Learn Python Packages and Pandas.ipynb

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    "<h1>Learn Python Pandas, Packages, and Modules</h1>\n",
    "\n",
    "**April 2021**<br>\n",
    "\n",
    "Instruction, media content, examples and links to resources that will help you build a foundation for Python competency. Jupyter Notebooks are available on [Google Colab](https://colab.research.google.com/drive/1K6bn0CPn-WLXRwqzXpcptI4Lcw6aJUY6?usp=sharing) and [Github](https://github.com/AlphaWaveData/Jupyter-Notebooks/blob/master/Learn%20Python%20Packages%20and%20Pandas.ipynb).\n",
    "\n",
    "<h1>Modules</h1>\n",
    "\n",
    "<b>Web Resources</b>\n",
    "<br> <a href='https://docs.python.org/3/tutorial/modules.html#packages'>Docs.python.org - Packages</a>\n",
    "\n",
    "\n",
    "#### <b>What are Python Modules?</b>\n",
    "\n",
    "Modules are simply python files (.py) which contain python code. This code can define functions, classes, variables etc.\n",
    "\n",
    "\n",
    "#### <b>Why do we use Modules?</b>\n",
    "\n",
    "Modules allow us to organize our code by grouping related functionalities, which makes it easier to use and understand. Writing code into smaller, more manageable pieces will help you 1) debug easier, 2) create reusable code and 3) make the code more understandable to the end user.\n",
    "\n",
    "#### <b>How do we use Modules?</b>\n",
    "\n",
    "We can use the code in a module by using the <b>import</b> function. For example, let's say we created a .py file called 'Square.py'. Inside this file we defined a function called <b>calculateSquare</b>. \n",
    "```python\n",
    "def calculateSquare(x):\n",
    "    return x**2\n",
    "```\n",
    "We can use the <b>calculateSquare</b> function by using either of the following commands:\n",
    "\n",
    "```python\n",
    "import Square \n",
    "x = 2\n",
    "print(Square.calculateSquare(x))\n",
    "\n",
    "#Output: 4\n",
    "\n",
    "from Square import calculateSquare\n",
    "x = 2\n",
    "print(calculateSquare(x))\n",
    "\n",
    "#Output: 4\n",
    "\n",
    "from Square import * #The asterisk imports all functions in the module [not always best practice]\n",
    "x = 2\n",
    "print(calculateSquare(x))\n",
    "\n",
    "#Output: 4\n",
    "```\n",
    "\n",
    "#### <b>Where can I find more information on Modules?</b>\n",
    "\n",
    "[Official python documentation on modules](https://docs.python.org/3.7/tutorial/modules.html)\n",
    "\n",
    "<h1>Packages</h1>\n",
    "\n",
    "#### <b>What are Python Packages?</b>\n",
    "\n",
    "Python packages are an organized collection of related python modules in a single directory. \n",
    "\n",
    "#### <b>How do we use Packages?</b>\n",
    "\n",
    "Packages are used exactly like modules (see the Modules section above).\n",
    "\n",
    "#### <b>What are some common Packages?</b>\n",
    "\n",
    "Below are some of the most widely used modules in finance and data science for data analysis:\n",
    "- Pandas\n",
    "- Numpy\n",
    "- Scipy\n",
    "- Scikit-Learn\n",
    "\n",
    "Visualizations and dashboard creation:\n",
    "- Matplotlib\n",
    "- ipywidgets\n"
   ]
  },
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   "source": [
    "<h1>PANDAS</h1>\n",
    "\n",
    "<b>Web Resources</b>\n",
    "<br> [Official Pandas documentation](https://pandas.pydata.org/docs/)\n",
    "<br> [Getting started with Pandas](https://pandas.pydata.org/docs/getting_started/intro_tutorials/index.html)\n",
    "\n",
    "#### <b>What is Pandas?</b>\n",
    "\n",
    "Pandas is a fast, powerful, flexible and easy to use data analysis and manipulation tool. Pandas was created in 2008 at AQR Capital Management as has been open sourced since 2009. Pandas aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. \n",
    "\n",
    "#### <b>What can we use Pandas for?</b>\n",
    "\n",
    "- A fast and efficient DataFrame object for **data manipulation** with integrated indexing\n",
    "\n",
    "- Tools for **reading and writing data** between in-memory data structures and different formats: CSV and text files, Microsoft Excel, SQL databases, and the fast HDF5 format\n",
    "\n",
    "- Intelligent **data alignment** and integrated **handling of missing data**: gain automatic label-based alignment in computations and easily manipulate messy data into an orderly form\n",
    "\n",
    "- Flexible **reshaping and pivoting** of data sets\n",
    "\n",
    "- Intelligent label-based **slicing, fancy indexing, and subsetting** of large data sets\n",
    "\n",
    "- Columns can be inserted and deleted from data structures for size **mutability**\n",
    "\n",
    "- **Aggregating or transforming data** with a powerful group by engine allowing split-apply-combine operations on data sets\n",
    "\n",
    "- High performance **merging and joining** of data sets\n",
    "\n",
    "- **Hierarchical axis indexing** provides an intuitive way of working with high-dimensional data in a lower-dimensional data structure\n",
    "\n",
    "- **Time series-functionality**: date range generation and frequency conversion, moving window statistics, date shifting and lagging. Even create domain-specific time offsets and join time series without losing data\n",
    "\n",
    "- **Highly optimized for performance**, with critical code paths written in Cython or C\n",
    "\n",
    "- Python with pandas is in use in a wide variety of academic and commercial domains, including Finance, Neuroscience, Economics, Statistics, Advertising, Web Analytics, and more\n"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Quick PANDAS Tutorial</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "dtype: int64"
