Bodensee.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
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   "source": [
    "## Intention\n",
    "In this Notebook, we describe how to get, analyze, and visualize level data from Lake Constance (Bodensee).\n",
    "Creation Date 2022-08.\n",
    "\n",
    "## Setup\n",
    "We analyze the data in Python, whereby the data is stored and processed in AWS (S3 and SageMaker).\n",
    "\n",
    "## Data\n",
    "\n",
    "#### Download the data\n",
    "\n",
    "We process public data taken from Hochwasservorhersagezentrale Baden-Württemberg (HVZ BaWü).\n",
    "Go to\n",
    "https://udo.lubw.baden-wuerttemberg.de/public/pages/home/welcome.xhtml\n",
    "(Sorry, this is all in German.)\n",
    "\n",
    "Go to\n",
    "Select Wasser -> Oberflächengewässer -> Hydrologische Landespegel -> Hydrologische Landespegel (2x)\n",
    "\n",
    "On the next page, select\n",
    "* Gewässer = Bodensee\n",
    "* Station = Konstanz Bodensee\n",
    "* Komponente = Wasserstand, W-Stand, cm\n",
    "* Produkt = Tagesmaximalwerte\n",
    "* Zeitraum = 01.01.1850 - 31.12.2022\n",
    "\n",
    "Export the data as *.csv (roughly 6 MB).\n",
    "\n",
    "#### Adjust the file\n",
    "* rows 1 - 6 contain the metadata - I removed this\n",
    "* rows 7 - 8 are empty - deleted as well\n",
    "* row 9 contains the column headers - I replaced the word “Gewässer” with “Gewasser”, just to get rid of the special character\n",
    "\n",
    "#### Store in AWS\n",
    "We store the file in S3.\n",
    "* bucket ='s3davibec/Bodensee'\n",
    "* data_key = 'Max_KN_1850-bis-heute_clean.csv'\n",
    "\n",
    "## Data Processing\n",
    "We use AWS service SageMaker Studio for the processing of the data based on the Jupyter Notebook given below."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1/ Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import pandas\n",
    "import pandas as pd"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2/ Read CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Messstellennummer       Stationsname  Gewasser Parameter   Datum / Uhrzeit  \\\n",
      "0                906  Konstanz Bodensee  Bodensee   W-Stand  1850-01-01 00:00   \n",
      "1                906  Konstanz Bodensee  Bodensee   W-Stand  1850-01-02 00:00   \n",
      "2                906  Konstanz Bodensee  Bodensee   W-Stand  1850-01-03 00:00   \n",
      "3                906  Konstanz Bodensee  Bodensee   W-Stand  1850-01-04 00:00   \n",
      "4                906  Konstanz Bodensee  Bodensee   W-Stand  1850-01-05 00:00   \n",
      "\n",
      "  Zeitbezug  Wert Einheit           Produkt  \n",
      "0       MEZ   306      cm  Tagesmaximalwert  \n",
      "1       MEZ   303      cm  Tagesmaximalwert  \n",
      "2       MEZ   303      cm  Tagesmaximalwert  \n",
      "3       MEZ   300      cm  Tagesmaximalwert  \n",
      "4       MEZ   300      cm  Tagesmaximalwert  \n"
     ]
    }
   ],
   "source": [
    "# read csv file from S3 bucket\n",
    "bucket ='s3davibec/Bodensee'\n",
    "data_key = 'Max_KN_1850-bis-heute_clean.csv'\n",
    "data_location = 's3://{}/{}'.format(bucket, data_key)\n",
    "\n",
    "df = pd.read_csv(data_location)\n",
    "\n",
    "# print\n",
    "print(df.head())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3/ Rename all columns to English\n",
    "As mentioned before, the data is in German. Column names are translated to English in this step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   measuring_point_number       station_name    waters parameter  \\\n",
      "0                     906  Konstanz Bodensee  Bodensee   W-Stand   \n",
      "1                     906  Konstanz Bodensee  Bodensee   W-Stand   \n",
      "2                     906  Konstanz Bodensee  Bodensee   W-Stand   \n",
      "3                     906  Konstanz Bodensee  Bodensee   W-Stand   \n",
      "4                     906  Konstanz Bodensee  Bodensee   W-Stand   \n",
      "\n",
      "          date_time time_zone  value unit        value_type  \n",
      "0  1850-01-01 00:00       MEZ    306   cm  Tagesmaximalwert  \n",
      "1  1850-01-02 00:00       MEZ    303   cm  Tagesmaximalwert  \n",
      "2  1850-01-03 00:00       MEZ    303   cm  Tagesmaximalwert  \n",
      "3  1850-01-04 00:00       MEZ    300   cm  Tagesmaximalwert  \n",
      "4  1850-01-05 00:00       MEZ    300   cm  Tagesmaximalwert  \n"
     ]
    }
   ],
   "source": [
    "#Rename all columns to English\n",
    "\n",
    "df = df.rename(columns={\n",
    "    'Messstellennummer': 'measuring_point_number',\n",
    "    'Stationsname': 'station_name',\n",
    "    'Gewasser': 'waters',\n",
    "    'Parameter': 'parameter',\n",
    "    'Datum / Uhrzeit': 'date_time',\n",
    "    'Zeitbezug': 'time_zone',\n",
    "    'Wert': 'value',\n",
    "    'Einheit': 'unit',\n",
    "    'Produkt': 'value_type'\n",
    "    })\n",
    "\n",
    "# print\n",
    "print(df.head())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4/ Add Data Conversions\n",
    "We need new different data points, like the month-day combination [“mm_dd”]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   measuring_point_number       station_name    waters parameter  \\\n",
      "0                     906  Konstanz Bodensee  Bodensee   W-Stand   \n",
      "1                     906  Konstanz Bodensee  Bodensee   W-Stand   \n",
