In this tutorial, I’ll show you how to export pandas DataFrame to a JSON file using a simple example. I’ll also review the different JSON formats that you may apply.
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To provide you some context, here is a template that you may use in Python to export pandas DataFrame to JSON:
Writing to JSON File in Python. The same table will now be used to convert python data types to json equivalents. To convert a python dict to a json object we will use the method dumps from the json module. It will return a string which will be converted into json format. The python code looks as below. Or, you can upload an XML file into the tool, click on ‘XML to JSON’ button and then click on the ‘Download’ button to get the JSON file downloaded in your device. Our online JSON to XML conversion is a 3 step basic tool that doesn’t require much time and effort, except for if you are hardcore developer and write the code on your own. Here are 2 python scripts which convert XML to JSON and JSON to XML. XML to JSON Create the sample XML file, with the below contents. Sample.xml Run the below python script and and it will output t. Convert XML to JSON in Python We can convert XML to the Python dictionary using the ‘ xmltodict’ module. So, first of all, ‘xmltodict’ should be installed using the ‘ pip ‘ command from the command prompt like this.
![Xml in json Xml in json](https://codebeautify.org/img/cb/xml-to-json.png)
Next, you’ll see the steps to apply this template in practice.
Steps to Export Pandas DataFrame to JSON
Step 1: Gather the Data
Let’s suppose that you have the following data about different products and their prices:
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Product | Price |
Desktop Computer | 700 |
Tablet | 250 |
iPhone | 800 |
Laptop | 1200 |
The goal is to export the above data into JSON.
But before you can export that data, you’ll need to capture it in Python.
One way to accomplish this task is by creating pandas DataFrame.
Step 2: Create the DataFrame
You may then use the following code to capture the data about the products and prices:
September zodiac sign. Once you run the code in Python, you’ll get this DataFrame:
![Convert Convert](https://i.stack.imgur.com/9IVkZ.png)
Step 3: Export Pandas DataFrame to JSON File
Finally, you may use the following template to export pandas DataFrame to JSON:
For example, the path where I’ll be storing the exported JSON file is:
C:UsersRonDesktopExport_DataFrame.json
![Format Format](https://windows-cdn.softpedia.com/screenshots/thumbs/XML-To-JSON-Converter-Software-thumb.png)
You’ll need to adjust the path (in the Python code below) to reflect the location where you’d like to store the JSON file on your computer:
Run the code (adjusted to your path), and the JSON file will be created at your specified location.
There are few ways in which you could view the JSON content. An easy way is to drag the file created into your web browser. You should then get the following result:
You may then use online tools, such as the JSON Formatter, in order to format your JSON into a more readable form:
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In the next section, I’ll review the different JSON formats that you may apply.
Different JSON Formats:
There are different ways to format the JSON string. You’ll need to set the orient to your desired format. Here are the options:
- split
- records
- index
- values
- table
- columns (the default format)
For example, this is how the general syntax would look like if you set orient=’split’
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And here is the full Python code for our example:
Once you run the above code, you’ll get this output:
And if you format that output using the JSON Formatter, you’ll see:
In the context of our example, here is the output that you’ll get for each of the other formats:
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orient=’records’
orient=’index’
orient=’values’
orient=’table’
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orient=’columns’ (default)
You may refer to the pandas documentation to see the different formatting options that you may apply.
![](https://cdn-ak.f.st-hatena.com/images/fotolife/r/ruriatunifoefec/20200910/20200910011329.png)