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{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "reverse-batch",
   "metadata": {},
   "source": [
    "3) [P] Create a dataframe named df_uniform that contains 2000 observations. It should have two variables,\n",
    "named x and y. For each observation, x should be generated from a uniform distribution between 10 and 90,\n",
    "and y should be generated from a uniform between 20 and 80. Show the head() of the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "under-howard",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import seaborn as sb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "pediatric-thompson",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35.268031</td>\n",
       "      <td>51.828337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49.321667</td>\n",
       "      <td>49.541725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>67.598721</td>\n",
       "      <td>64.768751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24.621642</td>\n",
       "      <td>47.319462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32.858934</td>\n",
       "      <td>62.514663</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           x          y\n",
       "0  35.268031  51.828337\n",
       "1  49.321667  49.541725\n",
       "2  67.598721  64.768751\n",
       "3  24.621642  47.319462\n",
       "4  32.858934  62.514663"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = pd.Series(np.random.uniform(10,90,2000),name=\"x\")\n",
    "y = pd.Series(np.random.uniform(20,80,2000),name=\"y\")\n",
    "df_uniform = pd.DataFrame()\n",
    "df_uniform['x'] = x\n",
    "df_uniform['y'] = y\n",
    "df_uniform.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "featured-albert",
   "metadata": {},
   "source": [
    "8) [P] Generate a single figure that contains two axes that are adjacent to each other. You should have:\n",
    "a. at least one shared axis\n",
    "b. appropriate axis labels\n",
    "c. make the range of the axis on both plots the same\n",
    "d. display a legend on each to be sure both are labeled correctly as \"normal\" or \"uniform\"\n",
    "e. One title at the top"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "colonial-background",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f1fb3bfc5b0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 100, 100)\n",
    "\n",
    "figure, (ax1, ax2) = plt.subplots(2,1, sharex=True)\n",
    "ax2.set(xlabel='x-label',ylabel='y-label')\n",
    "ax1.set(ylabel='y-label')\n",
    "ax1.set_xlim(0,100)\n",
    "ax1.set_ylim(0,100)\n",
    "ax2.set_ylim(0,100)\n",
    "ax1.plot(x,x,color='m')\n",
    "ax2.plot(x,x**2,color = 'g')\n",
    "ax1.legend(['linear'])\n",
    "ax2.legend(['quadratic'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "portuguese-entry",
   "metadata": {},
   "source": [
    "6) [P] Generate a data frame called df_normal with 2000 observations, two variables names x and y again. This\n",
    "time, x should be generated from a normal distribution with mean 50 and standard deviation 15, and y with\n",
    "mean 50 and standard deviation 5. Again, show the head() of df_normal.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "direct-hughes",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>62.068896</td>\n",
       "      <td>42.972707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50.942869</td>\n",
       "      <td>51.197962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>46.761095</td>\n",
       "      <td>45.211459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>66.816502</td>\n",
       "      <td>53.627801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>48.196453</td>\n",
       "      <td>58.904883</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           x          y\n",
       "0  62.068896  42.972707\n",
       "1  50.942869  51.197962\n",
       "2  46.761095  45.211459\n",
       "3  66.816502  53.627801\n",
       "4  48.196453  58.904883"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = pd.Series(np.random.normal(50,15,2000),name=\"x\")\n",
