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baldwint/dumb_multiindex_tricks.ipynb

Created January 26, 2017 20:40
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dumb_multiindex_tricks.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Inserting index levels"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def insert_index_level(index, val, level=None, name=None):\n",
" \"\"\"Insert a new level to a MultiIndex. Will be called `val` for all data.\n",
" \n",
" Optionally specify which `level`the new level will be, otherwise it will be innermost.\n",
" \n",
" \"\"\" \n",
" if index.nlevels > 1: # already a multiindex\n",
" tuples = index.values\n",
" else: # only a single index\n",
" tuples = ((v,) for v in index.values)\n",
" if level is None:\n",
" level = index.nlevels\n",
" new_names = list(index.names)\n",
" new_names.insert(level, name)\n",
" new_labels = [(tup[:level] + (val,) + tup[level:]) for tup in tuples]\n",
" return pd.MultiIndex.from_tuples(new_labels, names=new_names)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"example usage:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"np.random.seed(0)\n",
"data = np.random.randn(4,4)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"idx = pd.MultiIndex.from_tuples([(1, u'one'), (1, u'two'),\n",
" (2, u'one'), (2, u'two')],\n",
" names=['foo', 'bar'])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th>foo</th>\n",
" <th colspan=\"2\" halign=\"left\">1</th>\n",
" <th colspan=\"2\" halign=\"left\">2</th>\n",
" </tr>\n",
" <tr>\n",
" <th>bar</th>\n",
" <th>one</th>\n",
" <th>two</th>\n",
" <th>one</th>\n",
" <th>two</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.764052</td>\n",
" <td>0.400157</td>\n",
" <td>0.978738</td>\n",
" <td>2.240893</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.867558</td>\n",
" <td>-0.977278</td>\n",
" <td>0.950088</td>\n",
" <td>-0.151357</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-0.103219</td>\n",
" <td>0.410599</td>\n",
" <td>0.144044</td>\n",
" <td>1.454274</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.761038</td>\n",
" <td>0.121675</td>\n",
" <td>0.443863</td>\n",
" <td>0.333674</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"foo 1 2 \n",
"bar one two one two\n",
"0 1.764052 0.400157 0.978738 2.240893\n",
"1 1.867558 -0.977278 0.950088 -0.151357\n",
"2 -0.103219 0.410599 0.144044 1.454274\n",
"3 0.761038 0.121675 0.443863 0.333674"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test = pd.DataFrame(data=data, columns=idx)\n",
"test"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th>foo</th>\n",
" <th colspan=\"2\" halign=\"left\">1</th>\n",
" <th colspan=\"2\" halign=\"left\">2</th>\n",
" </tr>\n",
" <tr>\n",
" <th>new</th>\n",
" <th colspan=\"2\" halign=\"left\">hi</th>\n",
" <th colspan=\"2\" halign=\"left\">hi</th>\n",
" </tr>\n",
" <tr>\n",
" <th>bar</th>\n",
" <th>one</th>\n",
" <th>two</th>\n",
" <th>one</th>\n",
" <th>two</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.764052</td>\n",
" <td>0.400157</td>\n",
" <td>0.978738</td>\n",
" <td>2.240893</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.867558</td>\n",
" <td>-0.977278</td>\n",
" <td>0.950088</td>\n",
" <td>-0.151357</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-0.103219</td>\n",
" <td>0.410599</td>\n",
" <td>0.144044</td>\n",
" <td>1.454274</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.761038</td>\n",
" <td>0.121675</td>\n",
" <td>0.443863</td>\n",
" <td>0.333674</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"foo 1 2 \n",
"new hi hi \n",
"bar one two one two\n",
"0 1.764052 0.400157 0.978738 2.240893\n",
"1 1.867558 -0.977278 0.950088 -0.151357\n",
"2 -0.103219 0.410599 0.144044 1.454274\n",
"3 0.761038 0.121675 0.443863 0.333674"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test.columns = insert_index_level(test.columns, 'hi', level=1, name='new')\n",
