Read multiple json files into dataframe python. json () converts response to a Python dictionary/list.
Read multiple json files into dataframe python read_json () to Read JSON Files in Pandas The pd. I have setup the environment in databricks and have the connection linked. If your JSON code is not in a file, but in a Python Dictionary, you can load it into a DataFrame directly: I would like to read several yaml files from a directory into pandas dataframe and concatenate them into one big DataFrame. json ) is like this: [ { "info1": { "name": "Jo The main reason for doing this is because json_normalize gets slow for very large json file (and might not always produce the output you want). open() to pandas. There are about 50 files with a couple lines in each file. Reading JSON Files using Pandas To read the files, we use read_json () function and through it, we pass the path to the JSON file we want to read. The following JSON structure serves as an example: Jun 6, 2021 · In this short guide, we'll explore how to read multiple JSON files from archive and load them into Pandas DataFrame. Aug 25, 2023 · The first piece of code you need to write is to copy the JSON file to a data frame. The orient parameter Jul 23, 2025 · Pandas Dataframe To Nested Json in Python Below are some of the ways in which we can convert Pandas DataFrames into Nested JSON in Python: Use to_json () method The most straightforward approach is to use the `to_json` method of pandas, specifying the orientation as 'records'. Printing the data frame. dump method. Importing CSV files into DataFrames helps you work on the data using Python functionalities for data analysis. JSON readers JSON readers, such as pandas. write. If not specified, JSON string is returned. json that you want to import into a Pandas DataFrame to perform data analysis operations such as sorting, filtering, and summarizing. from_dict() functions. By default, PySpark considers every record in a JSON file as a fully qualified record in a single line. Whether you're working with simple JSON structures or more complex nested objects, Polars can handle them with ease. Nov 25, 2021 · If you’ve read the first blog post, you have already learned some tips and tricks on how to handle a large JSON file in Python. read_csv takes a file path as an argument Jul 24, 2022 · Data stored in a csv file can often contain JSON objects in one of the fields. Sep 16, 2019 · There are a few ways to do this a little more efficiently: You could try reading the JSON file directly as a JSON object (i. Feb 19, 2025 · The good news? Pandas makes reading JSON ridiculously simple with just one function: read_json(). Oct 25, 2025 · Converting JSON data into a Pandas DataFrame makes it easier to analyze, manipulate, and visualize. mode (). Jul 17, 2019 · I'm having a hard time loading multiple line delimited JSON files into a single pandas dataframe. This will convert it into a Python dictionary, and we can then create the DataFrame directly from the resulting Python data structure. Python supports JSON through a built-in package called JSON. e. json ("path") to seamlessly store DataFrame content into JSON format at the specified path. Jun 16, 2021 · I have a directory full of JSON files that I need to extract information from and convert into a Pandas dataframe. Then use pd. I'd like to know if there is a memory efficient way of reading multi record JSON file ( each line is a JSON dict) into a pandas dataframe. response. read()) df = pd. Nov 13, 2025 · JSON (JavaScript Object Notation) has become the de facto standard for data exchange due to its lightweight, human-readable structure and compatibility with most programming languages. columns = df. By leveraging PySpark’s distributed computing model, users can process massive CSV datasets with lightning speed, unlocking valuable insights and accelerating decision-making processes. columns. How would I go about doing this in python in an efficient manner? Each json is approx 200 MB. Feb 19, 2024 · Introduction Pandas, a powerful and flexible open-source data analysis and manipulation library for Python, offers numerous functionalities for data processing. Line-Delimited JSON (JSONL, also known as NDJSON) has emerged as a popular format for such scenarios. concat to combine in a final step. It is primarily used for transmitting data between an internet application and a server. This method parses JSON files and automatically infers the schema, making it convenient for handling structured and semi-structured data. Dec 17, 2024 · JSON file You can read JSON files in single-line or multi-line mode. You can use the read method of the SparkSession object to read a JSON file into a DataFrame, and the write method of a DataFrame… Apr 1, 2022 · As we have understood how to read the JSON data into