      "2                     906  Konstanz Bodensee  Bodensee   W-Stand   \n",
      "3                     906  Konstanz Bodensee  Bodensee   W-Stand   \n",
      "4                     906  Konstanz Bodensee  Bodensee   W-Stand   \n",
      "\n",
      "          date_time time_zone  value unit        value_type        date  year  \\\n",
      "0  1850-01-01 00:00       MEZ    306   cm  Tagesmaximalwert  1850-01-01  1850   \n",
      "1  1850-01-02 00:00       MEZ    303   cm  Tagesmaximalwert  1850-01-02  1850   \n",
      "2  1850-01-03 00:00       MEZ    303   cm  Tagesmaximalwert  1850-01-03  1850   \n",
      "3  1850-01-04 00:00       MEZ    300   cm  Tagesmaximalwert  1850-01-04  1850   \n",
      "4  1850-01-05 00:00       MEZ    300   cm  Tagesmaximalwert  1850-01-05  1850   \n",
      "\n",
      "  month day  mm_dd  \n",
      "0    01  01  01_01  \n",
      "1    01  02  01_02  \n",
      "2    01  03  01_03  \n",
      "3    01  04  01_04  \n",
      "4    01  05  01_05  \n"
     ]
    }
   ],
   "source": [
    "#add all date conversions\n",
    "df['date'] = pd.to_datetime(df['date_time']).dt.date\n",
    "df['year'] = pd.to_datetime(df['date_time']).dt.year\n",
    "df['month'] = pd.to_datetime(df['date_time']).dt.month\n",
    "df['day'] = pd.to_datetime(df['date_time']).dt.day\n",
    "\n",
    "#convert Month and Day to two digit format\n",
    "import io\n",
    "\n",
    "df[\"month\"] = df.month.map(\"{:02}\".format)\n",
    "df[\"day\"] = df.day.map(\"{:02}\".format)\n",
    "\n",
    "#new column\n",
    "df[\"mm_dd\"] = df[\"month\"].astype(str) + \"_\" + df[\"day\"].astype(str)\n",
    "\n",
    "\n",
    "# print\n",
    "print(df.head())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5/ Reduce Data to Minimum\n",
    "Create a new dataset with a minimum of data to make things easier in the next couple of steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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",
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       "    }\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>value</th>\n",
       "      <th>date</th>\n",
       "      <th>year</th>\n",
       "      <th>mm_dd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>306</td>\n",
       "      <td>1850-01-01</td>\n",
       "      <td>1850</td>\n",
       "      <td>01_01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>303</td>\n",
       "      <td>1850-01-02</td>\n",
       "      <td>1850</td>\n",
       "      <td>01_02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>303</td>\n",
       "      <td>1850-01-03</td>\n",
       "      <td>1850</td>\n",
       "      <td>01_03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>300</td>\n",
       "      <td>1850-01-04</td>\n",
       "      <td>1850</td>\n",
       "      <td>01_04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>300</td>\n",
       "      <td>1850-01-05</td>\n",
       "      <td>1850</td>\n",
       "      <td>01_05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   value        date  year  mm_dd\n",
       "0    306  1850-01-01  1850  01_01\n",
       "1    303  1850-01-02  1850  01_02\n",
       "2    303  1850-01-03  1850  01_03\n",
       "3    300  1850-01-04  1850  01_04\n",
       "4    300  1850-01-05  1850  01_05"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating a new dataset dfs with the minimum data I will need in the next couple of steps\n",
    "dfs = df[['value','date','year','mm_dd']]\n",
    "\n",
    "# print\n",
    "dfs.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6/ Check Data Types\n",
    "This might be a good time to check data types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output:\n",
      "value     int64\n",
      "date     object\n",
      "year      int64\n",
      "mm_dd    object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# check the data types of the various fields\n",
    "# apply the dtype attribute\n",
    "result = dfs.dtypes\n",
    "\n",
    "print(\"Output:\")\n",
    "print(result)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 7/ Get Min, Max, Mean\n",
    "Now I am creating a new data set with the three long-term values. For every day of the year, we calculate the Max, the Min, and the Mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mm_dd</th>\n",
       "      <th>value_min</th>\n",
       "      <th>value_mean</th>\n",
       "      <th>value_max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01_01</td>\n",
       "      <td>242</td>\n",
       "      <td>294.433526</td>\n",
       "      <td>390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01_02</td>\n",
       "      <td>242</td>\n",
       "      <td>293.976879</td>\n",
       "      <td>388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01_03</td>\n",
       "      <td>242</td>\n",
       "      <td>293.433526</td>\n",
       "      <td>391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01_04</td>\n",
       "      <td>241</td>\n",
       "      <td>293.271676</td>\n",
       "      <td>392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01_05</td>\n",
       "      <td>241</td>\n",
       "      <td>292.976879</td>\n",
       "      <td>391</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mm_dd  value_min  value_mean  value_max\n",
       "0  01_01        242  294.433526        390\n",
       "1  01_02        242  293.976879        388\n",
