Pandas json normalize nested dictionary. Aug 25, 2024 · import pandas as pd df = pd. Mar 9, 2022 · Pandas provides a number of different ways in which to convert dictionaries into a DataFrame. JSON with multiple levels In this case, the nested JSON data contains another JSON object as the value for some of its attributes. It's designed specifically for turning semi-structured JSON into a flat table. json_normalize # pandas. load (f): Loads the raw JSON into a Python dictionary. This means that JSONs that load with pd DataFrame will load with JSON normalize. Nested attribute column names follow the default pattern attribute dot nested attribute. This method is designed to transform semi-structured JSON data, such as nested dictionaries or lists, into a flat table. Jul 23, 2025 · Explanation: parse_json () function recursively navigates each level of the JSON structure. json_normalize(data, record_path=None, meta=None, meta_prefix=None, record_prefix=None, errors='raise', sep='. DataFrame' dtypes: float64(8), int64(3), object(9) So far it worked out nicely except for one column. We would like to show you a description here but the site won’t allow us. With only a few GB of data, Json_normalize is taking me around 3 hours to complete. The desired CSV data is created using the generate_csv_data () function. Having difficulty building a dataframe with pandas from json data. So, instead of using the read_json, I used the json. This makes the data multi-level and we need to flatten it as per the project requirements for better readability, as explained below. ', max_level=None) [source] # Normalize semi-structured JSON data into a flat table. json_normalize() converts the nested dictionaries into separate columns for each key. json_normalize(nested_dict) flat_dict = df. json_normalize() that can be used to flatten nested dictionaries and turn them into a DataFrame. Jul 15, 2025 · 0 Since I'm not really sure about what you want your end object to be, and ignoring the Pandas side, I've coded a recursive flattener for the type of dictionary you exhibited. Here is an example of three rows of the column named Info Jan 13, 2021 · I can get the REST API to run, but I'm having problems correctly structuring the resulting json data as a dataframe. Jul 2, 2025 · Converting nested dictionaries to Pandas DataFrames is a fundamental skill for Python developers working with complex data structures. json_normalize function is often the most powerful tool. json_normalize() The following code uses pandas v. Jul 27, 2021 · As an alternative, we can use popular data manipulation libraries such as pandas. It can flatten the JSON data, including the nested list, into a structured format suitable for analysis. Dec 13, 2023 · Learn how to convert nested JSON to CSV using Python's Pandas with examples covering different structures using json_normalize() and to_csv(). This seemed like a long and tenuous work. Quick Examples of Convert a List of Dictionaries to a DataFrame If you are in a hurry, below are some quick examples of how to convert a list of dictionaries (dict) to a Pandas DataFrame. record_pathstr or list of str, default None Path in each object to list of records. Feb 25, 2024 · This demonstrates how json_normalize() can handle deeply nested structures by specifying the path to the data and meta-information to include additional details at each level. This is the code I have. Use pandas. json_normalize function from the Pandas library is utilized to flatten the nested 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. Mar 8, 2024 · The result is a DataFrame where each dictionary becomes a row, and nested dictionaries remain nested within cells. Nov 20, 2023 · Optimize Fabric notebook JSON visualization with panel, Bokeh, and pandas' json_normalize for interactive, efficient analysis May 18, 2017 · 225 If you are already using pandas, you can do it with json_normalize() like so: Aug 26, 2020 · I have been trying using Pandas json_normalize which requires a dictionary. Nov 27, 2024 · Pandas json_normalize enables flattening out-of-box but incurs over 100x speed decrease. Mar 23, 2021 · pandas. May 20, 2020 · df = json_normalize(d) Additional Info: class 'pandas. In the case of a column of dictionaries, you can use json_normalize after converting the column to a list of dictionaries. Master inner, outer, left, right joins, and handle duplicates, nested JSONs, and more. Pandas: use of json_normalize () with nested list of lists of dicts Ask Question Asked 5 years, 10 months ago Modified 5 years, 10 months ago Feb 25, 2024 · The json_normalize() function in Pandas is a powerful tool for flattening JSON objects into a flat table. I tried