Json explode python.
Oct 6, 2016 · It uses pandas' pd.
Json explode python Sep 22, 2022 · By combining both explode and normalize, we can get a JSON file into a Data Frame for processing. explode(eDF. Oct 13, 2023 · we will explore how to use two essential functions, “from_json” and “exploed”, to manipulate JSON data within CSV files using PySpark. loads(x[1:-1])) Add these new columns to the existing dataframe using join. g. For multiple columns, specify a non-empty list with each element be str or tuple, and all specified columns their list-like data on same row of the frame must have matching length. Before we start, let’s create a DataFrame with a nested array column. Jul 23, 2025 · Python Extract Nested Data From Complex Json Below are some of the ways by which we can extract nested data from complex JSON in Python: Using dot Notation Using List Comprehension Using Recursion Using dot Notation In this approach, we directly access the nested elements by chaining the keys using dot notation. 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? Jul 23, 2025 · When working with Pandas, you may encounter columns with multiple values separated by a delimiter. Nov 22, 2021 · In this article, we are going to see how to convert nested JSON structures to Pandas DataFrames. explode # DataFrame. The VARIANT data type is available in Databricks Runtime 15. intlist). series. I have searched around on here and throughout the web and I seem unable to find the answer to my question. json_normalize(indict, max_level=5) n_dict = df. from_json For parsing json string we'll use from_json () SQL function to parse the Mar 7, 2024 · Flattening multi-nested JSON columns in Spark involves utilizing a combination of functions like json_regexp_extract, explode, and… Apr 25, 2023 · I have a JSON string substitutions as a column in dataframe which has multiple array elements that I want to explode and create a new row for each element present in that array. Series using pd. Apr 30, 2021 · In this How To article I will show a simple example of how to use the explode function from the SparkSQL API to unravel multi-valued fields. Oct 25, 2021 · df. explode(column, ignore_index=False) [source] # Transform each element of a list-like to a row, replicating index values. I currently have from pyspa When working with JSON source files in Databricks, it's common to load that data into DataFrames with nested arrays. If you want to see examples for querying semi-structured data Aug 24, 2024 · Effortlessly Flatten JSON Strings in PySpark Without Predefined Schema: Using Production Experience In the ever-evolving world of big data, dealing with complex and nested JSON structures is a Aug 8, 2023 · I need to flatten JSON file so that I can get output in table format. I have found this to be a pretty common use case when doing data cleaning using PySpark, particularly when working with nested JSON documents in an Extract Transform and Load workflow. Ihavetried but not getting the output that I want This is my JSON file :- { "records": [ { " polars. Covering popular subjects like HTML, CSS, JavaScript, Python, SQL, Java, and many, many more. Databricks recommends using VARIANT over JSON strings. , arrays from Reading Data: JSON —can be cumbersome to analyze directly, while explode flattens it—e. Jul 26, 2025 · Using pandas. Mar 22, 2023 · TL;DR Having a document based format such as JSON may require a few extra steps to pivoting into tabular format. Step-by-step guide with examples. The problem is that some of the columns are lists. I tried a few methods like explode () and json_normalize (data, max_level=3), flatten_json. Dec 10, 2022 · I want to get the result as a new JSON, but without using pandas (and all those explode, flatten and normalize functions). This makes the data multi-level and we need to flatten it as per the project requirements for better readability, as explained below. explode('details'). createDataFrame([Row(a=1, intlist=[1,2,3], mapfield={"a": "b"})]) eDF. This blog post explains how we might choose to preserve that nested array of objects in a single table column and then use the LATERAL VIEW clause to explode that array into multiple rows within a Spark SQL query. This works great for processing data coming from, for example, an API which is usually in JSON format. One solution is to use the json. explode () transforms the list into separate rows, where each list item gets its own row. explode(col) [source] # Returns a new row for each element in the given array or map. Strangely, I didn't find anyone else mention this function before. json_normalize(df['details']), left_index=True, right_index=True). merge(pd. First step im converting data i Nov 8, 2021 · df = df. This blog talks through how using explode() in PySpark can help to transform JSON data into a PySpark DataFrame which takes advantage of Spark clusters to increase processing speeds whilst managing your nested properties. sql import functions as F from pyspark. Jun 28, 2018 · As mentioned by @jxc, json_tuple should work fine if you were not able to define the schema beforehand and you only needed to deal with a single level of json string. There are other col Nov 25, 2025 · This article describes how you can query and transform semi-structured data stored as VARIANT. explode # pyspark. json_normalize(df['val'])) is slower simply because json_normalize is meant to work with a much more complex input data - particularly deeply nested JSON with multiple record paths and metadata. loads(x[1:-1]) Then, convert the dict to a pd. loads() function from the json module. How to explode nested json in pandas as rows? Asked 5 years, 6 months ago Modified 3 years, 1 month ago Viewed 10k times Oct 25, 2025 · Python Pandas Flatten Nested JSON Examples Most of the data extracted from the web through scraping are in the form of JSON datatype because JSON is a preferred datatype for transmitting data in web applications. explode(), more precisely, works on Python lists, tuples and sets, Pandas Series, and Numpy n-dimensional arrays. Jan 17, 2024 · Pyspark: Explode vs Explode_outer Hello Readers, Are you looking for clarification on the working of pyspark functions explode and explode_outer? I got your back! Flat data structures are easier The previous section defined the explode() behavior as ' unpacking list-like values '. 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. functions. select(F. You can parse the array as using ArrayType data structure: Jul 15, 2025 · 🧩 Unpacking Nested JSON Columns with Pandas in Real-World APIs Working with APIs that return complex nested data and flattening it cleanly When working with modern APIs — especially in Jul 23, 2025 · In this article, we are going to discuss how to parse a column of json strings into their own separate columns. We have a simple flat dict for which pd. Learn how to effectively `explode JSON` data in Python and map it to structured outputs using Pandas or PySpark. . Contribute to amancevice/flatsplode development by creating an account on GitHub. Let’s explore how to master the explode function in Spark DataFrames to unlock structured insights from nested data. Dec 15, 2021 · This is my first question on here. Sep 14, 2023 · I have the data coming via REST api with nested json, Trying to explode the response but its flatteing in only the first level. In my use case, original dataframe schema: StructType(List(StructField(a,StringType,true))), json string Aug 26, 2021 · Pandas: How to explode data frame with json arrays Asked 4 years, 3 months ago Modified 4 years, 2 months ago Viewed 5k times Dec 29, 2023 · PySpark ‘explode’ : Mastering JSON Column Transformation” (DataBricks/Synapse) “Picture this: you’re exploring a DataFrame and stumble upon a column bursting with JSON or array-like … Jul 6, 2022 · Explode - Does this code below give you the same error? from pyspark. I'm trying to explode out a list in a json file out into Feb 27, 2025 · The `json_normalize` function and the `explode` method in Pandas can be used to transform deeply nested JSON data from APIs into a Pandas DataFrame. , space, comma). Note, I can modify the response using json_dumps to return only the response piece of the string or Flatten/Explode JSON objects. Nov 20, 2023 · Optimize Fabric notebook JSON visualization with panel, Bokeh, and pandas' json_normalize for interactive, efficient analysis Dec 30, 2021 · This tutorial explains how to use the explode() function in pandas, including several examples. Then you may flatten the struct as described above to have individual columns. alias("anInt")). explode("details") basically duplicates each row in the details N times, where N is the number of items in the array (if any) of details of that row Since explode duplicates the rows, the original rows' indices (0 and 1) are copied to Mar 11, 2022 · Ok great, but now I want to retain a way that lets me link the first two rows as belonging to id 1 and the second two rows belonging two id 2 for an arbitrarily deep nested JSON xd. The underlying columns being exploded must be of the List or Array data type Sep 26, 2025 · Learn JSON parsing in Python with this ultimate guide. Parse a column containing json - from_json() can be used to turn a string column with json data into a struct. That said, I'd like to read the data into a Pandas DataFrame in a more obvious flattened structure that would have columns "a", "b", "c", "d", "e", "index". Example 1: Parse a Column of JSON Strings Using pyspark. Jul 1, 2024 · Flatten JSON format different methods using Python! Flattening a JSON object can be useful for various data processing tasks, such as transforming nested JSON structures into a more tabular format … pyspark. split () splits the string into a list of substrings based on a delimiter (e. JSON with multiple levels In this case, the nested JSON data contains another JSON object as the value for some of its attributes. May 30, 2025 · Flatsplode Flatten/Explode JSON objects. Nov 25, 2025 · This article describes how you can query and transform semi-structured data stored as VARIANT. For instance, data["location"]["country"] retrieves the country value. Series(json. Simple to use: Feb 12, 2025 · What is pandas explode () and Why is It Useful? I understand that learning data science can be really challenging… …especially when you are just starting out. sql import Row eDF = spark. Parameters: columns Column names, expressions, or a selector defining them. show() For the SQL method, what is the column name in the table that holds this JSON structure in each row? Let's say that it is "contacts" and Apr 30, 2025 · Note in this