 Tutorial 11- Python Pandas Working With Json Tutorials- Part 3.ipynb

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    "### Tutorial 11- Python Pandas Working With Json Tutorials- Part 3\n",
    "\n",
    "In Part we are going to learn about\n",
    "\n",
    "1. read Json (read_json)\n",
    "2. To Json(to_json)\n",
    "3. Json Normalize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "77f0cdbe",
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   "source": [
    "data = '{\"employee_name\": \"James\", \"email\": \"james@gmail.com\", \"job_profile\": [{\"title1\":\"Team Lead\", \"title2\":\"Sr. Developer\"}]}'"
   ]
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    "type(data)"
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   "cell_type": "code",
   "execution_count": 3,
   "id": "e1dd264e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n"
   ]
  },
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       "      <td>James</td>\n",
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       "  employee_name            email  \\\n",
       "0         James  james@gmail.com   \n",
       "\n",
       "                                         job_profile  \n",
       "0  {'title1': 'Team Lead', 'title2': 'Sr. Develop...  "
      ]
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "pd.read_json(data)"
   ]
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       "  employee_name            email  \\\n",
       "0         James  james@gmail.com   \n",
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       "                                         job_profile  \n",
       "0  {'title1': 'Team Lead', 'title2': 'Sr. Develop...  "
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     "execution_count": 5,
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   "source": [
    "pd.read_json(data,orient='records')"
   ]
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       "      <td>james@gmail.com</td>\n",
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       "      <th>job_profile</th>\n",
       "      <td>[{'title1': 'Team Lead', 'title2': 'Sr. Develo...</td>\n",
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       "                                                               0\n",
       "employee_name                                              James\n",
       "email                                            james@gmail.com\n",
       "job_profile    [{'title1': 'Team Lead', 'title2': 'Sr. Develo..."
      ]
     },
     "execution_count": 6,
     "metadata": {},
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    "pd.read_json(data,orient='index')"
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       "  employee_name            email  \\\n",
       "0         James  james@gmail.com   \n",
       "\n",
       "                                         job_profile  \n",
       "0  {'title1': 'Team Lead', 'title2': 'Sr. Develop...  "
      ]
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     "execution_count": 8,
     "metadata": {},
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   "source": [
    "pd.read_json(data,orient='columns')"
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  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8d733180",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame([['a', 'b'], ['c', 'd']],\n",
    "                  index=['row 1', 'row 2'],\n",
    "                  columns=['col 1', 'col 2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "20c777c0",
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       "      <th>row 2</th>\n",
       "      <td>c</td>\n",
       "      <td>d</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "      col 1 col 2\n",
       "row 1     a     b\n",
       "row 2     c     d"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7a904ef4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\"row 1\":{\"col 1\":\"a\",\"col 2\":\"b\"},\"row 2\":{\"col 1\":\"c\",\"col 2\":\"d\"}}'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.to_json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4b582031",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\"row 1\":{\"col 1\":\"a\",\"col 2\":\"b\"},\"row 2\":{\"col 1\":\"c\",\"col 2\":\"d\"}}'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.to_json(orient='index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "167068e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\"col 1\":{\"row 1\":\"a\",\"row 2\":\"c\"},\"col 2\":{\"row 1\":\"b\",\"row 2\":\"d\"}}'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.to_json(orient='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "755ce09d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[{\"col 1\":\"a\",\"col 2\":\"b\"},{\"col 1\":\"c\",\"col 2\":\"d\"}]'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.to_json(orient='records')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "27beab0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\"columns\":[\"col 1\",\"col 2\"],\"index\":[\"row 1\",\"row 2\"],\"data\":[[\"a\",\"b\"],[\"c\",\"d\"]]}'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.to_json(orient='split')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "95128740",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\"schema\":{\"fields\":[{\"name\":\"index\",\"type\":\"string\"},{\"name\":\"col 1\",\"type\":\"string\"},{\"name\":\"col 2\",\"type\":\"string\"}],\"primaryKey\":[\"index\"],\"pandas_version\":\"0.20.0\"},\"data\":[{\"index\":\"row 1\",\"col 1\":\"a\",\"col 2\":\"b\"},{\"index\":\"row 2\",\"col 1\":\"c\",\"col 2\":\"d\"}]}'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.to_json(orient='table')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d7eae41a",
   "metadata": {},
   "outputs": [],
   "source": [
    "schema='{\"schema\":{\"fields\":[{\"name\":\"index\",\"type\":\"string\"},{\"name\":\"col 1\",\"type\":\"string\"},{\"name\":\"col 2\",\"type\":\"string\"}],\"primaryKey\":[\"index\"],\"pandas_version\":\"0.20.0\"},\"data\":[{\"index\":\"row 1\",\"col 1\":\"a\",\"col 2\":\"b\"},{\"index\":\"row 2\",\"col 1\":\"c\",\"col 2\":\"d\"}]}'"
