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Update See Also. Add more examples and specifies return type
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‎Pandas Documentation Day.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": 1,
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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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"ename": "ImportError",
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"evalue": "C extension: numpy.core.multiarray failed to import not built. If you want to import pandas from the source directory, you may need to run 'python setup.py build_ext --inplace --force' to build the C extensions first.",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m__init__.pxd\u001b[0m in \u001b[0;36mnumpy.import_array\u001b[0;34m()\u001b[0m\n",
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"\u001b[0;31mRuntimeError\u001b[0m: module compiled against API version 0xb but this version of numpy is 0xa",
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"\nDuring handling of the above exception, another exception occurred:\n",
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"\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m/home/aaron/projects/pandas/pandas/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m from pandas._libs import (hashtable as _hashtable,\n\u001b[0m\u001b[1;32m 27\u001b[0m \u001b[0mlib\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0m_lib\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/home/aaron/projects/pandas/pandas/_libs/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mtslib\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0miNaT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNaT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTimestamp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTimedelta\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mOutOfBoundsDatetime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/home/aaron/projects/pandas/pandas/_libs/tslibs/conversion.pxd\u001b[0m in \u001b[0;36minit pandas._libs.tslib\u001b[0;34m()\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mcdef\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0m_TSObject\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 12\u001b[0m \u001b[0mcdef\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/home/aaron/projects/pandas/pandas/_libs/tslibs/conversion.pyx\u001b[0m in \u001b[0;36minit pandas._libs.tslibs.conversion\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;31m# -*- coding: utf-8 -*-\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;31m# cython: profile=False\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/home/aaron/projects/pandas/pandas/_libs/tslibs/nattype.pyx\u001b[0m in \u001b[0;36minit pandas._libs.tslibs.nattype\u001b[0;34m()\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0mcimport\u001b[0m \u001b[0mint64_t\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m \u001b[0mcnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimport_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m__init__.pxd\u001b[0m in \u001b[0;36mnumpy.import_array\u001b[0;34m()\u001b[0m\n",
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"\u001b[0;31mImportError\u001b[0m: numpy.core.multiarray failed to import",
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"\nDuring handling of the above exception, another exception occurred:\n",
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"\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-1-a9be8501a591>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m9\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/home/aaron/projects/pandas/pandas/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 33\u001b[0m \u001b[0;34m\"pandas from the source directory, you may need to run \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 34\u001b[0m \u001b[0;34m\"'python setup.py build_ext --inplace --force' to build \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 35\u001b[0;31m \"the C extensions first.\".format(module))\n\u001b[0m\u001b[1;32m 36\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 37\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mdatetime\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;31mImportError\u001b[0m: C extension: numpy.core.multiarray failed to import not built. If you want to import pandas from the source directory, you may need to run 'python setup.py build_ext --inplace --force' to build the C extensions first."
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"df = pd.DataFrame([[1,2,3], [4,5,6], [7,8,9]])"
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]
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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 3",
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"language": "python",
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"name": "python3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎pandas/core/indexing.py

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@@ -1438,10 +1438,10 @@ class _LocIndexer(_LocationIndexer):
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--------
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DateFrame.at
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Access a single value for a row/column label pair
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DateFrame.iat
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Access a single value for a row/column pair by integer position
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DateFrame.iloc
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Access group of rows and columns by integer position(s)
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Series.loc
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Access group of values using labels
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Examples
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--------
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r1 0 4 1
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r2 10 20 30
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Single label for row (note it would be faster to use ``DateFrame.at`` in
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this case)
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Single label. Note this returns a Series.
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>>> df.loc['r1']
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c0 0
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c1 4
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c2 1
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Name: r1, dtype: int64
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Single label for row and column (note it would be faster to use
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``DateFrame.at`` in this case)
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List with a single label. Note using ``[[]]`` returns a DataFrame.
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>>> df.loc[['r1']]
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c0 c1 c2
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r1 0 4 1
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Single label for row and column
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>>> df.loc['r0', 'c1']
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8 0 4 1
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9 10 20 30
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A number of examples using a DataFrame with a multi-index
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>>> tuples = [('r0', 'bar'), ('r0', 'foo'), ('r1', 'bar'),
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... ('r1', 'foo'), ('r2', 'bar'), ('r2', 'baz')]
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>>> index = pd.MultiIndex.from_tuples(tuples)
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>>> values = [[12,2,3], [0,4,1], [10,20,30],
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... [1, 4, 1], [7, 1, 2], [16, 36, 40]]
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>>> df = pd.DataFrame(values, columns=['c0', 'c1', 'c2'], index=index)
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>>> df
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c0 c1 c2
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r0 bar 12 2 3
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foo 0 4 1
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r1 bar 10 20 30
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foo 1 4 1
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r2 bar 7 1 2
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baz 16 36 40
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Single label. Note this returns a DataFrame with a single index.
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>>> df.loc['r0']
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c0 c1 c2
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bar 12 2 3
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foo 0 4 1
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Single index tuple. Note this returns a Series.
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>>> df.loc[('r0', 'bar')]
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c0 12
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c1 2
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c2 3
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Name: (r0, bar), dtype: int64
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Single label for row and column. Similar to passing in a tuple, this
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returns a Series.
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>>> df.loc['r0', 'foo']
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c0 0
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c1 4
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c2 1
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Name: (r0, foo), dtype: int64
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Single tuple. Note using ``[[]]`` returns a DataFrame.
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>>> df.loc[[('r0', 'bar')]]
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c0 c1 c2
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r0 bar 12 2 3
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Single tuple for the index with a single label for the column
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>>> df.loc[('r0', 'foo'), 'c1']
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4
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Boolean list
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>>> df.loc[[True, False, True, False, True, True]]
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c0 c1 c2
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r0 bar 12 2 3
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r1 bar 10 20 30
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r2 bar 7 1 2
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baz 16 36 40
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Slice from index tuple to single label
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>>> df.loc[('r0', 'foo'):'r1']
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c0 c1 c2
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r0 foo 0 4 1
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r1 bar 10 20 30
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foo 1 4 1
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Slice from index tuple to index tuple
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>>> df.loc[('r0', 'foo'):('r1', 'bar')]
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c0 c1 c2
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r0 foo 0 4 1
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r1 bar 10 20 30
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Raises
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------
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KeyError:

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