01 Statistical data analysis with Pandas and Pingouin (extended).ipynb

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    "<h1>Statistical data analysis with Pandas and Pingouin (extended)<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Exercise-1:-Create-some-dummy-data-samples\" data-toc-modified-id=\"Exercise-1:-Create-some-dummy-data-samples-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Exercise 1: Create some dummy data samples</a></span></li><li><span><a href=\"#Statistical-signficance-test\" data-toc-modified-id=\"Statistical-signficance-test-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Statistical signficance test</a></span></li><li><span><a href=\"#Exercise-2\" data-toc-modified-id=\"Exercise-2-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Exercise 2</a></span></li><li><span><a href=\"#Let's-apply-the-analysis-to-real-world-data\" data-toc-modified-id=\"Let's-apply-the-analysis-to-real-world-data-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Let's apply the analysis to real world data</a></span></li><li><span><a href=\"#A-few-Pandas-basics\" data-toc-modified-id=\"A-few-Pandas-basics-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>A few Pandas basics</a></span></li><li><span><a href=\"#Exercise-3\" data-toc-modified-id=\"Exercise-3-6\"><span class=\"toc-item-num\">6&nbsp;&nbsp;</span>Exercise 3</a></span></li><li><span><a href=\"#Exercise-4:-Extended-real-world-data-example\" data-toc-modified-id=\"Exercise-4:-Extended-real-world-data-example-7\"><span class=\"toc-item-num\">7&nbsp;&nbsp;</span>Exercise 4: Extended real world data example</a></span></li><li><span><a href=\"#Outlook\" data-toc-modified-id=\"Outlook-8\"><span class=\"toc-item-num\">8&nbsp;&nbsp;</span>Outlook</a></span></li></ul></div>"
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    "This chapter is an extension of the [_Statistical data analysis with Pingouin_](https://www.fabriziomusacchio.com/teaching/python_course/13_pingouin) chapter from the [_Python Basics_](https://www.fabriziomusacchio.com/teaching/python_course/) course. \n",
    "\n",
    "We start again from scratch and first create an artificial test data set:"
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    "## Exercise 1: Create some dummy data samples\n",
    "\n",
    "1. Create a new script and define the following [_NumPy_](https://www.fabriziomusacchio.com/teaching/python_course/10_numpy) arrays as dummy data arrays:\n",
    "```python\n",
    "np.random.seed(1)\n",
    "Group_A = np.random.randn(10) * 10 + 5\n",
    "Group_B = np.random.randn(10) * 10 + 2\n",
    "```\n",
    "2. Plot the averages of `Group_A` and `Group_B` in a bar-plot in a new figure window:\n",
    "    - use the plot command:\n",
    "    \n",
    "        `plt.bar([1, 2], [\"Mean of Group A\", \"Mean of Group B\"])`. \n",
    "       \n",
    "       Hint: \"_Mean of Group A_\" and \"_Mean of Group B_\" are just placeholders! Replace this with the according _NumPy_ averaging command :-)\n",
    "    - define the x-tick labels via `plt.xticks([1,2], labels=[\"A\", \"B\"])`\n",
    "    - add appropriate _x_- and _y_-labels and a title to your plot. \n",
    "    - save your plot as a PDF.\n",
    "3. Plot the values of `Group_A` and `Group_B`, respectively, in a boxplot in another figure window:\n",
    "    - set the figure aspect ratio to 5x6 via `fig=plt.figure(2, figsize=(5,6))`\n",
    "    - use the plot command `plt.boxplot([Group_A, Group_B])`\n",
    "    - define the x-tick labels as in 2.\n",
    "    - add appropriate _x_- and _y_-labels and a title to your plot. \n",
    "    - save your plot as a PDF.\n",
    "4.  Same as 3., but now use the command `plt.violinplot([Group_A, Group_B], showmedians=True)` to plot a violin plot in another figure."
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Your solution 1.1-1.2 here\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary><strong>Toggle solution</strong></summary>\n",
    "\n",
    "~~~python\n",
    "# Solution 2.1\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Generate some random dummy data:\n",
    "np.random.seed(1)\n",
    "Group_A = np.random.randn(10)*10+5\n",
    "Group_B = np.random.randn(10)*10+2\n",
    "\n",
    "# Solution 2.2\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(\"measurments\")\n",
    "plt.title(\"Bar-plot of group averages\")\n",
    "\n",
    "plt.tight_layout\n",
    "plt.show()\n",
    "fig.savefig(\"barplot.pdf\", dpi=120)\n",
    "~~~\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "code_folding": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Your solution 1.3 here:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary><strong>Toggle solution</strong></summary>\n",
    "\n",
    "~~~python\n",
    "# Solution 1.3:\n",
    "fig=plt.figure(3, figsize=(5,6))\n",
    "fig.clf()\n",
    "\n",
    "#x_ticks_A = np.ones(len(Group_A))\n",
    "#x_ticks_B = np.ones(len(Group_B))\n",
    "\n",
    "plt.boxplot([Group_A, Group_B])\n",
    "\n",
    "plt.xticks([1,2], labels=[\"A\", \"B\"])\n",
    "plt.xlabel(\"Groups\")\n",
    "plt.ylabel(\"measurments\")\n",
    "plt.title(\"Boxplot diagram\")\n",
    "\n",
    "plt.tight_layout\n",
    "plt.show()\n",
    "fig.savefig(\"boxlot.pdf\", dpi=120)\n",
    "~~~\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "code_folding": [],
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Your solution 1.4 here:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary><strong>Toggle solution</strong></summary>\n",
    "\n",
    "~~~python\n",
    "# Solution 1.4\n",
    "fig=plt.figure(3, figsize=(5,6))\n",
    "fig.clf()\n",
    "\n",
    "#x_ticks_A = np.ones(len(Group_A))\n",
    "#x_ticks_B = np.ones(len(Group_B))\n",
    "\n",
    "plt.violinplot([Group_A, Group_B], showmedians=True)\n",
    "plt.boxplot([Group_A, Group_B])\n",
    "\n",
    "plt.xticks([1,2], labels=[\"A\", \"B\"])\n",
    "plt.xlabel(\"Groups\")\n",
    "plt.ylabel(\"measurments\")\n",
    "plt.title(\"Violin plot\")\n",
    "\n",
    "plt.tight_layout\n",
    "plt.xlim(0.5, 2.5)\n",
    "plt.ylim(-40, 40)\n",
    "\n",
    "plt.show()\n",
    "fig.savefig(\"violinplot.pdf\", dpi=120)\n",
    "~~~\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistical signficance test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We would like to know, whether the difference between the two groups is significant or not. \n",
    "\n",
    "To perform a significance test in _Python_ we use [_Pingouin_](https://pingouin-stats.org), a compact package that provides the most important test tools for a significance study. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![img/pingouin.png](img/pingouin.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Info</b>: It is worth visiting the [_Pingouin_ website](https://pingouin-stats.org). It provides a very good overview of available significance tests and also a decision tree that helps selecting the correct test for the respective data set.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Screenshots from [pingouin-stats.org/guidelines.html](https://pingouin-stats.org/guidelines.html) (taken on March 2, 2021):\n",
    "![img/pingouin.png](img/pingouin_1.png)\n",
    "![img/pingouin.png](img/pingouin_2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's assume that our data is normally distributed and the two samples are independent. The corresponding test would be an unpaired, two-sample student's t-test. The corresponding Pingouin command is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>T</th>\n",
       "      <th>dof</th>\n",
       "      <th>tail</th>\n",
       "      <th>p-val</th>\n",
       "      <th>CI95%</th>\n",
       "      <th>cohen-d</th>\n",
       "      <th>BF10</th>\n",
       "      <th>power</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>T-test</th>\n",
       "      <td>0.718772</td>\n",
       "      <td>18</td>\n",
       "      <td>two-sided</td>\n",
       "      <td>0.481509</td>\n",
       "      <td>[-7.16, 14.61]</td>\n",
       "      <td>0.321445</td>\n",
       "      <td>0.477</td>\n",
       "      <td>0.104604</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               T  dof       tail     p-val           CI95%   cohen-d   BF10  \\\n",
       "T-test  0.718772   18  two-sided  0.481509  [-7.16, 14.61]  0.321445  0.477   \n",
       "\n",
       "           power  \n",
       "T-test  0.104604  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pingouin as pg\n",
    "test_result = pg.ttest(Group_A, Group_B, paired=False)\n",
    "#print(test_result)\n",
    "pg.ttest(Group_A, Group_B, paired=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the output of _Pingouin_ is not just a single value, e.g., the p-value, but a table of useful statistical properties:\n",
    "- `T` : _T_-value\n",
    "- `p-val` : _p_-value\n",
    "- `dof` : degrees of freedom\n",
    "- `cohen-d` : [Cohen's _d_ effect size](https://en.wikipedia.org/wiki/Effect_size)\n",
    "- `CI95%` : 95% confidence intervals of the difference in means\n",
    "- `power` : achieved power of the test ( = 1 - type II error)\n",
    "- `BF10` : Bayes Factor of the alternative hypothesis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To be more correct, this output is actually a so-called [_Pandas_](https://www.fabriziomusacchio.com/teaching/python_course/12_pandas) _DataFrame_, a type of variable construct. _Pandas_ DataFrames are a handy and powerful representation of tables in _Python_, which has - similar to a 2D [_NumPy_](https://www.fabriziomusacchio.com/teaching/python_course/10_numpy) array - rows and columns, that can be directly accessed via their column names, called `key` (similar to the keys of [dictionaries](https://www.fabriziomusacchio.com/teaching/python_course/04_variables#dictionaries)). In very long tables/DataFrames, by default not all columns/keys are shown. To get a list of all available keys, use the following command: `DataFrame.keys()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['T', 'dof', 'tail', 'p-val', 'CI95%', 'cohen-d', 'BF10', 'power'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# show all keys of the test_result DataFrame:\n",
