data-normalisation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-15T08:21:39.632487Z",
     "start_time": "2019-05-15T08:21:38.401500Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "# define pandas options\n",
    "opts = {\n",
    "    'display.max_columns': None,\n",
    "    'display.max_colwidth': 50,\n",
    "    'display.expand_frame_repr': False,  # Don't wrap to multiple pages\n",
    "    'display.max_rows': 14, # maximum number of rows pandas should output \n",
    "    'display.max_seq_items': 50,         # Max length of printed sequence\n",
    "    'display.precision': 4, # Floating point output precision\n",
    "    'display.show_dimensions': True,\n",
    "    'display.width':200, # Width of the display in characters. in a terminal this can be set to None, not in notebook or qtconsole.\n",
    "    }\n",
    "\n",
    "# set pandas options\n",
    "[pd.set_option(n,o) for n,o in opts.items()]\n",
    "\n",
    "# plotting\n",
    "import matplotlib\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# useful for displaying html, images and so on\n",
    "from IPython.display import display\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-15T09:02:04.945010Z",
     "start_time": "2019-05-15T09:02:04.929769Z"
    }
   },
   "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>0.6611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.7424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.8250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.6117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.6216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.7763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.8110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.1272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4.1058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.8813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.8847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.1660</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      data\n",
       "0   0.6611\n",
       "1   1.7424\n",
       "3   1.8250\n",
       "9   2.6117\n",
       "5   2.6216\n",
       "4   2.7763\n",
       "7   2.8110\n",
       "6   3.1272\n",
       "10  4.1058\n",
       "8   4.8813\n",
       "2   4.8847\n",
       "11  5.1660\n",
       "\n",
       "[12 rows x 1 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [0.6610893966935933,\n",
    "1.7423882770775445,\n",
    "4.884723708070242,\n",
    "1.8249901622170461,\n",
    "2.7763139747735814,\n",
    "2.621601355004263,\n",
    "3.127249155066913,\n",
    "2.810962943840614,\n",
    "4.881331167897244,\n",
    "2.611656683144541,\n",
    "4.105819626936935,\n",
    "5.165968850726315]\n",
    "\n",
    "df = pd.DataFrame(data,columns=['data']).sort_values('data')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-15T09:02:06.709649Z",
     "start_time": "2019-05-15T09:02:06.705908Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-15T09:04:05.849513Z",
     "start_time": "2019-05-15T09:04:05.564313Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.675149533470947 2.5077768681686736\n",
      "{0: 0.0, 1: 0.08333333333333333, 2: 0.0, 3: 0.16666666666666666, 4: 0.16666666666666666, 5: 0.25, 6: 0.0, 7: 0.08333333333333333, 8: 0.08333333333333333, 9: 0.16666666666666666}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    12.0000\n",
       "mean      5.6751\n",
       "std       2.6193\n",
       "min       1.1259\n",
       "25%       4.3958\n",
       "50%       5.1018\n",
       "75%       7.9096\n",
       "max       9.5247\n",
       "Name: scaled, Length: 8, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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": [
    "scaler = preprocessing.StandardScaler()\n",
    "std = df.data.std()*1.785\n",
    "mean = df.data.mean()*1.83\n",
    "\n",
    "df['scaled'] = scaler.fit_transform(df)*std+mean\n",
    "\n",
    "print(mean,std)\n",
    "freq, bins = np.histogram(df['scaled'], bins=range(11),density=True)\n",
    "\n",
    "print(dict(zip(bins,freq)))\n",
    "display(df.scaled.describe())\n",
    "ax = df['scaled'].plot(kind='hist',bins=10,density=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}

Bow :-)

Bitte :-D 🍺