files, now let us go through the details about writing Pandas Dataframe to JSON Files. This method is used when we working with standard JSON structures. dfs = [] for file in json_files: df = json. Before that just recall some terms : JSON File: A JSON file may be a file that stores simple data structures and objects in JavaScript Object Notation (JSON) format, which may be a standard data interchange format. Notes Specific to orient='table', if a DataFrame with a literal Index name of index gets written with to_json(), the subsequent read operation will incorrectly set the Index name to None. Also supports optionally iterating or breaking of the file into chunks. Now, I want to read this file into a DataFrame in Spark, using pyspark. After extensive reading and experimenting, I Apr 9, 2023 · PySpark provides a DataFrame API for reading and writing JSON files. Jul 23, 2025 · Sometimes you might need to read multiple CSV files into separate Pandas DataFrames. To convert a file to the data frame, we need to have a JSON file to perform that operation. To use this feature, we import the Python JSON package into Python script. If you’re working with a multi-record JSON file where each line is a valid JSON dictionary, you might be wondering how to efficiently read these records into a Pandas Nov 22, 2021 · Here, the data contains multiple levels. Nov 13, 2025 · If you’ve ever tried to load a nested JSON file into a Pandas DataFrame and ended up with messy columns full of dictionaries or lists, this guide is for you. First, we import Panda's library from Python. json_normalize and playing with the arguments record_path, meta and max_level but so far I was not able to, in a few steps, convert this type of json to a DataFrame. Sep 16, 2025 · Reading CSV files into a structured DataFrame becomes easy and efficient with PySpark DataFrame API. It looks like all the files are loading when I look through file_list, but cannot figure out how to get each file into a dataframe. Jun 19, 2023 · This blog will show you how to efficiently convert nested JSON files into a Pandas DataFrame, a vital skill for data scientists and software engineers. A DataFrame is a data structure that organizes data into a 2-dimensional table of rows and columns, much like a Nov 24, 2022 · In this article, we will learn how to convert multiple JSON files to CSV file in Python. This is the code I'm using: import os, json import pandas as pd import numpy as np import glob pd. read_csv() to construct a pandas. Jul 4, 2023 · The result shows that this json file has been read and is ready for data science operations. JSON is a ubiquitous file format, especially when working with data from the internet, such as from APIs. json_normalize(df) df. json files and get them into a spreadsheet to work with later. The string could be a URL. Jan 17, 2024 · Often you may need to combine multiple JSON files into a single file for easier processing and analysis. Options See the following Apache Spark reference articles for supported read and write options. Jun 15, 2017 · 64 You can pass ZipFile. In JSON, objects are represented as key-value pairs enclosed in curly braces {}. May 5, 2020 · 3 I have a lot of line delimited json files in S3 and want to read all those files in spark and then read each line in the json and output a Dict/Row for that line with the filename as a column. map(lambda x: x . The demonstrative files can be download from here Method 1: Reading CSV files If our data files are in CSV format then the read_csv () method must be used. Pandas Convert JSON to DataFrame Importing the pandas This is the first step to working with the data frames in Pandas. I need to read these parquet files starting from file1 in order and write it to a singe csv file. Jan 14, 2019 · I am trying to load multiple json files from a directory in my Google Drive into one pandas dataframe. 6 and trying to download json file (350 MB) as pandas dataframe using the code below. So far I can get the values I need from all JSON files in a folder, but I would also need the JSON filename and Sep 20, 2023 · In this article, we will delve into the process of converting it step by step. Jul 23, 2025 · Using pd. Jul 23, 2025 · Using json Module Using List Comprehension Using os Module with json Module Using glob Module with json Module Using Pandas Library Merge Multiple JSON Files Using json Module In this example, a Python function merge_json_files is defined to combine data from multiple JSON files specified by file_paths into a list called merged_data. As a coincidence, I used a GeoJSON file as a motivating example in the documentation, though I'm working on a few more tutorials that take larger Parquet files as example data, unrelated to pandas I'd like to know if there is a memory efficient way of reading multi