    "print(test_result.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To access a specific column/key, just use the same syntax as for [dictionaries](https://www.fabriziomusacchio.com/teaching/python_course/04_variables#dictionaries) and add `.values` (in order to extract the values from the DataFrame structure and retrieve a _NumPy_ array):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the p-value of our test is: [0.48150881]\n"
     ]
    }
   ],
   "source": [
    "print(f\"the p-value of our test is:\", \n",
    "      f\"{test_result['p-val'].values}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 2\n",
    "\n",
    "Add a new cell to the script of the solution from Exercise 1. In the new cell, extend your script by the following functions:\n",
    "    \n",
    "1. Write an [_if_-statement](https://www.fabriziomusacchio.com/teaching/python_course/08_if_conditions) that checks whether the p-value of the significance test is lower or greater than 0.05. If the p-value is lower, then print out \"_there is a significant difference_\" together with the according p-value. Otherwise print out \"_no significant difference_\", again, together with the according p-value.\n",
    "2. Extent your _if_-statement, so that also Cohen's _d_ effect size is printed out, but only if the p-value indicates a significant difference.\n",
    "3. Plug both, the significance test and your written _if_-statement into a function called `normal_unpaired_sigtest(A, B)`, which also returns the p-value back to your main script.\n",
    "4. In your main script, call your newly created function with the `Group_A` and `Group_B` data.\n",
    "5. Modify the `Group_A` definition by `Group_A = np.random.randn(10)*10+15` and re-run your script.\n",
    "6. Create another function for the two-sample, non-parametric, un-paired signficance test:\n",
    "    - visit [pingouin-stats.org/guidelines.html](https://pingouin-stats.org/guidelines.html) to find the appropriate test\n",
    "    - copy your function from 2. and rename the copy to `notnormal_unpaired_sigtest(A, B)`\n",
    "    - adjust the significance test according to your website search result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "code_folding": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no significant difference (p-value: [0.48150881])\n"
     ]
    }
   ],
   "source": [
    "# Your solution 2.2 - 2.4:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary><strong>Toggle solution</strong></summary>\n",
    "\n",
    "~~~python\n",
    "# Solution 2.2 - 2.4:\n",
    "def normal_unpaired_sigtest(A, B):\n",
    "    test_result = pg.ttest(A, B, paired=False)\n",
    "    p_value = test_result[\"p-val\"].values\n",
    "    cohen_d = test_result[\"cohen-d\"].values\n",
    "\n",
    "    if p_value>0.05:\n",
    "        print(f\"no significant difference (p-value: {p_value})\")\n",
    "    else:\n",
    "        print(f\"here is a significant difference \"\n",
    "              f\"(p-value: {p_value}, cohen-d:{cohen_d})\")\n",
    "\n",
    "    return p_value\n",
    "\n",
    "pvalue = normal_unpaired_sigtest(Group_A, Group_B)\n",
    "~~~\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "code_folding": [],
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no significant difference (p-value: [0.42735531])\n"
     ]
    }
   ],
   "source": [
    "# Your solution 2.6 here:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary><strong>Toggle solution</strong></summary>\n",
    "\n",
    "~~~python\n",
    "# Solution 2.6:\n",
    "def notnormal_unpaired_sigtest(A, B):\n",
    "    test_result = pg.mwu(A, B)\n",
    "    p_value = test_result[\"p-val\"].values\n",
    "\n",
    "    if p_value>0.05:\n",
    "        print(f\"no significant difference (p-value: {p_value})\")\n",
    "    else:\n",
    "        print(f\"here is a significant difference \"\n",
    "              f\"(p-value: {p_value})\")\n",
    "\n",
    "    return p_value\n",
    "\n",
    "pvalue = notnormal_unpaired_sigtest(Group_A, Group_B)\n",
    "~~~\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "code_folding": []
   },
   "source": [
    "<details>\n",
    "<summary><strong>Toggle full code solution</strong></summary>\n",
    "\n",
    "~~~python\n",
    "# Solution 2 (full code):\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# %% Generate some random dummy data:\n",
    "np.random.seed(1)\n",
    "Group_A = np.random.randn(10)*10+5\n",
    "Group_B = np.random.randn(10)*10+2\n",
    "Group_B2= np.random.randn(10)*10+20\n",
    "\n",
    "# %% FUNCTION DEFINITION\n",
    "\"\"\"not a rule, but usually you place functions at the beginning\n",
    "   of your script (e.g. after the imports) to a) quickly find \n",
    "   them and b) they have to be defined before their first call.\n",
    "\"\"\"\n",
    "\n",
    "def normal_unpaired_sigtest(A, B):\n",
    "    test_result = pg.ttest(A, B, paired=False)\n",
    "    p_value = test_result[\"p-val\"].values\n",
    "    cohen_d = test_result[\"cohen-d\"].values\n",
    "\n",
    "    if p_value>0.05:\n",
    "        print(f\"no significant difference (p-value: {p_value})\")\n",
    "    else:\n",
    "        print(f\"here is a significant difference \"\n",
    "              f\"(p-value: {p_value}, cohen-d:{cohen_d})\")\n",
    "\n",
    "    return p_value, cohen_d\n",
    "\n",
    "def notnormal_unpaired_sigtest(A, B):\n",
    "    test_result = pg.mwu(A, B)\n",
    "    p_value = test_result[\"p-val\"].values\n",
    "\n",
    "    if p_value>0.05:\n",
    "        print(f\"no significant difference (p-value: {p_value})\")\n",
    "    else:\n",
    "        print(f\"here is a significant difference \"\n",
    "              f\"(p-value: {p_value})\")\n",
    "\n",
    "    return p_value\n",
    "\n",
    "\n",
    "# %% PLOTS\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(\"measurments\")\n",
    "plt.title(\"Bar-plot of group averages\")\n",
    "\n",
    "plt.tight_layout\n",
    "plt.show()\n",
    "\n",
    "# VIOLIN-PLOTS:\n",
    "def my_violin_AB_plot(A, B, A_label=\"A\", B_label=\"B\",\n",
    "                     title=\"\"):\n",
    "    \"\"\" This is Group A vs Group B Violin plot function.\n",
    "        Written by me on 2021, April\n",
    "        \n",
    "        A     Group A sample data\n",
    "        B     Group B sample data\n",
    "        A_label....\n",
    "    \"\"\"\n",
    "    fig=plt.figure(3, figsize=(5,6))\n",
    "    fig.clf()\n",
    "\n",
    "    x_ticks_A = np.ones(len(A))\n",
    "    x_ticks_B = np.ones(len(B))\n",
    "\n",
    "    plt.violinplot([A, B], showmedians=True)\n",
    "    plt.boxplot([A, B])\n",
    "    plt.xticks([1,2], labels=[A_label, B_label])\n",
    "    plt.xlabel(\"Groups\")\n",
    "    plt.ylabel(\"measurments\")\n",
    "    plt.title(title)\n",
    "    plt.tight_layout\n",
    "    # plt.ylim(-40, 40)\n",
    "    \n",
    "    # some add-on commands (remove some borders):\n",
    "    axis = plt.gca()\n",
    "    axis.spines['right'].set_visible(False)\n",
    "    axis.spines['top'].set_visible(False)\n",
    "    axis.spines['bottom'].set_visible(False)\n",
    "    axis.spines['left'].set_visible(False)\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "\"\"\" OLD:\n",
    "    fig=plt.figure(3, figsize=(5,6))\n",
    "    fig.clf()\n",
    "\n",
    "    x_ticks_A = np.ones(len(Group_A))\n",
    "    x_ticks_B = np.ones(len(Group_B))\n",
    "\n",
    "    plt.violinplot([Group_A, Group_B], showmedians=True)\n",
    "    plt.xticks([1,2], labels=[\"A\", \"B\"])\n",
    "    plt.xlabel(\"Groups\")\n",
    "    plt.ylabel(\"measurments\")\n",
    "    plt.title(\"Violin plot\")\n",
    "    plt.tight_layout\n",
    "    # plt.ylim(-40, 40)\n",
    "    plt.show()\n",
    "\"\"\"\n",
    "\n",
    "# Group A vs B:\n",
    "my_violin_AB_plot(A=Group_A, B=Group_B, A_label=\"A\", B_label=\"B\",\n",
    "                     title=\"Group A vs. B\")\n",
    "pvalue_1, cohen_d_1 = normal_unpaired_sigtest(Group_A, Group_B)\n",
    "\n",
    "# Group A vs B2:\n",
    "my_violin_AB_plot(A=Group_A, B=Group_B2, A_label=\"A\", \n",
    "                     B_label=\"B 2\", title=\"Group A vs. B2\")\n",
    "pvalue_3, cohen_d_3 = normal_unpaired_sigtest(Group_A, Group_B2)\n",
    "\n",
    "\n",
    "# SIGNIFICANCE TEST: \n",
    "#pvalue_1, cohen_d_1 = normal_unpaired_sigtest(Group_A, Group_B)\n",
    "#pvalue_2 = notnormal_unpaired_sigtest(Group_A, Group_B)\n",
    "\n",
    "#pvalue_3, cohen_d_3 = normal_unpaired_sigtest(Group_A, Group_B2)\n",
    "\n",
    "#print(f\"again p-value1: {pvalue_1} and cohen's 1: {cohen_d_1}\")\n",
    "~~~\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "## Let's apply the analysis to real world data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to import some data, which are stored in _Excel_ files, we use the [_Pandas_](https://www.fabriziomusacchio.com/teaching/python_course/12_pandas) command `pd.read_excel(path_to_file, index_col=0)`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "code_folding": []
   },
