evap.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "29434371-81d0-407e-abc8-de0850752564",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "  \n",
    "df = pd.DataFrame({ \"alice\" : [ 32,32,32,24,24,24,16,16,16,8,8,8,0 ],\n",
    "                    \"km3net\": [ 0,0,0, 8,8,8, 16,16,16, 24,24,24, 32],\n",
    "                  \"mndl\": [ 4,4,4, 4,4,4, 8,8,8, 8,8,8, 8 ],\n",
    "                   \"web\": [ 4,4,4, 4,4,4, 0,0,0, 0,0,0, 0 ]\n",
    "                 \n",
    "                  } ,\n",
    "        index=['2023-1-1','2023-2-1','2023-3-1',\n",
    "               '2023-4-1','2023-5-1','2023-6-1',\n",
    "               '2023-7-1','2023-8-1','2023-9-1',\n",
    "               '2023-10-1','2023-11-1','2023-12-1',\n",
    "               '2024-1-1' ]\n",
    "                 )\n",
    "  \n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "e7c17ab5-6b18-4385-8c79-d00b6ef2c20d",
   "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>alice</th>\n",
       "      <th>km3net</th>\n",
       "      <th>mndl</th>\n",
       "      <th>web</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-1-1</th>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-2-1</th>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-3-1</th>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-4-1</th>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-5-1</th>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-6-1</th>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-7-1</th>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-8-1</th>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-9-1</th>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-1</th>\n",
       "      <td>8</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-11-1</th>\n",
       "      <td>8</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-1</th>\n",
       "      <td>8</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-1-1</th>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           alice  km3net  mndl  web\n",
       "2023-1-1      32       0     4    4\n",
       "2023-2-1      32       0     4    4\n",
       "2023-3-1      32       0     4    4\n",
       "2023-4-1      24       8     4    4\n",
       "2023-5-1      24       8     4    4\n",
       "2023-6-1      24       8     4    4\n",
       "2023-7-1      16      16     8    0\n",
       "2023-8-1      16      16     8    0\n",
       "2023-9-1      16      16     8    0\n",
       "2023-10-1      8      24     8    0\n",
       "2023-11-1      8      24     8    0\n",
       "2023-12-1      8      24     8    0\n",
       "2024-1-1       0      32     8    0"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "41831340-df29-473b-8d34-e0c7b7e0038e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='hours/week'>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='bar',ylabel='hours/week',width=1.0,stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6f343ec-2a79-4dc3-b321-b9b342167b84",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}