       "2  01_03        242  293.433526        391\n",
       "3  01_04        241  293.271676        392\n",
       "4  01_05        241  292.976879        391"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create new dataframe with min max mean values for each month date combination\n",
    "df_mmm = dfs.groupby('mm_dd').agg(value_min=('value', 'min'), \n",
    "                             value_mean=('value', 'mean'), \n",
    "                             value_max=('value', 'max')).reset_index()\n",
    "\n",
    "# print\n",
    "df_mmm.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 8/ Missed the month info on my way, so I need to bring this back in\n",
    "I reduced the data in step 5/. But I missed to keep to month info in. For the data visualization, this info is handy. Therefore I re-create a month column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\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>mm_dd</th>\n",
       "      <th>value_min</th>\n",
       "      <th>value_mean</th>\n",
       "      <th>value_max</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01_01</td>\n",
       "      <td>242</td>\n",
       "      <td>294.433526</td>\n",
       "      <td>390</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01_02</td>\n",
       "      <td>242</td>\n",
       "      <td>293.976879</td>\n",
       "      <td>388</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01_03</td>\n",
       "      <td>242</td>\n",
       "      <td>293.433526</td>\n",
       "      <td>391</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01_04</td>\n",
       "      <td>241</td>\n",
       "      <td>293.271676</td>\n",
       "      <td>392</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01_05</td>\n",
       "      <td>241</td>\n",
       "      <td>292.976879</td>\n",
       "      <td>391</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mm_dd  value_min  value_mean  value_max month\n",
       "0  01_01        242  294.433526        390    01\n",
       "1  01_02        242  293.976879        388    01\n",
       "2  01_03        242  293.433526        391    01\n",
       "3  01_04        241  293.271676        392    01\n",
       "4  01_05        241  292.976879        391    01"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extract the first to characters from field mm_dd in order to get the month information back\n",
    "df_mmm['month'] = df_mmm['mm_dd'].str[:2]\n",
    "\n",
    "# print\n",
    "df_mmm.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 9/ Visualize: Max, Min, Avg\n",
    "As a first visualization, I am simply setting up the Max, Min, and Mean value. Comparing this to the original graph from the data source, I can check whether my processing is okay. I use matplotlib for this purpose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# print min, max, and mean for long-term data\n",
    "# ---\n",
    "\n",
    "# import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# plot\n",
    "df_mmm.plot(x='month', kind='line')\n",
    "\n",
    "plt.ylim(bottom=0, top=800)\n",
    "\n",
    "# description\n",
    "plt.title(\"Level of Lake Constance: 1850 to 2021\")\n",
    "plt.xlabel(\"Month\")\n",
    "plt.ylabel(\"Level [cm]\")\n",
    "\n",
    "\n",
    "# add grid\n",
    "plt.grid()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 10/ Add 2022 data\n",
    "The intention in this step is to create a data frame with Max, Min, Mean, and 2022 data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create dataframe with 2022 data based on dfs\n",
    "dfs2022 = dfs.loc[dfs['year']==2022]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop not needed columns\n",
    "dfs2022 = dfs2022.drop(columns=['date','year'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "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>value_2022</th>\n",
       "      <th>mm_dd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>62822</th>\n",
       "      <td>325</td>\n",
       "      <td>01_01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62823</th>\n",
       "      <td>326</td>\n",
       "      <td>01_02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62824</th>\n",
       "      <td>325</td>\n",
       "      <td>01_03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62825</th>\n",
       "      <td>325</td>\n",
       "      <td>01_04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62826</th>\n",
       "      <td>327</td>\n",
       "      <td>01_05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       value_2022  mm_dd\n",
       "62822         325  01_01\n",
       "62823         326  01_02\n",
       "62824         325  01_03\n",
       "62825         325  01_04\n",
       "62826         327  01_05"
      ]
     },
     "execution_count": 380,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#rename value column\n",
    "dfs2022 = dfs2022.rename(columns={'value': 'value_2022'})\n",
    "\n",
    "# print\n",
    "dfs2022.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "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>mm_dd</th>\n",
       "      <th>value_min</th>\n",
       "      <th>value_mean</th>\n",
       "      <th>value_max</th>\n",
       "      <th>month</th>\n",
       "      <th>value_2022</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01_01</td>\n",
       "      <td>242</td>\n",