   "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>Data</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.074201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.086849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.272670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20.283832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26.357661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>45.342706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>47.139108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.427794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7.846927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>26.665037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Data\n",
       "0  18.074201\n",
       "1  13.086849\n",
       "2   2.272670\n",
       "3  20.283832\n",
       "4  26.357661\n",
       "5  45.342706\n",
       "6  47.139108\n",
       "7   1.427794\n",
       "8   7.846927\n",
       "9  26.665037"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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",
    "\n",
    "\"\"\" file_path is the main root path. \n",
    "    \n",
    "    The definition above is a so-called relative file path -\n",
    "    relative from the folder, that contains the script you \n",
    "    are currently executing.\n",
    "    \n",
    "    A so-called absolute path (and therefore indepenent from\n",
    "    your script's folder) can be defined as follows:\n",
    "    \n",
    "    file_path = \"/Users/Fabrizio/Python/Kurs/Data/Pandas_1/\"\n",
    "    \n",
    "    or\n",
    "    \n",
    "    file_path = \"C:/Users/Fabrizio/Python/Kurs/Data/Pandas_1/\"\n",
    "    \n",
    "    (adjust this to the absolute path on YOUR machine!)\n",
    "\"\"\"\n",
    "\n",
    "file_name_1 = \"Group_A_data.xls\"\n",
    "file_name_2 = \"Group_B_data.xls\"\n",
    "  \n",
    "file_1 = os.path.join(file_path, file_name_1) \n",
    "#  actually it does: file_1 = 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",
    "    \n",
    "    file_1 = file_path + file_name_1\n",
    "    file_2 = file_path + file_name_2\n",
    "    \n",
    "    Never forget to put the requiered '/' at the end of \n",
    "    the file_path defintion::\n",
    "       \n",
    "       file_path = \"Data/Pandas_1/\"  ⟵ okay\n",
    "       file_path = \"Data/Pandas_1\"   ⟵ not okay\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)\n",
    "\n",
    "# some print test:\n",
    "Group_A_df.iloc[0:10] # with the iloc command we can access\n",
    "                      # DataFrame rows by standard indexing and\n",
    "                      # slicing rules. Here, we didn't want to\n",
    "                      # print out all rows of the DataFrame.\n",
    "#print(Group_A_df.iloc[0:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two _Excel_ files are imported as DataFrames into `Group_A_df` and `Group_B_df`, respectively. In the next step, we extract the DataFrame data into two _NumPy_ arrays:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extracting the DataFrame import data:\n",
    "Group_A = Group_A_df[\"Data\"].values\n",
    "Group_B = Group_B_df[\"Data\"].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A few Pandas basics\n",
    "As stated in the [introductory course](https://www.fabriziomusacchio.com/teaching/python_course/12_pandas), _Pandas_ can do much more than _Excle_ file reading. The _Pandas_ DataFrame comes - simialar to [_NumPy_](https://www.fabriziomusacchio.com/teaching/python_course/10_numpy#useful-helper-functions-for-array-generation) -  equipped with a bunch of useful built-in function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of Group A data: (50, 1)\n",
      "shape of Group B data: (50, 1)\n"
     ]
    }
   ],
   "source": [
    "print(f\"shape of Group A data: {Group_A_df.shape}\")\n",
    "print(f\"shape of Group B data: {Group_B_df.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the data of our two groups is one-dimensional, and"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "keys (columns) in Group A data: Index(['Data'], dtype='object')\n",
      "keys (columns) in Group B data: Index(['Data'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(f\"keys (columns) in Group A data: {Group_A_df.keys()}\")\n",
    "print(f\"keys (columns) in Group B data: {Group_B_df.keys()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the main (and only - take a look at the Excel files-) column's key-name is \"_Data_\".\n",
    " \n",
    " We can do some further first examination of our data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first 5 rows in Group A data:         Data\n",
      "0  18.074201\n",
      "1  13.086849\n",
      "2   2.272670\n",
      "3  20.283832\n",
      "4  26.357661\n"
     ]
    }
   ],
   "source": [
    "print(f\"first 5 rows in Group A data: {Group_A_df.head(5)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "last 5 rows in Group A data:          Data\n",
      "45  24.808727\n",
      "46   9.782053\n",
      "47   2.438080\n",
      "48  31.641609\n",
      "49  24.247804\n"
     ]
    }
   ],
   "source": [
    "print(f\"last 5 rows in Group A data: {Group_A_df.tail(5)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average of Group A data: [17.47999084] +/- [14.40494573]\n",
      "average of Group B data: [14.43276578] +/- [11.85600989]\n"
     ]
    }
   ],
   "source": [
    "print(f\"average of Group A data: {Group_A_df.mean().values}\",\n",
    "      f\"+/- {Group_A_df.std().values}\",)\n",
    "print(f\"average of Group B data: {Group_B_df.mean().values}\",\n",
    "      f\"+/- {Group_B_df.std().values}\",)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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>Data</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>17.479991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.404946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.922640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.307007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13.226441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>26.161001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>60.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Data\n",
       "count  50.000000\n",
       "mean   17.479991\n",
       "std    14.404946\n",
       "min     0.922640\n",
       "25%     7.307007\n",
       "50%    13.226441\n",
       "75%    26.161001\n",
       "max    60.000000"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# general descriptive statistis of your dataframe:\n",
    "Group_A_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 3\n",
    "\n",
    "1. Copy your script from the _Pingouin_ Exercise 2. \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 by the following expression:\n",
    "    ```python\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "code_folding": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no significant difference (p-value: [0.25092508])\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a significant difference (p-value: [0.02157014], cohen-d:[0.46704775])\n"
     ]
    }
   ],
   "source": [
    "# Your solution 3 here (full script):\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary><strong>Toggle full script solution</strong></summary>\n",
    "\n",
    "~~~python\n",
    "# Solution 3 (Full script):\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_B_data.xls\"\n",
    "file_name_3 = \"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",
    "file_3 = os.path.join(file_path, file_name_3)\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",
    "Group_B2_df= pd.read_excel(file_3, index_col=0)\n",
    "\n",
    "# Broadcast the DataFrame data into the appropriate variables:\n",
    "Group_A = Group_A_df[\"Data\"].values\n",
    "Group_B = Group_B_df[\"Data\"].values\n",
    "Group_B2= Group_B2_df[\"Data\"].values\n",
    "\n",
    "\n",
    "\"\"\" The following code is simply your copied solution from the \n",
    "    Pingouin section:\n",
    "\"\"\"\n",
    "\n",
    "# %% FUNCTION DEFINITIONS\n",
    "def normal_unpaired_sigtest(A, B):\n",
    "    test_result = pg.ttest(A, B, paired=False)\n",
    "    p_value = test_result[\"p-val\"].values\n",
    "    cohen_d = test_result[\"cohen-d\"].values\n",
    "\n",
    "    if p_value>0.05:\n",
    "        print(f\"no significant difference (p-value: {p_value})\")\n",
    "    else:\n",
    "        print(f\"here is a significant difference \"\n",
    "              f\"(p-value: {p_value}, cohen-d:{cohen_d})\")\n",
    "\n",
    "    return p_value, cohen_d\n",
    "\n",
    "def notnormal_unpaired_sigtest(A, B):\n",
    "    test_result = pg.mwu(A, B)\n",
    "    p_value = test_result[\"p-val\"].values\n",
    "\n",
    "    if p_value>0.05:\n",
    "        print(f\"no significant difference (p-value: {p_value})\")\n",
    "    else:\n",
    "        print(f\"here is a significant difference \"\n",
    "              f\"(p-value: {p_value})\")\n",
    "\n",
    "    return p_value\n",
    "\n",
    "# VIOLIN-PLOTS:\n",
    "def my_violin_AB_plot(A, B, A_label=\"A\", B_label=\"B\",\n",
    "                     title=\"\"):\n",
    "    \"\"\" This is Group A vs Group B Violin plot function.\n",
    "        Written by me on 2021, April\n",
    "        \n",
    "        A     Group A sample data\n",
    "        B     Group B sample data\n",
    "        A_label....\n",
    "    \"\"\"\n",
    "    fig=plt.figure(3, figsize=(5,6))\n",
    "    fig.clf()\n",
    "\n",
    "    x_ticks_A = np.ones(len(A))\n",
    "    x_ticks_B = np.ones(len(B))\n",
    "\n",
    "    plt.violinplot([A, B], showmedians=True)\n",
    "    plt.boxplot([A, B])\n",
    "    \n",
    "    plt.xticks([1,2], labels=[A_label, B_label], fontsize=14,\n",
    "              fontweight=\"bold\")\n",
    "    plt.xlabel(\"Groups\")\n",
    "    plt.ylabel(\"measurments\")\n",
    "    plt.title(title, fontweight=\"bold\")\n",
    "    plt.tight_layout\n",