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     "execution_count": 12,
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     "output_type": "execute_result"
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   ],
   "source": [
    "###IMPORT PANDAS AND NUMPY PACKAGES###\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "###CREATING A SERIES###\n",
    "s = pd.Series([1,2,3])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
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       "      <td>2013-01-01</td>\n",
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       "    <tr>\n",
       "      <td>2013-01-06</td>\n",
       "      <td>0.485850</td>\n",
       "      <td>0.085890</td>\n",
       "      <td>1.870669</td>\n",
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       "                   A         B         C\n",
       "2013-01-01  0.169424  0.166691 -2.385720\n",
       "2013-01-02  0.542676  0.978059  1.152731\n",
       "2013-01-03 -0.547186  0.918728  2.226278\n",
       "2013-01-04  1.033752 -0.712600  1.169230\n",
       "2013-01-05 -0.534291  0.985898 -1.705124\n",
       "2013-01-06  0.485850  0.085890  1.870669"
      ]
     },
     "execution_count": 13,
     "metadata": {},
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   ],
   "source": [
    "###CREATING A TIMESERIES BASED DATAFRAME###\n",
    "dates = pd.date_range('20130101', periods=6)\n",
    "df = pd.DataFrame(np.random.randn(6, 3), index=dates, columns=list('ABC'))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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     "data": {
      "text/plain": [
       "2013-01-01    0.169424\n",
       "2013-01-02    0.542676\n",
       "2013-01-03   -0.547186\n",
       "2013-01-04    1.033752\n",
       "2013-01-05   -0.534291\n",
       "2013-01-06    0.485850\n",
       "Freq: D, Name: A, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###GET DATA FOR A SINGLE COLUMN###\n",
    "df['A']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
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       "      <th>A</th>\n",
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       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>0.191704</td>\n",
       "      <td>0.403778</td>\n",
       "      <td>0.388011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>0.631232</td>\n",
       "      <td>0.683725</td>\n",
       "      <td>1.941515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>-0.547186</td>\n",
       "      <td>-0.712600</td>\n",
       "      <td>-2.385720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>-0.358362</td>\n",
       "      <td>0.106090</td>\n",
       "      <td>-0.990660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>0.327637</td>\n",
       "      <td>0.542709</td>\n",
       "      <td>1.160980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>0.528469</td>\n",
       "      <td>0.963226</td>\n",
       "      <td>1.695309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>1.033752</td>\n",
       "      <td>0.985898</td>\n",
       "      <td>2.226278</td>\n",
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       "              A         B         C\n",
       "count  6.000000  6.000000  6.000000\n",
       "mean   0.191704  0.403778  0.388011\n",
       "std    0.631232  0.683725  1.941515\n",
       "min   -0.547186 -0.712600 -2.385720\n",
       "25%   -0.358362  0.106090 -0.990660\n",
       "50%    0.327637  0.542709  1.160980\n",
       "75%    0.528469  0.963226  1.695309\n",
       "max    1.033752  0.985898  2.226278"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###CALCULATE SUMMARY STATISTICS###\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6, 3)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###GET THE NUMBER OF ROWS AND COLUMNS###\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###GET THE NUMBER OF ROWS ONLY###\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11efd5990>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "###PLOT THE DATAFRAME###\n",
    "%matplotlib inline\n",
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>0.169424</td>\n",
       "      <td>0.166691</td>\n",
       "      <td>-2.385720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>0.542676</td>\n",
       "      <td>0.978059</td>\n",
       "      <td>1.152731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2013-01-03</td>\n",
       "      <td>-0.547186</td>\n",
       "      <td>0.918728</td>\n",
       "      <td>2.226278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2013-01-04</td>\n",
       "      <td>1.033752</td>\n",
       "      <td>-0.712600</td>\n",
       "      <td>1.169230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2013-01-05</td>\n",
       "      <td>-0.534291</td>\n",
       "      <td>0.985898</td>\n",
       "      <td>-1.705124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2013-01-06</td>\n",
       "      <td>0.485850</td>\n",
       "      <td>0.085890</td>\n",
       "      <td>1.870669</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   X         Y         Z\n",
       "2013-01-01  0.169424  0.166691 -2.385720\n",
       "2013-01-02  0.542676  0.978059  1.152731\n",
       "2013-01-03 -0.547186  0.918728  2.226278\n",
       "2013-01-04  1.033752 -0.712600  1.169230\n",
       "2013-01-05 -0.534291  0.985898 -1.705124\n",
       "2013-01-06  0.485850  0.085890  1.870669"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###RENAME COLUMNS###\n",
    "df = df.rename(columns={\"A\":\"X\",\"B\":\"Y\",\"C\":\"Z\"})\n",
    "df"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}