python/pandas-list-in-cells-to-col-names.ipynb

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      "text/plain": [
       "'1.1.2'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
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   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "pd.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>gender</th>\n",
       "      <th>characters</th>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>active/earnest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>funny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>F</td>\n",
       "      <td>active/dedicated/earnest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>F</td>\n",
       "      <td>dedicated/disciplined</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>F</td>\n",
       "      <td>active/disciplined</td>\n",
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      "text/plain": [
       "   id gender                characters\n",
       "0   0      F            active/earnest\n",
       "1   1      M                     funny\n",
       "2   2      F  active/dedicated/earnest\n",
       "3   3      F     dedicated/disciplined\n",
       "4   4      F        active/disciplined"
      ]
     },
     "execution_count": 2,
     "metadata": {},
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   ],
   "source": [
    "df = pd.DataFrame({'id': range(5), \n",
    "                   'gender': np.random.choice(['M', 'F'], 5), \n",
    "                   'characters': ['active/earnest', \n",
    "                                  'funny', \n",
    "                                  'active/dedicated/earnest', \n",
    "                                  'dedicated/disciplined', \n",
    "                                  'active/disciplined']})\n",
    "df"
   ]
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     "end_time": "2020-09-20T05:24:56.200301Z",
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0               [active, earnest]\n",
       "1                         [funny]\n",
       "2    [active, dedicated, earnest]\n",
       "3        [dedicated, disciplined]\n",
       "4           [active, disciplined]\n",
       "Name: characters, dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
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   ],
   "source": [
    "series_characters = df['characters'].str.split('/')\n",
    "series_characters"
   ]
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       "  active earnest funny dedicated disciplined\n",
       "0   True    True   NaN       NaN         NaN\n",
       "1    NaN     NaN  True       NaN         NaN\n",
       "2   True    True   NaN      True         NaN\n",
       "3    NaN     NaN   NaN      True        True\n",
       "4   True     NaN   NaN       NaN        True"
      ]
     },
     "execution_count": 4,
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   ],
   "source": [
    "series_characters = series_characters.apply(lambda lst: pd.Series(dict.fromkeys(lst, True)))\n",
    "series_characters"
   ]
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      "text/plain": [
       "   id gender                characters  active  earnest  funny  dedicated  \\\n",
       "0   0      F            active/earnest    True     True  False      False   \n",
       "1   1      M                     funny   False    False   True      False   \n",
       "2   2      F  active/dedicated/earnest    True     True  False       True   \n",
       "3   3      F     dedicated/disciplined   False    False  False       True   \n",
       "4   4      F        active/disciplined    True    False  False      False   \n",
       "\n",
       "   disciplined  \n",
       "0        False  \n",
       "1        False  \n",
       "2        False  \n",
       "3         True  \n",
       "4         True  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
    "df_new = pd.concat([df, series_characters], axis=1).fillna(False)\n",
    "df_new"
   ]
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   "execution_count": null,
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