    "    # plt.ylim(-40, 40)\n",
    "    \n",
    "    # some add-on commands (remove some borders):\n",
    "    axis = plt.gca()\n",
    "    axis.spines['right'].set_visible(False)\n",
    "    axis.spines['top'].set_visible(False)\n",
    "    axis.spines['bottom'].set_visible(False)\n",
    "    axis.spines['left'].set_visible(False)\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "# BAR-PLOT:\n",
    "def my_bar_AB_plot(A, B, A_label=\"A\", B_label=\"B\", title=\"\",\n",
    "                  plotfilename=\"plot.pdf\"):\n",
    "    fig=plt.figure(1, figsize=(3,5))\n",
    "    fig.clf()\n",
    "\n",
    "    plt.bar([1, 2], [A.mean(), B.mean()], alpha=0.25)\n",
    "\n",
    "    plt.xticks([1,2], labels=[A_label, B_label])\n",
    "    plt.xlabel(\"Groups\")\n",
    "    plt.ylabel(\"measurments\")\n",
    "    #plt.title(\"Bar-plot of group averages\")\n",
    "    plt.title(title, fontweight=\"bold\")\n",
    "    \n",
    "    # some add-on commands (remove some borders):\n",
    "    axis = plt.gca()\n",
    "    axis.spines['right'].set_visible(False)\n",
    "    axis.spines['top'].set_visible(False)\n",
    "    axis.spines['bottom'].set_visible(False)\n",
    "    axis.spines['left'].set_visible(False)\n",
    "\n",
    "    plt.tight_layout\n",
    "    plt.show()\n",
    "    # eg here the plot-save commands:\n",
    "    plt.savefig(plotfilename)# please wait for check up.\n",
    "\n",
    "\n",
    "\n",
    "# %% STATISTICAL ANALYSIS AND PLOT:\n",
    "\"\"\" Please note, that the followin 3 commands per group \n",
    "    comparison has become the MAIN PART OF OUR ANALYSIS\n",
    "    script, i.e., only execute these three commands to \n",
    "    get your plots and significance test. How the sign-\n",
    "    ficance test is calculated and how the plots are \n",
    "    generated is done in our function, which we don't have \n",
    "    to touch, change and care about anymore.\n",
    "\"\"\"\n",
    "\n",
    "# Group A vs B:\n",
    "my_bar_AB_plot(A=Group_A, B=Group_B, A_label=\"A\", B_label=\"B\", \n",
    "               title=\"Group A vs B averages\",\n",
    "              plotfilename=\"bar_plot_AB.pdf\")\n",
    "my_violin_AB_plot(A=Group_A, B=Group_B, A_label=\"A\", B_label=\"B\",\n",
    "                     title=\"Group A vs. B\")\n",
    "pvalue_1, cohen_d_1 = normal_unpaired_sigtest(Group_A, Group_B)\n",
    "\n",
    "\n",
    "# Group A vs B2:\n",
    "my_bar_AB_plot(A=Group_A, B=Group_B2, A_label=\"A\", B_label=\"B2\", \n",
    "               title=\"Group A vs B2 averages\",\n",
    "              plotfilename=\"bar_plot_AB2.pdf\")\n",
    "my_violin_AB_plot(A=Group_A, B=Group_B2, A_label=\"A\", \n",
    "                  B_label=\"B2\", title=\"Group A vs B2\")\n",
    "pvalue_3, cohen_d_3 = normal_unpaired_sigtest(Group_A, Group_B2)\n",
    "~~~\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 4: Extended real world data example\n",
    "In the second part of our statistics tutorial we expand our script, so that it can analyse _Excel_ tables with more than just one column. Also, we work with `.xlsx` files instead of `.xls` files. Unfortuantely, I just discovered that `xlrd` has explicitly removed the support for anything other than `.xls` files (due to \"_potential security vulnerabilities_\", [source](https://stackoverflow.com/questions/65250207/pandas-cannot-open-an-excel-xlsx-file)). To overcome this problem, please install the \n",
    "\n",
    "* `openpyxl`\n",
    "\n",
    "package. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Copy the _Excel_ file reading part from your previous script (or copy it from \"Solution 3 (Full script)\") into a new script.\n",
    "2. Change the main data path to `Data/Pandas_3/` and adjust the file names to the Group A and Group B data in that folder.\n",
    "3. In order to be able to read `.xlsx` files, add the argument `engine='openpyxl` to the _Pandas_ read command, e.g.\n",
    "    ```python\n",
    "    pd.read_excel(file_1, index_col=0, engine='openpyxl')\n",
    "    ```\n",
    "4. Similar to dictionaries, print out all column names (\"`keys`\") of the read _Excel_ files, e.g.:\n",
    "    ```python\n",
    "    for key in Group_A_df.keys():\n",
    "        print(f\"{key}\")\n",
    "    ```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "code_folding": [],
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mouse 1\n",
      "Mouse 2\n",
      "Mouse 3\n",
      "Mouse 4\n",
      "Mouse 5\n",
      "Mouse 6\n",
      "Mouse 7\n",
      "Mouse 8\n",
      "Mouse 9\n",
      "Mouse 10\n"
     ]
    }
   ],
   "source": [
    "# Your solution 4.1-4.4 here:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary><strong>Toggle solution</strong></summary>\n",
    "\n",
    "~~~python\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_3/\"\n",
    "#file_name_1 = \"Group_A_data_2.xlsx\"\n",
    "#file_name_2 = \"Group_B_data_2.xlsx\"\n",
    "file_name_1 = \"Group_A_data.xlsx\"\n",
    "file_name_2 = \"Group_B_data.xlsx\"\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, engine='openpyxl')\n",
    "Group_B_df = pd.read_excel(file_2, index_col=0, engine='openpyxl')\n",
    "\n",
    "for column in Group_A_df.keys():\n",
    "    print(f\"{column}\")\n",
    "~~~\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Mouse 1', 'Mouse 2', 'Mouse 3', 'Mouse 4', 'Mouse 5', 'Mouse 6',\n",
       "       'Mouse 7', 'Mouse 8', 'Mouse 9', 'Mouse 10'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Group_A_df.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>Mouse 10</th>\n",
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       "      <th>0</th>\n",
       "      <td>18.074201</td>\n",
       "      <td>23.066936</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>20.711322</td>\n",
       "      <td>60.000000</td>\n",
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       "      <td>13.086849</td>\n",
       "      <td>10.323111</td>\n",
       "      <td>19.974086</td>\n",
       "      <td>38.248952</td>\n",
       "      <td>25.587832</td>\n",
       "      <td>53.735452</td>\n",
       "      <td>16.635397</td>\n",
       "      <td>16.495615</td>\n",
       "      <td>44.782381</td>\n",
       "      <td>23.109105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.272670</td>\n",
       "      <td>17.602005</td>\n",
       "      <td>22.705996</td>\n",
       "      <td>19.140288</td>\n",
       "      <td>24.739332</td>\n",
       "      <td>12.594575</td>\n",
       "      <td>41.966899</td>\n",
       "      <td>13.633330</td>\n",
       "      <td>25.891547</td>\n",
       "      <td>14.908281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20.283832</td>\n",
       "      <td>28.179423</td>\n",
       "      <td>36.580909</td>\n",
       "      <td>38.064844</td>\n",
       "      <td>29.033290</td>\n",
       "      <td>9.057918</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>20.301198</td>\n",
       "      <td>18.622461</td>\n",
       "      <td>13.755511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26.357661</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>29.650851</td>\n",
       "      <td>20.149923</td>\n",
       "      <td>44.583438</td>\n",
       "      <td>5.217009</td>\n",
       "      <td>48.860477</td>\n",
       "      <td>37.083126</td>\n",
       "      <td>18.306000</td>\n",
       "      <td>9.591119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>45.342706</td>\n",
       "      <td>9.259463</td>\n",
       "      <td>10.029454</td>\n",
       "      <td>35.167710</td>\n",
       "      <td>14.413221</td>\n",
       "      <td>26.552184</td>\n",
       "      <td>3.281541</td>\n",
       "      <td>9.668325</td>\n",
       "      <td>15.764203</td>\n",
       "      <td>19.503650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>47.139108</td>\n",
       "      <td>25.335681</td>\n",
       "      <td>10.954845</td>\n",
       "      <td>19.216845</td>\n",
       "      <td>45.489924</td>\n",
       "      <td>48.642575</td>\n",
       "      <td>26.566834</td>\n",
       "      <td>3.103062</td>\n",
       "      <td>12.845208</td>\n",
       "      <td>34.832844</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Mouse 1    Mouse 2    Mouse 3    Mouse 4    Mouse 5    Mouse 6  \\\n",
       "0  18.074201  23.066936  60.000000  20.711322  60.000000  28.763525   \n",
       "1  13.086849  10.323111  19.974086  38.248952  25.587832  53.735452   \n",
       "2   2.272670  17.602005  22.705996  19.140288  24.739332  12.594575   \n",
       "3  20.283832  28.179423  36.580909  38.064844  29.033290   9.057918   \n",
       "4  26.357661  60.000000  29.650851  20.149923  44.583438   5.217009   \n",
       "5  45.342706   9.259463  10.029454  35.167710  14.413221  26.552184   \n",
       "6  47.139108  25.335681  10.954845  19.216845  45.489924  48.642575   \n",
       "\n",
       "     Mouse 7    Mouse 8    Mouse 9   Mouse 10  \n",
       "0  42.427278  15.598740  13.465411  13.196517  \n",
       "1  16.635397  16.495615  44.782381  23.109105  \n",
       "2  41.966899  13.633330  25.891547  14.908281  \n",
       "3  60.000000  20.301198  18.622461  13.755511  \n",
       "4  48.860477  37.083126  18.306000   9.591119  \n",
       "5   3.281541   9.668325  15.764203  19.503650  \n",
       "6  26.566834   3.103062  12.845208  34.832844  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Group_A_df.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50, 10)\n"
     ]
    }
   ],
   "source": [
    "print(Group_A_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mouse 1</th>\n",
       "      <th>Mouse 2</th>\n",
       "      <th>Mouse 3</th>\n",
       "      <th>Mouse 4</th>\n",
       "      <th>Mouse 5</th>\n",
       "      <th>Mouse 6</th>\n",
