As per your suggestion, since there are multiple nested objects if we separate each nested object into a separate dataframe then aren't we looking at a much complex solution given the fact that we would have to combine them later?. via builtin open function) or StringIO. Summarizing Data in Python with Pandas October 22, 2013. It is dangerous to flatten deeply nested JSON objects with a recursive python solution. You could do type Dict map[string] Dict. Working with SQLite Databases using Python and Pandas SQLite is a database engine that makes it simple to store and work with relational data. The pandas. Introduction. Some basic understanding of Python (with Requests, Pandas and JSON libraries), REST APIs, Jupyter Notebook, AWS S3 and Redshift would be useful. Download Open Datasets on 1000s of Projects + Share Projects on One Platform. The ability to explode nested lists into rows in a very easy way (see the Notebook below) Speed! Following is an example Databricks Notebook (Python) demonstrating the above claims. The aim of this post is to help beginners get to grips with the basic data format for Pandas – the DataFrame. In this video we will see: What is JSON; Read JSON to a DataFrame; Read different JSON formats; Get JSON String from a DataFrame. Set the url to the endpoint which will be the target of the request and attempt to retrieve the first page of data. In the case of nested json, further transformation is. If your Ansible inventory fluctuates over time, with hosts spinning up and shutting down in response to business demands, the static inventory solutions described in Working with. Python provides built-in JSON libraries to encode and decode JSON. The ElementTree class can be used to wrap an element structure, and convert it from and to XML. Pandasのグラフ描画機能 この記事ではPandasのPlot機能について扱います。 Pandasはデータの加工・集計のためのツールとしてその有用性が広く知られていますが、同時に優れた可視化機能を. However the nested json objects are being written as one value. For example a for loop can be inside a while loop or vice versa. Flattening a nested JSON isn’t very easy. Because the python interpreter limits the depth of stack to avoid infinite recursions which could result in stack overflows. Pandas is a third-party python module that can manipulate different format data files, such as csv, json, excel, clipboard, html etc. JSON Editor Online is a web-based tool to view, edit, and format JSON. how do I get the 'screen_name' from the 'user' key without flattening the JSON). In the second call, it only flattens to the first level. Flattening somebody already helped me out with here. You can vote up the examples you like or vote down the ones you don't like. If statements are control flow statements which helps us to run a particular code only when a certain condition is satisfied. com/softhints/python/b * hierarchical data * mapping pandas columns * Pretty print json and. Python for Data Science – Importing XML to Pandas DataFrame November 3, 2017 Gokhan Atil 8 Comments Big Data pandas , xml In my previous post , I showed how easy to import data from CSV, JSON, Excel files using Pandas package. This module allows us to normalise the data in json format into a tabular format. For example, an application written in C++ How to Fix Windows Visual C++ Runtime Errors How to Fix Windows Visual C++ Runtime Errors Visual C++ Errors are a common problem for Windows users. My file contains multiple JSON objects (1 per line) I would like to keep number, date, name, and locations column. For many complex objects, such as those that make extensive use of references, this process is not straightforward. json import json_normalize #package for flattening json in pandas df #load json. I am trying to flatten the JSON into a table and have found the following example on the pandas. In this video, Peter first explains JSON's common data types. if None, normalizes all levels. We come across various circumstances where we receive data in json format and we need to send or store it in csv format. Remove u from json python. Design: High performance columnar data in Python. When the resulting series of bits is reread according to the serialization format, it can be used to create a semantically identical clone of the original object. 6 so I hadn't noticed. unstack¶ DataFrame. Pandas DataFrame generate n-level hierarchical JSON https://github. Suppose we want to convert a sample POJO (Plain Old Java Object) to JSON. JSON (JavaScript Object Notation) is a text file format designed to facilitate the transmission of data from server to browser. Please note that I know Python is not a promoter for functional programming. In your Python script, import a database connector. Using pandas and json_normalize to flatten nested JSON API response I have a deeply nested JSON that I am trying to turn into a Pandas Dataframe using json_normalize. Flattening somebody already helped me out with here. It takes an argument i. For example, an application written in C++ How to Fix Windows Visual C++ Runtime Errors How to Fix Windows Visual C++ Runtime Errors Visual C++ Errors are a common problem for Windows users. Dealing with classifications¶ When you request an export of the raw classification data using the