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

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   "source": [
    "# Growing Degree Days\n",
    "\n",
    "Dis notebook go load temperature data wey dem save for one CSV file, and e go analyze am. E go draw graph for the temperatures, show the highest and lowest value for each day, and calculate the GDD.\n",
    "\n",
    "How you go take use dis notebook:\n",
    "\n",
    "* Copy the `temperature.csv` file put for the same folder wey dis notebook dey.\n",
    "* Run all the cells using the **▶︎ Run** button wey dey up. Dis one go run the cell wey you select, then e go move to the next one.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For di cell wey dey below, set `base_temperature` to di base temperature of di plant.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_temperature = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Di CSV file now need to load am, 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": [
    "Di temperature fit now dey show for graph.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "plt.plot(df['date'], df['temperature'])\n",
    "plt.xticks(rotation='vertical');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wen dem don read di data, dem fit group am by di `date` column, and comot di minimum and maximum temperature 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": [
    "Di GDD fit dey calculate wit di 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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   "source": [
    "---\n\n<!-- CO-OP TRANSLATOR DISCLAIMER START -->\n**Disclaimer**:  \nDis dokyument don use AI transleshion service [Co-op Translator](https://github.com/Azure/co-op-translator) do di transleshion. Even as we dey try make am accurate, abeg make you sabi say automatik transleshion fit get mistake or no dey correct well. Di original dokyument for im native language na di main source wey you go fit trust. For important informashon, e good make you use professional human transleshion. We no go fit take blame for any misunderstanding or wrong interpretation wey fit happen because you use dis transleshion.\n<!-- CO-OP TRANSLATOR DISCLAIMER END -->\n"
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