    "y = pd.Series(np.random.normal(50,5,2000),name=\"y\")\n",
    "df_normal = pd.DataFrame()\n",
    "df_normal = pd.DataFrame()\n",
    "df_normal['x'] = x\n",
    "df_normal['y'] = y\n",
    "df_normal.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "civil-second",
   "metadata": {},
   "source": [
    "7) [P] Repeat your scatterplot above with df_normal . Use a different color point, and title your plot\n",
    "accordingly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "wanted-serial",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.scatter(x,y,s=5, c='m')\n",
    "plt.title(\"Scatter of normally distributed data\")\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")\n",
    "plt.xlim(0,100)\n",
    "plt.ylim(0,100)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "composed-characteristic",
   "metadata": {},
   "source": [
    "11) [P] Using pandas, generate a histogram of both the x and y variables for df_uniform. Use 30 bins, and set\n",
    "the range of both variables to be 0 – 100. Repeat this exercise on df_normal. (HINT:Use the hist()\n",
    "method of DataFrame.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "demographic-crash",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#df_uniform.hist(bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "sharp-cement",
   "metadata": {},
   "outputs": [],
   "source": [
    "#hist = df_normal.hist(bins=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "reliable-crown",
   "metadata": {},
   "source": [
    "12) [M] What is a quantile?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mathematical-profession",
   "metadata": {},
   "source": [
    "A quantile is the dataset divided into equal sized parts\n",
    "\n",
    "A quartile is a dataset divided into 4 equal quantiles.\n",
    "A percentile is a dataset divided into 100 equal quantiles.\n",
    "Inter-Quantile Range is the used to find the range of where the majority of values lie. To calculate this it is the Lower Quartile(Q1 = 25th percentile) subtracted from the Upper Quantile (Q3 = 75th percentile) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "defined-fifteen",
   "metadata": {},
   "source": [
    "16) [P] Write a function called IQR_outlier_limits that takes a dataframe as input, and computes the\n",
    "minimum and maximum outlier thresholds for each variable (column) in the dataframe. The result that is\n",
    "returned will be stored in a data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "physical-fundamental",
   "metadata": {},
   "outputs": [],
   "source": [
    "def IQR_outlier_limits(df):\n",
    "    Q1_x = df['x'].quantile(0.25)\n",
    "    Q3_x = df['x'].quantile(0.75)\n",
    "    IQR_x = Q3_x - Q1_x\n",
    "    \n",
    "    Q1_y = df['y'].quantile(0.25)\n",
    "    Q3_y = df['y'].quantile(0.75)\n",
    "    IQR_y = Q3_y - Q1_y\n",
    "    \n",
    "    mins = pd.Series([Q1_x-1.5*IQR_x, Q1_y-1.5*IQR_y], name='min_out',index=['x','y'])\n",
    "    maxs = pd.Series([Q3_x+1.5*IQR_x, Q3_y+1.5*IQR_y], name='max_out',index=['x','y'])\n",
    "\n",
    "    return pd.DataFrame([mins,maxs])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "republican-transparency",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min_out</th>\n",
       "      <td>11.305127</td>\n",
       "      <td>36.967823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_out</th>\n",
       "      <td>88.495416</td>\n",
       "      <td>63.153637</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 x          y\n",
       "min_out  11.305127  36.967823\n",
       "max_out  88.495416  63.153637"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_n_out = IQR_outlier_limits(df_normal)\n",
    "IQR_outlier_limits(df_normal)\n",
    "#df_n_out.loc['min_out']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "indie-vegetable",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min_out</th>\n",
       "      <td>-30.986834</td>\n",
       "      <td>-9.452148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_out</th>\n",