record JSON file ( each line is a JSON dict) into a pandas dataframe. Jul 12, 2025 · JSON stands for JavaScript Object Notation. We discussed alternative ways to read JSON files and how to deal with semi-structured JSON like data. Sep 16, 2024 · Python’s Pandas library provides powerful tools for data manipulation and analysis, and it includes functionality to load JSON data into a Pandas DataFrame. json import json_normalize data = [] path_to_json = '/Users Aug 5, 2018 · 39 I am new to python and I have a scenario where there are multiple parquet files with file names in order. pkl, Feb 15, 2016 · I've got this JSON file { "a": 1, "b": 2 } which has been obtained with Python json. Finally, the PySpark dataframe is written into a JSON file using the "dataframe. Sep 15, 2025 · Example: Reading JSON File using Python We will be using Python’s json module, which offers several methods to work with JSON data. In Python, we get the JSON modules to merge JSON files easily. But, I have a folder having 40 pickle files named as imdbnames0. blob import BlobServiceClient, BlobClient, ContainerClient import json import json import pa How to read and load json objects and files into pandas dataframe using pandas. Sep 16, 2025 · To read JSON files into a PySpark DataFrame, users can use the json() method from the DataFrameReader class. For Example if the JSON file reads: If you have a file containing individual JSON objects with delimiters in-between, use How do I use the 'json' module to read in one JSON object at a time? to parse out individual objects using a buffered method. Nov 24, 2024 · How to Efficiently Read Multiple JSON Records into a Pandas DataFrame Processing large datasets is a common task in data science, especially when dealing with JSON data generated from various sources such as logs or databases. One common task is parsing JSON data into a pandas DataFrame, enabling Nov 3, 2021 · I am trying to get all the json files stored in a single container in a subfolder in blob storage. json_normalize Jul 3, 2025 · In this article, I will cover how to convert JSON to DataFrame by using json_normalize(), read_json() and DataFrame. Jul 26, 2024 · Python, being a versatile language, offers built-in support for handling JSON data. May 14, 2021 · To parse a JSON file with multiple JSON objects read one JSON object at a time and Convert it into Python dict using a json. The same limitation is encountered with a Aug 14, 2022 · I can convert this type of json to a pandas DataFrame, but it requires multiple steps and for loops within a single file to concatenate everything at the end. To convert it to a dataframe we will use the json_normalize () function of the pandas library. loads() to handle the data efficiently. 6 days ago · Learn how to use the Apache Spark spark. It's not advisable to continually append to an existing dataframe, as pd. json ()" function. Once we do that, it returns a “DataFrame” ( A table of rows and columns) that stores data. requests. Concatenate DataFrames: Use pd. How can I convert a JSON File as such into a dataframe to do some transformations. So, here is an alternative way to flatten the nested dictionary in pandas using glom. Jun 14, 2024 · The process involves reading JSON from a file and converting it into a format that Python can understand. json", "r")) df = pd. Key Points – Use pandas. Jun 9, 2021 · If you are interested in combining CSV files into DataFrame then you can check this detailed article: How to merge multiple CSV files with Python This image illustrates the process of merging two JSON files into single on by Python: Nov 17, 2019 · I have a problem writing the code that will read multiple json files from a folder in Python. pkl, imdbnames1. json () method to load JavaScript Object Notation (JSON) data into a DataFrame, converting this versatile text format into a structured, queryable entity within Spark’s distributed environment. load() function to parse our JSON data. Ensure that the Pandas library is imported in your Python environment. append is expensive relative to list. In this, I want to focus on how to work efficiently with multiple JSON files. In particular, loads () and load () are used to read JSON from strings and files, respectively. storage. Using pd. CSV are easy to read when opened in a spreadsheet GUI Hey all, I am trying to convert a json file into a dataframe. Learn how to create DataFrames and store them. Simplify the process of working with complex data structures and achieve a specific format for your data analysis tasks. Example use would be to process output from Hadoop Pig JSonStorage function. The Awkward Array library (note: I'm the author) is meant for working with nested data structures like this at large scale. However, when working with data analysis in Python, we often