{"cells": [{"cell_type": "markdown", "metadata": {"papermill": {"exception": false, "start_time": "2020-10-29T02:55:11.198106", "end_time": "2020-10-29T02:55:11.211127", "duration": 0.013021, "status": "completed"}, "tags": []}, "source": "# Distributions"}, {"cell_type": "code", "execution_count": 1, "metadata": {"execution": {"iopub.execute_input": "2020-10-29T02:55:11.243122Z", "iopub.status.busy": "2020-10-29T02:55:11.243122Z", "iopub.status.idle": "2020-10-29T02:55:13.619126Z", "shell.execute_reply": "2020-10-29T02:55:13.619126Z"}, "papermill": {"exception": false, "start_time": "2020-10-29T02:55:11.225091", "end_time": "2020-10-29T02:55:13.620127", "duration": 2.395036, "status": "completed"}, "tags": []}, "outputs": [], "source": "import pandas as pd\nimport data_describe as dd"}, {"cell_type": "code", "execution_count": 2, "metadata": {"execution": {"iopub.execute_input": "2020-10-29T02:55:13.649135Z", "iopub.status.busy": "2020-10-29T02:55:13.648128Z", "iopub.status.idle": "2020-10-29T02:55:13.850124Z", "shell.execute_reply": "2020-10-29T02:55:13.849121Z"}, "papermill": {"exception": false, "start_time": "2020-10-29T02:55:13.630094", "end_time": "2020-10-29T02:55:13.850124", "duration": 0.22003, "status": "completed"}, "tags": []}, "outputs": [], "source": "from sklearn.datasets import load_boston\ndata = load_boston()\ndf = pd.DataFrame(data.data, columns=list(data.feature_names))\ndf['target'] = data.target\n\n# Create categorical (bin) features to demonstrate count plots\ndf['AGE'] = df['AGE'].map(lambda x: \"young\" if x < 29 else \"old\")\ndf['CRIM'] = df['CRIM'].map(lambda x: \"low\" if x < df.CRIM.median() else \"high\")"}, {"cell_type": "code", "execution_count": 3, "metadata": {"execution": {"iopub.execute_input": "2020-10-29T02:55:13.888092Z", "iopub.status.busy": "2020-10-29T02:55:13.887095Z", "iopub.status.idle": "2020-10-29T02:55:13.903120Z", "shell.execute_reply": "2020-10-29T02:55:13.902092Z"}, "papermill": {"exception": false, "start_time": "2020-10-29T02:55:13.861120", "end_time": "2020-10-29T02:55:13.903120", "duration": 0.042, "status": "completed"}, "tags": []}, "outputs": [{"output_type": "execute_result", "metadata": {}, "data": {"text/plain": "  CRIM    ZN  INDUS  CHAS    NOX     RM  AGE     DIS  RAD    TAX  PTRATIO  \\\n0  low  18.0   2.31   0.0  0.538  6.575  old  4.0900  1.0  296.0     15.3   \n1  low   0.0   7.07   0.0  0.469  6.421  old  4.9671  2.0  242.0     17.8   \n\n       B  LSTAT  target  \n0  396.9   4.98    24.0  \n1  396.9   9.14    21.6  ", "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>CRIM</th>\n      <th>ZN</th>\n      <th>INDUS</th>\n      <th>CHAS</th>\n      <th>NOX</th>\n      <th>RM</th>\n      <th>AGE</th>\n      <th>DIS</th>\n      <th>RAD</th>\n      <th>TAX</th>\n      <th>PTRATIO</th>\n      <th>B</th>\n      <th>LSTAT</th>\n      <th>target</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>low</td>\n      <td>18.0</td>\n      <td>2.31</td>\n      <td>0.0</td>\n      <td>0.538</td>\n      <td>6.575</td>\n      <td>old</td>\n      <td>4.0900</td>\n      <td>1.0</td>\n      <td>296.0</td>\n      <td>15.3</td>\n      <td>396.9</td>\n      <td>4.98</td>\n      <td>24.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>low</td>\n      <td>0.0</td>\n      <td>7.07</td>\n      <td>0.0</td>\n      <td>0.469</td>\n      <td>6.421</td>\n      <td>old</td>\n      <td>4.9671</td>\n      <td>2.0</td>\n      <td>242.0</td>\n      <td>17.8</td>\n      <td>396.9</td>\n      <td>9.14</td>\n      <td>21.6</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "execution_count": 3}], "source": "df.head(2)"}, {"cell_type": "markdown", "metadata": {"papermill": {"exception": false, "start_time": "2020-10-29T02:55:13.914092", "end_time": "2020-10-29T02:55:13.924121", "duration": 0.010029, "status": "completed"}, "tags": []}, "source": "## Diagnostic Summary\nThe default output summarizes diagnostics on the univariate data."