natsort.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Usage\n",
    "- Python built-in *sorted()* returns lexicographically sorted list \n",
    "- Often not ideal when dealing with lists composed of strings of alphanumeric items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['c002', 'c01', 'c02', 'c1', 'c2']"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l1 = ['c2', 'c1', 'c01', 'c002', 'c02']\n",
    "sorted(l1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Cell02-protocol1-sweep1',\n",
       " 'Cell02-protocol1-sweep2',\n",
       " 'Cell1-protocol02-sweep03',\n",
       " 'Cell1-protocol02-sweep1',\n",
       " 'Cell1-protocol1-sweep1',\n",
       " 'Cell1-protocol1-sweep10',\n",
       " 'Cell1-protocol1-sweep2']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l2 = ['Cell1-protocol1-sweep1'\n",
    "     , 'Cell1-protocol1-sweep2'\n",
    "     , 'Cell1-protocol1-sweep10'\n",
    "     , 'Cell1-protocol02-sweep1'\n",
    "     , 'Cell1-protocol02-sweep03'\n",
    "     , 'Cell02-protocol1-sweep1'\n",
    "     , 'Cell02-protocol1-sweep2']\n",
    "\n",
    "sorted(l2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** *natsort* provides 'natural' sorting of lists / tuples / etc **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from natsort import natsorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['c1', 'c01', 'c2', 'c002', 'c02']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "natsorted(l1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Cell1-protocol1-sweep1',\n",
       " 'Cell1-protocol1-sweep2',\n",
       " 'Cell1-protocol1-sweep10',\n",
       " 'Cell1-protocol02-sweep1',\n",
       " 'Cell1-protocol02-sweep03',\n",
       " 'Cell02-protocol1-sweep1',\n",
       " 'Cell02-protocol1-sweep2']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "natsorted(l2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Controlling behavior of *natsorted()*\n",
    "\n",
    "- the 'alg' keyword provides a means by which you can change the algorithm natsorted uses to sort\n",
    "- the ns module provides a series algorithm variations to use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from natsort import ns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*** Dealing with letter case ***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A', 'B', 'a', 'b']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = ['a', 'B', 'A', 'b']\n",
    "sorted(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A', 'B', 'a', 'b']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "natsorted(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 'A', 'B', 'b']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "natsorted(l, alg=ns.IGNORECASE) #ns.IC is the shorthand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 'b', 'A', 'B']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "natsorted(l, alg=ns.LOWERCASEFIRST) #ns.LF is the shorthand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A', 'a', 'B', 'b']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "natsorted(l, alg=ns.GROUPLETTERS) #ns.G is the shorthand"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*** Sorting floats ***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['0,5', '5', '5.0', '5.1']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = ['5.1', '5', '5.0', '0,5']\n",
    "sorted(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['0,5', '5', '5.0', '5.1']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "natsorted(l, alg=ns.FLOAT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "l = ['-43.21', '1.0e2', '1.1e1', '+103.2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['-43.21', '1.1e1', '1.0e2', '+103.2']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "natsorted(l, alg=ns.FLOAT | ns.SIGNED)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*** Sorting file paths ***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['..\\\\folder 2\\\\file.txt', '..\\\\folder\\\\file.txt']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = ['..\\\\folder\\\\file.txt', '..\\\\folder 2\\\\file.txt']\n",
    "sorted(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['..\\\\folder\\\\file.txt', '..\\\\folder 2\\\\file.txt']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "natsorted(l, alg=ns.PATH) # ns.P is the shorthand"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Misc. Behavior\n",
    "\n",
    "***Mixed types***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unorderable types: int() < str()",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-18-47b29de7bde2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0ml\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'a'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'c'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'1.2'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0msorted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ml\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: unorderable types: int() < str()"
     ]
    }
   ],
   "source": [
    "l = ['a', 'c', 2, '1.2']\n",
    "sorted(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1.2', 2, 'a', 'c']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "natsorted(l)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*** Reverse ***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['c', 'a', 2, '1.2']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "natsorted(l, reverse=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting natsorted key"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*** Aside: 'key' keyword in builtin sorted() / sort() functions ***\n",
    "\n",