need to convert this flexible JSON data into a structured tabular format—like a Pandas Feb 5, 2018 · I am using python 3. In multi-line mode, a file is loaded as a whole entity and cannot be split. DataFrame. Let's see with an example. load(open("your_file. I have also tried pd. Using the to_json () Method The to_json () function converts a DataFrame into a JSON string or file. Basic Syntax: Load JSON into a Pandas DataFrame Jul 23, 2025 · A nested JSON example In the above example, the key field " article " has a value which is another JSON format. into a Python dictionary) using the json module: import json import pandas as pd data = json. In single-line mode, a file can be split into many parts and read in parallel. The csv data can easily be loaded into a Pandas Dataframe for analysis. Read a comma-separated values (csv) file into DataFrame. Fields of interest in the JSON object can also be extracted into their own column easily using Dataframe. read_json () function. import pandas as pd import os from pandas. Copy and paste the following code into an empty notebook cell. I have 22 json files which i want to gather in one pandas dataframe. Feb 2, 2024 · In summary, the transformation of JSON data into CSV files using Python's Pandas library is easy and effective. May 3, 2023 · Multi-level Nested JSON Recently, I went down a rabbit hole, trying to figure out JSON file parsing in Python from the Jupyter Notebook platform. read_json () function helps to read JSON data directly into a DataFrame. But if you want to show it in some specific tabular forms, you need to do more operation on dataframe. concat call. from_dict(data, orient="index") Using orient="index" might be necessary, depending on the shape/mappings of your JSON file Aug 30, 2022 · In this article, we saw how to read JSON files, JSON lines objects and multiple JSON formats. Read Python Scala Write Python Jul 15, 2025 · Pandas a powerful Python library for data manipulation provides the to_json() function to convert a DataFrame into a JSON file and the read_json() function to read a JSON file into a DataFrame. In this article we will explore how to export a Pandas DataFrame to a JSON file with detailed explanations and beginner-friendly steps. Below is a 2 line example with working solution, I need it Dec 12, 2023 · Read Multiple JSON Files: Loop through your JSON files and read each one into a DataFrame. Feb 20, 2025 · To use information from JSON Lines in data processing pipelines, the tokens must often be converted into a Dataframe or columnar format, such as Apache Arrow. The merged data is then written to a new JSON file named Notes Specific to orient='table', if a DataFrame with a literal Index name of index gets written with to_json(), the subsequent read operation will incorrectly set the Index name to None. This blog post aims to guide you through reading nested JSON files using PySpark, a Python library for Apache Spark. We’ll break down the most common techniques to flatten nested JSON structures, with step-by-step examples, code snippets, and solutions to common challenges. Nov 13, 2025 · How to Read Multiple Line-Delimited JSON Files into a Single Pandas DataFrame: Step-by-Step Guide In the era of big data, structured data is often split across multiple files for easier storage, processing, or streaming. Nested JSON to CSV conversion Our job is to convert the JSON file to a CSV format. Below is a 2 line example with working solution, I need it for potentially very large number of records. However, I get the following error: Jun 15, 2024 · Parsing a JSONL (JSON Lines) file into a pandas DataFrame is very useful because it allows you to convert the unstructured, line-delimited JSONL data into a well-structured, tabular format that’s easy to work with in Python. To begin, let’s first understand what a JSON file with nested objects means. DataFrame() functions The easiest and most straightforward approach is to use the built-in json. There can be many reasons as to why we need to perform this conversion. Now, let’s get straight to the practical part. For further information, see JSON Files. How to turn dataframe into json file or object using pandas. 