       "      <th>Mouse 7</th>\n",
       "      <th>Mouse 8</th>\n",
       "      <th>Mouse 9</th>\n",
       "      <th>Mouse 10</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>17.479991</td>\n",
       "      <td>24.648196</td>\n",
       "      <td>27.360234</td>\n",
       "      <td>26.124974</td>\n",
       "      <td>28.200733</td>\n",
       "      <td>18.042231</td>\n",
       "      <td>21.736532</td>\n",
       "      <td>18.573062</td>\n",
       "      <td>26.391034</td>\n",
       "      <td>24.842926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.404946</td>\n",
       "      <td>14.363733</td>\n",
       "      <td>12.986157</td>\n",
       "      <td>11.570712</td>\n",
       "      <td>11.908364</td>\n",
       "      <td>13.552271</td>\n",
       "      <td>15.680920</td>\n",
       "      <td>13.948133</td>\n",
       "      <td>12.477335</td>\n",
       "      <td>12.394661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.922640</td>\n",
       "      <td>9.259463</td>\n",
       "      <td>9.270425</td>\n",
       "      <td>9.429189</td>\n",
       "      <td>9.777123</td>\n",
       "      <td>0.062906</td>\n",
       "      <td>0.376473</td>\n",
       "      <td>0.472434</td>\n",
       "      <td>9.382286</td>\n",
       "      <td>9.549946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.307007</td>\n",
       "      <td>13.435652</td>\n",
       "      <td>19.699324</td>\n",
       "      <td>18.230936</td>\n",
       "      <td>18.421391</td>\n",
       "      <td>7.620068</td>\n",
       "      <td>7.607388</td>\n",
       "      <td>7.634176</td>\n",
       "      <td>17.837596</td>\n",
       "      <td>14.267775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13.226441</td>\n",
       "      <td>19.900726</td>\n",
       "      <td>24.955239</td>\n",
       "      <td>24.172694</td>\n",
       "      <td>26.158784</td>\n",
       "      <td>14.903794</td>\n",
       "      <td>18.857441</td>\n",
       "      <td>16.047178</td>\n",
       "      <td>23.196840</td>\n",
       "      <td>23.064810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>26.161001</td>\n",
       "      <td>28.072406</td>\n",
       "      <td>33.454855</td>\n",
       "      <td>32.736302</td>\n",
       "      <td>34.073416</td>\n",
       "      <td>27.297384</td>\n",
       "      <td>35.213392</td>\n",
       "      <td>26.562242</td>\n",
       "      <td>34.536572</td>\n",
       "      <td>31.782738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>53.735452</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>56.025684</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>60.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Mouse 1    Mouse 2    Mouse 3    Mouse 4    Mouse 5    Mouse 6  \\\n",
       "count  50.000000  50.000000  50.000000  50.000000  50.000000  50.000000   \n",
       "mean   17.479991  24.648196  27.360234  26.124974  28.200733  18.042231   \n",
       "std    14.404946  14.363733  12.986157  11.570712  11.908364  13.552271   \n",
       "min     0.922640   9.259463   9.270425   9.429189   9.777123   0.062906   \n",
       "25%     7.307007  13.435652  19.699324  18.230936  18.421391   7.620068   \n",
       "50%    13.226441  19.900726  24.955239  24.172694  26.158784  14.903794   \n",
       "75%    26.161001  28.072406  33.454855  32.736302  34.073416  27.297384   \n",
       "max    60.000000  60.000000  60.000000  60.000000  60.000000  53.735452   \n",
       "\n",
       "         Mouse 7    Mouse 8    Mouse 9   Mouse 10  \n",
       "count  50.000000  50.000000  50.000000  50.000000  \n",
       "mean   21.736532  18.573062  26.391034  24.842926  \n",
       "std    15.680920  13.948133  12.477335  12.394661  \n",
       "min     0.376473   0.472434   9.382286   9.549946  \n",
       "25%     7.607388   7.634176  17.837596  14.267775  \n",
       "50%    18.857441  16.047178  23.196840  23.064810  \n",
       "75%    35.213392  26.562242  34.536572  31.782738  \n",
       "max    60.000000  56.025684  60.000000  60.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print(Group_A_df.describe())\n",
    "Group_A_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 4 continued**:\n",
    "\n",
    "5. Calculate and print the median of each column of each data table. Hint: Take a look, how we calculated the mean with the built-in _Pandas_ mean function shown in Section 5.\n",
    "6. Save the calculated medians into the variables `Group_A` and `Group_B`.\n",
    "7. Copy the remaining plot and analysis part from your previous script/\"Solution 3 (Full script)\" into your  script and run your pipeline.\n",
    "8. Re-run your script with the Group A2 and Group B2 data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "code_folding": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Group A medians: [13.22644106 19.90072561 24.95523869 24.17269392 26.15878378 14.9037943\n",
      " 18.85744129 16.04717778 23.19683969 23.06480993]\n",
      "Group B medians: [23.59495388 11.61376136 18.68395653 20.8270858  21.82640825 14.64554514\n",
      " 20.27448066 17.02615332 11.41902818 17.77334372]\n"
     ]
    }
   ],
   "source": [
    "# Your solution 4.5-4.6 here:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary><strong>Toggle solution</strong></summary>\n",
    "\n",
    "~~~python\n",
    "# Solution 4.5-4.6:\n",
    "Group_A = Group_A_df.median().values\n",
    "Group_B = Group_B_df.median().values\n",
    "print(f\"Group A medians: {Group_A}\")\n",
    "print(f\"Group B medians: {Group_B}\")\n",
    "~~~\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print(f\"{type(Group_A_df)}\")\n",
    "print(f\"{type(Group_A)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "(50, 10)\n"
     ]
    }
   ],
   "source": [
    "dummy = Group_A_df.values\n",
    "print(f\"{type(dummy)}\")\n",
    "print(f\"{dummy.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>28.763525</td>\n",
       "      <td>42.427278</td>\n",
       "      <td>15.598740</td>\n",
       "      <td>13.465411</td>\n",
       "      <td>13.196517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.086849</td>\n",
       "      <td>10.323111</td>\n",
       "      <td>19.974086</td>\n",
       "      <td>38.248952</td>\n",
       "      <td>25.587832</td>\n",
       "      <td>53.735452</td>\n",
       "      <td>16.635397</td>\n",
       "      <td>16.495615</td>\n",
       "      <td>44.782381</td>\n",
       "      <td>23.109105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.272670</td>\n",
       "      <td>17.602005</td>\n",
       "      <td>22.705996</td>\n",
       "      <td>19.140288</td>\n",
       "      <td>24.739332</td>\n",
       "      <td>12.594575</td>\n",
       "      <td>41.966899</td>\n",
       "      <td>13.633330</td>\n",
       "      <td>25.891547</td>\n",
       "      <td>14.908281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20.283832</td>\n",
       "      <td>28.179423</td>\n",
       "      <td>36.580909</td>\n",
       "      <td>38.064844</td>\n",
       "      <td>29.033290</td>\n",
       "      <td>9.057918</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>20.301198</td>\n",
       "      <td>18.622461</td>\n",
       "      <td>13.755511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26.357661</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>29.650851</td>\n",
       "      <td>20.149923</td>\n",
       "      <td>44.583438</td>\n",
       "      <td>5.217009</td>\n",
       "      <td>48.860477</td>\n",
       "      <td>37.083126</td>\n",
       "      <td>18.306000</td>\n",
       "      <td>9.591119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>45.342706</td>\n",
       "      <td>9.259463</td>\n",
       "      <td>10.029454</td>\n",
       "      <td>35.167710</td>\n",
       "      <td>14.413221</td>\n",
       "      <td>26.552184</td>\n",
       "      <td>3.281541</td>\n",
       "      <td>9.668325</td>\n",
       "      <td>15.764203</td>\n",
       "      <td>19.503650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>47.139108</td>\n",
       "      <td>25.335681</td>\n",
       "      <td>10.954845</td>\n",
       "      <td>19.216845</td>\n",
       "      <td>45.489924</td>\n",
       "      <td>48.642575</td>\n",
       "      <td>26.566834</td>\n",
       "      <td>3.103062</td>\n",
       "      <td>12.845208</td>\n",
       "      <td>34.832844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.427794</td>\n",
       "      <td>12.321790</td>\n",
       "      <td>31.902779</td>\n",
       "      <td>18.709343</td>\n",
       "      <td>22.324181</td>\n",
       "      <td>13.551915</td>\n",
       "      <td>20.291572</td>\n",
       "      <td>28.838343</td>\n",
       "      <td>41.150187</td>\n",
       "      <td>42.268679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7.846927</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>26.200162</td>\n",
       "      <td>26.720212</td>\n",
       "      <td>19.633351</td>\n",
       "      <td>31.817556</td>\n",
       "      <td>33.593050</td>\n",
       "      <td>2.118014</td>\n",
       "      <td>40.808585</td>\n",
       "      <td>24.551126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>26.665037</td>\n",
       "      <td>14.995325</td>\n",
       "      <td>20.649107</td>\n",
       "      <td>25.755907</td>\n",
       "      <td>18.867057</td>\n",
       "      <td>10.498226</td>\n",
       "      <td>2.170260</td>\n",
       "      <td>3.044227</td>\n",
       "      <td>13.793600</td>\n",
       "      <td>14.054273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.922640</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>11.326179</td>\n",
       "      <td>23.560684</td>\n",
       "      <td>22.050686</td>\n",
