R           0\n",
       "S         853\n",
       "W        1254\n",
       "year     2019\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "na_prcp_by_type.max(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2429\n",
       "1          0\n",
       "C       6365\n",
       "E         17\n",
       "M        103\n",
       "N       3956\n",
       "P          2\n",
       "R       1384\n",
       "S        854\n",
       "W       1248\n",
       "year    2019\n",
       "dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "na_tmin.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2429\n",
       "1          0\n",
       "C       6380\n",
       "E         17\n",
       "M        103\n",
       "N       3957\n",
       "P          2\n",
       "R       1384\n",
       "S        854\n",
       "W       1248\n",
       "year    2019\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "na_tmax.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        2959\n",
       "1       17915\n",
       "C        9790\n",
       "E           0\n",
       "M           1\n",
       "N           0\n",
       "P           1\n",
       "R           0\n",
       "S           0\n",
       "W         856\n",
       "year     2019\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "na_snow.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2603\n",
       "1       9831\n",
       "C       9710\n",
       "E         26\n",
       "M         16\n",
       "N          0\n",
       "P          0\n",
       "R          0\n",
       "S        850\n",
       "W        967\n",
       "year    2019\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "na_snwd.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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