project builder, some of the columns we return will actually contain values in a format called JSON. I have a really deeply nested json with lots of records and I am using python 2. dumps(my_list) [/code]. listdir() method. python Json reduce pandas 2019-01-18 10:27:34 pandas json_normalize所有列都嵌套了字典展平 我有一个从非官方谷歌词典API返回的嵌套字典(json)。. JSON to CSV will convert an array of objects into a table. Quick HDF5 with Pandas HDF5 is a format designed to store large numerical arrays of homogenous type. In Flatten Tool terminology flattening is the process of converting a JSON document. json import json_normalize from flatten_json import. The parser function should use core Python functionality and not rely on external Python libraries. If you are looking for a more general way to unfold multiple hierarchies from a json you can use recursion and list comprehension to reshape your data. Quick HDF5 with Pandas HDF5 is a format designed to store large numerical arrays of homogenous type. Over the last couple of months working with clients, we've been working with a few new datasets containing nested JSON. Is there a better way? - df2json. json import json_normalize #package for flattening json in pandas df #load json. Converting Json file to Dataframe Python. We'll walk through how to deal with nested data using Pandas (for example - a JSON string column), transforming that data into a tabular format that's easier to deal with and analyze. I am trying to flatten the JSON into a table and have found the following example on the pandas. However, because DataFrames are built in Python, it’s possible to use Python to program more advanced operations and manipulations than SQL and Excel can offer. The Flickr JSON is a little confusing, and it doesn’t provide a direct link to the thumbnail version of our photos, so we’ll have to use some trickery on our end to get to it, which we’ll cover in just a moment. stringify() method converts a JavaScript object or value to a JSON string, optionally replacing values if a replacer function is specified or optionally including only the specified properties if a replacer array is specified. Remove u from json python. simplejson — JSON encoder and decoder¶ JSON (JavaScript Object Notation), specified by RFC 7159 (which obsoletes RFC 4627) and by ECMA-404, is a lightweight data interchange format inspired by JavaScript object literal syntax (although it is not a strict subset of JavaScript ). Advanced Search Remove u from json python. Because the python interpreter limits the depth of stack to avoid infinite recursions which could result in stack overflows. how do I get the 'screen_name' from the 'user' key without flattening the JSON). If you are looking for a more general way to unfold multiple hierarchies from a json you can use recursion and list comprehension to reshape your data. A package to easily open an instance of a Google spreadsheet and interact with worksheets through Pandas DataFrames. I want to write a code in which ; I can browse the folder and select 1000 or upto more than 1000 files, and covert them directly into a CSV file. Working with SQLite Databases using Python and Pandas SQLite is a database engine that makes it simple to store and work with relational data. If you are trying to gather some data using any API then most probably you are going to deal with JSON. Is there a simple way of grabbing nested keys when constructing a Pandas Dataframe from JSON. Although we. json library. Pandas offers easy way to normalize JSON data. For many complex objects, such as those that make extensive use of references, this process is not straightforward. Convert Nested JSON to Pandas DataFrame and Flatten List in a Column - gist:4ddc91ae47ea46a46c0b. While pandas only supports flat columns, the Table also provides nested columns, thus it can represent more data than a DataFrame, so a full conversion is not always possible. It automates the conversion of JSON to a database, text, or Hadoop. import pandas as pd from datetime import datetime, timedelta import time import requests import numpy as np import json import urllib from pandas. Nested JSON Parsing with Pandas: Nested JSON files can be time consuming and difficult process to flatten and load into Pandas. Dealing with classifications¶ When you request an export of the raw classification data using the project builder, some of the columns we return will actually contain values in a format called JSON. JSON uses UTF-8 encoded text strings, so JSON strings can be stored as CHAR or VARCHAR data types. py Explore Channels Plugins & Tools Pro Login About Us Report Ask Add Snippet. Pythonで多次元のリスト(リストのリスト、ネストしたリスト)を一次元に平坦化する方法について説明する。