05.10_Pandas_Working_With_String.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introducing Pandas String Operations\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([ 4,  6, 10, 14, 22, 26])"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "x = np.array([2, 3, 5, 7, 11, 13])\n",
    "x * 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numpy does not provide access like **vectorization** operation in arrays of string.  \n",
    "Thus we will have a some messy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-30T11:56:53.094356Z",
     "start_time": "2019-10-30T11:56:53.070108Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Peter', 'Paul', 'Mary', 'Guido']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = ['peter', 'Paul', 'MARY', 'gUIDO']\n",
    "[s.capitalize() for s in data]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is perhaps sufficient to work with some data, but it will raise a error if **there are any missing data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-30T11:56:54.037931Z",
     "start_time": "2019-10-30T11:56:53.995571Z"
    }
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'NoneType' object has no attribute 'capitalize'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-6-3924cc897f53>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'peter'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Paul'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m  \u001b[1;34m'MARY'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'gUIDO'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;33m[\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcapitalize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0ms\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-6-3924cc897f53>\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'peter'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Paul'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m  \u001b[1;34m'MARY'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'gUIDO'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;33m[\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcapitalize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0ms\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'capitalize'"
     ]
    }
   ],
   "source": [
    "data = ['peter', 'Paul',None,  'MARY', 'gUIDO']\n",
    "[s.capitalize() for s in data]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas includes features to address both this need for vetorized string operations and for correctly **handling missing data** via the `str` attribute of Pandas Series and Index objects containing string."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-30T11:56:55.020483Z",
     "start_time": "2019-10-30T11:56:54.955824Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    peter\n",
       "1     Paul\n",
       "2     None\n",
       "3     MARY\n",
       "4    gUIDO\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "names = pd.Series(data)\n",
    "names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas will **skipping over** any missing value while we do vetorized operation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-30T11:56:56.030249Z",
     "start_time": "2019-10-30T11:56:56.001393Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Peter\n",
       "1     Paul\n",
       "2     None\n",
       "3     Mary\n",
       "4    Guido\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names.str.capitalize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using `Tab` we can see more methods about `str`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-30T11:56:56.614293Z",
     "start_time": "2019-10-30T11:56:56.591608Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      peter   \n",
       "1       Paul   \n",
       "2          None\n",
       "3       MARY   \n",
       "4      gUIDO   \n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names.str.center(10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tables of Pandas String Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-30T11:56:56.912642Z",
     "start_time": "2019-10-30T11:56:56.890955Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Graham Chapman\n",
       "1       John Cleese\n",
       "2     Terry Gilliam\n",
       "3         Eric Idle\n",
       "4       Terry Jones\n",
       "5     Michael Palin\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monte = pd.Series(['Graham Chapman', 'John Cleese', 'Terry Gilliam',\n",
    "                   'Eric Idle', 'Terry Jones', 'Michael Palin'])\n",
    "monte"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Methods similar to Python string methods"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nearly all Python's built-in string method are mirrored by a Pandas vetorized string method.  \n",
    "![%E5%9B%BE%E7%89%87.png](attachment:%E5%9B%BE%E7%89%87.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-30T11:56:57.380044Z",
     "start_time": "2019-10-30T11:56:57.360507Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    graham chapman\n",
       "1       john cleese\n",
       "2     terry gilliam\n",
       "3         eric idle\n",
       "4       terry jones\n",
       "5     michael palin\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monte.str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-30T11:56:57.529838Z",
     "start_time": "2019-10-30T11:56:57.507399Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    14\n",
       "1    11\n",
       "2    13\n",
       "3     9\n",
       "4    11\n",
       "5    13\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monte.str.len()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-30T11:56:57.677628Z",
     "start_time": "2019-10-30T11:56:57.661324Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2     True\n",