normalizing the data, but I can only get it to work for the first nested level. Is the easiest way to write my own function to extract the data I want? However, nested JSON documents can be difficult to wrangle and analyze using typical data tools like pandas. Nov 24, 2021 · Need help on the below nested dictionary and I want to convert this to a pandas Data Frame My JSON have the following instance of CPU data and comes with random occurrence: Instance1 [{'datapoints' Normalize semi-structured JSON data into a flat table. DataFrame. It checks for the key-value pairs in the dict object. Jul 23, 2025 · The json_normalize () is used when we are working with nested JSON structues. The `pd. ---This video Jan 1, 2026 · Master Python's json_normalize to flatten complex JSON data. This might result in unexpected results or need to convert them to new columns. I have a nested json like this : I am working with extremely nested json data and need to flatten out the structure. For Example if the JSON file reads: The json_normalize() function from the Pandas library is a better way to manage nested JSON data. My data is like this: I've tried to use pandas. json_normalize () When dealing with a dictionary of nested JSON-like structure, pandas. May 31, 2021 · An alternative solution for flattening nested JSON files to a Pandas DataFrame with Jupyter-Notebook. pop is used to remove the specified column from the existing dataframe. Mar 8, 2021 · 3 I'm looking for a clean, fast way to expand a pandas dataframe column which contains a json object (essentially a dict of nested dicts), so I could have one column for each element in the json column in json normalized form; however, this needs to retain all of the original dataframe columns as well. The dedicated Flatdict library almost matches the efficiency of the custom implementation. The solution : pandas. Sep 8, 2022 · I have tried to use df=pd. By default, the nested parts have column names in the format <parent key>. Are there norms I'm not following? I had data in multiple spreadsheets that was a bit confusingly organized, so I wrote a custom script in python to pull everything into a dictionary, and then converted to a json file. Nov 22, 2021 · In this article, we are going to see how to convert nested JSON structures to Pandas DataFrames. Jul 13, 2024 · Fortunately, the pandas library provides a powerful function called json_normalize that can simplify this task by flattening nested JSON data into a more manageable tabular format. json. . Its json_normalize function is built specifically to flatten semi-structured JSON data into a flat table. Apr 28, 2023 · We can use the JSON normalize function of the Pandas library to flatten the nested dictionary. join to combine the original DataFrame, df, with the columns created using pd. Ultimately, pd. I have been using pandas json_normalize, but I have only been working with a fraction of the data and need to start flattening out all of the data. I searched a lot of similar Q&As, but can't find a solution. This enables easier manipulation, analysis, and Jul 15, 2025 · In this post, I’ll show how to flatten and normalize these nested JSON structures using pure Pandas, without needing heavyweight tools or writing custom recursive parsers. frame. If you are unfamiliar with the Pandas Library and its basic data structures, read this article on Introduction to Pandas. json_normalize. Feb 22, 2024 · Method 1: Using pandas. Normalize semi-structured JSON data into a flat table. Method 2: Pandas json_normalize Pandas provides a function called json_normalize that can handle the conversion of nested dictionary structures into a flat table. Parameters datadict or list of dicts Unserialized JSON objects. json_normalize ()` function helps to flatten nested JSON structures into a tabular format. Jul 6, 2022 · Exploding deeply nested JSON Pandas JSON_Normalize Ask Question Asked 3 years, 8 months ago Modified 3 years, 8 months ago May 30, 2023 · I have the following nested dictionary that contains the information that a person publicly reported to the organization. For example, it cannot add another metadata to the above example if it's nested inside another dictionary. If not passed, data Dec 12, 2017 · I propose an interesting answer I think using pandas. Using the Pandas library In this example, the pd. 