       "      <td>132.067035</td>\n",
       "      <td>109.338994</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  x           y\n",
       "min_out  -30.986834   -9.452148\n",
       "max_out  132.067035  109.338994"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_u_out = IQR_outlier_limits(df_uniform)\n",
    "IQR_outlier_limits(df_uniform)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "similar-coaching",
   "metadata": {},
   "source": [
    "18) [P] Use your function you defined above to print a subset of each dataframe that contains the outliers for\n",
    "df_normal and df_uniform. (HINT: You should get at many outliers for df_normal. However, your\n",
    "uniform data should not have outliers.). Print the number of outliers found for each dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "informative-cricket",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "duplicate-oxford",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "round-connecticut",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "encouraging-hierarchy",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "resistant-cigarette",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "naughty-vampire",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "south-disclosure",
   "metadata": {},
   "outputs": [],
   "source": [
    "normal_outlier_count = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "hearing-jesus",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normal x_min outliers\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "148      3.064909\n",
       "657      5.849486\n",
       "803      9.456794\n",
       "1094     5.940942\n",
       "1100    -0.254572\n",
       "1161    -5.007323\n",
       "1297     8.764273\n",
       "1396    10.419453\n",
       "1681     6.997233\n",
       "1877     5.718502\n",
       "Name: x, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Normal x_min outliers\")\n",
    "normal_outlier_count += df_normal['x'].where( df_normal['x'] < df_n_out['x'].at['min_out']).dropna().count()\n",
    "df_normal['x'].where( df_normal['x'] < df_n_out['x'].at['min_out']).dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "going-school",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normal x_max outliers\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "11       89.383861\n",
       "26       90.424452\n",
       "110      89.635527\n",
       "151      93.899935\n",
       "368     100.898475\n",
       "430      94.952343\n",
       "718      94.864568\n",
       "764      90.917368\n",
       "780      91.111968\n",
       "957      89.660245\n",
       "1199     90.232032\n",
       "1305     93.034806\n",
       "1311     89.692370\n",
       "1359    100.892818\n",
       "1371     94.901893\n",
       "1554     98.793578\n",
       "1799     90.456719\n",
       "Name: x, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Normal x_max outliers\")\n",
    "normal_outlier_count += df_normal['x'].where( df_normal['x'] > df_n_out['x'].at['max_out']).dropna().count()\n",
    "df_normal['x'].where( df_normal['x'] > df_n_out['x'].at['max_out']).dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "controlling-sauce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normal y_min outliers\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "193     36.640499\n",
       "279     35.553302\n",
       "343     36.861076\n",
       "350     35.776726\n",
       "562     35.416950\n",
       "861     33.810787\n",
       "878     34.370286\n",
       "1184    36.113551\n",
       "1720    36.699337\n",
       "Name: y, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Normal y_min outliers\")\n",
    "normal_outlier_count += df_normal['y'].where( df_normal['y'] < df_n_out['y'].at['min_out']).dropna().count()\n",
    "df_normal['y'].where( df_normal['y'] < df_n_out['y'].at['min_out']).dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "capital-aaron",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normal y_max outliers\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "342     64.066916\n",