       "3    False\n",
       "4     True\n",
       "5    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monte.str.startswith('T')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-30T11:56:57.829332Z",
     "start_time": "2019-10-30T11:56:57.814407Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    [Graham, Chapman]\n",
       "1       [John, Cleese]\n",
       "2     [Terry, Gilliam]\n",
       "3         [Eric, Idle]\n",
       "4       [Terry, Jones]\n",
       "5     [Michael, Palin]\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monte.str.split()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Methods using regular expressions\n",
    "There are several methods that accept regular expressions to examine the content of each string element."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "| Method | Description |\n",
    "|--------|-------------|\n",
    "| ``match()`` | Call ``re.match()`` on each element, returning a boolean. |\n",
    "| ``extract()`` | Call ``re.match()`` on each element, returning matched groups as strings.|\n",
    "| ``findall()`` | Call ``re.findall()`` on each element |\n",
    "| ``replace()`` | Replace occurrences of pattern with some other string|\n",
    "| ``contains()`` | Call ``re.search()`` on each element, returning a boolean |\n",
    "| ``count()`` | Count occurrences of pattern|\n",
    "| ``split()``   | Equivalent to ``str.split()``, but accepts regexps |\n",
    "| ``rsplit()`` | Equivalent to ``str.rsplit()``, but accepts regexps |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-30T11:56:58.313436Z",
     "start_time": "2019-10-30T11:56:58.296167Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "4    False\n",
       "5    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monte.str.match(r'^j.*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-30T11:56:58.462602Z",
     "start_time": "2019-10-30T11:56:58.442889Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     Graham\n",
       "1       John\n",
       "2      Terry\n",
       "3       Eric\n",
       "4      Terry\n",
       "5    Michael\n",
       "dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monte.str.extract('([A-Za-z]+)', expand=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-30T11:56:58.612401Z",
     "start_time": "2019-10-30T11:56:58.592839Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    [Graham Chapman]\n",
       "1                  []\n",
       "2     [Terry Gilliam]\n",
       "3                  []\n",
       "4       [Terry Jones]\n",
       "5     [Michael Palin]\n",
       "dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monte.str.findall(r'^[^AEIOU].*[^aeiou]$')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Miscellaneous methods\n",
    "| Method | Description |\n",
    "|--------|-------------|\n",
    "| ``get()`` | Index each element |\n",
    "| ``slice()`` | Slice each element|\n",
    "| ``slice_replace()`` | Replace slice in each element with passed value|\n",
    "| ``cat()``      | Concatenate strings|\n",
    "| ``repeat()`` | Repeat values |\n",
    "| ``normalize()`` | Return Unicode form of string |\n",
    "| ``pad()`` | Add whitespace to left, right, or both sides of strings|\n",
    "| ``wrap()`` | Split long strings into lines with length less than a given width|\n",
    "| ``join()`` | Join strings in each element of the Series with passed separator|\n",
    "| ``get_dummies()`` | extract dummy variables as a dataframe |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Vectorized item access and slicing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-30T11:56:59.094620Z",
     "start_time": "2019-10-30T11:56:59.081112Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Gra\n",
       "1    Joh\n",
       "2    Ter\n",
       "3    Eri\n",
       "4    Ter\n",
       "5    Mic\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# str.slice(0, 3) is equivalent to str[0:3]\n",
    "monte.str[0:3]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indexing via `df.str.get(i)` or `df.str[i]`   \n",
    "These `get()` and `slice()` methods alse let us access elements of arrays returned by `split()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-30T11:56:59.429258Z",
     "start_time": "2019-10-30T11:56:59.409650Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Chapman\n",
       "1     Cleese\n",
       "2    Gilliam\n",
       "3       Idle\n",
       "4      Jones\n",
       "5      Palin\n",
       "dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monte.str.split().str.get(-1)"
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    "#### Indicator variables\n",
    "Another method that requires a bit of extra explanation is the ``get_dummies()`` method.\n",
    "This is useful when your data has a column containing some sort of coded indicator.\n",
    "For example, we might have a dataset that contains information in the form of codes, such as A=\"born in America,\" B=\"born in the United Kingdom,\" C=\"likes cheese,\" D=\"likes spam\":"
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       "             name   info\n",
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       "2   Terry Gilliam    A|C\n",
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    "full_monte = pd.DataFrame({'name': monte,\n",
    "                           'info': ['B|C|D', 'B|D', 'A|C',\n",
    "                                    'B|D', 'B|C', 'B|C|D']})\n",
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    "The `get_dummies()` routine let us quickly split-out there indicator variables into a `DataFrame`"
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    "_ = full_monte['info'].str.get_dummies('|')\n",
    "_"
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