"test"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"this will turn Index to MultiIndex if necessary:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>one</th>\n",
" <th>two</th>\n",
" <th>three</th>\n",
" <th>four</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.764052</td>\n",
" <td>0.400157</td>\n",
" <td>0.978738</td>\n",
" <td>2.240893</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.867558</td>\n",
" <td>-0.977278</td>\n",
" <td>0.950088</td>\n",
" <td>-0.151357</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-0.103219</td>\n",
" <td>0.410599</td>\n",
" <td>0.144044</td>\n",
" <td>1.454274</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.761038</td>\n",
" <td>0.121675</td>\n",
" <td>0.443863</td>\n",
" <td>0.333674</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" one two three four\n",
"0 1.764052 0.400157 0.978738 2.240893\n",
"1 1.867558 -0.977278 0.950088 -0.151357\n",
"2 -0.103219 0.410599 0.144044 1.454274\n",
"3 0.761038 0.121675 0.443863 0.333674"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"single = pd.DataFrame(data=data, columns=('one', 'two', 'three', 'four'))\n",
"single"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th>new</th>\n",
" <th colspan=\"4\" halign=\"left\">hi</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>one</th>\n",
" <th>two</th>\n",
" <th>three</th>\n",
" <th>four</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.764052</td>\n",
" <td>0.400157</td>\n",
" <td>0.978738</td>\n",
" <td>2.240893</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.867558</td>\n",
" <td>-0.977278</td>\n",
" <td>0.950088</td>\n",
" <td>-0.151357</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-0.103219</td>\n",
" <td>0.410599</td>\n",
" <td>0.144044</td>\n",
" <td>1.454274</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.761038</td>\n",
" <td>0.121675</td>\n",
" <td>0.443863</td>\n",
" <td>0.333674</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"new hi \n",
" one two three four\n",
"0 1.764052 0.400157 0.978738 2.240893\n",
"1 1.867558 -0.977278 0.950088 -0.151357\n",
"2 -0.103219 0.410599 0.144044 1.454274\n",
"3 0.761038 0.121675 0.443863 0.333674"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"single.columns = insert_index_level(single.columns, 'hi', level=0, name='new')\n",
"single"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"bak = single.columns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# What's the point\n",
"\n",
"Sometimes I want to concatenate two frames whose indices do not have the same `nlevels`. When I do this, the index becomes unreadable because not all the tuples have the same length."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>(1, hi, one)</th>\n",
" <th>(1, hi, two)</th>\n",
" <th>(2, hi, one)</th>\n",
" <th>(2, hi, two)</th>\n",
" <th>(hi, one)</th>\n",
" <th>(hi, two)</th>\n",
" <th>(hi, three)</th>\n",
" <th>(hi, four)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.764052</td>\n",
" <td>0.400157</td>\n",
" <td>0.978738</td>\n",
" <td>2.240893</td>\n",
" <td>1.764052</td>\n",
" <td>0.400157</td>\n",
" <td>0.978738</td>\n",
" <td>2.240893</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.867558</td>\n",
" <td>-0.977278</td>\n",
" <td>0.950088</td>\n",
" <td>-0.151357</td>\n",
" <td>1.867558</td>\n",
" <td>-0.977278</td>\n",
" <td>0.950088</td>\n",
" <td>-0.151357</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-0.103219</td>\n",
" <td>0.410599</td>\n",
" <td>0.144044</td>\n",
" <td>1.454274</td>\n",
" <td>-0.103219</td>\n",
" <td>0.410599</td>\n",
" <td>0.144044</td>\n",
" <td>1.454274</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.761038</td>\n",
" <td>0.121675</td>\n",
" <td>0.443863</td>\n",
" <td>0.333674</td>\n",
" <td>0.761038</td>\n",
" <td>0.121675</td>\n",
" <td>0.443863</td>\n",
" <td>0.333674</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" (1, hi, one) (1, hi, two) (2, hi, one) (2, hi, two) (hi, one) \\\n",
"0 1.764052 0.400157 0.978738 2.240893 1.764052 \n",