2次元のリストを平坦化itertools. In drill we can use a few functions together the get the desired effect. pandas dataframe from a nested dictionary (elasticsearch result) I am having hard time translating results from elasticsearch aggregations to pandas. How to join or concatenate two strings with specified separator; how to concatenate or join the two string columns of dataframe in python. I'm trying to insert new array inside the array but I'm not sure where can I append the data. The library exposes many kinds of interactions with the Wolfram Language, many of which require representation of Wolfram Language expressions as Python objects. Preliminary # Import combinations with replacements from itertools from itertools import combinations_with_replacement Create a list of objects # Create a list of objects to combine list_of_objects = ['warplanes', 'armor', 'infantry'] Find all combinations (with replacement) for the list # Create an empty list object to hold the results of the loop combinations = [] # Create a loop for every. Yep – it's that easy. Nested or Inner Classes in Python Pandas Tutorial - Selecting Rows From a DataFrame. Working with. Python Flatten Multiply Nested Dictionary JSON with Pandas. It is useful in any situation where your program needs to look for a list of files on the filesystem with names matching a pattern. Nested List Comprehensions are nothing but a list comprehension within another list comprehension which is quite similar to nested for loops. Flattening a nested JSON isn’t very easy. Mapping object representing the DataFrame. What's Wrong With Python Pandas? We can't achieve these unless we flatten to the level of users and virtuals. Pandas writes Excel files using the Xlwt module for xls files and the Openpyxl or XlsxWriter modules for xlsx files. My target is to keep the information short, relevant and focus on the most important topics which are absolutely. With the prevalence of web and mobile applications, JSON has become the de-facto interchange format for web service API’s as well as long-term. The two method read csv data from csv_user_info. You can use the python library json or a command line tool like 'jq' to pre-process. In general, you could say that the Pandas DataFrame consists of three main components: the data, the index, and the columns. ### The `wcon` Python package. Pickling some data will write the python object (dictionary, list, tuple, class) as one long strea. The most straightforward way to build a pie chart is to use the pie method In this case, pie takes values corresponding to counts in a group. In previous versions of SQLite can be installed following the JSON1 official documentation) will be required to follow this tutorial. Flattening somebody already helped me out with here. 2 posts published by Bridgettobehere during October 2018. By file-like object, we refer to objects with a read() method, such as a file handler (e. How to flatten this nested Dict into dataframe (self. Or you could do map[string] interface{}, which is closer to what you have in Python or. com/softhints/python/b * hierarchical data * mapping pandas columns * Pretty print json and. For information on handling nested and repeated data in standard SQL, see the Standard SQL migration guide. To do this you first have to get the unique id for all the relevant patients, then get the the registered events for all the people associated with the ids. Remove u from json python. Quick HDF5 with Pandas HDF5 is a format designed to store large numerical arrays of homogenous type. loads(serialized_data) # Validate the raw file against the WCON schema jsonschema. json: This file is generated by the csv_2_json_by_reader or csv_2_json_by_dictreader method. JSON (stands for “JavaScript Object Notation”) is a text-based format which facilitates data interchange between diverse applications. My target is to keep the information short, relevant and focus on the most important topics which are absolutely. It enables you to easily pull data from Google spreadsheets into DataFrames as well as push data into spreadsheets from DataFrames. JSON uses UTF-8 encoded text strings, so JSON strings can be stored as CHAR or VARCHAR data types. Manipulating JSON With Python. I would like to have this JSON object written out to a CSV file so that the keys are header fields (for each of the columns) and the values are values that are associated with each header field. To flatten and load nested JSON file import json import pandas as pd from pandas. Take a nested Javascript object and flatten it, or unflatten an object with delimited keys. As per your suggestion, since there are multiple nested objects if we separate each nested object into a separate dataframe then aren't we looking at a much complex solution given the fact that we would have to combine them later?. Python - Pandas - expand nested json array within column Stackoverflow. Convert Nested JSON to Pandas DataFrame and Flatten List in a Column - gist:4ddc91ae47ea46a46c0b. data option is used to specify the property name for the row's data source object that should be used for a columns' data. python json pandas. flatten () # flatten a 2d NumPy array to 1d. Pythonで多重ループ(ネストしたforループ)からbreakする(抜け出す)方法について説明する。