01.12 Reading Data with Pandas.ipynb

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    "<h1>Reading Data with Pandas<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Exercise-1\" data-toc-modified-id=\"Exercise-1-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Exercise 1</a></span></li></ul></div>"
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    " "
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    "The so-called **_Pandas_** **DataFrame** is kind of a representation of tables in Python, which has - similar to a 2D _NumPy_ array - rows and columns, that can be directly accessed via their column names, called `key` (similar to the keys of dictionaries), instead of using an index value (which is also possible). The _Pandas_ DataFrame comes with the _Pandas_ package: [pandas.pydata.org/docs](https://pandas.pydata.org/docs/)."
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    "<figure class=\"image\">\n",
    "  <img src=\"img/pandas.png\" alt=\"Pandas Logo\" style=\"width: 30%;\">\n",
    "  <figcaption>The Pandas logo. Copyright © 2008 AQR Capital Management, LLC, Lambda Foundry, Inc. and PyData Development Team. (<a href=\"https://commons.wikimedia.org/wiki/File:Pandas_logo.svg\">commons.wikimedia.org</a>) </figcaption>\n",
    "</figure>"
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   "source": [
    "The scope of what we can do with _Pandas_ is huge. Originally from the financial world, _Pandas_ offers powerful spreadsheet tools (labeled columns and rows, descriptive statistics, pivoting, filtering and grouping, built-in plot functions - just to name a few). We can not go into detail here, but we will make use of _Pandas_' very handy _Excel_ file import function `pd.read_excel(path_to_file, index_col=0)`:"
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    "import pandas as pd\n",
    "import os # enables Operating System functions within Python\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Define file paths:\n",
    "file_path = \"Data/Pandas_1/\"\n",
    "\n",
    "\"\"\" file_path is the main root path. Adjust this to \n",
    "    the absolute path to your Data/Pandas_1/ folder\n",
    "    or put all Excel files into your script's folder \n",
    "    and set file_path = \"\"\n",
    "    \n",
    "    e.g. absolute path:\n",
    "    file_path = \"/Users/husker/Science/Python/Projekte/Python Course/Data/Pandas_1\"\n",
    "\"\"\"\n",
    "\n",
    "file_name_1 = \"Group_A_data.xls\"\n",
    "file_name_2 = \"Group_B_data.xls\"\n",
    "\n",
    "#file_1 = file_path+file_name_1\n",
    "file_1 = os.path.join(file_path, file_name_1) \n",
    "file_2 = os.path.join(file_path, file_name_2)\n",
    "\n",
    "\"\"\" The os.path.join() command just sticks the different \n",
    "    file-path components together. You can also just write\n",
    "    file_1 = file_path + file_name_1\n",
    "    file_2 = file_path + file_name_2\n",
    "\"\"\"\n",
    "\n",
    "# Read the Excel files with Pandas into a Pandas Dataframe:\n",
    "Group_A_df = pd.read_excel(file_1, index_col=0)\n",
    "Group_B_df = pd.read_excel(file_2, index_col=0)"
   ]
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   "cell_type": "code",
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   "metadata": {},
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   "source": [
    "#print( file_path+file_name_1)\n",
    "print(file_1)"
   ]
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   "cell_type": "code",
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   "metadata": {
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   "source": [
    "Group_A_df"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
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   "outputs": [],
   "source": [
    "display(Group_A_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "print(Group_A_df)"
   ]
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   "cell_type": "code",
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   "source": [
    "Group_A_df[\"Data\"]"
   ]
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   "source": [
    "The two _Excel_ files are imported as DataFrames into `Group_A_df` and `Group_B_df`, respectively. Next, we extract the DataFrame data into two _NumPy_ arrays:"
   ]
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   "execution_count": null,
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   "source": [
    "# Extracting the DataFrame import data:\n",
    "Group_A = Group_A_df[\"Data\"].values\n",
    "Group_B = Group_B_df[\"Data\"].values"
   ]
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   "source": [
    "print(Group_A)\n",
