IoT-For-Beginners/translations/en/2-farm/lessons/1-predict-plant-growth/code-notebook/gdd.ipynb

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    "# Growing Degree Days\n",
    "\n",
    "This notebook loads temperature data stored in a CSV file and analyzes it. It plots the temperatures, displays the highest and lowest values for each day, and calculates the GDD.\n",
    "\n",
    "To use this notebook:\n",
    "\n",
    "* Place the `temperature.csv` file in the same folder as this notebook.\n",
    "* Run all the cells using the **▶︎ Run** button above. This will execute the selected cell and then proceed to the next one.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the cell below, set `base_temperature` to the base temperature of the plant.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_temperature = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The CSV file now needs to be loaded, using pandas\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Read the temperature CSV file\n",
    "df = pd.read_csv('temperature.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
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   "cell_type": "code",
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   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "plt.plot(df['date'], df['temperature'])\n",
    "plt.xticks(rotation='vertical');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the data has been read it can be grouped by the `date` column, and the minimum and maximum temperatures extracted for each date.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert datetimes to pure dates so we can group by the date\n",
    "df['date'] = pd.to_datetime(df['date']).dt.date\n",
    "\n",
    "# Group the data by date so it can be analyzed by date\n",
    "data_by_date = df.groupby('date')\n",
    "\n",
    "# Get the minimum and maximum temperatures for each date\n",
    "min_by_date = data_by_date.min()\n",
    "max_by_date = data_by_date.max()\n",
    "\n",
    "# Join the min and max temperatures into one dataframe and flatten it\n",
    "min_max_by_date = min_by_date.join(max_by_date, on='date', lsuffix='_min', rsuffix='_max')\n",
    "min_max_by_date = min_max_by_date.reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The GDD can be calculated using the standard GDD equation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_gdd(row):\n",
    "    return ((row['temperature_max'] + row['temperature_min']) / 2) - base_temperature\n",
    "\n",
    "# Calculate the GDD for each row\n",
    "min_max_by_date['gdd'] = min_max_by_date.apply (lambda row: calculate_gdd(row), axis=1)\n",
    "\n",
    "# Print the results\n",
    "print(min_max_by_date[['date', 'gdd']].to_string(index=False))"
   ]
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   "execution_count": null,
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   "source": [
    "\n---\n\n**Disclaimer**:  \nThis document has been translated using the AI translation service [Co-op Translator](https://github.com/Azure/co-op-translator). While we aim for accuracy, please note that automated translations may include errors or inaccuracies. The original document in its native language should be regarded as the authoritative source. For critical information, professional human translation is advised. We are not responsible for any misunderstandings or misinterpretations resulting from the use of this translation.\n"
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