はじめに、多重ループの書き方とbreakの注意点 について説明したあと、多重ループからbreakする方法として、else, continueを活用 フラグ変数を追加 itertools. Sometimes the json data is very nested, we only want to. Is there a better way? - df2json. Keras can use external backends as well, and this can be performed by changing the keras. ## Develop Currently developed for Python 3 using the anaconda python. If not, or if you want a quick refresh, I've written an introduction to Designing a RESTful Web API. read_json() will fail to convert data to a valid DataFrame. json configuration file would be changed as follows:. json submodule has a function, json_normalize(), that does exactly this. Pandas fluency is essential for any Python-based data professional, people interested in trying a Kaggle challenge, or anyone seeking to automate a data process. ```python from google. Pandas provides a method called json_normalize that. There is more discussion on the use of JSON Pointer within JSON Schema in Structuring a complex schema. Free Bonus: Click here to download an example Python project with source code that shows you how to read large. If you want to learn more about these tools, check out our Data Analysis , Data Visualization , and Command Line courses on Dataquest. If 'orient' is 'records' write out line delimited json format. The following are code examples for showing how to use pandas. To format the output of the FOR JSON clause automatically based on the structure of the SELECT statement, specify the AUTO option. simplejson — JSON encoder and decoder¶ JSON (JavaScript Object Notation), specified by RFC 7159 (which obsoletes RFC 4627) and by ECMA-404, is a lightweight data interchange format inspired by JavaScript object literal syntax (although it is not a strict subset of JavaScript ). Then, we'll read in back from the file and play with it. I'd guess the most common use case would be pandas generating the json and then pandas reading it back. They are extracted from open source Python projects. 0, and tested on a variety of XML documents, but most notably a broad collection of GuideWire sample XMLs as part of a Client PoC. The categories attribute in the Yelp API response contains lists of objects. loads(serialized_data) # Validate the raw file against the WCON schema jsonschema. A Data frame is a two-dimensional data structure, i. ```python from google. Next: Write a JavaScript program to compute the union of two arrays. Here's a sketch of a table parser in Python built on top of JSON Lines using conventions 2) and 3):. functions therefore we will start off by importing that. CSVJSON format variant. Convert Nested JSON to Pandas DataFrame and Flatten List in a Column: gistfile1. And from performance standpoint, recursion is usually slower than an iterative solution. Then, you will use the json_normalize function to flatten the nested JSON data into a table. Is there a simple way of grabbing nested keys when constructing a Pandas Dataframe from JSON. See the Pen JavaScript - Flatten a nested array - array-ex- 21 by w3resource (@w3resource) on CodePen. Watch Now This tutorial has a related video course created by the Real Python team. This python recursive function flattens a JSON file or a dictionary with nested lists and/or dictionaries. In DataTables the columns. contact_phone from embedded. viewing nested JSON data into a pandas dataframe I am currently working with nutritional data for a project, where the data is in raw JSON format, and I want to use python and pandas to obtain an understandable data frame. Other sources talk about flattening data before feeding it to Pandas; but what is the point of using a vectorized library if you start with a by-every-element for-loop transformation. Here you go: We've seen here how we can use Databricks to flatten JSON with just a few lines of code. If the JSON file will not fit in memory then you'd need to processes it iteratively rather than loading it in bulk. 6 so I hadn't noticed. pandas dataframe from a nested dictionary (elasticsearch result) I am having hard time translating results from elasticsearch aggregations to pandas. Uwe Korn and I have built the Python interface and integration with pandas within the Python codebase (pyarrow) in Apache Arrow. One strength of Python is its relative ease in handling and manipulating string data. Spark DataFrames makes it easy to read from a variety of data formats, including JSON. This allows for reconstructing the JSON structure or converting it to other formats without loosing any structural information. Suppose we have some JSON data: [code]json_data = { "name": { "first": ". The easiest way I have found is to use [code ]pandas. JSON은 완벽하게 언어로 부터 독립적이지만 C-family 언어 - C, C++, C#, Java, JavaScript, Perl, Python 그외 다수 - 의 프로그래머들에게 친숙한 관습을 사용하는 텍스트 형식이다. e no matter how many levels of nesting is there in python list, all the nested has to be removed in order to convert it to a single containing all the values of all the lists inside the outermost brackets but without any brackets. Python list comprehensions can also be used for nested loops: [f(x, y) for x in xvalues for y in yvalues] This is equivalent to [email protected][f, xvalues, yvalues]. In any case, I improved on a posting for converting JSON to CSV in python. Flattens JSON objects in Python. APPLIES TO: SQL Server Azure SQL Database Azure SQL Data Warehouse Parallel Data Warehouse. Download the file for your platform. Work with dictionaries and JSON data in python. Let's push your code-writing skills a little further. NET is a popular high-performance JSON framework for. locations['name']. Using json_normalize, but it doesn't seem to be working. panda:将嵌套的json转换为扁平的表 [英] pandas: convert nested json to flattened