       "      <td>294.433526</td>\n",
       "      <td>390</td>\n",
       "      <td>01</td>\n",
       "      <td>325.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01_02</td>\n",
       "      <td>242</td>\n",
       "      <td>293.976879</td>\n",
       "      <td>388</td>\n",
       "      <td>01</td>\n",
       "      <td>326.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01_03</td>\n",
       "      <td>242</td>\n",
       "      <td>293.433526</td>\n",
       "      <td>391</td>\n",
       "      <td>01</td>\n",
       "      <td>325.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01_04</td>\n",
       "      <td>241</td>\n",
       "      <td>293.271676</td>\n",
       "      <td>392</td>\n",
       "      <td>01</td>\n",
       "      <td>325.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01_05</td>\n",
       "      <td>241</td>\n",
       "      <td>292.976879</td>\n",
       "      <td>391</td>\n",
       "      <td>01</td>\n",
       "      <td>327.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mm_dd  value_min  value_mean  value_max month  value_2022\n",
       "0  01_01        242  294.433526        390    01       325.0\n",
       "1  01_02        242  293.976879        388    01       326.0\n",
       "2  01_03        242  293.433526        391    01       325.0\n",
       "3  01_04        241  293.271676        392    01       325.0\n",
       "4  01_05        241  292.976879        391    01       327.0"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mmm_2022 = pd.merge(df_mmm, dfs2022, how=\"left\", on=\"mm_dd\")\n",
    "\n",
    "# print\n",
    "df_mmm_2022.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize\n",
    "# print min, max, and mean for long-term data\n",
    "# ---\n",
    "\n",
    "# import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# plot\n",
    "df_mmm_2022.plot(x='month', kind='line')\n",
    "\n",
    "plt.ylim(bottom=0, top=800)\n",
    "\n",
    "# description\n",
    "plt.title(\"Level of Lake Constance: 1850 to 2021\")\n",
    "plt.xlabel(\"Month\")\n",
    "plt.ylabel(\"Level [cm]\")\n",
    "\n",
    "\n",
    "# add grid\n",
    "plt.grid()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 11/ Check for years with low levels at that time of the year\n",
    "In August, people thought that Lake Constance might see a record low for this time of the year. All Germany saw a record droud. With that, I wanted to understand which years saw lower sea levels.\n",
    "\n",
    "Get lowest value for the fifth of August:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "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>value</th>\n",
       "      <th>date</th>\n",
       "      <th>year</th>\n",
       "      <th>mm_dd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>438</td>\n",
       "      <td>1850-08-05</td>\n",
       "      <td>1850</td>\n",
       "      <td>08_05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>546</td>\n",
       "      <td>1851-08-05</td>\n",
       "      <td>1851</td>\n",
       "      <td>08_05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947</th>\n",
       "      <td>411</td>\n",
       "      <td>1852-08-05</td>\n",
       "      <td>1852</td>\n",
       "      <td>08_05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1312</th>\n",
       "      <td>476</td>\n",
       "      <td>1853-08-05</td>\n",
       "      <td>1853</td>\n",
       "      <td>08_05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677</th>\n",
       "      <td>425</td>\n",
       "      <td>1854-08-05</td>\n",
       "      <td>1854</td>\n",
       "      <td>08_05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      value        date  year  mm_dd\n",
       "216     438  1850-08-05  1850  08_05\n",
       "581     546  1851-08-05  1851  08_05\n",
       "947     411  1852-08-05  1852  08_05\n",
       "1312    476  1853-08-05  1853  08_05\n",
       "1677    425  1854-08-05  1854  08_05"
      ]
     },
     "execution_count": 403,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the lowest values on a certain day of the year\n",
    "# ---\n",
    "\n",
    "# filter data frame by value\n",
    "filter_value = {'08_05'}\n",
    "dfs_min = dfs.loc[dfs['mm_dd'].isin(filter_value)]\n",
    "\n",
    "# print\n",
    "dfs_min.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "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>value</th>\n",
       "      <th>date</th>\n",
       "      <th>year</th>\n",
       "      <th>mm_dd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36375</th>\n",
       "      <td>303</td>\n",
       "      <td>1949-08-05</td>\n",
       "      <td>1949</td>\n",
       "      <td>08_05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56098</th>\n",
       "      <td>312</td>\n",
       "      <td>2003-08-05</td>\n",
       "      <td>2003</td>\n",
       "      <td>08_05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57194</th>\n",
       "      <td>315</td>\n",
       "      <td>2006-08-05</td>\n",
       "      <td>2006</td>\n",
       "      <td>08_05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41854</th>\n",
       "      <td>317</td>\n",
       "      <td>1964-08-05</td>\n",
       "      <td>1964</td>\n",
       "      <td>08_05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63039</th>\n",
       "      <td>323</td>\n",
       "      <td>2022-08-05</td>\n",
