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How to use the json_normalize Function in Pandas 

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Learn how to flatten JSON files using the pandas json_normalize() function!
Link to Jupyter Notebook: github.com/Data-Craftsman-Lea...
Key Moments:
- How to Open JSON Files - 00:55
- Convert JSON Files to Python Dictionary - 01:51
- Hierarchical JSON Structures - 03:26
- Navigating JSON Levels - 04:05
- Flattening JSON Files with json_normalize() - 07:20
- Creating a DataFrame from a JSON Key - 08:15
- Populating DataFrame Column Headers from Metadata - 09:31
- Adding JSON Metadata to DataFrame - 11:35
- Working with Flatter JSON File Structures - 12:45
- Adding Prefixes to JSON Metadata Columns in DataFrame - 15:54
- Adding Prefixes to JSON Record Path Columns in DataFrame - 16:33
- Make json_normalize() Work Without Errors - 17:15
- The Difference Between Prefixes and Separators - 19:19
- Customize Path Separators in json_normalize() - 19:55
- Choose How Much JSON Data You Want to Flatten - 20:52

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24 июл 2022

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Комментарии : 10   
@aminssssi
@aminssssi Год назад
Amazing and simple Thank you
@brucestuff
@brucestuff 9 месяцев назад
This is pairing nicely with my coursera : python for datascience course.
@Majixxxo
@Majixxxo 5 месяцев назад
11 minutes in, great video brother
@BigDataSport
@BigDataSport Год назад
Very good content
@eduardogpisco
@eduardogpisco 2 месяца назад
hi there , how the json flatten file is created ? or when can I download that file ?
@israelgonzalez677
@israelgonzalez677 4 месяца назад
Excellent! could you please share the github link with this repo? The one in the description is down. :(
@SQLPro1
@SQLPro1 3 месяца назад
Thank you for bringing this to my attention! I've fixed the link to the repo. I'll post the link here as well. The dataset for this video is large so you will have to download that directly from the source. The link should be on the readme. Thank you! github.com/Data-Craftsman-Learn/Pandas-Functions
@jesieniar
@jesieniar Год назад
We could really use a bigger font Other than that, a great video
@ErikS-
@ErikS- 7 месяцев назад
skip to 6:55...
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