table 本文翻译自 Pankaj Singhal 查看原文 2018/03/19 58 pandas / python / json / dataframe 收藏. How to join or concatenate two strings with specified separator; how to concatenate or join the two string columns of dataframe in python. Manipulating the JSON is done using the Python Data Analysis Library, called pandas. Over the last couple of months working with clients, we’ve been working with a few new datasets containing nested JSON. Your task is to write a parser function which extracts the essential data and converts it into a Pandas DataFrame. To work with JSON formatted data in python, we will use the integrated python json module. To flatten and load nested JSON file 2. Then the JSON objects could be loaded into pands via `pd. The following are code examples for showing how to use pandas. dumps 将 Python 对象编码成 JSON 字符串 json. This allows for reconstructing the JSON structure or converting it to other formats without loosing any structural information. Reading a nested JSON can be done in multiple ways. Some general use cases of JSON include: storing data, generating data from user input, transferring data from server to client and vice versa, configuring and verifying data. json) Text file (. More specifically, you’ll learn to create nested dictionary, access elements, modify them and so on with the help of examples. You can start using simplejson when the json library is not available by importing simplejson under a different name: import simplejson as json. Series() を用いて、1 次元のリスト (Series, シリーズと呼ばれます) を作成します。. I will cover: Importing a csv file using pandas,. We are in SaaS controller. The code is working fine for few input rows. json configuration file would be changed as follows:. The flattened object is made as a pandas. I demonstrate how to use WITH statements (Common Table Expressions), the json_agg function and SQLAlchemy to quickly convert complex SQL joins into nested Python data structures. Home; How to Convert Large CSV to JSON. json encoder in this video and see how. To output the DataFrame to JSON file. I believe the pandas library takes the expression "batteries included" to a whole new level (in a good way). Import Modules. Make sure it is in the same directory for this. Nested and repeated fields also reduce duplication when denormalizing the data. , data is aligned in a tabular fashion in. Convert CSV to JSON. Is there a better way? - df2json. In this post I will cover decision trees (for classification) in python, using scikit-learn and pandas. We want to flatten this result into a dataframe. JSON is text, written with JavaScript object notation. Suppose we want to convert a sample POJO (Plain Old Java Object) to JSON. Learn how to ingest and explore JSON data using Python in this video. I am trying to convert a JSON file to CSV format using Python. Below is an example of one of the truncated documents (removed some unneeded detail). Here is a snippet of the file: To query a file in a JAR file in the Drill classpath, you need to use the cp (classpath) storage plugin configuration, as shown in the sample query. 0 documentation pandas. js files used in D3. JSON uses UTF-8 encoded text strings, so JSON strings can be stored as CHAR or VARCHAR data types. When you load newline delimited JSON data from Cloud Storage, you can load the data into a new table or partition, or you can append to or overwrite an existing table or partition. locations['name']. For a while, I’ve primarily done analysis in R. When you are loading data from JSON files, the rows must be newline delimited. Enter your template below and press the Convert button below. json import json_normalize #package for flattening json in pandas df #load json. Working code: import json import pandas as pd from pandas. They are extracted from open source Python projects. GitHub Gist: instantly share code, notes, and snippets. JSON Normalize: 4. Enter your template below and press the Convert button below. So, from the start, you're not going to have any kind of nested dictionary at all until you fix that. What's Wrong With Python Pandas? We can't achieve these unless we flatten to the level of users and virtuals. In this article, you’ll learn about nested dictionary in Python. Please note this does not. A generic sample of the JSON data I'm working with looks looks like this (I've added context of what I'm trying to do at the bottom of the post):. Next: Write a JavaScript program to compute the union of two arrays. Sometimes you need to access a specific value from a key buried a dozen layers deep, and maybe some of those layers are actually arrays of nested json objects inside them. mat格式转化为dict / json. Getting this sort of data into pandas isn't very easy right now, without manual data structure munging, as the dicts reaing objects rather then converted into a flat naming hirerchy. BigQuery supports loading and exporting nested and repeated data in the form of JSON and Avro files. json import json_normalize. Or you could do map[string] interface{}, which is closer to what you have in Python or. In your Python script, import a database connector. To flatten and load nested JSON file import json import pandas as pd from pandas. json library. Let's see how