       "      <td>0.172303</td>\n",
       "      <td>32.466781</td>\n",
       "      <td>25.394094</td>\n",
       "      <td>23.179101</td>\n",
       "      <td>26.588131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.326630</td>\n",
       "      <td>27.751354</td>\n",
       "      <td>21.395401</td>\n",
       "      <td>19.939066</td>\n",
       "      <td>23.044234</td>\n",
       "      <td>28.377354</td>\n",
       "      <td>41.063390</td>\n",
       "      <td>2.535108</td>\n",
       "      <td>47.836518</td>\n",
       "      <td>50.371135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>9.157503</td>\n",
       "      <td>37.444383</td>\n",
       "      <td>22.488169</td>\n",
       "      <td>27.579036</td>\n",
       "      <td>33.046569</td>\n",
       "      <td>30.581410</td>\n",
       "      <td>24.489401</td>\n",
       "      <td>11.408426</td>\n",
       "      <td>12.707566</td>\n",
       "      <td>23.685907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>29.647979</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>10.276356</td>\n",
       "      <td>18.532377</td>\n",
       "      <td>50.371736</td>\n",
       "      <td>14.600049</td>\n",
       "      <td>6.179677</td>\n",
       "      <td>0.599478</td>\n",
       "      <td>9.884708</td>\n",
       "      <td>31.209116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>8.742000</td>\n",
       "      <td>13.402487</td>\n",
       "      <td>13.829782</td>\n",
       "      <td>40.112201</td>\n",
       "      <td>33.829378</td>\n",
       "      <td>15.207540</td>\n",
       "      <td>7.091670</td>\n",
       "      <td>32.604269</td>\n",
       "      <td>30.323622</td>\n",
       "      <td>31.973945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>25.571023</td>\n",
       "      <td>18.113789</td>\n",
       "      <td>30.915942</td>\n",
       "      <td>26.935311</td>\n",
       "      <td>45.771603</td>\n",
       "      <td>15.868325</td>\n",
       "      <td>9.154542</td>\n",
       "      <td>26.951625</td>\n",
       "      <td>24.522718</td>\n",
       "      <td>14.945380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>10.862005</td>\n",
       "      <td>43.721057</td>\n",
       "      <td>54.936913</td>\n",
       "      <td>25.452780</td>\n",
       "      <td>34.154762</td>\n",
       "      <td>4.038185</td>\n",
       "      <td>13.411006</td>\n",
       "      <td>41.784804</td>\n",
       "      <td>20.798726</td>\n",
       "      <td>9.617624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>4.951999</td>\n",
       "      <td>11.777482</td>\n",
       "      <td>19.827426</td>\n",
       "      <td>12.798417</td>\n",
       "      <td>34.864567</td>\n",
       "      <td>1.881381</td>\n",
       "      <td>50.341528</td>\n",
       "      <td>24.640953</td>\n",
       "      <td>13.600837</td>\n",
       "      <td>24.316330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>12.602611</td>\n",
       "      <td>44.989141</td>\n",
       "      <td>31.756518</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>50.767570</td>\n",
       "      <td>20.788953</td>\n",
       "      <td>10.415866</td>\n",
       "      <td>5.427315</td>\n",
       "      <td>32.450598</td>\n",
       "      <td>38.791586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>11.634356</td>\n",
       "      <td>18.588259</td>\n",
       "      <td>24.700600</td>\n",
       "      <td>17.620996</td>\n",
       "      <td>43.425558</td>\n",
       "      <td>45.023374</td>\n",
       "      <td>21.967371</td>\n",
       "      <td>9.176215</td>\n",
       "      <td>27.008075</td>\n",
       "      <td>24.106791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>34.598150</td>\n",
       "      <td>23.195795</td>\n",
       "      <td>26.490087</td>\n",
       "      <td>12.446582</td>\n",
       "      <td>30.696737</td>\n",
       "      <td>5.961492</td>\n",
       "      <td>2.909811</td>\n",
       "      <td>0.472434</td>\n",
       "      <td>39.430237</td>\n",
       "      <td>44.301112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>8.010439</td>\n",
       "      <td>17.133937</td>\n",
       "      <td>21.597454</td>\n",
       "      <td>17.322758</td>\n",
       "      <td>12.003452</td>\n",
       "      <td>13.956856</td>\n",
       "      <td>17.423311</td>\n",
       "      <td>4.319208</td>\n",
       "      <td>49.054840</td>\n",
       "      <td>47.313228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>14.213581</td>\n",
       "      <td>13.013907</td>\n",
       "      <td>42.684656</td>\n",
       "      <td>52.144749</td>\n",
       "      <td>43.825369</td>\n",
       "      <td>20.861080</td>\n",
       "      <td>39.967672</td>\n",
       "      <td>16.577972</td>\n",
       "      <td>13.891236</td>\n",
       "      <td>35.827849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>17.283408</td>\n",
       "      <td>45.924542</td>\n",
       "      <td>51.593317</td>\n",
       "      <td>55.568849</td>\n",
       "      <td>33.648052</td>\n",
       "      <td>15.822137</td>\n",
       "      <td>35.904421</td>\n",
       "      <td>8.848024</td>\n",
       "      <td>24.704299</td>\n",
       "      <td>11.604016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>7.231297</td>\n",
       "      <td>18.351918</td>\n",
       "      <td>15.818481</td>\n",
       "      <td>28.129001</td>\n",
       "      <td>31.764006</td>\n",
       "      <td>7.846267</td>\n",
       "      <td>22.262767</td>\n",
       "      <td>12.155190</td>\n",
       "      <td>25.912714</td>\n",
       "      <td>27.630061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>37.431148</td>\n",
       "      <td>14.271629</td>\n",
       "      <td>10.372286</td>\n",
       "      <td>18.130455</td>\n",
       "      <td>9.777123</td>\n",
       "      <td>43.152748</td>\n",
       "      <td>3.728966</td>\n",
       "      <td>49.090144</td>\n",
       "      <td>18.187796</td>\n",
       "      <td>51.745546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>27.090419</td>\n",
       "      <td>28.996761</td>\n",
       "      <td>14.138737</td>\n",
       "      <td>44.961747</td>\n",
       "      <td>15.467501</td>\n",
       "      <td>2.651519</td>\n",
       "      <td>18.647221</td>\n",
       "      <td>19.375649</td>\n",
       "      <td>19.259184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>15.263485</td>\n",
       "      <td>24.453000</td>\n",
       "      <td>39.158395</td>\n",
       "      <td>29.291996</td>\n",
       "      <td>24.467756</td>\n",
       "      <td>28.393238</td>\n",
       "      <td>30.909583</td>\n",
       "      <td>15.293277</td>\n",
       "      <td>35.231897</td>\n",
       "      <td>26.813921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>19.726142</td>\n",
       "      <td>19.591767</td>\n",
       "      <td>17.227544</td>\n",
       "      <td>15.210792</td>\n",
       "      <td>31.066936</td>\n",
       "      <td>7.544668</td>\n",
       "      <td>17.086464</td>\n",
       "      <td>16.578254</td>\n",
       "      <td>21.923898</td>\n",
       "      <td>18.220372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>53.398856</td>\n",
       "      <td>35.515243</td>\n",
       "      <td>24.882107</td>\n",
       "      <td>23.198237</td>\n",
       "      <td>16.201861</td>\n",
       "      <td>11.142045</td>\n",
       "      <td>0.376473</td>\n",
       "      <td>3.806705</td>\n",
       "      <td>37.843522</td>\n",
       "      <td>21.460346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1.999801</td>\n",
       "      <td>41.338417</td>\n",
       "      <td>19.234954</td>\n",
       "      <td>34.182026</td>\n",
       "      <td>15.984169</td>\n",
       "      <td>20.502174</td>\n",
       "      <td>37.557506</td>\n",
       "      <td>10.702520</td>\n",
       "      <td>18.995861</td>\n",
       "      <td>11.977590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>23.165529</td>\n",
       "      <td>9.897616</td>\n",
       "      <td>10.463554</td>\n",
       "      <td>9.429189</td>\n",
       "      <td>15.821055</td>\n",
       "      <td>14.059610</td>\n",
       "      <td>5.548009</td>\n",
       "      <td>30.102911</td>\n",
       "      <td>18.080977</td>\n",
       "      <td>15.059878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>17.817273</td>\n",
       "      <td>26.074054</td>\n",
       "      <td>26.262574</td>\n",
       "      <td>31.639008</td>\n",
       "      <td>13.894720</td>\n",
       "      <td>49.942595</td>\n",
       "      <td>0.544130</td>\n",
       "      <td>7.229560</td>\n",
       "      <td>27.405940</td>\n",
       "      <td>18.695652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>26.506697</td>\n",
       "      <td>12.475491</td>\n",
       "      <td>28.759075</td>\n",
       "      <td>26.614726</td>\n",
       "      <td>29.884389</td>\n",
       "      <td>6.496984</td>\n",
       "      <td>13.228164</td>\n",
       "      <td>29.737218</td>\n",
       "      <td>19.913229</td>\n",
       "      <td>17.977265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1.204476</td>\n",
       "      <td>37.155684</td>\n",
       "      <td>9.270425</td>\n",
       "      <td>18.976416</td>\n",
       "      <td>34.428537</td>\n",
       "      <td>22.663872</td>\n",
       "      <td>12.654010</td>\n",
       "      <td>5.705055</td>\n",
       "      <td>17.756469</td>\n",
       "      <td>36.962189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>7.534135</td>\n",
       "      <td>11.614547</td>\n",
       "      <td>30.238235</td>\n",
       "      <td>47.987283</td>\n",
       "      <td>20.302559</td>\n",