       "      <td>2022</td>\n",
       "      <td>08_05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61577</th>\n",
       "      <td>330</td>\n",
       "      <td>2018-08-05</td>\n",
       "      <td>2018</td>\n",
       "      <td>08_05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56829</th>\n",
       "      <td>330</td>\n",
       "      <td>2005-08-05</td>\n",
       "      <td>2005</td>\n",
       "      <td>08_05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26148</th>\n",
       "      <td>330</td>\n",
       "      <td>1921-08-05</td>\n",
       "      <td>1921</td>\n",
       "      <td>08_05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7521</th>\n",
       "      <td>333</td>\n",
       "      <td>1870-08-05</td>\n",
       "      <td>1870</td>\n",
       "      <td>08_05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37471</th>\n",
       "      <td>337</td>\n",
       "      <td>1952-08-05</td>\n",
       "      <td>1952</td>\n",
       "      <td>08_05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       value        date  year  mm_dd\n",
       "36375    303  1949-08-05  1949  08_05\n",
       "56098    312  2003-08-05  2003  08_05\n",
       "57194    315  2006-08-05  2006  08_05\n",
       "41854    317  1964-08-05  1964  08_05\n",
       "63039    323  2022-08-05  2022  08_05\n",
       "61577    330  2018-08-05  2018  08_05\n",
       "56829    330  2005-08-05  2005  08_05\n",
       "26148    330  1921-08-05  1921  08_05\n",
       "7521     333  1870-08-05  1870  08_05\n",
       "37471    337  1952-08-05  1952  08_05"
      ]
     },
     "execution_count": 404,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort the data\n",
    "dfs_min = dfs_min.sort_values(by='value', ascending=True)\n",
    "\n",
    "# print\n",
    "dfs_min.head(10)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "now we know that the water level was lower than in 2022 in four occasions before: \n",
    "* 1949\n",
    "* 2003\n",
    "* 2006\n",
    "* 1964\n",
    "This is in ascending order. 1949 saw the lowest value ever recorded on 08/05.\n",
    "\n",
    "It is important to note that there are five years after 2000 in this (negative) Top10 list."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 12a/ Dive deeper\n",
    "\n",
    "Let us have a closer look at the 1949 data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let us look at 1949 vs 2022 vs min, max, mean\n",
    "# ---\n",
    "\n",
    "# create dataframe with 1949 data based on dfs\n",
    "dfs1949 = dfs.loc[df['year'] == 1949]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop not needed columns\n",
    "dfs1949 = dfs1949.drop(columns=['date','year'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "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>value_1949</th>\n",
       "      <th>mm_dd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36159</th>\n",
       "      <td>242</td>\n",
       "      <td>01_01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36160</th>\n",
       "      <td>242</td>\n",
       "      <td>01_02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36161</th>\n",
       "      <td>242</td>\n",
       "      <td>01_03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36162</th>\n",
       "      <td>242</td>\n",
       "      <td>01_04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36163</th>\n",
       "      <td>242</td>\n",
       "      <td>01_05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       value_1949  mm_dd\n",
       "36159         242  01_01\n",
       "36160         242  01_02\n",
       "36161         242  01_03\n",
       "36162         242  01_04\n",
       "36163         242  01_05"
      ]
     },
     "execution_count": 409,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#rename value column\n",
    "dfs1949 = dfs1949.rename(columns={'value': 'value_1949'})\n",
    "\n",
    "# print\n",
    "dfs1949.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "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>mm_dd</th>\n",
       "      <th>value_min</th>\n",
       "      <th>value_mean</th>\n",
       "      <th>value_max</th>\n",
       "      <th>month</th>\n",
       "      <th>value_2022</th>\n",
       "      <th>value_1949</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01_01</td>\n",
       "      <td>242</td>\n",
       "      <td>294.433526</td>\n",
       "      <td>390</td>\n",
       "      <td>01</td>\n",
       "      <td>325.0</td>\n",
       "      <td>242.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01_02</td>\n",
       "      <td>242</td>\n",
       "      <td>293.976879</td>\n",
       "      <td>388</td>\n",
       "      <td>01</td>\n",
       "      <td>326.0</td>\n",
       "      <td>242.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01_03</td>\n",
       "      <td>242</td>\n",
       "      <td>293.433526</td>\n",
       "      <td>391</td>\n",
       "      <td>01</td>\n",
       "      <td>325.0</td>\n",
       "      <td>242.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01_04</td>\n",
       "      <td>241</td>\n",
       "      <td>293.271676</td>\n",
       "      <td>392</td>\n",
       "      <td>01</td>\n",
       "      <td>325.0</td>\n",
       "      <td>242.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01_05</td>\n",
       "      <td>241</td>\n",
       "      <td>292.976879</td>\n",