JSON's main website defines it:. You could probably also do the above solution without use of _. js; Read JSON ; Read JSON from file; Making Pandas Play Nice With Native Python Datatypes; Map Values; Merge, join, and. This isn't a fast function but has quite a bit of functionality. Let’s look at an example of using SparkSQL to import a simple flat JSON file, before then considering how we handle nested and array formats. We do this, because sadly sometimes the kinds of data we track are too complicated to easily fit into a table structure. Data Structures supported by JSON. loads can be used to load JSON data from string to dictionary. Learn Python online: Python tutorials for developers of all skill levels, Python books and courses, Python news, code examples, articles, and more. Here we'll review JSON parsing in Python so that you can get to the interesting data faster. It is used for reading and writing JSON among other tasks. However the nested json objects are as it is. Spark SQL supports many built-in transformation functions in the module pyspark. The Flickr JSON is a little confusing, and it doesn’t provide a direct link to the thumbnail version of our photos, so we’ll have to use some trickery on our end to get to it, which we’ll cover in just a moment. Learn JSON array example with object, array, schema, encode, decode, file, date etc. "' to create a flattened pandas data frame from one nested array then unpack a deeply nested array. The pandas. We do this, because sadly sometimes the kinds of data we track are too complicated to easily fit into a table structure. It seems only fair that if we are going to talk about how to handle pseudo JSON files in R, that we should also talk about how to handle them in python. Interactive Course Streamlined Data Ingestion with pandas. I am trying to convert JSON to CSV file, that I can use for further analysis. It can handle JSON of any complexity. Not able to Iterate through JSON response object with JQ in Unix shell Tag: json , loops , unix , jq I am trying to iterate through a JSON object in UNIX, where the idea is to pickup different values and append it to a string and forward it as a syslog. Parse JSON using Python and store in MySQL JSON is one the most widely used data format. pandas dataframe from a nested dictionary (elasticsearch result) I am having hard time translating results from elasticsearch aggregations to pandas. If you have a simple one-level json, this step is sufficient to get the result data frame. I have a column in pandas data frame, which stores json. 6 - dfCat = json_normalize(json_data['SuccessResponse']['Body'],'children') But couldn't get all values of required columns due to this nested json data. dumps() function convert a Python datastructure to a JSON string, but it can also dump a JSON string directly into a file. This allows for reconstructing the JSON structure or converting it to other formats without loosing any structural information. While this works, it's clutter you can do without. How do I convert 1000 json files in to 1000 csv files using python. If you are new to the Wolfram Language, the Fast Introduction for Programmers is a good place to start. It automates the conversion of JSON to a database, text, or Hadoop. Typically, they are written in a single line of code. For example, you want to print a message on the screen only when a condition is true then you can use if statement to accomplish this in programming. If the JSON file will not fit in memory then you'd need to processes it iteratively rather than loading it in bulk. The sample file, employee. To flatten and load nested JSON file import json import pandas as pd from pandas. I am trying to write an abstract function which would take nested dictionary (arbitrary number of levels) and flatten them into a pandas dataframe. Convert JSON to Python Object (Dict) To convert JSON to a Python dict use this:. Pandas module does support json normalization. 利用Python进行数据分析----Numpy中扁平化函数ravel()和flatten()的区别 2019年05月31日 16:23:39 马小歪丷 阅读数 14 版权声明:本文为博主原创文章,遵循 CC 4. How could I use Apache Spark Python script to flatten it in a columnar manner so that I could use it via AWS Glue and use AWS Athena or AWS redshift to query the data. Complex (nested) JSON data source Like DataTables, Editor has the ability to work with virtually any JSON data source. Place double underscore within the column header name to create nested data. If you have a web service that takes data from the database layer and provides a response in JSON format, or client-side JavaScript frameworks or libraries that accept data formatted as JSON, you can format your database content as JSON directly in a SQL query. org Mailing Lists: Welcome! Below is a listing of all the public Mailman 2 mailing lists on mail. Installation pip install flatten_json flatten Usage. So, from the start, you're not going to have any kind of nested dictionary at all until you fix that. This allows for reconstructing the JSON structure or converting it to other formats without loosing any structural information. What we’re going to do is display the thumbnails of the latest 16 photos, which will link to the medium-sized display of the image.