       "      <td>3.249226</td>\n",
       "      <td>17.072194</td>\n",
       "      <td>22.333842</td>\n",
       "      <td>9.382286</td>\n",
       "      <td>28.667410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>10.269124</td>\n",
       "      <td>25.800587</td>\n",
       "      <td>49.434714</td>\n",
       "      <td>39.438373</td>\n",
       "      <td>32.977586</td>\n",
       "      <td>5.673104</td>\n",
       "      <td>1.781834</td>\n",
       "      <td>48.147802</td>\n",
       "      <td>49.861266</td>\n",
       "      <td>9.549946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1.679551</td>\n",
       "      <td>22.546229</td>\n",
       "      <td>25.028371</td>\n",
       "      <td>34.567647</td>\n",
       "      <td>15.254913</td>\n",
       "      <td>18.676291</td>\n",
       "      <td>39.989288</td>\n",
       "      <td>14.035692</td>\n",
       "      <td>26.532216</td>\n",
       "      <td>22.792065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>34.870874</td>\n",
       "      <td>12.062923</td>\n",
       "      <td>33.691626</td>\n",
       "      <td>19.715068</td>\n",
       "      <td>18.272835</td>\n",
       "      <td>27.545784</td>\n",
       "      <td>1.809349</td>\n",
       "      <td>46.898462</td>\n",
       "      <td>18.139465</td>\n",
       "      <td>11.661021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>3.548040</td>\n",
       "      <td>20.209684</td>\n",
       "      <td>19.656624</td>\n",
       "      <td>31.864163</td>\n",
       "      <td>25.997314</td>\n",
       "      <td>0.553830</td>\n",
       "      <td>35.753506</td>\n",
       "      <td>3.232649</td>\n",
       "      <td>17.635220</td>\n",
       "      <td>11.559999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>13.366033</td>\n",
       "      <td>13.707039</td>\n",
       "      <td>34.500235</td>\n",
       "      <td>23.116394</td>\n",
       "      <td>27.482170</td>\n",
       "      <td>31.895751</td>\n",
       "      <td>12.781879</td>\n",
       "      <td>18.189214</td>\n",
       "      <td>17.681920</td>\n",
       "      <td>33.061807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>3.950274</td>\n",
       "      <td>10.492658</td>\n",
       "      <td>33.823570</td>\n",
       "      <td>15.996040</td>\n",
       "      <td>19.019034</td>\n",
       "      <td>4.585402</td>\n",
       "      <td>22.200923</td>\n",
       "      <td>56.025684</td>\n",
       "      <td>40.288950</td>\n",
       "      <td>12.298093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>4.664843</td>\n",
       "      <td>32.356748</td>\n",
       "      <td>35.535390</td>\n",
       "      <td>14.709471</td>\n",
       "      <td>17.504142</td>\n",
       "      <td>22.546489</td>\n",
       "      <td>27.582803</td>\n",
       "      <td>14.589289</td>\n",
       "      <td>48.318855</td>\n",
       "      <td>22.106622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>8.136822</td>\n",
       "      <td>13.535150</td>\n",
       "      <td>21.903188</td>\n",
       "      <td>10.697075</td>\n",
       "      <td>10.426118</td>\n",
       "      <td>28.748127</td>\n",
       "      <td>16.332092</td>\n",
       "      <td>33.934607</td>\n",
       "      <td>26.286119</td>\n",
       "      <td>60.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>28.295757</td>\n",
       "      <td>22.036282</td>\n",
       "      <td>48.424268</td>\n",
       "      <td>24.784703</td>\n",
       "      <td>41.437238</td>\n",
       "      <td>0.062906</td>\n",
       "      <td>40.008105</td>\n",
       "      <td>18.073233</td>\n",
       "      <td>23.214578</td>\n",
       "      <td>39.247507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>24.808727</td>\n",
       "      <td>11.418740</td>\n",
       "      <td>24.051138</td>\n",
       "      <td>33.027015</td>\n",
       "      <td>24.838067</td>\n",
       "      <td>17.460130</td>\n",
       "      <td>12.595379</td>\n",
       "      <td>31.639951</td>\n",
       "      <td>10.456252</td>\n",
       "      <td>10.361826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>9.782053</td>\n",
       "      <td>12.168356</td>\n",
       "      <td>32.744544</td>\n",
       "      <td>14.103582</td>\n",
       "      <td>32.015211</td>\n",
       "      <td>8.679609</td>\n",
       "      <td>25.330024</td>\n",
       "      <td>21.230605</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>23.020514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2.438080</td>\n",
       "      <td>17.025026</td>\n",
       "      <td>14.555416</td>\n",
       "      <td>25.361097</td>\n",
       "      <td>15.838776</td>\n",
       "      <td>12.936283</td>\n",
       "      <td>5.739870</td>\n",
       "      <td>4.977422</td>\n",
       "      <td>29.575528</td>\n",
       "      <td>25.947061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>31.641609</td>\n",
       "      <td>18.787834</td>\n",
       "      <td>21.511244</td>\n",
       "      <td>14.716543</td>\n",
       "      <td>26.320254</td>\n",
       "      <td>12.441082</td>\n",
       "      <td>43.123952</td>\n",
       "      <td>14.849948</td>\n",
       "      <td>44.978574</td>\n",
       "      <td>13.966244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>24.247804</td>\n",
       "      <td>26.292905</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>35.324496</td>\n",
       "      <td>17.926226</td>\n",
       "      <td>6.274465</td>\n",
       "      <td>29.598034</td>\n",
       "      <td>20.822737</td>\n",
       "      <td>21.749836</td>\n",
       "      <td>27.680942</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            0          1          2          3          4          5  \\\n",
       "0   18.074201  23.066936  60.000000  20.711322  60.000000  28.763525   \n",
       "1   13.086849  10.323111  19.974086  38.248952  25.587832  53.735452   \n",
       "2    2.272670  17.602005  22.705996  19.140288  24.739332  12.594575   \n",
       "3   20.283832  28.179423  36.580909  38.064844  29.033290   9.057918   \n",
       "4   26.357661  60.000000  29.650851  20.149923  44.583438   5.217009   \n",
       "5   45.342706   9.259463  10.029454  35.167710  14.413221  26.552184   \n",
       "6   47.139108  25.335681  10.954845  19.216845  45.489924  48.642575   \n",
       "7    1.427794  12.321790  31.902779  18.709343  22.324181  13.551915   \n",
       "8    7.846927  60.000000  26.200162  26.720212  19.633351  31.817556   \n",
       "9   26.665037  14.995325  20.649107  25.755907  18.867057  10.498226   \n",
       "10   0.922640  60.000000  11.326179  23.560684  22.050686   0.172303   \n",
       "11   4.326630  27.751354  21.395401  19.939066  23.044234  28.377354   \n",
       "12   9.157503  37.444383  22.488169  27.579036  33.046569  30.581410   \n",
       "13  29.647979  60.000000  10.276356  18.532377  50.371736  14.600049   \n",
       "14   8.742000  13.402487  13.829782  40.112201  33.829378  15.207540   \n",
       "15  25.571023  18.113789  30.915942  26.935311  45.771603  15.868325   \n",
       "16  10.862005  43.721057  54.936913  25.452780  34.154762   4.038185   \n",
       "17   4.951999  11.777482  19.827426  12.798417  34.864567   1.881381   \n",
       "18  12.602611  44.989141  31.756518  60.000000  50.767570  20.788953   \n",
       "19  11.634356  18.588259  24.700600  17.620996  43.425558  45.023374   \n",
       "20  34.598150  23.195795  26.490087  12.446582  30.696737   5.961492   \n",
       "21   8.010439  17.133937  21.597454  17.322758  12.003452  13.956856   \n",
       "22  14.213581  13.013907  42.684656  52.144749  43.825369  20.861080   \n",
       "23  17.283408  45.924542  51.593317  55.568849  33.648052  15.822137   \n",
       "24   7.231297  18.351918  15.818481  28.129001  31.764006   7.846267   \n",
       "25  37.431148  14.271629  10.372286  18.130455   9.777123  43.152748   \n",
       "26  60.000000  27.090419  28.996761  14.138737  44.961747  15.467501   \n",
       "27  15.263485  24.453000  39.158395  29.291996  24.467756  28.393238   \n",
       "28  19.726142  19.591767  17.227544  15.210792  31.066936   7.544668   \n",
       "29  53.398856  35.515243  24.882107  23.198237  16.201861  11.142045   \n",
       "30   1.999801  41.338417  19.234954  34.182026  15.984169  20.502174   \n",
       "31  23.165529   9.897616  10.463554   9.429189  15.821055  14.059610   \n",
       "32  17.817273  26.074054  26.262574  31.639008  13.894720  49.942595   \n",
       "33  26.506697  12.475491  28.759075  26.614726  29.884389   6.496984   \n",
       "34   1.204476  37.155684   9.270425  18.976416  34.428537  22.663872   \n",
       "35   7.534135  11.614547  30.238235  47.987283  20.302559   3.249226   \n",
       "36  10.269124  25.800587  49.434714  39.438373  32.977586   5.673104   \n",
       "37   1.679551  22.546229  25.028371  34.567647  15.254913  18.676291   \n",
       "38  34.870874  12.062923  33.691626  19.715068  18.272835  27.545784   \n",
       "39   3.548040  20.209684  19.656624  31.864163  25.997314   0.553830   \n",
       "40  13.366033  13.707039  34.500235  23.116394  27.482170  31.895751   \n",
       "41   3.950274  10.492658  33.823570  15.996040  19.019034   4.585402   \n",
       "42   4.664843  32.356748  35.535390  14.709471  17.504142  22.546489   \n",
       "43   8.136822  13.535150  21.903188  10.697075  10.426118  28.748127   \n",
       "44  28.295757  22.036282  48.424268  24.784703  41.437238   0.062906   \n",
       "45  24.808727  11.418740  24.051138  33.027015  24.838067  17.460130   \n",
       "46   9.782053  12.168356  32.744544  14.103582  32.015211   8.679609   \n",
       "47   2.438080  17.025026  14.555416  25.361097  15.838776  12.936283   \n",