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). Jan 1, 2026 · Master Python's json_normalize to flatten complex JSON data. Oct 1, 2025 · If your original data comes directly from a JSON file, or if the dictionaries in your column are nested (dictionaries inside dictionaries), the pandas. A few months ago I was tasked to work on a machine learning project and I came across a very 4 It would be way simpler if you use json_normalize in the following way to flatten your data: Feb 23, 2024 · The output is a DataFrame assembled from normalized JSON derived from the XML document. No lengthy theory — just the fact that it saves you from manually untangling nested JSON. json_normalize() Pandas offers a convenient function pandas. Feb 23, 2024 · The pandas library provides json_normalize, a powerful function specifically designed to flatten nested JSON objects into a flat table. Learn to handle nested dictionaries, lists, and one-to-many relationships for clean analysis. This method is helpful when we don't know how deep is the nesting. load () method to load json file into a list and then used the json_normalize on this. I am processing a house listing file and trying to pull out prices. json_normalize — pandas 1. It is particularly useful for JSON objects with nested arrays or dictionaries. The Panacea: json_normalize for Nested Data A strong, robust alternative to the methods outlined above is the json_normalize function which works with lists of dictionaries (records), and in addition can also handle nested dictionaries. json. This function is specifically designed to handle nested JSON data, but it works equally well with Python dictionaries. This method is designed to transform semi-structured JSON data, such as nested dictionaries or lists, into a flat table. Jul 25, 2025 · Efficiently process and flatten large nested JSON files using Pandas, orjson, and json_normalize. I tried to look for a solution but I can't find one that helps me. This is a great opportunity for us to not recreate existing solutions and use a more robust one. It automatically flattens the nested structure of the JSON data, creating a DataFrame from the resulting data. 3 documentation Web APIなどで取得できるJSONによく使われる形式なので、そ Jan 24, 2022 · 1 I have a large amount of JSON data and I want to perform some tasks. Unlike traditional methods of dealing with JSON data, which often require nested loops or verbose transformations, json_normalize() simplifies the process, making data analysis and manipulation more straightforward. Feb 2, 2024 · The pd. Nov 22, 2025 · The best and most idiomatic tool in Pandas for this task is the pandas. So, here is an alternative way to flatten the nested dictionary in pandas using . So, I figure I would convert the attributes column to a dictionary but it does not quite work out as expected for the dictionary has the form: Feb 16, 2024 · To convert a nested JSON object into a flat table, pandas provides the json_normalize() function. pandas. This approach offers a more concise and readable solution compared to manual iteration, reducing the risk of errors and improving code maintainability. If the value is again a dict then it concatenates the key string with the key string of the nested dict. JSON normalize takes a dictionary or list of dictionaries. Sep 22, 2025 · It's the intended and most efficient way to use json_normalize for this kind of structure. This process often entails using the json_normalize() function in Pandas to flatten nested dictionaries or lists within the JSON object and create a DataFrame with appropriate columns. Sometimes, you have a list of records, and within each record, there's another nested list you want to normalize. json_normalize (data) to turn it into a data frame, however, the result gives a lot of NaNs and didn't work out as expected. It’s an ideal choice when dealing with JSON data with multiple nested levels. The hard part is those fruits don't have a uniform key such as 'fruit', but each fruit's name is its own key. json_normalize() function to flatten the nested objects and create a pandas DataFrame. Apr 5, 2019 · And, it takes a list or a dictionary as an input. It’s particularly useful when dealing with nested dictionaries or when you need to select certain parts of the dictionary to be expanded into columns. json_normalize() cannot handle anything more complex than this kind of structure. core. Jul 23, 2025 · Using json_normalize Normalizing a nested JSON object into a Pandas DataFrame involves converting the hierarchical structure of the JSON into a tabular format. 