598 asked Jul 17 '19 02:07 What is Reading JSON Files in PySpark? Reading JSON files in PySpark means using the spark. Conversely, you can employ dataframe. Mar 2, 2024 · The goal is to read the JSON file into a DataFrame using the Python Pandas library, enabling a seamless transition from raw data to actionable insights. For instance, you may have a file data. Sep 7, 2022 · I am not a programmer and have no experience with Pyton, so I would really appreciate any help to solve the few remaining issues I’ll explain bellow: What I am trying to do is collect a few but same values from many . If the file is located on a remote server we can also pass the URL instead of a local file path. In this article, we will explore how to load and parse a JSON file with multiple objects using Python 3. Key parameters include: path_or_buf: File path or buffer. infer_schema_length: int | None = 100, ) → DataFrame [source] # Read into a DataFrame from a JSON file. I managed to flatten them with json_normalize to the second level, but I am not able to parse it further. loads() Jul 13, 2022 · I m using below code to read json file from Azure storage into a dataframe in Python. Code: python The answers above are excellent, but here's something a little different. Pandas Dataframe objects have several methods to write Jan 10, 2025 · Pandas read_json() function can be used to read JSON file or string into DataFrame. You’ll use methods like json. Create a Dask DataFrame from various data storage formats like CSV, HDF, Apache Parquet, and others. Jun 12, 2025 · Now, let’s dive deeper into the process and explore different options available for customization. Understanding JSON JSON is a lightweight data format that is easy for humans to read and write and for machines to parse and generate. dumps (obj, indent=4): converts the Python object back to a JSON string with 4-space indentation. to_json() to denote a missing Index name, and the subsequent read_json() operation cannot distinguish between the two. loads(open(file). Understanding JSON Files Before diving into the process of reading multiple JSON files, it is important to understand the structure of JSON files. Apply. Imagine receiving a JSON file with multiple levels of hierarchy, and you need to flatten this structure for use within a pandas DataFrame. When working with large datasets, it is common to have JSON files that span multiple lines. Jul 13, 2018 · The problem isn't that I have multiple files - the problem is that, in a single file, I have multiple JSON documents without newlines between. I have tried quite a few solutions but nothing seems to be yielding a positive result. JSON is still the most common format in modern data storage and exchange, notably in NoSQL databases and REST APIs. Dec 6, 2024 · How can I efficiently read and manipulate nested JSON data using Pandas? Navigating through complex nested JSON structures can be challenging, especially when trying to convert them into a format that is more workable for data analysis, such as a Pandas DataFrame. When dealing with JSON data, it is common to have multiple records stored in a single file. load() and pd. It supports JSON in several formats by using orient param. Sep 7, 2023 · To convert a Pandas dataframe to a JSON file, we use the to_json() function on the dataframe, and pass the path to the soon-to-be file as a parameter. json () converts response to a Python dictionary/list. read_json convert input character data into a Dataframe organized by columns and rows. from azure. read. Oct 31, 2024 · Convert JSON to CSV using Pandas, Pandas is a library in Python that can be used to convert JSON (String or file) to CSV file, all you need is first read the JSON into a pandas DataFrame and then write pandas DataFrame to CSV file. Parameters: filepath_or_bufferstr, path object or file-like object Any valid string path is acceptable. DataFrame from a csv-file packed into a multi-file zip. From APIs to configuration files, JSON is everywhere. Use appropriate methods to read JSON data from a file, URL, or a JSON string. read_json() to read JSON data directly into a DataFrame. Whether you are extracting data from web APIs, reading files, or working with data received in JSON format, understanding how to seamlessly transform it into a Pandas DataFrame is a valuable skill. Let's create a JSON file from the tips dataset, which is included in the Seaborn library for data visualization. Jul 1, 2024 · Flattening JSON data using PySpark involves reading the JSON data into a DataFrame, then using various DataFrame operations to transform nested structures into a flat format. In the below example it reads and prints JSON data from the specified API endpoint using the pandas library in Python. io. Mar 20, 2025 · Understanding Data Reading in PySpark: Working with CSV and JSON Files PySpark is the Python API for Apache Spark, which allows Python developers to harness the power of Spark in data processing … Jan 23, 2018 · I am reading large pickle files to pandas dataframe, I loaded one of them and it is loaded i the manner, I need. json. Sometimes the jsons have more than 5 levels. I have not been able to figure it out though Jul 11, 2025 · Read the JSON File directly from Web Data You can fetch JSON data from online sources using the requests library and then convert it to a DataFrame. Dataframe () Methods 1. json ("path") for efficiently parsing both single-line and multiline