    "- Specifies a function to be applied to each element prior to making the comparison used for the sorting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['John', 'Mike', 'kate', 'sally']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = ['John', 'sally', 'Mike', 'kate']\n",
    "sorted(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['John', 'kate', 'Mike', 'sally']"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(l, key=str.lower)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['Kate', 8], ['Tim', 9], ['John', 12]]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = [['John', 12], ['Kate', 8], ['Tim', 9]]\n",
    "sorted(l, key=lambda x: x[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***natsort*** provides utility function to generate a key to use with e.g. inplace ***sort***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from natsort import natsort_keygen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['c1', 'c01', 'c2', 'c002', 'c02']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = ['c2', 'c1', 'c01', 'c002', 'c02']\n",
    "nskey = natsort_keygen()\n",
    "l.sort(key=nskey)\n",
    "l"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting index of sorted list\n",
    "\n",
    "- utility function that instead returns the indices use to sort the series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from natsort import index_natsorted, order_by_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 0, 3, 4]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = ['c2', 'c1', 'c01', 'c002', 'c02']\n",
    "ixs = index_natsorted(l)\n",
    "ixs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['c1', 'c01', 'c2', 'c002', 'c02']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_by_index(l, ixs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*** working with pandas ***\n",
    "\n",
    "Adapted from example in docs / [this StackOverflow answer](http://stackoverflow.com/a/29582718/1399279)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0hr</th>\n",
       "      <td>a5</td>\n",
       "      <td>b1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128hr</th>\n",
       "      <td>a1</td>\n",
       "      <td>b1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72hr</th>\n",
       "      <td>a10</td>\n",
       "      <td>b2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48hr</th>\n",
       "      <td>a2</td>\n",
       "      <td>b2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96hr</th>\n",
       "      <td>a12</td>\n",
       "      <td>b1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         a   b\n",
       "0hr     a5  b1\n",
       "128hr   a1  b1\n",
       "72hr   a10  b2\n",
       "48hr    a2  b2\n",
       "96hr   a12  b1"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas import DataFrame\n",
    "df = DataFrame({'a': ['a5', 'a1', 'a10', 'a2', 'a12']\n",
    "                , 'b': ['b1', 'b1', 'b2', 'b2', 'b1']}\n",
    "               , index=['0hr', '128hr', '72hr', '48hr', '96hr'])\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0hr</th>\n",
       "      <td>a5</td>\n",
       "      <td>b1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48hr</th>\n",
       "      <td>a2</td>\n",
       "      <td>b2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72hr</th>\n",
       "      <td>a10</td>\n",
       "      <td>b2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96hr</th>\n",
       "      <td>a12</td>\n",
       "      <td>b1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128hr</th>\n",
       "      <td>a1</td>\n",
       "      <td>b1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         a   b\n",
       "0hr     a5  b1\n",
       "48hr    a2  b2\n",
       "72hr   a10  b2\n",
       "96hr   a12  b1\n",
       "128hr   a1  b1"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reindex(index=natsorted(df.index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>128hr</th>\n",
       "      <td>a1</td>\n",
       "      <td>b1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48hr</th>\n",
       "      <td>a2</td>\n",
       "      <td>b2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0hr</th>\n",
       "      <td>a5</td>\n",
       "      <td>b1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72hr</th>\n",
       "      <td>a10</td>\n",
       "      <td>b2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96hr</th>\n",
       "      <td>a12</td>\n",
       "      <td>b1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
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       "         a   b\n",
       "128hr   a1  b1\n",
       "48hr    a2  b2\n",
       "0hr     a5  b1\n",
       "72hr   a10  b2\n",
       "96hr   a12  b1"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[index_natsorted(df.a)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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      "text/plain": [
       "         a   b\n",
       "128hr   a1  b1\n",
       "0hr     a5  b1\n",
       "96hr   a12  b1\n",
       "48hr    a2  b2\n",
       "72hr   a10  b2"
      ]
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     "execution_count": 32,
     "metadata": {},
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   "source": [
    "df.iloc[index_natsorted(zip(df.b, df.a))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "         a   b\n",
       "0hr     a5  b1\n",
       "96hr   a12  b1\n",
       "128hr   a1  b1\n",
       "48hr    a2  b2\n",
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     "execution_count": 33,
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   "source": [
    "df.iloc[index_natsorted(zip(df.b, df.index))]"
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