}, {"cell_type": "code", "execution_count": 4, "metadata": {"execution": {"iopub.execute_input": "2020-10-29T02:55:13.950120Z", "iopub.status.busy": "2020-10-29T02:55:13.949092Z", "iopub.status.idle": "2020-10-29T02:55:14.134287Z", "shell.execute_reply": "2020-10-29T02:55:14.135277Z"}, "papermill": {"exception": false, "start_time": "2020-10-29T02:55:13.935122", "end_time": "2020-10-29T02:55:14.135277", "duration": 0.200155, "status": "completed"}, "tags": []}, "outputs": [{"output_type": "stream", "name": "stdout", "text": "Distribution Summary:\n        Skew detected in 1 columns.\n        Spikey histograms detected in 0 columns.\n\n        Use the method plot_distribution(\"column_name\") to view plots for each feature.\n\n        Example:\n            dist = DistributionWidget(data)\n            dist.plot_distribution(\"column1\")\n        \n"}, {"output_type": "display_data", "metadata": {}, "data": {"text/plain": "None"}}, {"output_type": "execute_result", "metadata": {}, "data": {"text/plain": "<data_describe.core.distributions.DistributionWidget at 0x1fa324be4c8>"}, "execution_count": 4}], "source": "dist = dd.distribution(df)\ndist"}, {"cell_type": "markdown", "metadata": {"papermill": {"exception": false, "start_time": "2020-10-29T02:55:14.149287", "end_time": "2020-10-29T02:55:14.162289", "duration": 0.013002, "status": "completed"}, "tags": []}, "source": "## Plot one feature"}, {"cell_type": "code", "execution_count": 5, "metadata": {"execution": {"iopub.execute_input": "2020-10-29T02:55:14.191283Z", "iopub.status.busy": "2020-10-29T02:55:14.190285Z", "iopub.status.idle": "2020-10-29T02:55:14.459251Z", "shell.execute_reply": "2020-10-29T02:55:14.458254Z"}, "papermill": {"exception": false, "start_time": "2020-10-29T02:55:14.174285", "end_time": "2020-10-29T02:55:14.459251", "duration": 0.284966, "status": "completed"}, "tags": []}, "outputs": [{"output_type": "execute_result", "metadata": {}, "data": {"text/plain": "<Figure size 720x720 with 1 Axes>", "image/png": "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\n"}, "execution_count": 5}], "source": "dist.plot_distribution(\"CRIM\")"}, {"cell_type": "code", "execution_count": 6, "metadata": {"execution": {"iopub.execute_input": "2020-10-29T02:55:14.550255Z", "iopub.status.busy": "2020-10-29T02:55:14.549256Z", "iopub.status.idle": "2020-10-29T02:55:14.877255Z", "shell.execute_reply": "2020-10-29T02:55:14.878252Z"}, "papermill": {"exception": false, "start_time": "2020-10-29T02:55:14.472249", "end_time": "2020-10-29T02:55:14.878252", "duration": 0.406003, "status": "completed"}, "tags": []}, "outputs": [{"output_type": "execute_result", "metadata": {}, "data": {"text/plain": "<Figure size 720x720 with 2 Axes>", "image/png": "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\n"}, "execution_count": 6}], "source": "dist.plot_distribution(\"ZN\")"}, {"cell_type": "code", "execution_count": 7, "metadata": {"execution": {"iopub.execute_input": "2020-10-29T02:55:14.912255Z", "iopub.status.busy": "2020-10-29T02:55:14.912255Z", "iopub.status.idle": "2020-10-29T02:55:14.921256Z", "shell.execute_reply": "2020-10-29T02:55:14.920254Z"}, "papermill": {"exception": false, "start_time": "2020-10-29T02:55:14.893251", "end_time": "2020-10-29T02:55:14.921256", "duration": 0.028005, "status": "completed"}, "tags": []}, "outputs": [{"output_type": "execute_result", "metadata": {}, "data": {"text/plain": "'0.11.0'"}, "execution_count": 7}], "source": "import seaborn as sns\nsns.