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 … pandas. I use it to expand the nested json -- maybe there is a better way, but you definitively should consider using this feature. Feb 23, 2024 · Pandas is a powerful Python Data Analysis Library that simplifies data operations. read_json (data)df = Feb 14, 2025 · Nested Structures: Anytime you see {} within {} or lists inside dictionaries, this is your go-to tool. so I choose pandas for this. I went through the pandas. json_normalize function. `person_json={'basicInformation': {'individualId': 5429958, 'firstName': ' Dec 12, 2023 · Learn to merge JSON files using Pandas in Python. <child key>. Let‘s quantify tradeoffs… pandas Performance Reusing our benchmarks: Feb 21, 2024 · Method 3: Using pandas. json_normalize to explode the dictionaries (creating new columns), and pandas' explode to explode the lists (creating new rows). Jan 16, 2019 · I think using json_normalize 's record_path parameter will solve your problem. Jul 23, 2025 · Here we will apply some techniques to normalize the data and discuss these with the help of examples. Aug 3, 2021 · json_normalize to pandas dataframe with nested dict/list combos Asked 4 years, 7 months ago Modified 4 years, 7 months ago Viewed 158 times Jul 5, 2019 · I am trying to flatten nested dictionaries by using json_normalize. Jul 11, 2025 · To turn deeply nested JSON into a table use json_normalize () from pandas making it easier to analyze or manipulate in a table format. We'll employ the json_normalize function from the pandas library, combined with list comprehension for efficient data extraction. For this let's understand the steps needed for data normalization with Pandas. This is particularly useful when handling JSON-like data structures that contain deeply nested fields. io. json_normalize If the index isn't integers (as in the example), first use df. Jan 1, 2022 · 2 You can explode, convert the dictionaries to columns with json_normalize, then join and concat to the original DataFrame: Feb 23, 2023 · The Pandas Library provides a method to normalize the JSON data. Converting Dataframe column of list with dictionaries into separate columns and expand Dataframe For this purpose, we will first create a nested dictionary, then we will create the DataFrame by normalizing the JSON format (the nested dictionary) with its specific keys. to_dict(‘records‘)[0] Benefits include: Leveraging robust existing library Integration with other pandas workflows Helper methods for analysis tasks Cost is added overhead. It uses pandas' pd. We'll use its JSON normalize function to flatten nested data. json_normalize Pandas offers a function to easily flatten nested JSON objects and select the keys we care about in 3 simple steps: Make a python list of the keys we care May 3, 2023 · This pandas object shows two multi-level key-value pairs — a list and a dictionary. json_normalize on a data file that has highly varied, nested json, where the content of the records can vary considerably. JSON (JavaScript Object Notation) data and dictionaries can be stored and imported in different ways. reset_index() to get an index of integers, before doing the normalize and join. The combination of strings, dictionaries, and lists makes data normalization in one step, complicated. Apr 29, 2021 · 4 Use pandas. JSON from APIs often comes in nested form and this method helps to flatten it into a tabular format that’s easier to work with in Pandas. DataFrame() converts the nested dictionaries directly as elements. This is where pandas json_normalize () comes in very handy, providing a convenient way to flatten nested JSON into a normalized DataFrame for easier data processing in Python. DataFrameに変換できる。 pandas. The author explains how to use the function in Pandas to convert JSON data into a tabular form, which is essential for further analysis. Aug 3, 2020 · The data Nested JSON object structure I was only interested in keys that were at different levels in the JSON. It can handle multiple levels of nesting more readily than the previous methods. Each row has a Nested dictionary. Feb 16, 2024 · For JSON-like nested structures, pandas. 1. It returns a flat dictionary, whose keys are constructed by concatenating the successive keys for nested dicts, and appending indexes for the successive items of lists: Sep 24, 2023 · We can perform certain operations on both rows & column values. Mar 16, 2023 · Use the following list of nested dictionaries as an example. Sep 24, 2022 · You can see that lineup dictionary key’s nested key-value pairs have been expanded into individual columns. json_normalize ()を使うと共通のキーをもつ辞書のリストをpandas. Scale your data pipeline without bottlenecks. Oct 19, 2021 · I have attempted using some guides I found online using Panda's json_normalize () function to "unfold" or get the list of dictionaries into their own rows in a Panda dataframe. If you feel that is unnecessary, we can restrict expansion by using max_level argument. I think it's because there are multiple nested levels in the data. Aug 26, 2024 · Now that we have the JSON data loaded into a dictionary, we can use the pandas. The json_normalize