ghcn_demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "center-gnome",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pandas-gbq\n",
      "  Downloading pandas_gbq-0.14.1-py3-none-any.whl (24 kB)\n",
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      "Collecting google-cloud-bigquery[bqstorage,pandas]<3.0.0dev,>=1.11.1\n",
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      "Requirement already satisfied: numpy>=1.16.5 in /home/dave/anaconda3/envs/ai-dask/lib/python3.8/site-packages (from pandas>=0.20.1->pandas-gbq) (1.19.2)\n",
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      "\u001b[?25hCollecting pyarrow<4.0dev,>=1.0.0\n",
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      "Building wheels for collected packages: proto-plus\n",
      "  Building wheel for proto-plus (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for proto-plus: filename=proto_plus-1.13.0-py3-none-any.whl size=41592 sha256=0c2cbfc7fdfa0cf387c7ce38f1028a2c606aa6d189048a2b16c6ec5d8088e43d\n",
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      "Successfully built proto-plus\n",
      "Installing collected packages: pyasn1, rsa, pyasn1-modules, protobuf, cachetools, mypy-extensions, googleapis-common-protos, google-auth, typing-inspect, oauthlib, grpcio, google-crc32c, google-api-core, requests-oauthlib, proto-plus, libcst, google-resumable-media, google-cloud-core, pyarrow, google-cloud-bigquery-storage, google-cloud-bigquery, google-auth-oauthlib, pydata-google-auth, pandas-gbq\n",
      "Successfully installed cachetools-4.2.1 google-api-core-1.26.0 google-auth-1.25.0 google-auth-oauthlib-0.4.2 google-cloud-bigquery-2.7.0 google-cloud-bigquery-storage-2.2.1 google-cloud-core-1.6.0 google-crc32c-1.1.2 google-resumable-media-1.2.0 googleapis-common-protos-1.52.0 grpcio-1.35.0 libcst-0.3.16 mypy-extensions-0.4.3 oauthlib-3.1.0 pandas-gbq-0.14.1 proto-plus-1.13.0 protobuf-3.14.0 pyarrow-3.0.0 pyasn1-0.4.8 pyasn1-modules-0.2.8 pydata-google-auth-1.1.0 requests-oauthlib-1.3.0 rsa-4.7 typing-inspect-0.6.0\n"
     ]
    }
   ],
   "source": [
    "!pip install pandas-gbq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "supposed-mambo",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting matplotlib\n",
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      "\u001b[K     |████████████████████████████████| 1.2 MB 38.0 MB/s eta 0:00:01\n",
      "\u001b[?25hInstalling collected packages: kiwisolver, cycler, matplotlib\n",
      "Successfully installed cycler-0.10.0 kiwisolver-1.3.1 matplotlib-3.3.4\n"
     ]
    }
   ],
   "source": [
    "!pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "spatial-corpus",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "actual-reducing",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    SELECT *\n",
      "    FROM `bigquery-public-data.ghcn_d.ghcnd_2018`\n",
      "    WHERE \n",
      "    `id` = \"USC00325754\"  \n",
      "    -- `date` BETWEEN \"2020-01-01\" AND \"2020-12-31\" \n",
      "    -- `element` = \"TMAX\";\n",
      "    \n",
      "\n",
      "    SELECT *\n",
      "    FROM `bigquery-public-data.ghcn_d.ghcnd_2019`\n",
      "    WHERE \n",
      "    `id` = \"USC00325754\"  \n",
      "    -- `date` BETWEEN \"2020-01-01\" AND \"2020-12-31\" \n",
      "    -- `element` = \"TMAX\";\n",
      "    \n",
      "\n",
      "    SELECT *\n",
      "    FROM `bigquery-public-data.ghcn_d.ghcnd_2020`\n",
      "    WHERE \n",
      "    `id` = \"USC00325754\"  \n",
      "    -- `date` BETWEEN \"2020-01-01\" AND \"2020-12-31\" \n",
      "    -- `element` = \"TMAX\";\n",
      "    \n",
      "\n",
      "    SELECT *\n",
      "    FROM `bigquery-public-data.ghcn_d.ghcnd_2021`\n",
      "    WHERE \n",
      "    `id` = \"USC00325754\"  \n",
      "    -- `date` BETWEEN \"2020-01-01\" AND \"2020-12-31\" \n",
      "    -- `element` = \"TMAX\";\n",
      "    \n"
     ]
    }
   ],
   "source": [
    "df_years = []\n",
    "for year in range(2018, 2022):\n",
    "\n",
    "    sql = f\"\"\"\n",
    "    SELECT *\n",
    "    FROM `bigquery-public-data.ghcn_d.ghcnd_{year}`\n",
    "    WHERE \n",
    "    `id` = \"USC00325754\"  \n",
    "    -- `date` BETWEEN \"2020-01-01\" AND \"2020-12-31\" \n",
    "    -- `element` = \"TMAX\";\n",
    "    \"\"\"\n",
    "\n",
    "    print(sql)\n",
    "    \n",
    "    df_ = pd.read_gbq(sql, project_id=\"yerrington\")\n",
    "    df_years.append(df_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "transparent-yield",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat(df_years)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "silent-sigma",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SNOW    1130\n",
       "SNWD    1129\n",
       "PRCP    1127\n",
       "TOBS    1122\n",
       "TMAX    1122\n",
       "TMIN    1122\n",
       "WT01      92\n",
       "WT03      85\n",
       "WT11       4\n",
       "WT05       4\n",
       "MDPR       1\n",
       "DAPR       1\n",
       "WT06       1\n",
       "Name: element, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['element'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "intimate-hampton",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.query(\"element == 'PRCP'\").set_index('date').plot(figsize=(15, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "blessed-elevation",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
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