__version__\n# sns.displot(df, x=\"ZN\", hue=\"CRIM\")"}, {"cell_type": "code", "execution_count": 8, "metadata": {"execution": {"iopub.execute_input": "2020-10-29T02:55:15.012255Z", "iopub.status.busy": "2020-10-29T02:55:15.008256Z", "iopub.status.idle": "2020-10-29T02:55:15.535301Z", "shell.execute_reply": "2020-10-29T02:55:15.534278Z"}, "papermill": {"exception": false, "start_time": "2020-10-29T02:55:14.937277", "end_time": "2020-10-29T02:55:15.535301", "duration": 0.598024, "status": "completed"}, "tags": []}, "outputs": [{"output_type": "execute_result", "metadata": {}, "data": {"text/plain": "<Figure size 720x720 with 2 Axes>", "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmcAAAJdCAYAAAB3U9DaAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAA8Y0lEQVR4nO3deZxcZZ33/e+vqnpfshMgW4ewJghBY5B90RlReYGMOIZnVOYeRpwRF2CUUYfbQW9FX4qOy4zcg+LI/dwOyIMwMCAg+6IYSAhCEhLIng5JyEISkk5vVb/nj6rqru703lV1rur6vF+vSp1znXOqfumru+vb51znHHN3AQAAIAyxqAsAAABAN8IZAABAQAhnAAAAASGcAQAABIRwBgAAEBDCGQAAQEAIZwAAAAEhnAEoa2b2V2a2v4+Hm9nXzOxJM2s1sxk527zPzDZEWDaAMYxwBqCsufuv3L0+9yHpaknbJf0ss9oBSf8zqhoBlBfCGQDkMLNTJP2LpEXuvjXT/GNJl5nZ0dFVBqBcEM4AIMPMxku6S9I33f3JnEVblN6LdkPxqwJQbhJRFwAAITAzk3SbpOWSvtvHKt+WtMbM5hW1MABlhz1nAJD2j5JOlHS5u3vvhe6+Q9K/SvpGsQsDUF7Ycwag7JnZuZL+SdLZ7r5ngFW/J2mdpOcLXxWAcsWeMwBlzcyOkHSHpKvdfdlA62aC2/clXVeE0gCUKcIZgHL3KUlTJf2oj2ud/e8+1v+RpGRxSwRQTqyPoRUAAACICHvOAAAAAkI4AwAACAjhDAAAICCEMwAAgIAQzgAAAAIyZi5Ce8EFF/hDDz0UdRkAAABDYf0tGDN7znbu3Bl1CQAAAKM2ZsIZAADAWEA4AwAACAjhDAAAICBj5oSAYpkxc5aaN28a0bbTZ8zU5k0b81wRAAClqaOjQ83NzWptbY26lIKprq7W9OnTVVFRMeRtCGfD1Lx5k37wu9Uj2vbaPz8uz9UAAFC6mpub1dDQoKamJpn1e/JiyXJ37dq1S83NzZo9e/aQt+OwJgAAiERra6smTZo0JoOZJJmZJk2aNOw9g4QzAAAQmbEazLJG8v8jnAEAgJK2bds2LVq0SHPmzNHcuXP1wQ9+UK+99ppqamo0f/58zZ07V5/85CfV0dEhSXryySd14YUXSpJ++ctfysz02GOPdb3ePffcIzPTXXfdFcn/h3AGAABKlrvrkksu0bnnnqu1a9dq5cqVuvHGG7V9+3bNmTNHL730kl555RU1Nzfrzjvv7PM13vGOd+j222/vmr/jjjt08sknF+u/cAjCGQAAKFlPPPGEKioq9Hd/93ddbfPnz9eMGTO65uPxuBYuXKgtW7b0+RpnnXWWnn/+eXV0dGj//v1as2aN5s+fX+jS+0U4AwAAJWv58uV617veNeA6ra2tWrx4sS644II+l5uZ3ve+9+nhhx/Wvffeq4suuqgQpQ4Z4QwAAIxJa9eu1fz58zVp0iTNnDlTJ510Ur/rLlq0SHfccYfuuOMOXXbZZUWs8lCEMwAAULLmzZunpUuX9rksO+ZszZo1+uMf/6j77ruv39dZuHChli9frp07d+rYY48tVLlDUpRwZmYzzOwJM3vVzFaY2Rcy7RPN7BEzez3zPCFnm6+Y2RozW21m7y9GnQAAoLScf/75amtr089+9rOuthdeeEEbN3bfkeeII47Qd77zHX37298e8LW+/e1v68YbbyxYrUNVrD1nnZL+wd1PkPQeSVeZ2VxJX5b0mLsfI+mxzLwyyxZJmifpAkk/NbN4kWoFAAAlwsx0zz336JFHHtGcOXM0b9483XDDDTryyCN7rPfhD39YLS0teuaZZ/p9rQ984AM677zzCl3yoMzdi/+mZvdK+tfM41x332pmR0h60t2PM7OvSJK7fzuz/sOSbnD35/p7zQULFviSJUuKUfuobt8UxdcbAIAQvfrqqzrhhBOiLqPg+vl/9nt12qKPOTOzJkmnSFosaaq7b5WkzPNhmdWmSdqcs1lzpg0AAGBMK2o4M7N6Sb+RdLW77xto1T7aDtnlZGZXmtkSM1uyY8eOfJUJAAAQmaKFMzOrUDqY/crd7840b88czlTm+c1Me7OkGTmbT5f0Ru/XdPdb3H2Buy+YMmVK4YoHAAAokmKdrWmSbpX0qrv/IGfRfZIuz0xfLunenPZFZlZlZrMlHSPp+WLUCgAAEKVEkd7nDEmfkPSKmb2UafuqpO9IutPMrpC0SdJHJcndV5jZnZJWKn2m51XunixSrQAAAJEpSjhz92fV/1kJ7+1nm29J+lbBigIAAAgQdwgAAABla8OGDTrxxBMPaf/a176mRx99dMBtb7jhBt100015r6lYhzUBAAAGNGPmLDVv3pS315s+Y6Y2b9o4+Ip9+MY3vpG3OoaLcAYAAILQvHnTiC/03pdr//y4Ia2XTCb1qU99Sn/4wx80bdo03Xvvvfr7v/97XXjhhbr00kv129/+Vtdee60mT56sd77znVq3bp3uv/9+SdLKlSt17rnnatOmTbr66qv1+c9/ftR1c1gTAACUtddff11XXXWVVqxYofHjx+s3v/lN17LW1lZ9+tOf1oMPPqhnn31Wva+rumrVKj388MN6/vnn9fWvf10dHR2jrodwBgAAytrs2bM1f/58SdK73vUubdiwoWvZqlWrdNRRR2n27NmSpMsuu6zHth/60IdUVVWlyZMn67DDDtP27dtHXQ/hDAAAlLWqqqqu6Xg8rs7Ozq75we6JPdC2I0U4AwAA6Mfxxx+vdevWde1N+/Wvf13w9+SEAAAAgH7U1NTopz/9qS644AJNnjxZCxcuLPh7Es4AAEAQps+YOeQzLIf6eoNpamrS8uXLu+a/+MUvHrLOeeedp1WrVsndddVVV2nBggWS0tc5y5X7OqNBOAMAAEEY6TXJCu1nP/uZbrvtNrW3t+uUU07Rpz/96YK+37DDmZmd4e6/H6wNAABgLLjmmmt0zTXXFO39RnJCwE+G2AYAAIBhGvKeMzM7TdLpkqaY2bU5ixolxfNdGAAAQDkazmHNSkn1mW0actr3Sbo0n0UBAACUqyGHM3d/StJTZvZLdw9zxB4AAECJG8nZmlVmdoukptzt3f38fBUFAABQDPX19dq/f3/UZfQwknD2/0n635J+LimZ33IAAEC5apo5XRs3b8nb682aMU0bNjXn7fWKZSThrNPdb857JQAAoKxt3LxF/viNeXs9O/+rQ17X3XXdddfpwQcflJnp+uuv18c+9jF95jOf0QUXXKCLLrpIl1xyiSZMmKBf/OIXuvXWW7V+/Xp985vfzFu9WSO5lMZ/m9lnzOwIM5uYfeS9MgAAgCK5++679dJLL+lPf/qTHn30UX3pS1/S1q1bdfbZZ+uZZ56RJG3ZskUrV66UJD377LM666yzClLLSMLZ5ZK+JOkPkpZmHkvyWRQAAEAxPfvss7rssssUj8c1depUnXPOOXrhhRd01lln6ZlnntHKlSs1d+5cTZ06VVu3btVzzz2n008/vSC1DPuwprvPLkQhpeTrX/961CUAAIA8cvc+26dNm6a33npLDz30kM4++2zt3r1bd955p+rr69XQ0NDnNqM1kts3fbKvdnf/P6MvpzT88+XvHdF21/7+P/NcCQAAyIezzz5b//7v/67LL79cu3fv1tNPP63vfe97kqTTTjtNP/zhD/X4449r165duvTSS3XppYW7xOtITgh4d850taT3SnpRUtmEMwAAMLZccskleu6553TyySfLzPTd735Xhx9+uCTprLPO0u9+9zsdffTRmjVrlnbv3l2w8WbSyA5rfi533szGSfp/81YRAAAoS7NmTBvWGZZDeb3BZK9xZmb63ve+17W3LNcVV1yhK664QpJUUVGhAwcO5K3Gvoxkz1lvLZKOycPrAACAMlaK1yQrhJGMOftvSdlRc3FJJ0i6M59FAQAAlKuR7Dm7KWe6U9JGdyfqAgAA5MGwr3OWuQH6KkkNkiZIas93UQAAoDz0dwmLsWIk/79hhzMz+0tJz0v6qKS/lLTYzAY8n9TMfmFmb5rZ8py2iWb2iJm9nnmekLPsK2a2xsxWm9n7h1sjAAAIX3V1tXbt2jVmA5q7a9euXaqurh7WdiM5rPlPkt7t7m9KkplNkfSopLsG2OaXkv5VPS+38WVJj7n7d8zsy5n5fzSzuZIWSZon6UhJj5rZse7OTdYBABhDpk+frubmZu3YsSPqUgqmurpa06dPH9Y2IwlnsWwwy9ilQfbAufvTZtbUq/liSedmpm+T9KSkf8y03+HubZLWm9kaSQslPTeCWgEAQKAqKio0e3bZ33joECMJZw+Z2cOSbs/Mf0zSb0fwOlPdfaskuftWMzss0z5N0h9z1mvOtAEAAIx5Qw5nZna00oHqS2b2F5LOlGRK79H6VR5rsj7a+jwYbWZXSrpSkmbOnJnHEgAAAKIxnBMCfijpbUly97vd/Vp3v0bpvWY/HMF7bzezIyQp85w9VNosaUbOetMlvdHXC7j7Le6+wN0XTJkyZQQlAAAAhGU44azJ3V/u3ejuSyQ1jeC975N0eWb6ckn35rQvMrMqM5ut9N0Hnh/B6wMAAJSc4Yw5G+g80JqBNjSz25Ue/D/ZzJol/bOk70i608yukLRJ6UtzyN1XmNmdklYqfZHbqzhTEwAAlIvhhLMXzOxT7v6z3MZMuFo60Ibuflk/i97bz/rfkvStYdQGAAAwJgwnnF0t6R4z+yt1h7EFkiolXZLnugAAAMrSkMOZu2+XdLqZnSfpxEzzA+7+eEEqAwAAKEPDvs6Zuz8h6YkC1AIAAFD2hn1vTQAAABQO4QwAACAghDMAAICAEM4AAAACQjgDAAAICOEMAAAgIIQzAACAgBDOAAAAAkI4AwAACAjhDAAAICCEMwAAgIAQzgAAAAJCOAMAAAgI4QwAACAghDMAAICAEM4AAAACQjgDAAAICOEMAAAgIIQzAACAgBDOAAAAAkI4AwAACAjhDAAAICCEMwAAgIAQzgAAAAJCOAMAoACaZk6XmY340TRzetT/BUQkEXUBwFAlKiqV7OwY8fbxRIU6O9pHtG3TzOnauHnLiN971oxp2rCpecTbAyg9GzdvkT9+44i3t/O/msdqUEqCDWdmdoGkH0mKS/q5u38n4pLKXtQBJdnZoR/ccuuIt7/2yitGvC2/ZAEAxRJkODOzuKR/k/RnkpolvWBm97n7ymgrK29jIaCYWdQljEjUwTjq9weAchJkOJO0UNIad18nSWZ2h6SLJRHOMCojDZejDZZxG30wjDIYRxnMCYajw9cPIxH1903U7x+1UMPZNEmbc+abJZ0aUS3Ik3wElFGx2MhDgo3u3JmkRxuuIv/aj0LUe2xH+yFRVRFXW0dyxNuP9kMm6q/faET9AT3a9x+t0f7cjvZ7r1z/IAyBuXvUNRzCzD4q6f3u/reZ+U9IWujun+u13pWSrszMHidpdRHKmyxpZxHeB0NHn4SHPgkT/RIe+iRMxeiXne5+QV8LQt1z1ixpRs78dElv9F7J3W+RdEuxipIkM1vi7guK+Z4YGH0SHvokTPRLeOiTMEXdL6Fe5+wFSceY2Wwzq5S0SNJ9EdcEAABQcEHuOXP3TjP7rKSHlb6Uxi/cfUXEZQEAABRckOFMktz9t5J+G3UdfSjqYVQMCX0SHvokTPRLeOiTMEXaL0GeEAAAAFCuQh1zBgAAUJYIZ0NkZheY2WozW2NmX466nnJkZjPM7Akze9XMVpjZFzLtE83sETN7PfM8Iepay5GZxc1smZndn5mnXyJkZuPN7C4zW5X5mTmNPomemV2T+f213MxuN7Nq+qW4zOwXZvammS3Paeu3D8zsK5nP/tVm9v5i1Eg4G4Kc20l9QNJcSZeZ2dxoqypLnZL+wd1PkPQeSVdl+uHLkh5z92MkPZaZR/F9QdKrOfP0S7R+JOkhdz9e0slK9w19EiEzmybp85IWuPuJSp/wtkj0S7H9UlLv64v12QeZz5hFkuZltvlpJhMUFOFsaLpuJ+Xu7ZKyt5NCEbn7Vnd/MTP9ttIfNtOU7ovbMqvdJunDkRRYxsxsuqQPSfp5TjP9EhEza5R0tqRbJcnd2919j+iTECQk1ZhZQlKt0tfwpF+KyN2flrS7V3N/fXCxpDvcvc3d10tao3QmKCjC2dD0dTupaRHVAklm1iTpFEmLJU11961SOsBJOizC0srVDyVdJymV00a/ROcoSTsk/UfmUPPPzaxO9Emk3H2LpJskbZK0VdJed/+d6JcQ9NcHkXz+E86Gpq+bm3Gaa0TMrF7SbyRd7e77oq6n3JnZhZLedPelUdeCLglJ75R0s7ufIumAOFQWucw4poslzZZ0pKQ6M/t4tFVhEJF8/hPOhmZIt5NC4ZlZhdLB7FfufnemebuZHZFZfoSkN6Oqr0ydIekiM9ug9CH/883s/4p+iVKzpGZ3X5yZv0vpsEafROt9kta7+w5375B0t6TTRb+EoL8+iOTzn3A2NNxOKgBmZkqPoXnV3X+Qs+g+SZdnpi+XdG+xaytn7v4Vd5/u7k1K/2w87u4fF/0SGXffJmmzmR2XaXqvpJWiT6K2SdJ7zKw28/vsvUqPnaVfotdfH9wnaZGZVZnZbEnHSHq+0MVwEdohMrMPKj2uJns7qW9FW1H5MbMzJT0j6RV1j236qtLjzu6UNFPpX34fdffegz1RBGZ2rqQvuvuFZjZJ9EtkzGy+0idoVEpaJ+l/KP0HOX0SITP7uqSPKX32+TJJfyupXvRL0ZjZ7ZLOlTRZ0nZJ/yzpv9RPH5jZP0n6G6X77Gp3f7DgNRLOAAAAwsFhTQAAgIAQzgAAAAJCOAMAAAhIIuoC8mXy5Mne1NQUdRkAAACDWrp06U53n9LXsjETzpqamrRkyZKoywAAABiUmW3sbxmHNQEAAAJCOAMAAAgI4QwAACAghDMAAICAEM4AAAACQjgDAAAICOEMAAAgIIQzAACAgBDOAAAAAkI4AwAACAjhDAAAICCEMwAAgIAQzgAAAAJCOAMAAAgI4QwAACAghDMAAICAEM4AAAACQjgDAAAICOFsGNxd7h51GQAAYAwjnA3DzTffrI985CNau3Zt1KUAAIAxinA2DK+88op2796tL3zharW0tERdDgAAGIMIZ8MwceJESdL+/W/rgQceiLgaAAAwFhHOhsnrJinVcLh+feed6uzsjLocAAAwxhDORqBtyvHauWOHVq1aFXUpAABgjCGcjUDnuGmSmRYvXhx1KQAAYIwhnI1Eokqp+sP0R8IZAADIM8LZCHU0TtPrr72mt956K+pSAADAGEI4G6HOxiMlScuXL4+4EgAAMJYQzkYoVTtRsphWrlwZdSkAAGAMIZyNVCyhVO0krVz5atSVAACAMYRwNgqddZO1atUqJZPJqEsBAABjBOFsFJJ1U9TW1qoNGzZEXQoAABgjCGejkKyfIklavXp1xJUAAICxgnA2Cl7VIIsntG7duqhLAQAAYwThbDQspmTNBK1ZsybqSgAAwBhBOBulzuoJWrN2rdw96lIAAMAYQDgbpVTtRO1/+23t2rUr6lIAAMAYQDgbpVTtREnS2rVrI64EAACMBYSzUUrWTJAkTgoAAAB5QTgbrUSVrKqOa50BAIC8IJzlQUfVOK0nnAEAgDwgnOVBqnq8Nm7cyBmbAABg1AhneZCqGa+21lbt2LEj6lIAAECJI5zlQapmvCQx7gwAAIwa4SwPUtXjJUkbN26MthAAAFDyCGd54BXVssoa9pwBAIBRI5zlSWdVozZt2hR1GQAAoMQRzvIkWTVOGzYSzgAAwOgQzvIkVTNOb+/bq71790ZdCgAAKGGEszzJnhSwefPmaAsBAAAljXCWJ6nqcZIIZwAAYHQIZ3niVfVSLM5JAQAAYFQIZ/liMal6HNc6AwAAo0I4y6OOqkZtIJwBAIBRIJzlUap6nLZt26bOzs6oSwEAACWKcJZHqepxSiWT2rJlS9SlAACAEkU4y6PsGZucFAAAAEaKcJZHqRoupwEAAEaHcJZP8UpZVS17zgAAwIgRzvKss5LLaQAAgJEjnOVZsnqcNm7aJHePuhQAAFCCCGd5lqoZp5YDB7R79+6oSwEAACWIcJZn2RugM+4MAACMBOEsz7KX02DcGQAAGAnCWZ55ZZ0sXsGeMwAAMCKEs3wzU7J6HPfYBAAAI0I4K4Bk9TitX78h6jIAAEAJIpwVQKpmvN7avUv79++PuhQAAFBiCGcFkKyZIEnasGFDtIUAAICSQzgrgBThDAAAjBDhrAC8sl4Wr9D69eujLgUAAJQYwlkhmClZPZ5wBgAAho1wViDJmvFau3Zd1GUAAIASQzgrkGTNBO3du0d79uyJuhQAAFBCCGcFkqqdKElau3ZtxJUAAIBSQjgrkFRNOpytWbMm4koAAEApIZwViFdUy6rqCGcAAGBYCGcF1FE9Ua+99nrUZQAAgBJCOCugZO1Ebd68SW1tbVGXAgAASgThrIBStROVSqW43hkAABgywlkBJesmS5JWr14dcSUAAKBUEM4KyCvrZZU1WrlyZdSlAACAEkE4KyQzddRM1oqVr0ZdCQAAKBGEswJL1k/RlubN2r9/f9SlAACAEkA4K7Bk3RS5O+POAADAkBDOCixZN0WS9Morr0RcCQAAKAWEs0JLVMrrJmnZspeirgQAAJQAwlkRdNQfrpUrV6i9vT3qUgAAQOAIZ0WQbDhcHR0dWrVqVdSlAACAwBHOiqCz4XBJ0osvvhhxJQAAIHSEs2JIVClVf5j+8NxzUVcCAAACRzgrko5x0/Xa6tXavXt31KUAAICAEc6KpHP8DEnS4sWLI64EAACEjHBWJKmaiVJVvZ586qmoSwEAAAEjnBWLmdomzNYLL7ygPXv2RF0NAAAIFOGsiDonzVEqmdTjjz8edSkAACBQhLMiStVOlNdN0n3/fb/cPepyAABAgAhnRdY65QRtWL9OS5cujboUAAAQIMJZkXVOmiNV1uo/b7896lIAAECACGfFFourdeqJenHpUv3xj3+MuhoAABCYRNQFlKOOw05Q9c7X9KMf/1gnnXSSamtroy6ppGzZskXLli3T6tWrtWnzZu1+6y0lO5Oqq63R1KlT1dTUpLlz5+rkk09WXV1d1OUCADAsBQtnZtYk6X53P7FX+zckPe3ujw6w7Q2S9rv7TYWqL1KxuFpmnqatrz2k73//+7r++utlZlFXFbTm5mY98sgjeuzxJ9S8eZMkySqq1Fk1TqmKGkkVsv0ten3LCj37+99L7orF4zrppJN09lln6ZxzztGkSZOi/U8AADAERd9z5u5fK/Z7hijZeITajjxFjz32mBobG/W5z31OsRhHmXPt3btXTz75pB56+GG9unKlJCnZcIQ6Zp6qzsbp8upGqa9Qm+xU/MAOxfc266XVG/TSsmX6yU9+one84yRdd92XNH369CL/TwAAGLpCh7O4mf1M0umStki6WNLNSu9Ru8vMPijpB5J2SnpR0lHufmFm27lm9qSkmZJ+6O4/LnCtg9q3b5/UflCx/W8qVX/YqF+v/YiTZcl23XPPPVq3fr2uveYazZo1Kw+Vlq7t27fr+eef19PPPKOlS5cqlUzKayeobfq71TnpKHnlEA5TxhNKNh6hZOMRate7FTv4lhI71+jll/+kJUuWEM4AAEErdDg7RtJl7v4pM7tT0keyC8ysWtK/Szrb3debWe/TF4+XdJ6kBkmrzexmd+8ocL39WrFihdatW6dLL/6gHnjwIe2aee7oA5qZ2qa/W8nq8Xr5lcW6/K//WgsWLNAZp5+u448/XkceeaQaGxvH3CHPjo4O7du3T7t27dK2bdu0efNmrV27VstXrNSb27elV6puVPuUueqYeJRStRP73kM2RKmaCeo4/ERVbXslT/8DAAAKp9DhbL27v5SZXiqpKWfZ8ZLWufv6zPztkq7MWf6Au7dJajOzNyVNldSc++JmdmV2m5kzZ+a9+FwvvfSSPvCBD+izn/2sJNN/PrdO7XnYeyYzdU45Vm+Pn6mKN1dqycsrteSFF0b/uiXGqurUXjtFyRmnKjnuSKWqx48qkAEAUKoKHc7acqaTkmpy5gf75O297SG1uvstkm6RpAULFhT0kvvz58/XddddJ3fXAw89rM6Z5+b3DZJtsvYWWbJDZXnvgI42xTtalOpokbW3SFWNksWjrgoAgKKL8lIaqyQdZWZN7r5B0scirGVQ8+bN01FHHaW77ntQLUefn5cxZ5IkT6ly68uqeuMlVVQk9N73na/TM4c1p0yZMuYOaWYlk0nt2bOn67DmunXrtHz5Cq1evVLJba/IEpVqb5yhjklHKTlummScLAEAKA+RhTN3P2hmn5H0kJntlPR8VLUMVWNjo1RZk9dgVr3+GVXsWqvzzjtfn/3sVWVzuYd4PK5JkyZp0qRJmjdvXlf7wYMHtWzZMj377LN66qmndeD1tbLKGrVNOEodk44e/vgzTym+b6sqdq+TpDEbdgEAY0fBwllmb9iJOfN9XbPsCXc/3tKfmP8maUlm3Rt6vdaJfWxb8qo2v6CKXWt1xRVX6BOf+ETU5QShpqZGp59+uk4//XRdc801Wrx4sR5++GH94Q9/UOX2FVLNeLWNm6nkuGlK1k2R4r2+hd1l7QcU379dib1bVLmvWd7Rqqqqap1xfnqvJAAAIYv6DgGfMrPLJVVKWqb02ZtlIfHWRlVuX6FLLrmEYNaPiooKnXnmmTrzzDO1d+9ePfXUU3r8iSf08ssvK7XtZclMVt2gzniVXDHFvVOx9v3yjvRwxbr6Bp12zpk6++yzdeqpp6qqqiri/xEAAIOLNJy5+79I+pcoa4hEskM1mxfrqDlz9JnPfCbqakrCuHHjdNFFF+miiy7S/v379fLLL2v16tVqbm7Wnj17lEwmVVOTvn3TrFmzNHfuXB199NGKxzmpAABQWqLec1aWKrctl9r26+ov3KiKioqoyyk59fX1XYc+AQAYawhnxZbsUPWOlTrjzLN00kknRV0NAAAIDNcnKLKKna/LO9p02WWLoi4FAAAEiHBWTO6q3vGqTpg7t8flIwAAALIIZ0UUO7BTOrhXF37oQ1GXAgAAAkU4K6KKXWuVSFTonHPOiboUAAAQKMJZsbiras96nXHG6aqvr4+6GgAAECjCWZHEDuyQtx/UWWedFXUpAAAgYISzIkns2Swz08KFC6MuBQAABIxwViSV+5o1