basics/10.modules.ipynb

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   "source": [
    "# 10. Modules and packages"
   ]
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   "source": [
    "## 10.1 What is a module?\n",
    "To be able to package and re-use code, Python relies on **modules** and **packages**. A module is a library of functions. It stores a bunch of earlier created functions, so that you can re-use them anywhere. By importing such modules and packages into your own software, a whole range of functions and functionalities become available immediately for you, without you having to build them from scratch. This is a major advantage to advance fast to better results.\n",
    "\n",
    "Different kinds of modules are available:\n",
    "1. Built-in modules: `math`, `datetime`, `os`\n",
    "2. External modules built by others: `numpy`, `matplotlib`, `pandas`\n",
    "3. Self-built modules"
   ]
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   "source": [
    "import math"
   ]
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   "source": [
    "So what is a module? It is a .py file that contains functions or variables that you can use in another python file."
   ]
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   "source": [
    "## 10.2 Using built-in Python modules\n",
    "\n",
    "One common library that comes as part of the Python Standard Library is `datetime`. `datetime` helps you work with dates and times in Python.\n",
    "\n",
    "Let’s get started by importing and using the `datetime` module. In this case, you will notice that `datetime` is both the name of the library and the name of the object that you are importing."
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     "text": [
      "2023-11-13 15:10:21.571721\n"
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   "source": [
    "from datetime import datetime\n",
    "\n",
    "current_time = datetime.now()\n",
    "print(current_time)"
   ]
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   "metadata": {},
   "source": [
    "Similarly, a whole range of other modules can be loaded, with important functionality readily available in easy-to-understand functions."
   ]
  },
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   "execution_count": 3,
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     "text": [
      "Windows\n",
      "AMD64\n"
     ]
    }
   ],
   "source": [
    "import platform\n",
    "\n",
    "print(platform.system())\n",
    "\n",
    "print(platform.machine())"
   ]
  },
  {
   "cell_type": "markdown",
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   },
   "source": [
    "## 10.3 Using external modules"
   ]
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   "source": [
    "A module that is very often used to handle data, is the `pandas` module. We will use it to handle temperature values."
   ]
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     "text": [
      "Collecting pandas"
     ]
    },
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     "text": [
      "WARNING: You are using pip version 21.1.3; however, version 23.3.1 is available.\n",
      "You should consider upgrading via the 'c:\\users\\20194060\\appdata\\local\\programs\\python\\python39\\python.exe -m pip install --upgrade pip' command.\n"
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     "text": [
      "\n",
      "  Downloading pandas-2.1.3-cp39-cp39-win_amd64.whl (10.8 MB)\n",
      "Collecting numpy<2,>=1.22.4\n",
      "  Downloading numpy-1.26.2-cp39-cp39-win_amd64.whl (15.8 MB)\n",
      "Collecting tzdata>=2022.1\n",
      "  Downloading tzdata-2023.3-py2.py3-none-any.whl (341 kB)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\20194060\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from pandas) (2022.1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\20194060\\appdata\\roaming\\python\\python39\\site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\20194060\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n",
      "Installing collected packages: tzdata, numpy, pandas\n",
      "  Attempting uninstall: tzdata\n",
      "    Found existing installation: tzdata 2021.5\n",
      "    Uninstalling tzdata-2021.5:\n",
      "      Successfully uninstalled tzdata-2021.5\n",
      "Successfully installed numpy-1.26.2 pandas-2.1.3 tzdata-2023.3\n"
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   "source": [
    "!pip install pandas"
   ]
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   "source": [
    "Using `pandas`, it is possible to read an input CSV file (comma-separated values - CSV). This input file is made available in the same folder as this script (`temp.csv`), and it can be changed as preferred, using Notepad++ or MS Excel or any code editor. The below code loads the data into a `dataframe`."
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       "<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",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sensor_id</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>temp1</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>temp2</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>temp3</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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       "  sensor_id   value\n",
       "0     temp1      12\n",
       "1     temp2      23\n",
       "2     temp3      12"
      ]
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     "execution_count": 5,
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   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "#  Creating a DataFrame by passing a dictionary of objects that can be converted into a series-like structure:\n",
    "df = pd.read_csv('temp.csv')\n",
    "df"
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