       "      <td>391</td>\n",
       "      <td>01</td>\n",
       "      <td>327.0</td>\n",
       "      <td>242.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mm_dd  value_min  value_mean  value_max month  value_2022  value_1949\n",
       "0  01_01        242  294.433526        390    01       325.0       242.0\n",
       "1  01_02        242  293.976879        388    01       326.0       242.0\n",
       "2  01_03        242  293.433526        391    01       325.0       242.0\n",
       "3  01_04        241  293.271676        392    01       325.0       242.0\n",
       "4  01_05        241  292.976879        391    01       327.0       242.0"
      ]
     },
     "execution_count": 410,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge the 1949 data to the dataframe created for 2022\n",
    "df_mmm_1949 = pd.merge(df_mmm_2022, dfs1949, how=\"left\", on=\"mm_dd\")\n",
    "\n",
    "# print\n",
    "df_mmm_1949.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize\n",
    "# print min, max, and mean for long-term data\n",
    "# ---\n",
    "\n",
    "# import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# plot\n",
    "df_mmm_1949.plot(x='month', kind='line')\n",
    "\n",
    "plt.ylim(bottom=0, top=800)\n",
    "\n",
    "# description\n",
    "plt.title(\"Level of Lake Constance: 1850 to 2021 + 2022 and 1949\")\n",
    "plt.xlabel(\"Month\")\n",
    "plt.ylabel(\"Level [cm]\")\n",
    "\n",
    "# add grid\n",
    "plt.grid()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 12b/ Zoom in summer 1949\n",
    "We need to have a closer look here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "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>mm_dd</th>\n",
       "      <th>value_min</th>\n",
       "      <th>value_mean</th>\n",
       "      <th>month</th>\n",
       "      <th>value_2022</th>\n",
       "      <th>value_1949</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>05_01</td>\n",
       "      <td>272</td>\n",
       "      <td>331.023121</td>\n",
       "      <td>05</td>\n",
       "      <td>311.0</td>\n",
       "      <td>340.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>05_02</td>\n",
       "      <td>272</td>\n",
       "      <td>332.508671</td>\n",
       "      <td>05</td>\n",
       "      <td>311.0</td>\n",
       "      <td>341.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>05_03</td>\n",
       "      <td>272</td>\n",
       "      <td>334.196532</td>\n",
       "      <td>05</td>\n",
       "      <td>311.0</td>\n",
       "      <td>343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>05_04</td>\n",
       "      <td>273</td>\n",
       "      <td>335.826590</td>\n",
       "      <td>05</td>\n",
       "      <td>312.0</td>\n",
       "      <td>343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>05_05</td>\n",
       "      <td>275</td>\n",
       "      <td>337.780347</td>\n",
       "      <td>05</td>\n",
       "      <td>314.0</td>\n",
       "      <td>344.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     mm_dd  value_min  value_mean month  value_2022  value_1949\n",
       "121  05_01        272  331.023121    05       311.0       340.0\n",
       "122  05_02        272  332.508671    05       311.0       341.0\n",
       "123  05_03        272  334.196532    05       311.0       343.0\n",
       "124  05_04        273  335.826590    05       312.0       343.0\n",
       "125  05_05        275  337.780347    05       314.0       344.0"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let us zoom in a little bit\n",
    "# ---\n",
    "\n",
    "# filter data frame by value\n",
    "filter_value = {'05','06','07','08'}\n",
    "df_mmm_1949_zoom = df_mmm_1949.loc[df_mmm_1949['month'].isin(filter_value)]\n",
    "\n",
    "# drop not needed columns\n",
    "df_mmm_1949_zoom = df_mmm_1949_zoom.drop(columns=['value_max'], axis=1)\n",
    "\n",
    "# print\n",
    "df_mmm_1949_zoom.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "124"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check number of rows in df\n",
    "len(df_mmm_1949_zoom)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize\n",
    "# print zoom\n",
    "# ---\n",
    "\n",
    "# import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# plot\n",
    "df_mmm_1949_zoom.plot(x='mm_dd', kind='line', rot=90)\n",
    "\n",
    "# plt.ylim(bottom=0, top=800)\n",
    "\n",
    "# description\n",
    "plt.title(\"Level of Lake Constance: 1850 to 2021 + 2022 and 1949\")\n",
    "plt.xlabel(\"MM_DD\")\n",
    "plt.ylabel(\"Level [cm]\")\n",
    "\n",
    "# add grid\n",
    "plt.grid()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 13/ All time Max\n",
    "Next, we have a look into the all-time max value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "value           576\n",
      "date     2022-08-06\n",
      "year           2022\n",
      "mm_dd         12_31\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# now let us check out the max value\n",
    "# ---\n",
    "\n",
    "# get the max values for all columns\n",
    "print(dfs.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "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>value</th>\n",
       "      <th>date</th>\n",
       "      <th>year</th>\n",
       "      <th>mm_dd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14855</th>\n",
       "      <td>576</td>\n",