    "print(type(Group_A))"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Group_A_df.median()"
   ]
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.median(Group_A)"
   ]
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   "source": [
    "# Concatenate the two data columns:\n",
    "#print(np.array([Group_A_df.values, Group_B_df.values]).T.shape)\n",
    "np.array([Group_A_df.values, Group_B_df.values]).T"
   ]
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   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "# create a new merged dataframe from Group_A and Group_B:\n",
    "pd.DataFrame(data=np.array([Group_A_df.values, Group_B_df.values]).T[0,:,:])"
   ]
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   "source": [
    "We can apply any operation to the two _NumPy_ arrays `Group_A` and `Group_B`, that we have previously learned in the _NumPy_ chapter. By redefining our previous plot script from the _Matplotlib_ chapter (Exercise 2 there), we are able to re-run the entire script, now applied to the imported _Excel_ data:"
   ]
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   "source": [
    "## Exercise 1\n",
    "\n",
    "1. Copy your solution from Exercise 3 from the _Matplotlib_ chapter into a new script.\n",
    "2. Add the _Pandas_ _Excel_ file import commands from above to your script. \n",
    "3. Uncomment or redefine your `Group_A` and `Groud_B` variable definitions according to:\n",
    "    ```\n",
    "        Group_A = Group_A_df[\"Data\"].values\n",
    "        Group_B = Group_B_df[\"Data\"].values\n",
    "    ```\n",
    "4. Run your new script. \n",
    "5. Now, instead of reading the file \"Group_B_data.xls\", read \"Group_B2_data.xls\" as Group B data and re-run your script"
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.tight_layout(*, pad=1.08, h_pad=None, w_pad=None, rect=None)>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Your solution 1 here:\n"
   ]
  },
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   "source": [
    "<details>\n",
    "<summary><strong>Toggle solution</strong></summary>\n",
    "\n",
    "```python\n",
    "# Solution 1:\n",
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pingouin as pg\n",
    "\n",
    "# Define file paths:\n",
    "file_path = \"Data/Pandas_1/\"\n",
    "file_name_1 = \"Group_A_data.xls\"\n",
    "file_name_2 = \"Group_B2_data.xls\"\n",
    "\n",
    "file_1 = os.path.join(file_path, file_name_1)\n",
    "file_2 = os.path.join(file_path, file_name_2)\n",
    "\n",
    "# Read the Excel files with Pandas into a Pandas Dataframe:\n",
    "Group_A_df = pd.read_excel(file_1, index_col=0)\n",
    "Group_B_df = pd.read_excel(file_2, index_col=0)\n",
    "\n",
    "# Broadcast the DataFrame data into the approproate variables:\n",
    "Group_A = Group_A_df[\"Data\"].values\n",
    "Group_B = Group_B_df[\"Data\"].values\n",
    "\n",
    "\n",
    "\"\"\" The following code is simply your copied solution from the \n",
    "    Matplotlib Exercise:\n",
    "\"\"\"\n",
    "\n",
    "# BAR-PLOT\n",
    "fig=plt.figure(1)\n",
    "fig.clf()\n",
    "\n",
    "plt.bar([1, 2], [Group_A.mean(), Group_B.mean()])\n",
    "\n",
    "plt.xticks([1,2], labels=[\"A\", \"B\"])\n",
    "plt.xlabel(\"Groups\")\n",
    "plt.ylabel(\"measurements\")\n",
    "plt.title(\"Bar-plot of group averages\")\n",
    "\n",
    "plt.tight_layout\n",
    "plt.show()\n",
    "fig.savefig(\"barplot with data.pdf\", dpi=120)\n",
    "\n",
    "\n",
    "# BOX-PLOTS:\n",
    "fig=plt.figure(2, figsize=(5,6))\n",
    "fig.clf()\n",
    "\n",
    "plt.boxplot([Group_A, Group_B])\n",
    "\n",
    "plt.xticks([1,2], labels=[\"A\", \"B\"])\n",
    "plt.xlabel(\"Groups\")\n",
    "plt.ylabel(\"measurements\")\n",
    "plt.title(\"Boxplot diagram\")\n",
    "plt.tight_layout\n",
    "plt.show()\n",
    "fig.savefig(\"boxplot with data.pdf\", dpi=120)\n",
    "\n",
    "\n",
    "# VIOLIN-PLOTS:\n",
    "fig=plt.figure(3, figsize=(5,6))\n",
    "fig.clf()\n",
    "\n",
    "plt.violinplot([Group_A, Group_B], showmedians=True)\n",
    "\n",
    "plt.xticks([1,2], labels=[\"A\", \"B\"])\n",
    "plt.xlabel(\"Groups\")\n",
    "plt.ylabel(\"measurements\")\n",
    "plt.title(\"Violin plot\")\n",
    "plt.tight_layout\n",
    "# plt.ylim(-40, 40)\n",
    "plt.show()\n",
    "fig.savefig(\"violinplot with data.pdf\", dpi=120)\n",
    "``` \n",
    "<script src=\"https://gist.github.com/username/a39a422ebdff6e732753b90573100b16.js\"></script>\n",
    "</details>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Group_A_df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Group_A_df.mean().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Group_A_df.plot()"
   ]
  }
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