JSON files into Spark DataFrames. Dec 5, 2024 · Discover five effective ways to load and parse JSON files with multiple JSON objects in Python, including practical code examples. You invoke this method on a SparkSession object—your central hub for Spark’s SQL capabilities—and JSON = Python Dictionary JSON objects have the same format as Python dictionaries. In this guide, we will explore how to load a multi-line JSON file into Pandas in Python 3. Method 1: Using the json. Whether you’re working with gigabytes or petabytes of data, PySpark’s CSV file integration offers a Oct 3, 2020 · 0 I'm trying to flatten deeply nested json files. concat () to merge these DataFrames into one. The same limitation is encountered with a Feb 6, 2024 · The "multiline_dataframe" value is created for reading records from JSON files that are scattered in multiple lines so, to read such files, use-value true to multiline option and by default multiline option is set to false. ex: par_file1,par_file2,par_file3 and so on upto 100 files in a folder. Parameters: source Path to a file or a file-like object (by “file-like object” we refer to objects that have a read() method, such as a file handler like the builtin open function, or a BytesIO instance). My json file example (file name: 20191111. Aug 23, 2021 · In this article, we are going to see how to read multiple data files into pandas, data files are of multiple types, here are a few ways to read multiple files by using the pandas package in python. Converting into data frame . append and a single pd. Feb 25, 2024 · In this article, we will explore how to efficiently read multiple JSON files from a folder in Python 3. Understanding JSON Records Feb 23, 2024 · Problem Formulation: In the era of big data, developers often find themselves needing to convert JSON structures with nested arrays and objects into tidy pandas DataFrames for analysis. My current solution works, but I have a feeling that there is a more elegant way of Jul 30, 2020 · The countries column is a JSON with multiple rows of data, the year applies to all that data, so how can I convert it to a dataframe with all the rows and the corresponding year in each row? May 18, 2024 · Spark SQL offers spark. JSON supports multiple nests to create complex JSON files if required. load() or json. Feb 24, 2023 · Pandas read_json – Reading JSON Files Into DataFrames February 24, 2023 In this tutorial, you’ll learn how to use the Pandas read_json function to read JSON strings and files into a Pandas DataFrame. Pandas provides a built-in function- json_normalize (), which efficiently flattens simple to moderately nested JSON data into a flat tabular format. get (url) fetches data from the URL. Reading into a single DataFrame To read multiple files into a single DataFrame, we can use globbing patterns: Aug 26, 2024 · One common task is to read a JSON file with nested objects into a pandas DataFrame, a powerful data manipulation tool in Python. In this article, we will explore how to read multiple JSON records into a Pandas dataframe in Python 3. Additional help can be found in the online docs for IO Tools. Mar 12, 2024 · Python, with its powerful libraries such as Pandas, makes it easy to work with JSON data. format() method to read JSON data from a directory into a DataFrame. json. read_json and pandas. As already suggested, it is better to read a JSON file via Pandas, using the read_json () method and passing the chunksize parameter, to load and manipulate only a certain amount of rows Jul 23, 2025 · Polars offers a fast and efficient way to read JSON files into DataFrames using the polars. Similar use case for CSV files is shown here: Parallel Processing Zip Archive CSV Files With Python and Pandas The full code and the explanation: from multiprocessing import Pool from Jul 23, 2025 · Reading the JSON file. The text in JSON is done through quoted-string which contains a value in key-value Mar 27, 2024 · Problem: How to read JSON files from multiple lines (multiline option) in PySpark with Python example? Solution: PySpark JSON data source API provides the multiline option to read records from multiple lines. CSV Jul 21, 2023 · In the world of big data, JSON (JavaScript Object Notation) has become a popular format for data interchange due to its simplicity and readability. In this article, we will see how to read multiple CSV files into separate DataFrames. Jun 6, 2018 · You can make a list of dataframes via a for loop. This is because index is also used by DataFrame. It means that a script (executable) file which is made of text in a programming language, is used to store and transfer the data. However, when dealing with nested JSON files, data scientists often face challenges. nivl qkhwrnt jihj hdwdg ftxg qku qzv ztccc uujcxh xpxsu zkv heqefmdp qxsts nlfqni jbynma