       "      <td>1890-09-03</td>\n",
       "      <td>1890</td>\n",
       "      <td>09_03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14856</th>\n",
       "      <td>576</td>\n",
       "      <td>1890-09-04</td>\n",
       "      <td>1890</td>\n",
       "      <td>09_04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       value        date  year  mm_dd\n",
       "14855    576  1890-09-03  1890  09_03\n",
       "14856    576  1890-09-04  1890  09_04"
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now we know that the highest value is 576, but in which year did that occur?\n",
    "\n",
    "dfs_max = dfs.loc[df['value']==576]\n",
    "\n",
    "# print\n",
    "dfs_max"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The answer is 1890\n",
    "What did this year look like?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create dataframe with 1890 data based on dfs\n",
    "dfs1890 = dfs.loc[df['year'] == 1890]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop not needed columns\n",
    "dfs1890 = dfs1890.drop(columns=['date','year'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "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>value_1890</th>\n",
       "      <th>mm_dd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14610</th>\n",
       "      <td>289</td>\n",
       "      <td>01_01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14611</th>\n",
       "      <td>288</td>\n",
       "      <td>01_02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14612</th>\n",
       "      <td>287</td>\n",
       "      <td>01_03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14613</th>\n",
       "      <td>285</td>\n",
       "      <td>01_04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14614</th>\n",
       "      <td>284</td>\n",
       "      <td>01_05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       value_1890  mm_dd\n",
       "14610         289  01_01\n",
       "14611         288  01_02\n",
       "14612         287  01_03\n",
       "14613         285  01_04\n",
       "14614         284  01_05"
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#rename value column\n",
    "dfs1890 = dfs1890.rename(columns={'value': 'value_1890'})\n",
    "\n",
    "# print\n",
    "dfs1890.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "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>value_1890</th>\n",
       "      <th>mm_dd</th>\n",
       "      <th>value_min</th>\n",
       "      <th>value_mean</th>\n",
       "      <th>value_max</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>289</td>\n",
       "      <td>01_01</td>\n",
       "      <td>242</td>\n",
       "      <td>294.433526</td>\n",
       "      <td>390</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>288</td>\n",
       "      <td>01_02</td>\n",
       "      <td>242</td>\n",
       "      <td>293.976879</td>\n",
       "      <td>388</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>287</td>\n",
       "      <td>01_03</td>\n",
       "      <td>242</td>\n",
       "      <td>293.433526</td>\n",
       "      <td>391</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>285</td>\n",
       "      <td>01_04</td>\n",
       "      <td>241</td>\n",
       "      <td>293.271676</td>\n",
       "      <td>392</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>284</td>\n",
       "      <td>01_05</td>\n",
       "      <td>241</td>\n",
       "      <td>292.976879</td>\n",
       "      <td>391</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   value_1890  mm_dd  value_min  value_mean  value_max month\n",
       "0         289  01_01        242  294.433526        390    01\n",
       "1         288  01_02        242  293.976879        388    01\n",
       "2         287  01_03        242  293.433526        391    01\n",
       "3         285  01_04        241  293.271676        392    01\n",
       "4         284  01_05        241  292.976879        391    01"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge the data\n",
    "dfs_mmm_1890 = pd.merge(dfs1890, df_mmm, how=\"left\", on=\"mm_dd\")\n",
    "\n",
    "# print\n",
    "dfs_mmm_1890.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize\n",
    "# print min, max, and mean for long-term data\n",
    "# ---\n",
    "\n",
    "# import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# plot\n",
    "dfs_mmm_1890.plot(x='month', kind='line')\n",
    "\n",
    "plt.ylim(bottom=0, top=800)\n",
    "\n",
    "# description\n",
    "plt.title(\"Level of Lake Constance: 1850 to 2021 + 1890\")\n",
    "plt.xlabel(\"Month\")\n",
    "plt.ylabel(\"Level [cm]\")\n",
    "\n",
    "# add grid\n",
    "plt.grid()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 14/ Check year 1999\n",
    "I still remember the flood from summer 1999. At that time, the military took us to school. Let us have a look at this year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {},
   "outputs": [],
   "source": [
    "# and what was 1999\n",
    "# ---\n",
    "\n",
    "# create dataframe with 1999 data based on dfs\n",
    "dfs1999 = dfs.loc[df['year'] == 1999]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop not needed columns\n",
    "dfs1999 = dfs1999.drop(columns=['date','year'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "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>value_1999</th>\n",