"1 1.867558 -0.977278 0.950088 -0.151357 1.867558 \n",
"2 -0.103219 0.410599 0.144044 1.454274 -0.103219 \n",
"3 0.761038 0.121675 0.443863 0.333674 0.761038 \n",
"\n",
" (hi, two) (hi, three) (hi, four) \n",
"0 0.400157 0.978738 2.240893 \n",
"1 -0.977278 0.950088 -0.151357 \n",
"2 0.410599 0.144044 1.454274 \n",
"3 0.121675 0.443863 0.333674 "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.concat((test, single), axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"now I can resolve this by inserting a level to the shallow multiindex:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th>foo</th>\n",
" <th colspan=\"2\" halign=\"left\">1</th>\n",
" <th colspan=\"2\" halign=\"left\">2</th>\n",
" <th colspan=\"4\" halign=\"left\">3</th>\n",
" </tr>\n",
" <tr>\n",
" <th>new</th>\n",
" <th colspan=\"2\" halign=\"left\">hi</th>\n",
" <th colspan=\"2\" halign=\"left\">hi</th>\n",
" <th colspan=\"4\" halign=\"left\">hi</th>\n",
" </tr>\n",
" <tr>\n",
" <th>bar</th>\n",
" <th>one</th>\n",
" <th>two</th>\n",
" <th>one</th>\n",
" <th>two</th>\n",
" <th>one</th>\n",
" <th>two</th>\n",
" <th>three</th>\n",
" <th>four</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.764052</td>\n",
" <td>0.400157</td>\n",
" <td>0.978738</td>\n",
" <td>2.240893</td>\n",
" <td>1.764052</td>\n",
" <td>0.400157</td>\n",
" <td>0.978738</td>\n",
" <td>2.240893</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.867558</td>\n",
" <td>-0.977278</td>\n",
" <td>0.950088</td>\n",
" <td>-0.151357</td>\n",
" <td>1.867558</td>\n",
" <td>-0.977278</td>\n",
" <td>0.950088</td>\n",
" <td>-0.151357</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-0.103219</td>\n",
" <td>0.410599</td>\n",
" <td>0.144044</td>\n",
" <td>1.454274</td>\n",
" <td>-0.103219</td>\n",
" <td>0.410599</td>\n",
" <td>0.144044</td>\n",
" <td>1.454274</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.761038</td>\n",
" <td>0.121675</td>\n",
" <td>0.443863</td>\n",
" <td>0.333674</td>\n",
" <td>0.761038</td>\n",
" <td>0.121675</td>\n",
" <td>0.443863</td>\n",
" <td>0.333674</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"foo 1 2 3 \\\n",
"new hi hi hi \n",
"bar one two one two one two three \n",
"0 1.764052 0.400157 0.978738 2.240893 1.764052 0.400157 0.978738 \n",
"1 1.867558 -0.977278 0.950088 -0.151357 1.867558 -0.977278 0.950088 \n",
"2 -0.103219 0.410599 0.144044 1.454274 -0.103219 0.410599 0.144044 \n",
"3 0.761038 0.121675 0.443863 0.333674 0.761038 0.121675 0.443863 \n",
"\n",
"foo \n",
"new \n",
"bar four \n",
"0 2.240893 \n",
"1 -0.151357 \n",
"2 1.454274 \n",
"3 0.333674 "
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"single.columns = insert_index_level(single.columns, 3, 0)\n",
"pd.concat((test, single), axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"this is best, but occasionaly it is nicer to reduce the number of levels in the deep one:\n",
"\n",
"# combining index levels"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def flatten_levels(index, lo=0, hi=None, delimiter='_'):\n",
" \"\"\"reduce number of levels in a MultiIndex by string concatenation\"\"\"\n",
" assert index.nlevels > 1\n",
" if hi is None:\n",
" hi = index.nlevels\n",
" def collapse(tup):\n",
" return (tup[:lo] + (delimiter.join(el for el in tup[lo:hi] if el is not None),) + tup[hi:])\n",
" new_values = [collapse(tup) for tup in index.values]\n",
" new_names = collapse(index.names)\n",
" return pd.MultiIndex.from_tuples(new_values, names=new_names)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th>new</th>\n",
" <th colspan=\"4\" halign=\"left\">hi</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>one</th>\n",
" <th>two</th>\n",
" <th>three</th>\n",
" <th>four</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.764052</td>\n",
" <td>0.400157</td>\n",
" <td>0.978738</td>\n",
" <td>2.240893</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.867558</td>\n",
" <td>-0.977278</td>\n",
" <td>0.950088</td>\n",