       "48  31.641609  18.787834  21.511244  14.716543  26.320254  12.441082   \n",
       "49  24.247804  26.292905  60.000000  35.324496  17.926226   6.274465   \n",
       "\n",
       "            6          7          8          9  \n",
       "0   42.427278  15.598740  13.465411  13.196517  \n",
       "1   16.635397  16.495615  44.782381  23.109105  \n",
       "2   41.966899  13.633330  25.891547  14.908281  \n",
       "3   60.000000  20.301198  18.622461  13.755511  \n",
       "4   48.860477  37.083126  18.306000   9.591119  \n",
       "5    3.281541   9.668325  15.764203  19.503650  \n",
       "6   26.566834   3.103062  12.845208  34.832844  \n",
       "7   20.291572  28.838343  41.150187  42.268679  \n",
       "8   33.593050   2.118014  40.808585  24.551126  \n",
       "9    2.170260   3.044227  13.793600  14.054273  \n",
       "10  32.466781  25.394094  23.179101  26.588131  \n",
       "11  41.063390   2.535108  47.836518  50.371135  \n",
       "12  24.489401  11.408426  12.707566  23.685907  \n",
       "13   6.179677   0.599478   9.884708  31.209116  \n",
       "14   7.091670  32.604269  30.323622  31.973945  \n",
       "15   9.154542  26.951625  24.522718  14.945380  \n",
       "16  13.411006  41.784804  20.798726   9.617624  \n",
       "17  50.341528  24.640953  13.600837  24.316330  \n",
       "18  10.415866   5.427315  32.450598  38.791586  \n",
       "19  21.967371   9.176215  27.008075  24.106791  \n",
       "20   2.909811   0.472434  39.430237  44.301112  \n",
       "21  17.423311   4.319208  49.054840  47.313228  \n",
       "22  39.967672  16.577972  13.891236  35.827849  \n",
       "23  35.904421   8.848024  24.704299  11.604016  \n",
       "24  22.262767  12.155190  25.912714  27.630061  \n",
       "25   3.728966  49.090144  18.187796  51.745546  \n",
       "26   2.651519  18.647221  19.375649  19.259184  \n",
       "27  30.909583  15.293277  35.231897  26.813921  \n",
       "28  17.086464  16.578254  21.923898  18.220372  \n",
       "29   0.376473   3.806705  37.843522  21.460346  \n",
       "30  37.557506  10.702520  18.995861  11.977590  \n",
       "31   5.548009  30.102911  18.080977  15.059878  \n",
       "32   0.544130   7.229560  27.405940  18.695652  \n",
       "33  13.228164  29.737218  19.913229  17.977265  \n",
       "34  12.654010   5.705055  17.756469  36.962189  \n",
       "35  17.072194  22.333842   9.382286  28.667410  \n",
       "36   1.781834  48.147802  49.861266   9.549946  \n",
       "37  39.989288  14.035692  26.532216  22.792065  \n",
       "38   1.809349  46.898462  18.139465  11.661021  \n",
       "39  35.753506   3.232649  17.635220  11.559999  \n",
       "40  12.781879  18.189214  17.681920  33.061807  \n",
       "41  22.200923  56.025684  40.288950  12.298093  \n",
       "42  27.582803  14.589289  48.318855  22.106622  \n",
       "43  16.332092  33.934607  26.286119  60.000000  \n",
       "44  40.008105  18.073233  23.214578  39.247507  \n",
       "45  12.595379  31.639951  10.456252  10.361826  \n",
       "46  25.330024  21.230605  60.000000  23.020514  \n",
       "47   5.739870   4.977422  29.575528  25.947061  \n",
       "48  43.123952  14.849948  44.978574  13.966244  \n",
       "49  29.598034  20.822737  21.749836  27.680942  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the other way around: create a new dataframe from a given\n",
    "# NumPy array:\n",
    "df2 = pd.DataFrame(data=dummy)\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.to_csv(\"Data/outcsv_df2.csv\")\n",
    "# there is also an Excel export. But I have to look it up (one\n",
    "# needs an \"ExcelWriter\" module)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "code_folding": [],
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no significant difference (p-value: [0.1858791])\n"
     ]
    }
   ],
   "source": [
    "# Your solution 4.7-4.8 here:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary><strong>Toggle solution</strong></summary>\n",
    "\n",
    "~~~python\n",
    "# Solution 4.7-4.8:\n",
    "\n",
    "# %% FUNCTION DEFINITIONS\n",
    "def normal_unpaired_sigtest(A, B):\n",
    "    test_result = pg.ttest(A, B, paired=False)\n",
    "    p_value = test_result[\"p-val\"].values\n",
    "    cohen_d = test_result[\"cohen-d\"].values\n",
    "\n",
    "    if p_value>0.05:\n",
    "        print(f\"no significant difference (p-value: {p_value})\")\n",
    "    else:\n",
    "        print(f\"here is a significant difference \"\n",
    "              f\"(p-value: {p_value}, cohen-d:{cohen_d})\")\n",
    "\n",
    "    return p_value, cohen_d\n",
    "\n",
    "def notnormal_unpaired_sigtest(A, B):\n",
    "    test_result = pg.mwu(A, B)\n",
    "    p_value = test_result[\"p-val\"].values\n",
    "\n",
    "    if p_value>0.05:\n",
    "        print(f\"no significant difference (p-value: {p_value})\")\n",
    "    else:\n",
    "        print(f\"here is a significant difference \"\n",
    "              f\"(p-value: {p_value})\")\n",
    "\n",
    "    return p_value\n",
    "\n",
    "# VIOLIN-PLOTS:\n",
    "def my_violin_AB_plot(A, B, A_label=\"A\", B_label=\"B\",\n",
    "                     title=\"\"):\n",
    "    \"\"\" This is Group A vs Group B Violin plot function.\n",
    "        Written by me on 2021, April\n",
    "        \n",
    "        A     Group A sample data\n",
    "        B     Group B sample data\n",
    "        A_label....\n",
    "    \"\"\"\n",
    "    fig=plt.figure(3, figsize=(5,6))\n",
    "    fig.clf()\n",
    "\n",
    "    x_ticks_A = np.ones(len(A))\n",
    "    x_ticks_B = np.ones(len(B))\n",
    "\n",
    "    plt.violinplot([A, B], showmedians=True)\n",
    "    plt.boxplot([A, B])\n",
    "    \n",
    "    plt.xticks([1,2], labels=[A_label, B_label], fontsize=14,\n",
    "              fontweight=\"bold\")\n",
    "    plt.xlabel(\"Groups\")\n",
    "    plt.ylabel(\"measurments\")\n",
    "    plt.title(title, fontweight=\"bold\")\n",
    "    plt.tight_layout\n",
    "    # plt.ylim(-40, 40)\n",
    "    \n",
    "    # some add-on commands (remove some borders):\n",
    "    axis = plt.gca()\n",
    "    axis.spines['right'].set_visible(False)\n",
    "    axis.spines['top'].set_visible(False)\n",
    "    axis.spines['bottom'].set_visible(False)\n",
    "    axis.spines['left'].set_visible(False)\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "# BAR-PLOT:\n",
    "def my_bar_AB_plot(A, B, A_label=\"A\", B_label=\"B\", title=\"\"):\n",
    "    fig=plt.figure(1, figsize=(3,5))\n",
    "    fig.clf()\n",
    "\n",
    "    plt.bar([1, 2], [A.mean(), B.mean()], alpha=0.25)\n",
    "\n",
    "    plt.xticks([1,2], labels=[A_label, B_label])\n",
    "    plt.xlabel(\"Groups\")\n",
    "    plt.ylabel(\"measurments\")\n",
    "    #plt.title(\"Bar-plot of group averages\")\n",
    "    plt.title(title, fontweight=\"bold\")\n",
    "    \n",
    "    # some add-on commands (remove some borders):\n",
    "    axis = plt.gca()\n",
    "    axis.spines['right'].set_visible(False)\n",
    "    axis.spines['top'].set_visible(False)\n",
    "    axis.spines['bottom'].set_visible(False)\n",
    "    axis.spines['left'].set_visible(False)\n",
    "\n",
    "    plt.tight_layout\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "\n",
    "# %% STATISTICAL ANALYSIS AND PLOT:\n",
    "\"\"\" Please note, that the followin 3 commands per group \n",
    "    comparison has become the MAIN PART OF OUR ANALYSIS\n",
    "    script, i.e., only execute these three commands to \n",
    "    get your plots and significance test. How the sign-\n",
    "    ficance test is calculated and how the plots are \n",
    "    generated is done in our function, which we don't have \n",
    "    to touch, change and care about anymore.\n",
    "\"\"\"\n",
    "\n",
    "# Group A vs B:\n",
    "my_bar_AB_plot(A=Group_A, B=Group_B, A_label=\"A\", B_label=\"B\", \n",
    "               title=\"Group A vs B averages\")\n",
    "my_violin_AB_plot(A=Group_A, B=Group_B, A_label=\"A\", B_label=\"B\",\n",
    "                     title=\"Group A vs. B\")\n",
    "pvalue_1, cohen_d_1 = normal_unpaired_sigtest(Group_A, Group_B)\n",
    "~~~\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outlook\n",
    "We can also easily and quickly plot all data columns of an _Excel_ file via:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc7184ceb20>,\n",
       " <matplotlib.lines.Line2D at 0x7fc7184ced90>,\n",
       " <matplotlib.lines.Line2D at 0x7fc70b57c850>,\n",
       " <matplotlib.lines.Line2D at 0x7fc7295b6cd0>,\n",
       " <matplotlib.lines.Line2D at 0x7fc7295b6df0>,\n",
       " <matplotlib.lines.Line2D at 0x7fc7295b6f10>,\n",
       " <matplotlib.lines.Line2D at 0x7fc738893070>,\n",
       " <matplotlib.lines.Line2D at 0x7fc738893190>,\n",
       " <matplotlib.lines.Line2D at 0x7fc7388932b0>,\n",
       " <matplotlib.lines.Line2D at 0x7fc7388933d0>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Matplotlib:\n",
    "plt.plot(Group_A_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# or use the Pandas built-in plot function:\n",
    "Group_A_df.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also, we can easily plot the _Grand_ average over all columns via:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fc738950e20>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Group_A_df.plot()\n",
    "Group_A_df.plot(xlabel=\"time\", ylabel=\"voltage\")\n",
    "#Group_A_df.mean(axis=1).plot()\n",
    "Group_A_df.mean(axis=1).plot(lw=5, c=\"k\", label=\"average\")\n",
    "plt.legend(loc=\"best\",bbox_to_anchor=(1.05, 1), title=\"Legend:\")"
   ]
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Statistical data analysis with Pandas and Pingouin (extended)",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}