chapters 6 and 7

Author: Soledad Galli

ch6-outliers/Recipe1-Outlier-Trimming.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5, 3)"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.random.seed(29)\n",
+    "\n",
+    "rng_min = pd.date_range('2019-03-05', periods=5, freq='T')\n",
+    "rng_day = pd.date_range('2019-03-05', periods=5, freq='D')\n",
+    "\n",
+    "df = pd.DataFrame({ 'Date1': rng_min, 'Date2': rng_day, 'Val': np.random.randn(len(rng_min)) })\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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",
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+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Date1</th>\n",
+       "      <th>Date2</th>\n",
+       "      <th>Val</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2019-03-05 00:00:00</td>\n",
+       "      <td>2019-03-05</td>\n",
+       "      <td>-0.417482</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2019-03-05 00:01:00</td>\n",
+       "      <td>2019-03-06</td>\n",
+       "      <td>0.706032</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2019-03-05 00:02:00</td>\n",
+       "      <td>2019-03-07</td>\n",
+       "      <td>1.915985</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2019-03-05 00:03:00</td>\n",
+       "      <td>2019-03-08</td>\n",
+       "      <td>-2.141755</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2019-03-05 00:04:00</td>\n",
+       "      <td>2019-03-09</td>\n",
+       "      <td>0.719057</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                Date1      Date2       Val\n",
+       "0 2019-03-05 00:00:00 2019-03-05 -0.417482\n",
+       "1 2019-03-05 00:01:00 2019-03-06  0.706032\n",
+       "2 2019-03-05 00:02:00 2019-03-07  1.915985\n",
+       "3 2019-03-05 00:03:00 2019-03-08 -2.141755\n",
+       "4 2019-03-05 00:04:00 2019-03-09  0.719057"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
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+}