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Serialize Pandas Dataframe Rows as JSON Objects
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "id": "752ef609-3a97-43a8-962e-57b6d62b4b79", | |
| "metadata": {}, | |
| "source": [ | |
| "# Serialize Pandas Dataframe Rows as JSON Objects.\n", | |
| "\n", | |
| "An example demonstrating how to produce JSON objects from rows in a Pandas Dataframe." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 10, | |
| "id": "fd1e0c7b-fd06-4704-a194-85740b164cee", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "import pandas as pd\n", | |
| "from json import loads, dumps" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "7612ff75-5cb2-45c6-a094-faf2ecad6542", | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "source": [ | |
| "Create a dataframe with a single row." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 18, | |
| "id": "6ede7bc2-0acb-40da-acaf-6ec6bcae6a02", | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<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", | |
| " </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." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 19, | |
| "id": "ca21530c-ed0f-421d-89bb-d9cbda086950", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<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>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", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " age color\n", | |
| "0 27 red" | |
| ] | |
| }, | |
| "execution_count": 19, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "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." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 20, | |
| "id": "d9ec5023-a18c-4fa4-9607-e0f23504f2a2", | |
| "metadata": { | |
| "tags": [] | |
| }, | |
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| { | |
| "data": { | |
| "text/html": [ | |
| "<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", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " name age color\n", | |
| "0 fred 27 red" | |
| ] | |
| }, | |
| "execution_count": 20, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df3 = pd.concat([df1, df2], axis=1)\n", | |
| "df3" | |
| ] | |
| }, | |
| { | |
| "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." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 21, | |
| "id": "97fb93c7-5d24-44d0-a0cb-35075e18dd99", | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "'{\"name\":{\"0\":\"fred\"},\"age\":{\"0\":\"27\"},\"color\":{\"0\":\"red\"}}'" | |
| ] | |
| }, | |
| "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", | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\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>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", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " name age color\n", | |
| "0 fred 27 red\n", | |
| "1 wilma 29 green" | |
| ] | |
| }, | |
| "execution_count": 33, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "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." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 39, | |
| "id": "11ec2e79-1ddb-4081-af1a-285b4f9741f0", | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "'[{\"name\":\"fred\",\"age\":\"27\",\"color\":\"red\"},{\"name\":\"wilma\",\"age\":\"29\",\"color\":\"green\"}]'" | |
| ] | |
| }, | |
| "execution_count": 39, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df5.to_json(orient=\"records\", force_ascii=False)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "27ce5cbd-f3a7-4f7e-99d2-76d55fa16a3b", | |
| "metadata": {}, | |
| "source": [ | |
| "Convert the first row to JSON." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 47, | |
| "id": "a9b04b89-b8a8-409e-971c-134a48db1361", | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "'[{\"name\":\"fred\",\"age\":\"27\",\"color\":\"red\"}]'" | |
| ] | |
| }, | |
| "execution_count": 47, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df5.iloc[[0]].to_json(orient=\"records\", force_ascii=False)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "ef1c76f0-a542-4cf3-9ab0-d0b14f991f98", | |
| "metadata": {}, | |
| "source": [ | |
| "Strip the list parens." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 48, | |
| "id": "ffcd8b92-2bb2-4e3f-b519-f48a571a884e", | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "outputs": [ | |
| { | |
| "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]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "id": "fbfea533-8af2-4ba6-aa60-91636bb74439", | |
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| ], | |
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| "kernelspec": { | |
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| "language": "python", | |
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| }, | |
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| } |
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