function is your go-to for flattening JSON into a DataFrame. It returns a dataframe. Jul 30, 2022 · In this article, we will see how to convert JSON or string representation of dictionaries in Pandas. Since record_path is intended to be a single path to a list of json objects or records, I had to call json_normalize more than once and then concatenate the results to get a dataframe with the data you want. Nov 12, 2024 · If dictionaries contain nested structures, use json_normalize() to flatten them into a DataFrame format. Dec 10, 2025 · Converting JSON data into a Pandas DataFrame makes it easier to analyze, manipulate, and visualize. Oct 13, 2018 · In this case the OP wants all the values for 1 event, to be on a single row, so flatten_json works If the desired result is for each position in positions to have a separate row, then pandas. json_normalize () function is particularly useful in this context. json_normalize() can be used effectively to flatten the dictionary and then export it to CSV. To work around it, you need help from a 3rd module, for example, the Python json module: Dec 29, 2022 · Normalizing json using pandas with inconsistent nested lists/dictionaries Asked 3 years ago Modified 3 years ago Viewed 571 times May 18, 2023 · Solved: Hello Team, For below nested json, the array is not getting normalized using import pandas as pd import json def on_input (data): df = pd. Nov 17, 2017 · I have been trying to normalize a very nested json file I will later analyze. 2. I am trying to run pandas. Like the two examples above, the keys are compressed with an underscore. Feb 22, 2021 · However, Pandas json_normalize() function only accepts a dict or a list of dicts. json_normalize but it takes 'name' as the column instead and doesn't give me one "list" of all values from 't'. Whether you're using built-in Pandas functions like json_normalize(), creating custom flattening functions, or employing hybrid approaches, the key is to transform your data into a format that's conducive to How can I convert a JSON File as such into a dataframe to do some transformations. Learn how to use Pandas' `json_normalize` function to unravel complex nested dictionaries and create a clean DataFrame from your JSON-like data. Oct 6, 2016 · It takes a dataframe that may have nested lists and/or dicts in its columns, and recursively explodes/flattens those columns. You’ll learn how to use the Pandas from_dict method, the DataFrame constructor, and the json_normalize function. drop to remove any other unwanted columns from df. This code reads the XML file, parses it with xmltodict to get an OrderedDict, and then uses Pandas json_normalize method to create a DataFrame. I've spent many hours reading tutorials for pandas json_normalize function, but I'm still pretty lost on how I should go about working with json data formatted a certain way, so I figured I'd ask for some more specific help. I ended up having one column with a List of dictionaries in each row. Pandas provides a built-in function- json_normalize (), which efficiently flattens simple to moderately nested JSON data into a flat tabular format. What I am struggling with is how to go more than one level deep to normalize. The article "All Pandas json_normalize () you should know for flattening JSON" is a detailed guide for data scientists and machine learning practitioners who frequently deal with JSON data. It builds a dictionary with the same nested structure, making it easier to access specific values later. json_normalize is the better option. Nov 24, 2021 · Need help on the below nested dictionary and I want to convert this to a pandas Data Frame My JSON have the following instance of CPU data and comes with random occurrence: Instance1 [{'datapoints' I have been trying to normalize a very nested json file I will later analyze. Jul 23, 2025 · We normalize the dict object using the normalize_json () function. json_normalize() is a powerful tool that can flatten the data and create a DataFrame. pandas comes with a generic function to normalize JSON objects which are represented in Python as a dictionary. You can pass complex JSON objects and specify the record path to extract nested data, and it will create a DataFrame that can then be easily written to a CSV Jul 23, 2025 · Parsing Json Nested Dictionary Using Pandas Library In this example, below code uses the `pandas` library to parse JSON data into a DataFrame (`parsed_data`) and then extracts and prints the value associated with the 'city' key within the 'address' column. 4 If you don't want the other columns, remove the list of keys assigned to meta Use pandas. dyovy ifxusdj mbagyerk ulaxm nulvg kqjq hwf yoii ujysc hypw