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This PR adds all the guides from [Visual Guides](https://bytebytego.com/guides/) section on bytebytego to the repository with proper links. - [x] Markdown files for guides and categories are placed inside `data/guides` and `data/categories` - [x] Guide links in readme are auto-generated using `scripts/readme.ts`. Everytime you run the script `npm run update-readme`, it reads the categories and guides from the above mentioned folders, generate production links for guides and categories and populate the table of content in the readme. This ensures that any future guides and categories will automatically get added to the readme. - [x] Sorting inside the readme matches the actual category and guides sorting on production
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title, description, image, createdAt, draft, categories, tags
| title | description | image | createdAt | draft | categories | tags | |||
|---|---|---|---|---|---|---|---|---|---|
| 5 Functions to Merge Data with Pandas | Explore 5 Pandas functions for efficient data merging and analysis. | https://assets.bytebytego.com/diagrams/0192-five-pandas.jpg | 2024-03-08 | false |
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How do we quickly merge data without Microsoft Excel?
Here are 5 useful pandas functions for production data analysis.
- Concat: this function supports the vertical and horizontal combination of two tables. Concat can quickly combine the data from different shards.
- Append: this function supports the adding of data to an existing table. Append can be used in web crawlers. The new data can be appended to the table when it is crawled.
- Merge: this function supports horizontal combination on keys. It works similarly to database joins. Merge can be used to combine data from different domains with the same keys.
- Join: this function works similarly to database outer joins.
- Combine: this function can apply calculations while combining two tables. The example below chooses the smaller value for the cell. Combine is useful for a data cleansing process.