example each json list in the original dataframe is the same, but of course the idea here is to explode each json to its neighbouring new columns, and they could be different of course. Need to explode the nested part also. json_normalize to explode the dictionaries (creating new columns), and pandas' explode to explode the lists (creating new rows). Subsequently, we access specific values within the JSON structure using dictionary keys, demonstrating how to retrieve information such as the name, age, city and zipcode. W3Schools offers free online tutorials, references and exercises in all the major languages of the web. For users currently using JSON strings looking to migrate, see How is variant different than JSON strings?. I'll walk you through the steps with a real-world Jun 15, 2022 · This question shows research effort; it is useful and clear Why Use the PySpark Explode Function? Nested data—e. DataFrame suffices, so use that if your dicts are flat. About Explode a JSON file to a hierarchical file structure that mirrors the data. explode( columns: ColumnNameOrSelector | Iterable[ColumnNameOrSelector], *more_columns: ColumnNameOrSelector, ) → DataFrame [source] # Explode the dataframe to long format by exploding the given columns. For Python users, related PySpark operations are discussed at PySpark Explode Function and other blogs. reset_index(drop=True) df = df. Let's learn Jul 15, 2025 · I have the following json data and i've been trying to flatten it out into a single row. Installation pip install flatsplode Usage Use the flatsplode() function to recursively flatten and explode complex JSON Feb 14, 2024 · Learn all you need to know about the pandas . drop('details', axis=1) df. pandas. DataFrame. From below example column “subjects” is an array of ArraType which holds subjects learned. Here we will parse or read json string present in a csv file and convert it into multiple dataframe columns using Python Pyspark. , for SQL queries with Running SQL Queries —enabling straightforward processing. str. May 20, 2022 · This article shows you how to flatten nested JSON, using only $"column. Feb 27, 2024 · To flatten (explode) a JSON file into a data table using PySpark, you can use the explode function along with the select and alias functions. Oct 13, 2025 · Solution: PySpark explode function can be used to explode an Array of Array (nested Array) ArrayType(ArrayType(StringType)) columns to rows on PySpark DataFrame using python example. Jun 7, 2022 · I have some JSON I have exploded however I need to filter the return based on where the "locale" is en_GB and I only wish to return that data in the dataframe. *" and explode methods. pd. series with lambda function Using the explode function The way of flattening nested Series objects and DataFrame columns by splitting their content into multiple rows is known as the explode function. Uses the default column name col for elements in the array and key and value for elements in the map unless specified otherwise. If you want to see examples for querying semi-structured data May 24, 2025 · Learn how to use PySpark explode (), explode_outer (), posexplode (), and posexplode_outer () functions to flatten arrays and maps in dataframes. After extensive reading and experimenting, I May 9, 2024 · このように、 json_normalize 関数と explode 関数を組み合わせることで、多対多の関係を持つネストされたJSONデータを適切に扱うことができます。 Nov 25, 2025 · In this article, you have learned how to explode or convert array or map DataFrame columns to rows using explode and posexplode PySpark SQL functions and their’s respective outer functions and also learned differences between these functions using Python example. items(): print(key, value) where indict looks like th Oct 6, 2016 · It uses pandas' pd. To split these strings into separate rows, you can use the split () and explode () functions. Dec 23, 2017 · The producer of the json chose an unnecessary nested structure whereas a flat structure would have been perfectly sufficient. The code becomes json. I think it's more straight forward and easier to use. Also, the data is very big and because of that I cannot use the available solutions on the Jul 23, 2025 · Using the JSON module In this example, we use the json module to parse a nested JSON string. Apr 29, 2020 · Since the "Data" column is a string and we actually want a JSON, we need to convert it. withColumn("col3",explode(from_json("col1"))) However, I'm not sure how to explode given I want two columns instead of one and need the schema. Aug 23, 2017 · I am reading multiple JSON objects into one DataFrame. explode() method, covering single and multiple columns, handling nested data, and common pitfalls with practical Python code examples. In this method, we will see how we can unnest multiple list columns using the explode function. explode function Using pandas. 3 and above. Oct 5, 2022 · If Input_array is string then you need to parse this string as a JSON and then explode it into rows and expand the keys to columns. to_dict() for key, value in n_dict. Explore basic and advanced techniques using json, and tools like ijson and nested-lookup. sql. Parameters: columnIndexLabel Column (s) to explode. Is there any option to get this structure without using pandas or having an Out of memory issue? Jul 6, 2022 · I have the below code: import pandas as pd df = pd.