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Yoav Ram
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‎pandas duplicate column bug.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"from StringIO import StringIO"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"data = \"\"\"A,A,B,B,B\n",
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" 1,2,3,4,5\n",
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" 6,7,8,9,10\n",
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" 11,12,13,14,15\"\"\"\n",
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"\n",
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"# check default beahviour\n",
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"df = pd.read_table(StringIO(data), sep=',')\n",
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"assert (list(df.columns) == ['A', 'A.1', 'B', 'B.1', 'B.2'])\n",
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"\n",
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"df = pd.read_table(StringIO(data), sep=',', mangle_dupe_cols=False)\n",
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"assert (list(df.columns)==['A', 'A', 'B', 'B', 'B'])\n",
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"\n",
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"df = pd.read_table(StringIO(data), sep=',', mangle_dupe_cols=True)\n",
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"assert (list(df.columns)== ['A', 'A.1', 'B', 'B.1', 'B.2'])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 30,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>A</th>\n",
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" <th>A.1</th>\n",
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" <th>B</th>\n",
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" <th>B.1</th>\n",
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" <th>B.2</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>1</td>\n",
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" <td>2</td>\n",
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" <td>3</td>\n",
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" <td>4</td>\n",
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" <td>5</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>6</td>\n",
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" <td>7</td>\n",
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" <td>8</td>\n",
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" <td>9</td>\n",
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" <td>10</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>11</td>\n",
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" <td>12</td>\n",
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" <td>13</td>\n",
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" <td>14</td>\n",
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" <td>15</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" A A.1 B B.1 B.2\n",
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"0 1 2 3 4 5\n",
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"1 6 7 8 9 10\n",
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"2 11 12 13 14 15"
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]
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},
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"execution_count": 30,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df = pd.read_table(StringIO(data), sep=',', mangle_dupe_cols=True)\n",
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"df"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 31,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"| | A | A.1 | B | B.1 | B.2 |\n",
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"|---:|----:|------:|----:|------:|------:|\n",
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"| 0 | 1 | 2 | 3 | 4 | 5 |\n",
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"| 1 | 6 | 7 | 8 | 9 | 10 |\n",
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"| 2 | 11 | 12 | 13 | 14 | 15 |\n"
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]
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}
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],
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"source": [
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"import tabulate\n",
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"print tabulate.tabulate(df, headers=df.columns.tolist(), tablefmt='pipe')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>A</th>\n",
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" <th>A</th>\n",
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" <th>B</th>\n",
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" <th>B</th>\n",
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" <th>B</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>2</td>\n",
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" <td>2</td>\n",
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" <td>5</td>\n",
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" <td>5</td>\n",
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" <td>5</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>7</td>\n",
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" <td>7</td>\n",
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" <td>10</td>\n",
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" <td>10</td>\n",
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" <td>10</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>12</td>\n",
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" <td>12</td>\n",
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" <td>15</td>\n",
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" <td>15</td>\n",
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" <td>15</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" A A B B B\n",
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"0 2 2 5 5 5\n",
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"1 7 7 10 10 10\n",
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"2 12 12 15 15 15"
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]
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},
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"execution_count": 25,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df = pd.read_table(StringIO(data), sep=',', mangle_dupe_cols=False)\n",
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"df"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 29,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"| | A | A | B | B | B |\n",
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"|---:|----:|----:|----:|----:|----:|\n",
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"| 0 | 2 | 2 | 5 | 5 | 5 |\n",
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"| 1 | 7 | 7 | 10 | 10 | 10 |\n",
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"| 2 | 12 | 12 | 15 | 15 | 15 |\n"
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]
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}
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],
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"source": [
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"print tabulate.tabulate(df, headers=df.columns.tolist(), tablefmt='pipe')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[u'fancy_grid',\n",
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" u'grid',\n",
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" u'html',\n",
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" u'latex',\n",
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" u'latex_booktabs',\n",
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" u'mediawiki',\n",
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" u'orgtbl',\n",
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" u'pipe',\n",
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" u'plain',\n",
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" u'psql',\n",
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" u'rst',\n",
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" u'simple',\n",
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" u'tsv']"
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]
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},
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"execution_count": 27,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"tabulate.tabulate_formats"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 2",
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"language": "python",
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"name": "python2"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.8"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}

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