Serialize Pandas Dataframe Rows as JSON Objects

notebook.ipynb

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   "source": [
    "# Serialize Pandas Dataframe Rows as JSON Objects.\n",
    "\n",
    "An example demonstrating how to produce JSON objects from rows in a Pandas Dataframe."
   ]
  },
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   "id": "fd1e0c7b-fd06-4704-a194-85740b164cee",
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   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from json import loads, dumps"
   ]
  },
  {
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   "id": "7612ff75-5cb2-45c6-a094-faf2ecad6542",
   "metadata": {
    "tags": []
   },
   "source": [
    "Create a dataframe with a single row."
   ]
  },
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>fred</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   name\n",
       "0  fred"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame({\"name\": \"fred\"}, index=[0])\n",
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd16ba0e-e3eb-4113-b02a-639d722236c2",
   "metadata": {},
   "source": [
    "Create a second dataframe with two additional columns."
   ]
  },
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   "cell_type": "code",
   "execution_count": 19,
   "id": "ca21530c-ed0f-421d-89bb-d9cbda086950",
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   "outputs": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "  age color\n",
       "0  27   red"
      ]
     },
     "execution_count": 19,
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    }
   ],
   "source": [
    "df2 = pd.DataFrame({\"age\": \"27\", \"color\": \"red\"}, index=[0])\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d11f2f1d-cb0c-4f03-a4f5-5a5c050f4cf6",
   "metadata": {},
   "source": [
    "Create a single dataframe with all three columns."
   ]
  },
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   "cell_type": "code",
   "execution_count": 20,
   "id": "d9ec5023-a18c-4fa4-9607-e0f23504f2a2",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>fred</td>\n",
       "      <td>27</td>\n",
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       "   name age color\n",
       "0  fred  27   red"
      ]
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   ],
   "source": [
    "df3 = pd.concat([df1, df2], axis=1)\n",
    "df3"
   ]
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   "cell_type": "markdown",
   "id": "462e9d13-f682-4f91-b4a4-6c6f730e2d71",
   "metadata": {},
   "source": [
    "Convert the dataframe to JSON. Note that there is a JSON attribute per column, each with a dictionary of values keyed by row."
   ]
  },
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   "cell_type": "code",
   "execution_count": 21,
   "id": "97fb93c7-5d24-44d0-a0cb-35075e18dd99",
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      "text/plain": [
       "'{\"name\":{\"0\":\"fred\"},\"age\":{\"0\":\"27\"},\"color\":{\"0\":\"red\"}}'"
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     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.to_json()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e02c7f8c-7ece-499f-8f84-bc0834796bfa",
   "metadata": {},
   "source": [
    "Add another row to the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6cb53944-f8ce-4d99-ad34-62bc2981f5ee",
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>fred</td>\n",
       "      <td>27</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>wilma</td>\n",
       "      <td>29</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "    name age  color\n",
       "0   fred  27    red\n",
       "1  wilma  29  green"
      ]
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     "execution_count": 33,
     "metadata": {},
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    }
   ],
   "source": [
    "df4 = pd.DataFrame({'name':'wilma', 'age':'29', 'color':'green'}, index=[1])\n",
    "df5 = pd.concat([df3, df4], axis=0)\n",
    "df5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ddaadbe-b4d2-4ddb-a63e-97b9cd9d06e3",
   "metadata": {},
   "source": [
    "Convert the dataframe to JSON oriented so that each item in the list is a row."
   ]
  },
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   "execution_count": 39,
   "id": "11ec2e79-1ddb-4081-af1a-285b4f9741f0",
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    {
     "data": {
      "text/plain": [
       "'[{\"name\":\"fred\",\"age\":\"27\",\"color\":\"red\"},{\"name\":\"wilma\",\"age\":\"29\",\"color\":\"green\"}]'"
      ]
     },
     "execution_count": 39,
     "metadata": {},
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   "source": [
    "df5.to_json(orient=\"records\", force_ascii=False)"
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   "id": "27ce5cbd-f3a7-4f7e-99d2-76d55fa16a3b",
   "metadata": {},
   "source": [
    "Convert the first row to JSON."
   ]
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   "execution_count": 47,
   "id": "a9b04b89-b8a8-409e-971c-134a48db1361",
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    {
     "data": {
      "text/plain": [
       "'[{\"name\":\"fred\",\"age\":\"27\",\"color\":\"red\"}]'"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df5.iloc[[0]].to_json(orient=\"records\", force_ascii=False)"
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   "cell_type": "markdown",
   "id": "ef1c76f0-a542-4cf3-9ab0-d0b14f991f98",
   "metadata": {},
   "source": [
    "Strip the list parens."
   ]
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   "execution_count": 48,
   "id": "ffcd8b92-2bb2-4e3f-b519-f48a571a884e",
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    {
     "data": {
      "text/plain": [
       "'{\"name\":\"fred\",\"age\":\"27\",\"color\":\"red\"}'"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.iloc[[0]].to_json(orient=\"records\", force_ascii=False)[1:-1]"
   ]
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   "execution_count": null,
   "id": "fbfea533-8af2-4ba6-aa60-91636bb74439",
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