Скачать или смотреть How to Create a Pandas DataFrame from JSON with Custom Column Names and Multiple Values

Learn how to convert a JSON object into a Pandas DataFrame with unique values and different column names effortlessly.
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Converting JSON Data into a Pandas DataFrame with Unique Values

When working with data in Python, especially with complex JSON structures, it is common to encounter the need to transform JSON objects into a Pandas DataFrame. This is particularly essential when you want to manage and analyze data more effectively. In this guide, we will explore a specific scenario where you need to create a Pandas DataFrame from a JSON object with different column names and accommodate multiple values. Let's dive in!

Understanding the JSON Object

The given JSON object looks something like this:

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From this JSON, we want to extract pertinent information and transform it into a DataFrame that retains the uniqueness of values while populating the corresponding columns with the same values.

Expected Output

The expected output data structure would appear as follows:

emailitem_namesupplyquantitypriceemail-gmail.comabc23FEG1100email-gmail.comabc23FEG2150email-gmail.comabc23FEG5400Each unique price associated with its quantity should be reflected while duplicating the other column values.

Steps to Create the DataFrame

Here’s a step-by-step breakdown of how to accomplish this transformation using Python and Pandas:

1. Import Required Libraries

Make sure you have Pandas installed. You can do this by running pip install pandas in your terminal.

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2. Prepare the JSON Object

Here we convert the JSON object into a dictionary format in Python:

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3. Create the DataFrame

To handle the JSON data correctly, we use the following commands:

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Explanation of the Code:

pd.DataFrame.from_dict(dic, orient="index"): This creates a DataFrame by turning the dictionary into a DataFrame where the keys become the index.

df.T.explode('price'): The explode function is particularly useful here. It allows us to expand the list of prices while retaining the other data points aligned, thus creating rows for each individual price.

df['item'] = pd.Series(df['item'].to_list()[0]): Since item consists of a list, we are creating separate entries for each component.

df[['quantity', 'price']] = pd.DataFrame(df['price'].to_list(), index=df.index): This line splits the price list into two new columns: quantity and price.

Final Output

After executing the above lines of code, the output is as shown below:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

Transforming a JSON object into a Pandas DataFrame while customizing the column names and handling multiple values isn't just straightforward—it's also a crucial skill for data analysts and developers. By leveraging the power of the Pandas library, we can efficiently manipulate complex data structures and gain insights with ease.

Feel free to utilize the provided code and adapt it to your own JSON structures to streamline your data workflow! If you have any further queries or want to share experiences, drop your thoughts in the comments below.