       "383     63.526821\n",
       "403     63.272509\n",
       "447     63.199411\n",
       "478     63.357331\n",
       "991     63.516516\n",
       "1332    64.981928\n",
       "1599    64.160841\n",
       "1673    66.286915\n",
       "1900    66.097458\n",
       "Name: y, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Normal y_max outliers\")\n",
    "normal_outlier_count += df_normal['y'].where( df_normal['y'] > df_n_out['y'].at['max_out']).dropna().count()\n",
    "df_normal['y'].where( df_normal['y'] > df_n_out['y'].at['max_out']).dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "interracial-independence",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "46\n"
     ]
    }
   ],
   "source": [
    "print(normal_outlier_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "agricultural-shelf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normal x_min outliers\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "-30.986834379745922"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Normal x_min outliers\")\n",
    "df_uniform['x'].where( df_uniform['x'] < df_u_out['x'].at['min_out']).dropna()\n",
    "df_u_out['x'].at['min_out']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "editorial-space",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normal x_max outliers\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Series([], Name: x, dtype: float64)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Normal x_max outliers\")\n",
    "df_uniform['x'].where( df_uniform['x'] > df_u_out['x'].at['max_out']).dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "reserved-pleasure",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normal y_min outliers\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Series([], Name: y, dtype: float64)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Normal y_min outliers\")\n",
    "df_uniform['y'].where( df_uniform['y'] < df_u_out['y'].at['min_out']).dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "executive-castle",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normal y_max outliers\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Series([], Name: y, dtype: float64)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Normal y_max outliers\")\n",
    "df_uniform['y'].where( df_uniform['y'] > df_u_out['y'].at['max_out']).dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "indirect-suffering",
   "metadata": {},
   "source": [
    "30) [P] Generally, seaborn works best when all of your data to be plotted is in one dataframe. So, create a new\n",
    "dataframe that contains both df_uniform and df_normal, with a new categorical variable (hint = set it to\n",
    "pd.Categorical!) called \"type\", with levels \"uniform\" and \"normal\" respectively.\n",
    "Show the output of value_counts() on the \"type\" variable. It should look like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "hydraulic-upper",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "normal     2000\n",
       "uniform    2000\n",
       "Name: type, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined = pd.DataFrame()\n",
    "df_uniform['type'] = 'uniform'\n",
    "df_normal['type'] = 'normal'\n",
    "combined = combined.append(df_uniform)\n",
    "combined = combined.append(df_normal)\n",
    "\n",
    "combined['type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bottom-strengthening",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f1fb32629a0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sb.FacetGrid(combined, col='type')\n",