" <td>-0.151357</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-0.103219</td>\n",
" <td>0.410599</td>\n",
" <td>0.144044</td>\n",
" <td>1.454274</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.761038</td>\n",
" <td>0.121675</td>\n",
" <td>0.443863</td>\n",
" <td>0.333674</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"new hi \n",
" one two three four\n",
"0 1.764052 0.400157 0.978738 2.240893\n",
"1 1.867558 -0.977278 0.950088 -0.151357\n",
"2 -0.103219 0.410599 0.144044 1.454274\n",
"3 0.761038 0.121675 0.443863 0.333674"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"single.columns = bak\n",
"single"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th>foo</th>\n",
" <th colspan=\"2\" halign=\"left\">1</th>\n",
" <th colspan=\"2\" halign=\"left\">2</th>\n",
" </tr>\n",
" <tr>\n",
" <th>new_bar</th>\n",
" <th>hi_one</th>\n",
" <th>hi_two</th>\n",
" <th>hi_one</th>\n",
" <th>hi_two</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.764052</td>\n",
" <td>0.400157</td>\n",
" <td>0.978738</td>\n",
" <td>2.240893</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.867558</td>\n",
" <td>-0.977278</td>\n",
" <td>0.950088</td>\n",
" <td>-0.151357</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-0.103219</td>\n",
" <td>0.410599</td>\n",
" <td>0.144044</td>\n",
" <td>1.454274</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.761038</td>\n",
" <td>0.121675</td>\n",
" <td>0.443863</td>\n",
" <td>0.333674</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"foo 1 2 \n",
"new_bar hi_one hi_two hi_one hi_two\n",
"0 1.764052 0.400157 0.978738 2.240893\n",
"1 1.867558 -0.977278 0.950088 -0.151357\n",
"2 -0.103219 0.410599 0.144044 1.454274\n",
"3 0.761038 0.121675 0.443863 0.333674"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test.columns = flatten_levels(test.columns, lo=1)\n",
"test"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th>new</th>\n",
" <th colspan=\"4\" halign=\"left\">hi</th>\n",
" <th colspan=\"2\" halign=\"left\">1</th>\n",
" <th colspan=\"2\" halign=\"left\">2</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>one</th>\n",
" <th>two</th>\n",
" <th>three</th>\n",
" <th>four</th>\n",
" <th>hi_one</th>\n",
" <th>hi_two</th>\n",
" <th>hi_one</th>\n",
" <th>hi_two</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.764052</td>\n",
" <td>0.400157</td>\n",
" <td>0.978738</td>\n",
" <td>2.240893</td>\n",
" <td>1.764052</td>\n",
" <td>0.400157</td>\n",
" <td>0.978738</td>\n",
" <td>2.240893</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
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" <td>0.950088</td>\n",
" <td>-0.151357</td>\n",
" <td>1.867558</td>\n",
" <td>-0.977278</td>\n",
" <td>0.950088</td>\n",
" <td>-0.151357</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-0.103219</td>\n",
" <td>0.410599</td>\n",
" <td>0.144044</td>\n",
" <td>1.454274</td>\n",
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" <td>0.144044</td>\n",
" <td>1.454274</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.761038</td>\n",
" <td>0.121675</td>\n",
" <td>0.443863</td>\n",
" <td>0.333674</td>\n",
" <td>0.761038</td>\n",
" <td>0.121675</td>\n",
" <td>0.443863</td>\n",
" <td>0.333674</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"new hi 1 2 \\\n",
" one two three four hi_one hi_two hi_one \n",
"0 1.764052 0.400157 0.978738 2.240893 1.764052 0.400157 0.978738 \n",
"1 1.867558 -0.977278 0.950088 -0.151357 1.867558 -0.977278 0.950088 \n",
"2 -0.103219 0.410599 0.144044 1.454274 -0.103219 0.410599 0.144044 \n",
"3 0.761038 0.121675 0.443863 0.333674 0.761038 0.121675 0.443863 \n",
"\n",
"new \n",
" hi_two \n",
"0 2.240893 \n",
"1 -0.151357 \n",
"2 1.454274 \n",
"3 0.333674 "
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.concat([single, test], axis=1)"
]
}
],
"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": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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