       "      <th>mm_dd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>54421</th>\n",
       "      <td>302</td>\n",
       "      <td>01_01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54422</th>\n",
       "      <td>300</td>\n",
       "      <td>01_02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54423</th>\n",
       "      <td>297</td>\n",
       "      <td>01_03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54424</th>\n",
       "      <td>296</td>\n",
       "      <td>01_04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54425</th>\n",
       "      <td>294</td>\n",
       "      <td>01_05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       value_1999  mm_dd\n",
       "54421         302  01_01\n",
       "54422         300  01_02\n",
       "54423         297  01_03\n",
       "54424         296  01_04\n",
       "54425         294  01_05"
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#rename value column\n",
    "dfs1999 = dfs1999.rename(columns={'value': 'value_1999'})\n",
    "\n",
    "# print\n",
    "dfs1999.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "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>value_1890</th>\n",
       "      <th>mm_dd</th>\n",
       "      <th>value_1999</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>289</td>\n",
       "      <td>01_01</td>\n",
       "      <td>302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>288</td>\n",
       "      <td>01_02</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>287</td>\n",
       "      <td>01_03</td>\n",
       "      <td>297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>285</td>\n",
       "      <td>01_04</td>\n",
       "      <td>296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>284</td>\n",
       "      <td>01_05</td>\n",
       "      <td>294</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   value_1890  mm_dd  value_1999\n",
       "0         289  01_01         302\n",
       "1         288  01_02         300\n",
       "2         287  01_03         297\n",
       "3         285  01_04         296\n",
       "4         284  01_05         294"
      ]
     },
     "execution_count": 350,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs_1890_1999 = pd.merge(dfs1890, dfs1999, how=\"left\", on=\"mm_dd\")\n",
    "\n",
    "# print\n",
    "\n",
    "dfs_1890_1999.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "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>value_1890</th>\n",
       "      <th>mm_dd</th>\n",
       "      <th>value_1999</th>\n",
       "      <th>value_min</th>\n",
       "      <th>value_mean</th>\n",
       "      <th>value_max</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>289</td>\n",
       "      <td>01_01</td>\n",
       "      <td>302</td>\n",
       "      <td>242</td>\n",
       "      <td>294.433526</td>\n",
       "      <td>390</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>288</td>\n",
       "      <td>01_02</td>\n",
       "      <td>300</td>\n",
       "      <td>242</td>\n",
       "      <td>293.976879</td>\n",
       "      <td>388</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>287</td>\n",
       "      <td>01_03</td>\n",
       "      <td>297</td>\n",
       "      <td>242</td>\n",
       "      <td>293.433526</td>\n",
       "      <td>391</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>285</td>\n",
       "      <td>01_04</td>\n",
       "      <td>296</td>\n",
       "      <td>241</td>\n",
       "      <td>293.271676</td>\n",
       "      <td>392</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>284</td>\n",
       "      <td>01_05</td>\n",
       "      <td>294</td>\n",
       "      <td>241</td>\n",
       "      <td>292.976879</td>\n",
       "      <td>391</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   value_1890  mm_dd  value_1999  value_min  value_mean  value_max month\n",
       "0         289  01_01         302        242  294.433526        390    01\n",
       "1         288  01_02         300        242  293.976879        388    01\n",
       "2         287  01_03         297        242  293.433526        391    01\n",
       "3         285  01_04         296        241  293.271676        392    01\n",
       "4         284  01_05         294        241  292.976879        391    01"
      ]
     },
     "execution_count": 351,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs_mmm_1890_1999 = pd.merge(dfs_1890_1999, df_mmm, how=\"left\", on=\"mm_dd\")\n",
    "\n",
    "# print\n",
    "dfs_mmm_1890_1999.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize\n",
    "# print min, max, and mean for long-term data\n",
    "# ---\n",
    "\n",
    "# import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# plot\n",
    "dfs_mmm_1890_1999.plot(x='month', kind='line')\n",
    "\n",
    "plt.ylim(bottom=0, top=800)\n",
    "\n",
    "# description\n",
    "plt.title(\"Level of Lake Constance: 1850 to 2021 + 1949 and 1999\")\n",
    "plt.xlabel(\"Month\")\n",
    "plt.ylabel(\"Level [cm]\")\n",
    "\n",
    "# add grid\n",
    "plt.grid()\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "instance_type": "ml.t3.medium",
  "kernelspec": {
   "display_name": "Python 3 (Data Science)",
   "language": "python",
   "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-east-1:081325390199:image/datascience-1.0"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}