    "g.map(sb.scatterplot, \"x\", \"y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "auburn-fossil",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='x', ylabel='Count'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JhOM8c3B2BvoL3ZxOSRWRRmNMT+7uO4FdudsPAw+IyJ1kB5qXA5vnMjb1qp6RKLd940VcDhvfvf3K85rdspBsvOIqAO68664pz/H5fNzxkY/QNxrj94cGWFrr5eKWc6t8mh4NsK6pgkOBMEcGxgjFU7RXebBdQCkPj9PBinofe3pGuXpp9alz9MXG0wcCbOsIsrTWy01rG3DM4Wyx+WJJjY9L2/y8fCL777Co2mt1SPNaPqekfg94HVAjIp3A3wCvE5ENZLuGjgF/CmCM2S0iDwJ7gBTwcZ15ZI1YMs3HvvsS4ViKh/58U1F0GfX09gKw6ebbpjznuYe/SzyV5tFdvXhdDt6wuv68pmk6HTZWN5TRMZwtFjgWT7G8zkdpyfnPiFrf6mdvb4i9PSE2tPoBGB5LUPfe/59tHUE2tPq5ZnkNtvkyrdQCVy+p5ujAGE/u6+e2qxZZHc68ls/ZR5NN+v7GNOd/CfhSvuJRZ2eM4Qs/2cX2zhH+87bL8rb4aqF6cl8/o7Ek77605YLexEWEtioPZaUODgfG2NU9yuJqz6RTJ2eivryUxopStnUEWd9SgdQs4W1f/R2lLWt5w+o61jad+14OhcZht3Hdyjp+/EoXLx0ftjqcea342pJqSg9sPsFDL3fyF9cvv6CVsoVIKls40BfmqiXVNPnds/KclR4n65rKKXXYOBQY42B/GHGe33Ovb/EzEk3y6K5e7G/4FCLQ+8D/0oQwQWuVh2V1PrYeH8bm8VsdzrylSWGBaG1bhIhc0Fdr29TN5iOBMF/8xR6uWV7DJ69fPodXNv/FU2mkcQ3NfjeXL6o8+wPOQWmJnbVN5bRWuglGk/g23IT/mtuIJWfee2qMydZ/Ag72hzFHnufRv7yWRM+BWY21EGxaWk3aGCqufo/VocxbWvtogejsOHHBq0qnWsWZTGf41A+24XLY+fJ71l/QwGehMcZwJDAGZHdAy0e5BxHJ1WtysnXbTio2vY9v/v4YKxp8rG4op7GidNLfG0mk2NcbYk/3KINjCTxOO5FEmvTeX+Nz/cOsx1kIKj1O1jSWs2vDmxmLp+asEuxCov8iiq89dYjtnSN87f2X6tTT0wRCcUZjKUzvXircq/P6u0pL7EQPvpAtI/L5b7OvJ8SurlFK7NnS0n5PCf43/Cm/2NE9HhdAQ3kpb1xTT0ulm28+dwzb0qvzGudCly0REmRbR5DXLJu98uOFQpNCkTsSCPO1pw5x8/om3nJxo9XhnLs8FmuLJ9McH4pQXuqgq3PftFNWI5HIrP3eZOAYN61tILEyw5FAmN7c5jMdQ1HcSzYyNJagvryUdc0ultR4qZ5QwqG92suRxVfqQq1p+D1OIvufY0fJtVzeXjXt4sNipEmhmEzyBlr3nr/D1byKr3309fz7+4PWxHUhZqFY25133UXnI3efcfzYUPaNfkmNl86MmXbK6hMP3HNBMUzG6bCxqrGcVY2vzgL7zFvfyad/sW3Kx6xpLOfowBjPHNQSMNMZ3fITvKv+gH29o1q65TSaFIrJaW+gh/rD/HJnD9cur+GSn7w446cphgqTwUiCYCRJa6Ub1wVMP51ri2u8mFiIH27ttDqUeS3RvZ/aMhc7uka4qLli/pQGnwe03VSkUukMzxwMUO1zsl4/KZ0iYwzHhyK4HLa8biCUD3abkDm2JbuTmlvXmUzn4uYKBsMJekZiVocyr2hSKFI7u0YIxVK8dnmtzjY6Tf9onFgyw6Jqz4JcBZw58jzJtMG75rVWhzKvragvw2ET9vWGrA5lXtGkUISS6Qxbjg3TUukuijIW5yKZztAZjFLhLsG/UAvHBbtZ11yOd931VkcyrzkdNpbUejnYF5rVkuYLnSaFIrS9M0g0mebqJdVWhzLvdA1HyWQMi6o8C7qf+T2XteJqWDZr1VgL1cqGMmKpDMeHxqwOZd7QpFBkEqkMLx0fZlG1Z9bKNRSKeCpDf24Xs9ncstMKN69vwqST7OvVPYqns6jKS6nDxn7tQhqnSaHI7OgKEktmuEpbCWfoCUYBaFpgg8uTqfQ6iR55mQN9YYzRrpGp2G3C8voyjgTGSKR0bQdoUiguNjvbO0ZoqXTToCuXT2H3VdMfilNT5lpQU1CnM7b3acLxFF25ZKcmt7K+jFTGcGQgbHUo84ImhSLiWXE14XiKS3I199Wryq98FwDNBdBKOCl66EVK7KJdI2fR5C/F53JwsE+TAmhSKCrlG99BhbuExTW689QpHC7KNtxEja9wWgkAJhlnSa2Pg/1hnV0zDRFhSY2XE0MRLQ+CJoWi0TMSxdW8ig2t/gU9qyYfpHYJ2Ow0+QunlXDSyvoy4qkMxwd1ds10Ftd4SWUMndrVpkmhWGzvGCETH2NNo65ynSieSiOVbYztefqCdlObr9qqPJSW2Njfp11I02mpdOOwCUcHNHlqUigC8WSaQ4Ew4V1PaUXI0+zuHkXsDkJbf2Z1KHlhtwnL63R2zdk47DbaqjwcHRgr+tla+g5RBA70ZfuUx3Y+YXUo80rGGLZ3BDFjQyT6DlsdTt7o7JqZWVzjJRRLMTiWsDoUS+WtSqqI3A+8Feg3xqzLHfsX4G1AAjgM/IkxJigi7cBe4GQJzxeMMR/LV2zFZk/PKNVeJ8cL+I3vfBwJjDEaS5EZOGp1KKcS27SVaGdSpXbiuNHJ2TX7e0OsatDuw6m0V2cnYBwdGKNmwh4VxSafpbO/BXwV+M6EY08AnzPGpETkn4DPAf8r97PDxpgNeYynKA2NJegdjXHN8hpetjqYeWZbR5CyUgfB0T6rQzmVyfDee56b9EdPPHAPN7x/+s9Lv/7+f+LxeOh85O7xjYGkYRWhmsXc+e//AekEPp+POz7ykdmOfEHzlTqoK3NxdGCMy9urrA7HMnnrPjLGPAMMnXbscWNMKnf3BaAlX79fZe3pGUUk24WgXtUfitEVjObKhhdWH7KZsCHQpptvY9PNt3Hx+ksQsbH0mrez6ebbCIe1K2ky7dVeekdixFNpq0OxjJVjCh8GHp1wf7GIvCIiT4vINVM9SETuEJGtIrI1ENDdpaaTMYZ9vaO0V3t1g/LTbOsIUmIX1jYVR3eKx2nHXWJnIFzc/eVn01rlxkBRrwK3JCmIyBeAFPDfuUM9QJsx5hLg08ADIjLpq9UYc68xZqMxZmNtbe3cBLxA9QRjjMXTrGrQVsJEsWSaA31hVjaUFeQ01MmICNU+J+F4iniyeD8Fn01DeSl2m9A5pElhzojIh8gOQH/A5OZ+GWPixpjB3O2XyA5Cr5jr2ArNwf4QdpuMD6CprH292fr5FzVXWB3KnKr2OgGKfnbNdBx2G00VpXQMR6wOxTJzmhRE5CayA8s3G2MiE47Xiog9d3sJsBw4MpexFRpjDIcCYdqrPbo2YQJjDLu6Rqgrc1FXVngrmKdTWmLH57JrUjiLlkoPA+EE0URxtqjy9m4hIt8DngdWikiniNxOdjZSGfCEiGwTkXtyp18L7BCR7cCPgI8ZY4YmfWI1Iz0j2a6jZXU+q0OZV3pHYwyOJYqulXBStddFJJEGl/5dTKW1KrvPSGeRthbyNvpojLl1ksPfmOLch4CH8hVLMTrUH8YuosXvTrOza4QSu7CiSGdjVfucHB+KIP5mq0OZt+rKSimxCx3DUZYX4d+J9isUoJNdR23VHlyO4hhInYl4Ms3BvjAr68uKtkutxG6jwu1A/E1FX85hKnab0Ox3F21LoThfGQWuLxQnFEtp19Fp9vWGSGUM64q06+ikaq8LcXroHY1ZHcq81VrpYTiSJBxPnf3kAqNJoQAdDYwhoF1Hp9ndPUptmYv6It91rtLrxGTS7NPNd6bUXJkdV+gaLr6pqZoUCtCxwTEaKkpxF8kc/JkIhOIEwnHWaulwHDbBjPZxsE8335lKrc9FiV3oGdGkoBa4sXiK/lCcdm0lnGJvzyg2gRW6kA8AM9JNNJku6vn407HZhPryUnpGiq+LTZNCgTk+lH2RL9YFa+PSGcO+3hCLa7zaejopFMDpsOn+zdNoqnATCMeLbh8KLYhTYI4NjOF12qnxOa0OZV649777CIsHe/vlHNjyNHc+eWZF1EikCD8tmwzL63wc6AuRTGcosevnw9M1+ksxx7JrW9qqPFaHM2c0KRSQTMZwfCjC8jqf7sOcEw6Hqb3yDYzGUlz+uhuwTfLv8sQD90zyyMK3sr6M3d2jHB0YK9p1G9NprMhOSOgJRosqKejHgwLSMxIjkcporaOJ7CUMR5JU+5yTJoRi1lzpxuuy6yykKbgcdqp9TrqLbFxBk0IBOTY4hk1eXaavQCoaMWRnk6hT2URY1VDOscExxopwPv5MNFW46R2JkSmihX6aFArIiaEIDRWluop5AqlswV1ix+PUf5PJrG4owxjY36ethck0VpSSSGcYLKJ9KDQpFIhYMk1/KE5rZfH0fZ7N8FgC8VRS43PqGMsUqn0u6std7O0ZtTqUeanJn211F9N6BU0KBaIzt/KytYgGxM5mb+8oxpii3oR9JlY3lDMQThAIxa0OZd4pL3XgcdqLar2CJoUC0TkcwWETGoq8hMNJxhj29oQgHCja4ncztaKhDJtk9/NWpxLJvqb6iqhOlL5aCkTHUJTmSjd2m3aTAHQMRwnHU5jhTqtDmffcJXYW13jZn9uRTp2qvryU4UiSeKo4Nt3RdQoFYCyeYiiSYE2RbEI/E/t7QzjtNqKjZy5WU9kFe3fedderB8rqsLdfzr9983sQ6gfA5/Nxx0c+Yk2A80h9ebb7sW80XhTrFTQpFICT9WtaK3UqKkAqneFQIMzSWi+7THGVKJgpkzFsuvm28fsZY3jlRJDKNZvGF7I99/B3rQpvXjlZVbevSFY2a/dRAegYiuJy2Kgp0wFVgGODERKpDCu1+N2M2USo8TkJRpKk0ppIJyotseP3lNBbJIPN2lIoAB3DEVoq3UW7YveUbhBAWi9BfNU89N37irOu0Xmq8bnoHY0zOJYo+j0nTtdQXsqJoQjGmIKf3jyjpCAirzHG/P5sx9TcG40lCcVSXNLqtzoUy0zsBkllDC+fGKbO56L95g8WbV2j8+F1ZadfBsJxTQqnqS8vZV9viHA8RVlpidXh5NVMu4/+fYbHxonI/SLSLyK7JhyrEpEnRORg7nvlhJ99TkQOich+EblxhnEVvZ5gtknb7NfxBMguWDMmu0G9Onc1Phdj8TSRRHHMtJmphvFxhcJfyzFtUhCRq0XkfwK1IvLpCV9/C5ytbsC3gJtOO/ZZ4DfGmOXAb3L3EZE1wC3A2txj/kNEtC7BDHQHo5TYRRdo5QyOJXA6bPhc2jN6Pmp8TgQYCBf+m9+5qPE5sQlFsa/12VoKTsBHtpupbMLXKPDu6R5ojHkGGDrt8NuBb+dufxt4x4Tj3zfGxI0xR4FDwBUzu4Ti1j0SpbHCjU3XJ5BMZxiJJqn2almL81Vit+H3lOSSgv4bnuSw26gtc9FXBIPN036cMsY8DTwtIt8yxhyfhd9Xb4zpyT13j4jU5Y43Ay9MOK8zd+wMInIHcAdAW1vbLIS0cMWTaQbCCa5a4rM6lHlhaCxbtEw3GLowNT4Xw5EklNVaHcq8Ul+WHVco9MHmmY4puETkXhF5XESePPk1i3FM9i886dJKY8y9xpiNxpiNtbXF/Ud7sh5LU4WOJwAMhBO5iqjadXQh/J4SHDbBVtlidSjzSm25i0Q6QzCatDqUvJrpq+eHwD3AfcCFjED1iUhjrpXQCPTnjncCrRPOawG6L+D3FIXukSg2gYYKnSkST6YJx1O06AK+C3ZyzUJPqp6oDjiPqy/Lvs76R+NUegq3NTrTlkLKGHO3MWazMealk1/n8fseBj6Uu/0h4GcTjt8iIi4RWQwsBzafx/MXle5gjNoyl+6vS3aAGaDaW7gv1rlUW+ZCbDbdZ2GCKq8Tu03oDxX2uMJM301+LiJ/LiKNuWmlVSJSNd0DROR7wPPAShHpFJHbgX8EbhCRg8ANufsYY3YDDwJ7gMeAjxtj9CPKNFKZDL2jsfF678VucCyBz2WntEQnrc0Gj9OBiQTZ062VU0+y27ItqP4CLzE+0+6jk5/uPzPhmAGWTPUAY8ytU/zo+inO/xLwpRnGU/QCoTjpjNHxBCCSSBFJpFlUBHVp5lK8/wgBj5+SusVnrBqH4iyYV1vm4kBfuKAHm2eUFIwxi/MdiDo33blFa01+HU84uVWiLlibXYnACdztl+K76IZTVo2fVIwF8+rLStnVNcpINIm/QMcVZlrm4o8mO26M+c7shqNmqjsYxe8p0Zk2ZLuOyksdOrYy21IJKr1OUmteS8aYoq2tNVFdruhkfyhesElhpq+iyyd8XQP8LXBznmJSZ2GMoTsY1a4jwNm4gngqoyu686TW58TuqSAYKexpmDNV7XNhEwp6XGGm3Uf/Y+J9EakAiq/tOE8MjSWIpTLadQR417wWEaj0FnaRMqtUuEtIhQYIuEuo0plducFmF/0FXO7ifNvbEbLTRpUFxhetFfnMo4wxeFZdg99dgsOmXUf5ICKM7XqSYDRJIqX7LEC2C6k/FMeYwty6dEavJBH5uYg8nPv6JbCfV9cYqDnWMxKjtMSG313cn447h6M4fFVUa9dRXoV3/hrQInkn1ZWVEk9lGI2lrA4lL2Y6SvnlCbdTwHFjjO6IbpHe0RgN5aUFOyVupvb3hsjEI1S6K89+sjpvqeFufC4HgXCcxgr9u6vL7dlcqF1IM2op5Arj7SNbIbUSSOQzKDW1eCrN0FhivL57sUplsvswRw48pxVi50BtmZNYMsNYXNeUVufKaBfqYPNMu4/eS7bsxHuA9wIvisi0pbNVfpzc5KPY6x0dG8juwzy29xmrQykKVd7srJuAdiHhsNmo9roKNinMtPvoC8Dlxph+ABGpBX4N/ChfganJndw8vNhbCvt7Q7hL7MSObbM6lKLgsAlVXieDYwkWVXmKvnVWV+7icH/Y6jDyYqZTNmwnE0LO4Dk8Vs2i3tEYlZ4SXEVc4yeeSnN0cIyV9WVgdEbMXKnxuUhnDEMR7T2uK3MRS2Wwlxde+f6ZthQeE5FfAd/L3X8f8Eh+QlJTMcbQOxKjvaa4a/wc6g+TzhhWNpRZHUpRKS914HLYCITiRb9YsC5XRtvZsMziSGbf2fZoXiYirzHGfAb4T+BiYD3Z6qf3zkF8aoLRWIpoMl30XUf7ekNUuEuoLy/uN6a5Jrl9FkZjKeJFvmahxufkhtX1JLr3Wx3KrDtbF9BdQAjAGPNjY8ynjTGfIttKuCu/oanTjY8nFPEgcziWonM4yqqGsqKfGmmFky2EwSIfcHbYbaxpKicdPn0b+oXvbEmh3Riz4/SDxpitQHteIlJT6h2N4bAJNd7i/YR8ILfpi3YdWaO0xI7P5WAgrOMKhepsSWG6j6TFXWPBAr0jMerKXUU982NfX4i6MldBb4c439X6nESTaXBXWB2KyoOzJYUtIvLR0w/mdlE7n+041fmyOwiE4kU9njA0liAQirNKWwmWqvI6EQHxN1sdisqDs80++iTwExH5AK8mgY2AE3hnHuNSp3HWLSFtTFGPJ+zrHUWAFfWaFKzksNuo9DgZ9DeRzhjsRdxyLUTTJgVjTB+wSUSuA9blDv/SGPNk3iNTp3A1rgCKd9GaMYb9vSFaqzx4XbqxkNVqfU6GxlwcGxxjaa3P6nDULJrpfgpPAU/lORY1DVfTKrwuO2WlxVkZtWckxmgsxZVLqq0ORZHdZ8Ek4+ztGdWkUGDm/COXiKwEfjDh0BLg/wB+4KNAIHf888YYXSCX42xaUbStBMiWtbDbhKW1XqtDUWTXLJiRLo46XcSSaUqLeIV9oZnzUhXGmP3GmA3GmA3AZWQ37PlJ7sf/evJnmhBeNRiOU1LZVLTjCal0hv19IZbWenE59M1nvjDDnWQM7M9NE1aFwer6RdcDh40xxy2OY17b3hkEoLG8OGcBHw6MEU9lWNukUyDnlViIGp+TfT2aFAqJ1UnhFl6tpwTwCRHZISL3i4junJKz7UQQk0mPb+5RbHb3jFBW6qC1sjiT4ny2uqGc3tEYw2O6mK1QWJYURMQJ3Az8MHfobmApsAHoAb4yxePuEJGtIrI1EAhMdkrBeaUjSDJwjBK71Tl87o1Gk3QMRVnTWK5lLeahlQ1lCLC3d9TqUNQssXJu35uAl3PTXk9OfwVARL4O/GKyBxlj7iVXjG/jxo2FuXP2BJmMYVtHkHj3AeAmq8OZU/fedx9jnkakbjnPPfojnktGrQ5JncbrctBW7WFvT4irl1Rr4i4AViaFW5nQdSQijcaYntzddwK7LIlqngnFU1y9pJr//v42q0OZc+FwGPfyVZQ6bKx+0+Qb/T34yN1zHJU63eqGch7b3UvncJTWquIu614ILOmPEBEPcAPw4wmH/1lEdorIDuA64FNWxDbfVLhLuPePNhLZ/3urQ5l73moSqQy1ZcU5lrJQLK314rTbtAupQFjSUjDGRIDq047dZkUsav6SqlbsNqFKi9/Naw67jeX1Pg70hbhuZaYox74Kif7vqXlpLJ5CKhqp8TmLuirsQrG6oZxk2nCoQPctLiaaFNS8tLNrBBEb9UW8inshafKXUl7q0C6kAqBJQc076YxhZ9cIJtSPW8snLAgiwurGcjqGooRiSavDURdAk4Kadw4HwkQSaTKDutB9ITm5z8W+Xl3hvJBpDWI172zvCFLhLmEo1G91KGoakUiEO++665RjtiVX8/vdIZ796XcA8Pl83PGRj1gQnTpfmhTUvBIIxekeiXHN8hp+u9nqaNR0TMaw6eZTJw32j8Y4Ohjh4htvxedy8NzD37UoOnW+tPtIzSvbOoI4bMKaxnKrQ1Hn4eRWnQPhuNWhqPOkSUHNG+FYin29o6xuLNf6/AvU+Fad4QQZU/BVaAqSJgU1b7x8YhgDXLZIC+QuZLU+J6mMIRjRWUgLkSYFNS9EE2l2do2wsr6MCndxbjlaKCrcJThsol1IC5QmBTUvbOsIksoYNmorYcETEWp8zmxLwa4JfqHRpKAsF0+l2d4ZZGmtl2qfFr8rBDU+FwYQf7PVoahzpElBWW5n1wjxVIaN7VVWh6JmidflwOu0I1VtGB1wXlA0KShLxVNpXj4epK3KQ4PWOSoo9eWlSGkZncO6OdJCoklBWerl40GiyTRXL60++8lqQan2OjGpBDs6R6wORZ0DXdGsLHHvffdh91by4pF+zGg/D9z/yzPOiUQiFkSmZovNJpjhDg6XOLVI3gKiSUFZIhwOU/GaW7HZHFy8bjWll6w745wnHrjHgsjUbDKDxzG1S9nVpSW1FwpNCsoaTi++tddQV+bS1cuFLBmlvdrDru4RsOnbzUKgYwrKEraGlZhknOZKt9WhqDxb3+onkkjjWbnJ6lDUDGhSUHPu+OAYUtHI6IsP6X6+RWBRlQe/p4Tyy9+p01MXAEtekSJyTER2isg2EdmaO1YlIk+IyMHcd13aWoBS6QxP7Q9g4mFGNv/Y6nDUHBARLmurxNW4nA6dnjrvWfkx7TpjzAZjzMbc/c8CvzHGLAd+k7uvCsxLJ4YZiSbJdO+GtM5IKRarGstIhQbZemzI6lDUWcyntvvbgW/nbn8beId1oah8CEYSbDk2zPI6H4QHrA5HzSGHzUZo68/oGI7SNxqzOhw1DauSggEeF5GXROSO3LF6Y0wPQO57nUWxqTwwxvDbAwHsIly7otbqcJQFQtsexemwsfX4sNWhqGlYNUfsNcaYbhGpA54QkX0zfWAuidwB0NbWlq/41Czb0zPK8cEIr11Ri8+lUxOLkUlEubi5gq3HhxmOJKj0OK0OSU3CkpaCMaY7970f+AlwBdAnIo0Aue+T7tpujLnXGLPRGLOxtlY/cS4Eo9EkzxwYoMXvZn1LhdXhKAttaPVjtwlbj2lrYb6a86QgIl4RKTt5G3gjsAt4GPhQ7rQPAT+b69jU7DPG8MSePgBuWFOPiFgckbKS1+XgouYK9vaMMjyWsDocNQkr2vH1wE9ybw4O4AFjzGMisgV4UERuB04A77EgNjVL7r3vPsLhMFLdjq1pLZnOHdy37efjP9e6RsXr8vZKdneP8MKRQd50UaPV4ajTzHlSMMYcAdZPcnwQuH6u41H5EQ6HWX/jrezuGaWitIQVf/DaU1oJWteoOEQiEe68667x+ydvS/0KDqSXs/ee+/E5MtzxkY9YE6A6g474qfywOTjYH8ZhE5bUerXbqEiZjGHTzbcB8OAjd4/fTqUzbOscwX/x6xnc/FMLI1Snm0/rFFSBMMZga1lPIpVheZ1PS1moMzjsNpoqSglGk+DR4gXzib5a1ax75UQQqWigtcpDWalu3K4mV19eSoldsDWs0ppI84gmBTWrjg2O8bvDA5iRHhrKXVaHo+Yxu01oqXQj3ioO9oetDkflaFJQs6Y/FOORnT3UeF1kOrfrOII6q1qfCxMd4XeHBkilM1aHo9CkoGbJaCzJw9u6cTns3LyhCTJpq0NSC4CIkOnZQyiW4uUTQavDUWhSULMgnkzz8LZukmnD2zc0aRkLdW7Ghlha62Xr8SHC8ZTV0RQ9ffWqC5LOGH6xs4fhSIK3b2imxqfjCOrcRCIRDjz7c2wrXsvXf/4spnP7KT/3+Xy6jmEOaVJQ580Yw2/29tE5HOWNa+ppq/JYHZJagEzGsOlN76ZjOEK3rYXVq1dT7n511tpzD3/XwuiKj3YfqfP2wtEh9vaGuGpxFasby60ORy1wzRVuXA4bRwfHyOgUVctoUlDnZXf3CJuPDrGmsZwrFldZHY4qADab0F7tIZbM0BPUjXisot1H6hQnC9lNp7R9A0/u66etysPrV9Xp1FM1a/weJ1VeJ10jUap9TkpL7FaHVHQ0KahThMPh8fo0k4kkUmxfcR1VXidvvqgBu00Tgppdi6o8jESSHB0YY1VDmdXhFB3tPlIzlkhl2NcbIpOIcPP6JlwO/RSnZp/TYaO1ys1oLMWg7rkw5zQpqBlJZwwH+kKkM4b+H/6d1jRSeVVX5sLrtHNiKAI27dCYS5oU1FkZYzgcCDOWSLOszkcycNTqkFSBExEW13hJpg3SuMbqcIqKpmB1Vp3DUYYjSdqq3OObrU/cOGUyurOaulBel4MmfyndtHJkIMySGp/VIRUFTQpqWoPhON0jMWp9LhrKS8ePTzcYDbqzmpodzX43XT39PLnXTtNVbqvDKQrafaSmFE2kOTIwhs/loL3Go1NP1ZyziZDp3E40mebpAwGrwykKmhTUpNIZw4H+EDYRltf5sGlCUFaJjXJ5exX7ekO4l19ldTQFT5OCOoMxhiMDY8SSGZbV+XA69M9EWevy9ipqfS6qb/w40YSWZc+nOX+1i0iriDwlIntFZLeI/GXu+N+KSJeIbMt9vXmuY1NZfaNxhsYStFa6qXDr1FNlPbtNuGFNPbZSH0/t79ftO/PIio+AKeB/GmNWA1cBHxeRk3PO/tUYsyH39YgFsSlPJSeGIlR6SmisKD37+UrNkdoyF8HfPcDB/jB7e0JWh1Ow5jwpGGN6jDEv526HgL1A81zHoc40Fk9ha7sUp8PGklqvDiyreWf0xYdo8bv57YF+hnW1c15Y2lksIu3AJcCLuUOfEJEdInK/iFRO8Zg7RGSriGwNBHQ2wmzJGMNju3vBXsLyOh8Om44jqHnIZLhxbbbm1qO7e0lldF/n2WbZOgUR8QEPAZ80xoyKyN3AFwGT+/4V4MOnP84Ycy9wL8DGjRu1Y3GWvHBkkM7hKKZ7F96l11kdjlLjIpHIKYsl773nq1BWR6z9cv79h09gevbq7myzyJKPgyJSQjYh/Lcx5scAxpg+Y0zaGJMBvg5cYUVsxejowBhbjg2ztqkcM9xpdThKncJkDJtuvm18weSmm29j03U3Ul/uwlazhJXXv++s5d7VzFkx+0iAbwB7jTF3TjjeOOG0dwK75jq2YjQaTfKr3b3U+Jy8bkWt1eEoNWNtVR58LgdHAmFwaQmM2WJF99FrgNuAnSKyLXfs88CtIrKBbPfRMeBPLYitqKQyGR7Z1YMx8JaLGnHYdRxBLRwnF1bu6h4hvegy4qm0lnOfBXOeFIwxvwMmm9aiU1DnkDGG3+4P0Dca5y0XNeLPFbpTaiFxOmwsq/WxpyfN47v7eOvFjTpr7gLpR8Mi9UpHkN3do1zeXsmyOm16q4Wr3F2C6dnLkYExfndowOpwFjxNCkXoyECYZw8OsKzOx9VLqq0OR6kLZgaPsb6lgpdPBNneEbQ6nAVNS2cXmUAozmO7eqkrc/HGNfXa1FYF49oVtYRiKZ4+EKCs1MGSWm0Bnw9tKRQRR1UzP3mlC5fDztsubqJEB5ZVAbGJcNO6BurKXTy6q5fOYd3o6XxoS6FIjEST1N/yJSLRCJkjz3Pv1rFJz9Md09RCVmK3cfP6Jh56uYufbevm7RuaaKn0WB3WgqJJoQiEYkl+/HIn4nBx8aJaPMv/cMpzdcc0tRCdvuoZhxPb4qv40dYkmWNbYGxIVz3PkCaFAjc0luCn27qIJzP0P/h/8Pztt60OSalZd3LV80TJdIa9PSHiS69maa2Pfb/5gUXRLSzaqVzAuoJRHtzaQTpjeNelzSR6D1odklJzpsRuY3VjGW6nnYP9YaR2me7DMAOaFArU/t4QP3mlC7fTzns3tlJXrnsjqOJTYrexpqGcaq8TW8NKHtvVSyKllVWno91HBSaVzvDswQF2dI3QWFHK2y5uwu3Upf+qeNlswtJaL4HDOzjAKnpHY7jaLrI6rHlLWwoFJBhJ8MOXOtnRNcKlbX7edWmLJgSlABHBBA7z7staEBEabv2/PLqzh2BEN+o5nbYUCkAmY9jWEeTZA30YkyHTsY0tO/vZYnVgSs0zzX43H7iyjX/4+y9y+NoPcDAQZnmtj9VN5bRVerDZdDGnJoUFrj8U4zd7++kPxTHhAS5ZuxLXshsnPffBR+6e4+iUmn9K7DZGfv8Af/XZL/DSiWH2dI9yoD+M12lnWZ2PZr+bJr8br+vMt8d777vvjL0bmtvaT7nf2NDA1s0v5PMS8kqTwgI1Gk3y/JFB9vWGcJfYedO6Bn7xvV/iWr/a6tCUWhC8LgfXLq9l09Jqjg1E2Nszyu7uUbZ3jgBQ4S6hxuek1ueipsxFrc9FOBw+Zerrg4/czV994/FTnvfLt79xTq9jtmlSWGBGo0le6Qiysyv7h3vZoko2LqqktETHDpQ6Hw6bjWV1PpbV+UhnDIFQnK5glN7RGAOhOIcDr67+t615I3u6R/G47HicdpwNy0mlMwW1F4kmhQXAGIOzaSWP7urhYH+26bqqoYyrl1RTVlpicXRKFYbJuoYAsNnBVYa4y0k53Ji2VQRCcTIGGj/0r/zHbw/j95RQW+aiye+GikYyGbNgxyc0KcxTxhgOB8b4xY5ufratm8bbvsKxgQiXtPrZ0OrXZKDULDu9a2gyTzxwD2uvugJjDPFUhif+/a9566e/wkAoTncwxoG+MCVv+f+49O+f4PL2Kq5cXMUVi6tY01i+YFoTmhTmCWMMxwcjbO8M8tyhQX53aICuYBQRuGpxNVu/+w/421axZVtqyllFWsxOqamdXh/plFpJnNvrR0QoLbETPfD8+J4kxhhGYynu+8oXueETn2PzsSGe2NMHgM/l4LJFlVyxOJsoLmqpmLdbh2pSmAMbr7iKnt7e3D0Bjx8pr0fK66G8ntKaVjzNKxmJJgEoK3XwmqU1/NnrlnL96joaK9zInz7Bm//876b9PVrMTqmpTayP9OAjd5/RKrjQ14+IUOEuwRx9gX95z3oAekdibD42xItHBtl8dIh/+dV+AFwOG5e0+blycTVXLq7ikrbKebOmSJNCHiTTGbqDUTqHo3QMReiru5w1t7yb4UiCYCRJKvNq/RWn3Uas/wjvvqiBi1v8XNRcwaqGsgXT1FRKTa2hopSb1zdx8/omAAbDcbYcG+aT//A1nu9v4vnDLYjYMJk0hAKYUD9mtJ9I3zHc6TAmOgLREUjFz3jufE19nXdJQURuAv4NsAP3GWP+0eKQAEhnDLFkmnA8xWA4weBYnMFwgoFwnIFwgkAoTudwhM7hKD0jUSa872NbcwP9oTh+TwmtlR4qPU4qvSVUepx4nHY++4738J1f1Vh3cUqpWRMYGDhj7cJk5/zTz14mnkrTHYzRHYwyHCknGGkjGE1iX3Nq4T6n3YbXZcfrcuBzOfC6HLz43X/KS/zzKimIiB34GnAD0AlsEZGHjTF7ZvP3dA5HuP93x4il0sQSaaLJNLFk9ns0mSF+8nYiezyWzJBIT11Eq8Qu1PhctFS6uWJxFa2VbloqPbRUuWmt9HDVJev44/sem/LxmXTmjLnOp/v0G1ee9/UqpebOTF7Pn3nrBgBcDjuLa7wsrvG++nhj+Pz7b+DjX3uYsXiKcCLFWDz7gXQsnqI7GGUsnsbWuiEv8c+rpABcARwyxhwBEJHvA28HZjUpBCNJHtzawdhokEwyBqkkJp2AdBJSCUgnIJUgHh3DaSd3LPezVBwTC0E8jImFGOnrpMLj5ARwYorfFxgYmM3wlVIFzCZCOjRAa9XUO8YZY/jK9/8SmH6c8XzIfKovLiLvBm4yxnwkd/824EpjzCcmnHMHcEfu7kpg/5wHCjVAob/TF8M1QnFcZzFcIxTHdc7WNS4yxtRO9oP51lKYbLXHKVnLGHMvcO/chDM5EdlqjNloZQz5VgzXCMVxncVwjVAc1zkX1zjfprh0Aq0T7rcA3RbFopRSRWe+JYUtwHIRWSwiTuAW4GGLY1JKqaIxr7qPjDEpEfkE8CuyU1LvN8bstjisyVjafTVHiuEaoTiusxiuEYrjOvN+jfNqoFkppZS15lv3kVJKKQtpUlBKKTVOk8I5EJGbRGS/iBwSkc9aHc9sEZFWEXlKRPaKyG4R+cvc8SoReUJEDua+V1od64USEbuIvCIiv8jdL8Rr9IvIj0RkX+7/9OpCu04R+VTub3WXiHxPREoL4RpF5H4R6ReRXROOTXldIvK53PvRfhGZfB/ec6RJYYYmlOB4E7AGuFVE1lgb1axJAf/TGLMauAr4eO7aPgv8xhizHPhN7v5C95fA3gn3C/Ea/w14zBizClhP9noL5jpFpBn4C2CjMWYd2Ukpt1AY1/gt4KbTjk16XbnX6C3A2txj/iP3PnVBNCnM3HgJDmNMAjhZgmPBM8b0GGNezt0OkX0TaSZ7fd/OnfZt4B2WBDhLRKQFeAtw34TDhXaN5cC1wDcAjDEJY0yQArtOsjMn3SLiADxk1zMt+Gs0xjwDDJ12eKrrejvwfWNM3BhzFDhE9n3qgmhSmLlmoGPC/c7csYIiIu3AJcCLQL0xpgeyiQOoszC02XAX8NfAxOqGhXaNS4AA8M1cN9l9IuKlgK7TGNMFfJlsubEeYMQY8zgFdI2nmeq68vKepElh5s5agmOhExEf8BDwSWPMqNXxzCYReSvQb4x5yepY8swBXArcbYy5BBhjYXajTCnXp/52YDHQBHhF5IPWRmWJvLwnaVKYuYIuwSEiJWQTwn8bY36cO9wnIo25nzcC/VbFNwteA9wsIsfIdv29XkT+i8K6Rsj+nXYaY17M3f8R2SRRSNf5BuCoMSZgjEkCPwY2UVjXONFU15WX9yRNCjNXsCU4RETI9kHvNcbcOeFHDwMfyt3+EPCzuY5tthhjPmeMaTHGtJP9v3vSGPNBCugaAYwxvUCHiJzcgON6sqXnC+k6TwBXiYgn97d7PdlxsEK6xommuq6HgVtExCUii4HlwOYL/m3GGP2a4RfwZuAAcBj4gtXxzOJ1/QHZZucOYFvu681ANdnZDgdz36usjnWWrvd1wC9ytwvuGoENwNbc/+dPgcpCu06yGwnsA3YB3wVchXCNwPfIjpMkybYEbp/uuoAv5N6P9gNvmo0YtMyFUkqpcdp9pJRSapwmBaWUUuM0KSillBqnSUEppdQ4TQpKKaXGaVJQSik1TpOCUkqpcZoUlJpFInK5iOzI1ff35mr+r7M6LqVmShevKTXLROTvgVLATbYO0f+1OCSlZkyTglKzLFcbawsQAzYZY9IWh6TUjGn3kVKzrwrwAWVkWwxKLRjaUlBqlonIw2TLcy8GGo0xn7A4JKVmzGF1AEoVEhH5IyBljHkgt1/ucyLyemPMk1bHptRMaEtBKaXUOB1TUEopNU6TglJKqXGaFJRSSo3TpKCUUmqcJgWllFLjNCkopZQap0lBKaXUuP8H8bk/229QV60AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.histplot(data=df_normal['x'], kde=True)\n",
    "sb.histplot(data=df_uniform['x'], kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "cognitive-mouth",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7f1fb119e9a0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},