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  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "49098048",
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    {
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   "cell_type": "code",
   "execution_count": 21,
   "id": "aae1fae8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data', header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "019af320",
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       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
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       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   0      1     2     3     4    5     6     7     8     9     10    11    12  \\\n",
       "0   1  14.23  1.71  2.43  15.6  127  2.80  3.06  0.28  2.29  5.64  1.04  3.92   \n",
       "1   1  13.20  1.78  2.14  11.2  100  2.65  2.76  0.26  1.28  4.38  1.05  3.40   \n",
       "2   1  13.16  2.36  2.67  18.6  101  2.80  3.24  0.30  2.81  5.68  1.03  3.17   \n",
       "3   1  14.37  1.95  2.50  16.8  113  3.85  3.49  0.24  2.18  7.80  0.86  3.45   \n",
       "4   1  13.24  2.59  2.87  21.0  118  2.80  2.69  0.39  1.82  4.32  1.04  2.93   \n",
       "\n",
       "     13  \n",
       "0  1065  \n",
       "1  1050  \n",
       "2  1185  \n",
       "3  1480  \n",
       "4   735  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f7aad5fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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    "data = [{\"employee_name\": \"James\", \"email\": \"james@gmail.com\", \"job_profile\": {\"title1\":\"Team Lead\", \"title2\":\"Sr. Developer\"}}]"
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    "data = [\n",
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    "        \"fitness\": {\"height\": 130, \"weight\": 60},\n",
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       "    id        name  fitness.height  fitness.weight\n",
       "0  1.0   Cole Volk             130              60\n",
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       "    id        name                        fitness\n",
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   "execution_count": 35,
   "id": "b5f30321",
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   "outputs": [
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       "    id        name  fitness.height  fitness.weight\n",
       "0  1.0   Cole Volk             130              60\n",
       "1  NaN    Mark Reg             130              60\n",
       "2  2.0  Faye Raker             130              60"
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   "execution_count": 40,
   "id": "c9ccd126",
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    "data = [\n",
    "    {\n",
    "        \"state\": \"Florida\",\n",
    "        \"shortname\": \"FL\",\n",
    "        \"info\": {\"governor\": \"Rick Scott\"},\n",
    "        \"counties\": [\n",
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    "            {\"name\": \"Broward\", \"population\": 40000},\n",
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    "        ],\n",
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    "        \"shortname\": \"OH\",\n",
    "        \"info\": {\"governor\": \"John Kasich\"},\n",
    "        \"counties\": [\n",
    "            {\"name\": \"Summit\", \"population\": 1234},\n",
    "            {\"name\": \"Cuyahoga\", \"population\": 1337},\n",
    "        ],\n",
    "    },\n",
    "]"
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  {
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   "execution_count": 41,
   "id": "612b5363",
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       "     state shortname                                           counties  \\\n",
       "0  Florida        FL  [{'name': 'Dade', 'population': 12345}, {'name...   \n",
       "1     Ohio        OH  [{'name': 'Summit', 'population': 1234}, {'nam...   \n",
       "\n",
       "  info.governor  \n",
       "0    Rick Scott  \n",
       "1   John Kasich  "
      ]
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     "execution_count": 41,
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    "pd.json_normalize(data)"
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  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7a0d6ace",
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   "outputs": [
    {
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       "      <th>1</th>\n",
       "      <td>Broward</td>\n",
       "      <td>40000</td>\n",
       "      <td>Florida</td>\n",
       "      <td>FL</td>\n",
       "      <td>Rick Scott</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Palm Beach</td>\n",
       "      <td>60000</td>\n",
       "      <td>Florida</td>\n",
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       "      <td>Rick Scott</td>\n",
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       "      <th>3</th>\n",
       "      <td>Summit</td>\n",
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       "      <td>OH</td>\n",
       "      <td>John Kasich</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cuyahoga</td>\n",
       "      <td>1337</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>OH</td>\n",
       "      <td>John Kasich</td>\n",
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      "text/plain": [
       "         name  population    state shortname info.governor\n",
       "0        Dade       12345  Florida        FL    Rick Scott\n",
       "1     Broward       40000  Florida        FL    Rick Scott\n",
       "2  Palm Beach       60000  Florida        FL    Rick Scott\n",
       "3      Summit        1234     Ohio        OH   John Kasich\n",
       "4    Cuyahoga        1337     Ohio        OH   John Kasich"
      ]
     },
     "execution_count": 45,
     "metadata": {},
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    }
   ],
   "source": [
    "pd.json_normalize(data, \"counties\",[\"state\", \"shortname\",[\"info\",'governor']])"
   ]
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