b9689M3TAQAA+kE4KwLrOCg7sEunnXZa1KUAAIDAEc6KIP72VknSKaecEnElAAAgdISzIoi/vU1VVdU69thjoy4FAAAEjnBWBBX7t+ukk96hRIJbmQIAgIERzgrMOlplLW9p/vz5UZcCAABKAOGswGIHdkiSTjzxxIgrAQAApYBwVmDxAztkZow3AwAAQ0I4K7D4gR2a1TRbNTU1UZcCAABKAOGskNxV0bJT8+aeEHUlAACgRBDOCsja9sk72nTCCYQzAAAwNISzAoof2ClJOu644yKuBAAAlArCWQHFWnYrHo+rqakp6lIAAECJIJwVULxlt2Y1NamioiLqUgAAQIkgnBVQRetbOvaYY6IuAwAAlBDCWYFYR4u8vUVHH3101KUAAIASQjgrkFjLbkkinAEAgGEhnBVIPBPO5syZE3ElAACglBDOCiR28C1NmDhJDQ0NUZcCAABKCOGsQBKte3T0nKOiLgMAAJQYwlkheEqxg3s1e/bsqCsBAAAlhnBWANb2tjzVSTgDAADDRjgrgPjBtySJOwMAAIBhI5wVQOzgHknSrFmzoi0EAACUHMJZAcQO7tHkKYeptrY26lIAAECJIZwVQKJtr46a3RR1GQAAoAQRzvLNXbHWvRzSBAAAI0I4yzNr3y9PdmrmzJlRlwIAAEoQ4SzPOBkAAACMBuEsz2KteyWJPWcAAGBECGd5Fju4R/UNjRo/fnzUpQAAgBJEOMuzeNteNXFIEwAAjBDhLM8Sbfs0axaHNAEAwMgQzvKps03efpDxZgAAYMQIZ3mUPRlgxowZEVcCAABKFeEsj7KX0WDPGQAAGCnCWR7FWvcqHo/r8MMPj7oUAABQoghneRRr3asjp01TIpGIuhQAAFCiCGd5VNG+j8toAACAUSGc5UsqJR3cx22bAADAqBDO8sTa3pY8xZmaAABgVAhneRJv3SOJMzUBAMDoEM7yhGucAQCAfCCc5Umsda/GT5io+vr6qEsBAAAljHCWJ/HWvWrinpoAAGCUCGf54K54217GmwEAgFEjnOWBdR6Ud7Spqakp6lIAAECJI5zlQfaemlzjDAAAjBbhLA+y4Yw9ZwAAYLQIZ3kQO7hHtXV1mjhxYtSlAACAEkc4y4P0mZqzZGZRlwIAAEoc4SwPEm17OaQJAADygnA2StbRKm9vIZwBAIC8IJyNUuzgbknSUUcdFXElAABgLCCcjVKsJR3O5syZE3ElAABgLCCcjVL84FsaN36CJkyYEHUpAABgDCCcjVLi4G4dczR7zQAAQH4QzkbDU7KDezikCQAA8oZwNgqxg3ulVJJwBgAA8oZwNgqxAzskSccdd1zElQAAgLGCcDYK8QM7VFNbqxkzZkRdCgAAGCMIZ6NQ0bJTc084QbEYX0YAAJAfpIqRSnbKWnZr7ty5UVcCAADGEMLZCMVbdkruOuGEE6IuBQAAjCGEsxGK73tDZqZ3vOMdUZcCAADGEMLZCFXs3aK5c+epoaEh6lIAAMAYQjgbAes4qNiBHTr11IVRlwIAAMYYwtkIxPc2S5JOPfXUiCsBAABjDeFs2FzVb76qadOn65hjjom6GAAAMMYQzobJDuyWHdipyxYt4vpmAAAg70gXw7B7925J0sRJk/Rnf/ZnEVcDAADGokTUBZSS0047TYlEQtdff72qqqqiLgcAAIxB5u5R15AXCxYs8CVLlkRdBgAAwKDMbKm7L+hrGYc1AQAAAkI4AwAACAjhDAAAICCEMwAAgIAQzgAAAAJCOAMAAAgI4QwAACAghDMAAICAEM4AAAACMuDtm8zsLwZa7u5357ccAACA8jbYvTXvkvRS5iFJlrPMJRHOAAAA8miwcPYRSR+TdJKkeyXd7u5rCl4VAABAmRpwzJm73+PuiySdI2mtpO+b2bNmdk5RqgMAACgzQz0hoFXSXkn7JNVJqi5YRQAAAGVssBMCzpN0maSFkh6V9CN3X1KMwgAAAMrRYGPOHpP0sqRnJVVJ+qSZfTK70N0/X8DaAAAAys5g4exvlD4rEwAAAEUwYDhz918WqQ4AAABo8DFn/60B9py5+0V5rwgAAKCMDXZY86aiVAEAAABJgx/WfKq/ZWZ2Rv7LAQAAKG+DHdaMS/pLSdMkPeTuy83sQklflVQj6ZTClwgAAFA+BjuseaukGZKel/RjM9so6TRJX3b3/ypwbQAAAGVnsHC2QNJJ7p4ys2pJOyUd7e7bCl8aAABA+Rns9k3t7p6SJHdvlfQawQwAAKBwBttzdryZvZyZNklzcubl7icVrDIAAIAyNFg4O1nSVEmbe7XPkvRGQSoCAAAoY4Md1vwXSfvcfWPuQ1JLZhkAAADyaLBw1uTuL/dudPclkpoKUhEAAEAZGyycVQ+wrCafhQAAAGDwcPaCmX2qd6OZXSFpaWFKAgAAKF+DnRBwtaR7zOyv1B3GFkiqlHRJAesCAAAoS4PdW3O7pNPN7DxJJ2aaH3D3xwteGQAAQBkabM+ZJMndn5D0RIFrAQAAKHuDjTkDAABAERHOAAAAAkI4AwAACAjhDAAAICCEMwAAgIAQzgAAAAJCOAMAAAgI4QwAACAghDMAAICAEM4AAAACQjgDAAAICOEMAAAgIIQzAACAgBDOAAAAAkI4AwAACAjhDAAAICCEMwAAgIAQzgAAAAJCOAMAAAgI4QwAACAghDMAAICAEM4AAAACQjgDAAAICOEMAAAgIIQzAACAgBDOAAAAAkI4AwAACAjhDAAAICCEMwAAgIAQzgAAAAJCOAMAAAgI4QwAACAghDMAAICAEM4AAAACQjgDAAAICOEMAAAgIIQzAACAgBDOAAAAAkI4AwAACAjhDAAAICCEMwAAgIAQzgAAAAJCOAMAAAgI4QwAACAghDMAAICAEM4AAAACQjgDAAAICOEMAAAgIIQzAACAgBDOAAAAAkI4AwAACAjhDAAAICCEMwAAgIAQzgAAAAJCOAMAAAgI4QwAACAghDMAAICAEM4AAAACQjgDAAAICOEMAAAgIIQzAACAgBDOAAAAAkI4AwAACAjhDAAAICCEMwAAgIAQzgAAAAJCOAMAAAgI4QwAACAghDMAAICAEM4AAAACQjgDAAAICOEMAAAgIIQzAACAgBDOAAAAAkI4AwAACAjhDAAAICCEMwAAgIAQzgAAAAJCOAMAAAgI4QwAACAghDMAAICAEM4AAAACQjgDAAAICOEMAAAgIImoCyglL774oh577DG5u1KplNy96xGLxWRmisViXdMVFRVKJBI9HpWVlaqqqurxqK6uVk1NjWpqarqma2trVVlZKTOL+r8NAACKiHA2DHfffbee+/2zGldtMrlivXJTyiWXyT09nXSpMyV1plydqeG/XzweU21Njerq6tKP+gbV19cf8mhoaOgxnX3U1NQQ7gAAKDGEs2Fwd02rT+mb735rBNumw1pHSupImdqTpvaU1J4ytSW7H63ZR6fUmjQd7GzRweRutewzte42bUnG1ZKMqaXD1NLh8gHeMx6PqaG+Xg0N9WpsHK+GxkY1NjZ2hbfGxsauQJc73dDQoIqKipF/oQAAwIgRzorETEqYlIhJNXJpwFg1NClPB7gDHaYDnaaWDtOBzpj2d5haOk37O2Jq6WzR/rYdOrA1pq3Nca3NLD/YMfD7V1VVqr6uLh3WGhtVV3foHru6ujrV1tZ279nLzGcPyxLwAAAYPsJZCYuZVJtw1SZcU4a5bTIltXSmQ92BjpgOdFpXqDvQEVNL50Ed6NynAy3b1LI3pq2puFo6YzrYaTrQ4fIhZMuKRDwd1GpqVF1TrZqaOlVnglt1dXXXeLvc58rKyq5xednniooKVVRUdC3Lzmcf2bF88Xicw7gAgJJX1HBmZvvdvb6Y74m+xWNSQ6WrodIlDW9AnLvUmpQOJtNhrbUzHeoOJtPT2UOzBztNrckDak1K7S2m1rdNB5Km3am42lIxtaektqSpvdPVMYIxeX1JxOOKJ+KqSCS6phPx9MkY8cxJGfF4XIlEheKJuOLx7rZ4PK5YLNY13d8jd52B1h/Kaw30yK2ru+6ebdmTTwAAYwd7zjBsZlJNQqpJpKSq/LxmyqX2ZGY8Xio9Hq8jZepImjpSUqdb13i9jpTUmbLMGD7LnHSRnu90UzKlHtOdLiVTpmSrlPT0etkTNjrclHRTyk0pZZel55Oejq2pzPYppfc4plxKuaeXp/JxgHp0EvG44vFYTmhLB9J4PN4VSLsfFb1CaveyvuYHa+sdHPsKkX2Fy/6W507HYlzpB0B5iiScWfpP/e9K+oDSn23fdPdfm9lPJT3k7veZ2T2S3nL3vzGzKyTNdvfro6hXkn7yk59o2bJlajto+p/Pj1NNIuqPZOSDSUqYSzby/vTMP54znz3s29XWtdwGWX7o9u7WY1n2vbrXS6anU5InTd4utfd6zeq464i6pJIpqd1NBzOhNClLB1fvDq5JTwfedPj0TLgd8ZdnxMxM8VisO3hmLlPT757KXs/pR6Lr8ja5ezRz24Yyb2aHTOdeOif30bs9O5/7nH3kzueuk/3/9zedffQ1n9uWne79de1rWkqf9NTXdHY++xjKfPZyQ7nTuZcg6t2W+5ydTiaTA7b1ns5dJ7vNSB+Hvn/m9VOulKfkvdb1VEqpzLPLlUq55Jl1XV3tnvnBdXflntI1lKEi6T7LPCunf81klm6zWExmUsxikpliMZNZ5nvL0st7fp9mvq9j1jWd/d7PrjPQz0l/RxP6WjbYkYehHm3o7zX62z63llIR1Z6zv5A0X9LJkiZLesHMnpb0tKSzJN0naZqkIzLrnynpjuKX2e1Pf/qTUqmULvnIR3X//ferdX9rlOUAw9KZSqk92f1BHDdXPBtIh/j7qq8A2TNc2iHhMXe7Q5fZAMt6vX5KUjL9Hsn0ZM8Am7O9Dnm97v93z+Dbs/2Q7Xu9B0qD5T5br7Ze8+lpH2CZ1P3deujr9Zi2nu291+vRlu+RCN7392huW20ipRn1Sblb1xGBrp9jl1IydeZMp6TMupY5WpDdLv0VS7m62pOeO59zZMGHHjqLJR7PhMrcP9p6h854TFMPP0I33fR9JRLRxKSowtmZkm5396Sk7Wb2lKR3S3pG0tVmNlfSSkkTzOwISadJ+nzvFzGzKyVdKUkzZ84saMH79+/XhRdeqM9+9rNyd/3mN78p6PsBobHeHz6HfMAM97dwYL+1h6G/vaV9htU+lnW9Rq/X6l6nV3DMWe+Qtty6+vmSjvYr3WfI6COodM1br5Akycx7zR8apEzdj+557/Fe/W2Hgc2s79THj20p+vv2uO5nZq98e85wlNzhKunn9LKuNu+5bnrbXuvkznt23e4hMF3ruJRMppRMptQxSN1bt72pt99+WxMmTCjK16m3qMJZnz9L7r7FzCZIukDpvWgTJf2lpP3u/nYf698i6RZJWrBgQUF/09fX1+v++++Xu+uBBx4o5FsBeTerIamvvnPfqF8ne72+HodBU/1MZ9fJjNPrdOv6JZ3bnrt+qseYwO71c9fra1l2O89ZlspdVz3/8s/uDfCctq6/9LPr5Pzln90b0LXHwXsGLkQjffguvfM3ZlLMTDHLac88shcNj2WCXMxcMaUDYywzb7nzmfXN0tPWNZ377F1hMdveHR79kCCZvWh5j6Ap9RhS0TvQ5soN73I7pK3nUIhez0pfWunmFfWZPWPKee7eE9b1vZ5zMfXsXrTePzfJ7LJePzddP5NdP9/etU5I0nvPLD0MIpazNy2zZ23q1MNVXx/d+YtRhbOnJX3azG5TOoCdLelLmWXPSbpa0vmSJkm6K/OI1Mknn6w33nhD/3X3XZpel1TN+MC+0xC5vvam9H+obghjz3psawMs6+vQYc/Xf6vN9KNX6vscX3boI3t3i0wASkmd7l1hKgq5Jz3EYjElsmPLcse1VGTHl3SPPUvEE0rEY11t/Y2VGWzc2VDHlw005kxS12v1Ne6s93i0/saZ5XPM2UDyNeYs95GPsWfFGnOWO8YsmZlPplJStsbMGLNUKpkZc5Ydi9Y9zqzHmLOctvR4s15f4+zXcYA+SYe5nv1q2eke486se8xZ9vsj05a7LB1G4ulxaJnxaPFYPBNQDv1ZGcr4zf7GnvUeA5Y923y448/6Wq+v1xto3FkpnOUeVTi7R+lDlX9S+nvxOnfflln2jKQ/d/c1ZrZR6fD2TDRldvvc5z6nbdu2afPLz+h/vXtv1OWMScmUunZbtw+wmzu7mzrpPc/c7Ezlnq1p3WdpevcZmz323ByytyZz5mbOGIvsXpeu5ZmB8t17kLzrr8Qo43rMrHvgfCKeuYxIQolE9xmQu+MJxXPu91oRT4zobM2Bzs7sa9uBztLsfXZm7+2zv1ABoJwUNZxlr3Hm6T8XvqTuvWW569wq6dbMdIekumLWiKHpTKnrumYHc65zdrAz9xZU6baet6dS5hpn6Udb0tSelNqSnrczAmNmmQ/5XpeWSCTSwaVHAEi3VSYqhnW2UF9/tQ31r8WhvvZQLz+R/YsRADA2cJ2zMuUutSWlA52xrts/pW/31PNWUAcywaulM6aWZFwHO2Nq6XC1JYf2PtVVlek7BFRXq6axVtU1NZpYW9d1V4DcOwT0vjNA9o4AuY/snQJy7wzQ8zpeCfa0AABKGuGsxHWklAlXmWDV0X1Lptx7bB7ozKyTjKfn29OnOvcnFoupvq5WDfX1qp/UoHENjZqWuZ9mfc7zQPfWrKmpYY8OAADDRDgrouw4pfaU9bgafltSmUN+3Yf/DuYcJsydbumM6WDmPpcHOlwdg+zBqqutSQesCY0aN268pjc0pG9m3uvR2Nio+vr6rvna2trgB0wCADAWEc6GacuBmL60eFKP6++Yuk8jzp6SnD2FP3ul9c6UqzM1/AvyVVdVqra2VrW1Naqf2KgJ9Q2aUV/ftfcqG6Zyg1X2UVdXF9kF9AAAwMjwyT0MF198sWpqaiTpkFPBe59ub2Zd46JyH9nxVNlHZWWlampqejyqq6u7DgsSrgAAKC988g/DwoULtXDhwqjLAAAAYxijtQEAAAJCOAMAAAgI4QwAACAghDMAAICAEM4AAAACQjgDAAAICOEMAAAgIIQzAACAgBDOAAAAAkI4AwAACAjhDAAAICCEMwAAgIAQzgAAAAJCOAMAAAgI4QwAACAghDMAAICAEM4AAAACQjgDAAAICOEMAAAgIIQzAACAgBDOAAAAAkI4AwAACAjhDAAAICCEMwAAgIAQzgAAAAJCOAMAAAgI4QwAACAghDMAAICAEM4AAAACQjgDAAAICOEMAAAgIIQzAACAgBDOAAAAAkI4AwAACAjhDAAAICCEMwAAgIAQzgAAAAJCOAMAAAgI4QwAACAghDMAAICAEM4AAAACQjgDAAAICOEMAAAgIIQzAACAgBDOAAAAAkI4AwAACAjhDAAAICCEMwAAgIAQzgAAAAJCOAMAAAgI4QwAACAghDMAAICAEM4AAAACQjgDAAAICOEMAAAgIIQzAACAgBDOAAAAAkI4AwAACAjhDAAAICCEMwAAgIAQzgAAAAJCOAMAAAgI4QwAACAghDMAAICAEM4AAAACQjgDAAAICOEMAAAgIIQzAACAgBDOAAAAAkI4AwAACAjhDAAAICCEMwAAgIAQzgAAAAJCOAMAAAgI4QwAACAghDMAAICAEM4AAAACQjgDAAAICOEMAAAgIIQzAACAgBDOAAAAAkI4AwAACAjhDAAAICCEMwAAgIAQzgAAAAJCOAMAAAgI4QwAACAghDMAAICAEM4AAAACQjgDAAAICOEMAAAgIIQzAACAgBDOAAAAAkI4AwAACAjhDAAAICCEMwAAgIAQzgAAAAJi7h51DXlhZjskbSzCW02WtLMI74Oho0/CQ5+EiX4JD30SpmL0yyx3n9LXgjETzorFzJa4+4Ko60A3+iQ89EmY6Jfw0CdhirpfOKwJAAAQEMIZAABAQAhnw3dL1AXgEPRJeOiTMNEv4aFPwhRpvzDmDAAAICDsOQMAAAgI4WyIzOwCM1ttZmvM7MtR11OOzGyGmT1hZq+a2Qoz+0KmfaKZPWJmr2eeJ0Rdazkys7iZLTOz+zPz9EuEzGy8md1lZqsyPzOn0SfRM7NrMr+/lpvZ7WZWTb8Ul5n9wszeNLPlOW399oGZfSXz2b/azN5fjBoJZ0NgZnFJ/ybpA5LmSrrMzOZGW1VZ6pT0D+5+gqT3SLoq0w9flvSYux8j6bHMPIrvC5JezZmnX6L1I0kPufvxkk5Wum/okwiZ2TRJn5e0wN1PlBSXtEj0S7H9UtIFvdr67IPMZ8wiSfMy2/w0kwkKinA2NAslrXH3de7eLukOSRdHXFPZcfet7v5iZvptpT9spindF7dlVrtN0ocjKbCMmdl0SR+S9POcZvolImbWKOlsSbdKkru3u/se0SchSEiqMbOEpFpJb4h+KSp3f1rS7l7N/fXBxZLucPc2d18vaY3SmaCgCGdDM03S5pz55kwbImJmTZJOkbRY0lR33yqlA5ykwyIsrVz9UNJ1klI5bfRLdI6StEPSf2QONf/czOpEn0TK3bdIuknSJklbJe1199+JfglBf30Qyec/4WxorI82TnONiJnVS/qNpKvdfV/U9ZQ7M7tQ0pvuvjTqWtAlIemdkm5291MkHRCHyiKXGcd0saTZko6UVGdmH4+2Kgwiks9/wtnQNEuakTM/Xeld0SgyM6tQOpj9yt3vzjRvN7MjMsuPkPRmVPWVqTMkXWRmG5Q+5H++mf1f0S9RapbU7O6LM/N3KR3W6JNovU/Senff4e4dku6WdLrolxD01weRfP4TzobmBUnHmNlsM6tUenDgfRHXVHbMzJQeQ/Oqu/8gZ9F9ki7PTF8u6d5i11bO3P0r7j7d3ZuU/tl43N0/LvolMu6+TdJmMzsu0/ReSStFn0Rtk6T3mFlt5vfZe5UeO0u/RK+/PrhP0iIzqzKz2ZKOkfR8oYvhIrRDZGYfVHpcTVzSL9z9W9FWVH7M7ExJz0h6Rd1jm76q9LizOyXNVPqX30fdvfdgTxSBmZ0r6YvufqGZTRL9Ehkzm6/0CRqVktZJ+h9K/0FOn0TIzL4u6WNKn32+TNLfSqoX/VI0Zna7pHMlTZa0XdI/S/ov9dMHZvZPkv5G6T672t0fLHiNhDMAAIBwcFgTAAAgIIQzAACAgBDOAAAAAkI4AwAACAjhDAAAICCEMwCQZGaXmNlLvR4pM/t7M3Mz+1zOuv9qZn8dYbkAxjDCGQBIcvd73H1+9iHpp0pfV+9hpa8W/oXMRagBoKAIZwDQi5kdK+lrkj6h9AWPd0h6TN1XEAeAgiGcAUCOzP1b/1PpOx1syln0HUn/YGbxaCoDUC4IZwDQ0/+StMLd78htdPf1St9T7/+JpCoAZSMRdQEAEIrMvUE/Iumd/axyo6S7JD1dpJIAlCH2nAGAJDObIOk/JH3S3d/uax13XyVppaQLi1kbgPLCnjMASPs7SYdJutnMcttv77XetyQtK1ZRAMqPuXvUNQAAACCDw5oAAAABIZwBAAAEhHAGAAAQEMIZAABAQAhnAAAAASGcAQAABIRwBgAAEBDCGQAAQED+fwaadxuTLyMjAAAAAElFTkSuQmCC\n"}, "execution_count": 8}], "source": "dist.plot_distribution(\"ZN\", contrast=\"CRIM\")"}, {"cell_type": "markdown", "metadata": {"papermill": {"exception": false, "start_time": "2020-10-29T02:55:15.552327", "end_time": "2020-10-29T02:55:15.567308", "duration": 0.014981, "status": "completed"}, "tags": []}, "source": "## Display diagnostic values"}, {"cell_type": "code", "execution_count": 9, "metadata": {"execution": {"iopub.execute_input": "2020-10-29T02:55:15.603274Z", "iopub.status.busy": "2020-10-29T02:55:15.602276Z", "iopub.status.idle": "2020-10-29T02:55:15.606278Z", "shell.execute_reply": "2020-10-29T02:55:15.607300Z"}, "papermill": {"exception": false, "start_time": "2020-10-29T02:55:15.581321", "end_time": "2020-10-29T02:55:15.607300", "duration": 0.025979, "status": "completed"}, "tags": []}, "outputs": [{"output_type": "execute_result", "metadata": {}, "data": {"text/plain": "ZN         2.219063\nINDUS      0.294146\nCHAS       3.395799\nNOX        0.727144\nRM         0.402415\nDIS        1.008779\nRAD        1.001833\nTAX        0.667968\nPTRATIO   -0.799945\nB         -2.881798\nLSTAT      0.903771\ntarget     1.104811\ndtype: float64"}, "execution_count": 9}], "source": "dist.skew_value"}], "metadata": {"kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, "language_info": {"name": "python", "version": "3.7.9", "mimetype": "text/x-python", "codemirror_mode": {"name": "ipython", "version": 3}, "pygments_lexer": "ipython3", "nbconvert_exporter": "python", "file_extension": ".py"}, "papermill": {"default_parameters": {}, "duration": 6.704517, "end_time": "2020-10-29T02:55:16.131270", "environment_variables": {}, "exception": null, "input_path": "C:\\workspace\\data-describe\\examples\\Distributions.ipynb", "output_path": "C:\\workspace\\data-describe\\examples\\Distributions.ipynb", "parameters": {}, "start_time": "2020-10-29T02:55:09.426753", "version": "2.2.0"}}, "nbformat": 4, "nbformat_minor": 4}