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Merge remote-tracking branch 'origin/main' into dependabot/npm_and_yarn/quiz-app/loader-utils-and-vue/cli-plugin-babel-and-vue/cli-plugin-eslint-and-vue/cli-service-1.4.2
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2-Working-With-Data/07-python/README.md

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df = pd.DataFrame([a,b])
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```
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This will create a horizontal table like this:
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| | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
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| --- | --- | ---- | --- | --- | ------ | --- | ------ | ---- | ---- |
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| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
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**Note** that we can also get this table layout by transposing the previous table, eg. by writing
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```python
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df = pd.DataFrame([a,b]).T..rename(columns={ 0 : 'A', 1 : 'B' })
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df = pd.DataFrame([a,b]).T.rename(columns={ 0 : 'A', 1 : 'B' })
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```
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Here `.T` means the operation of transposing the DataFrame, i.e. changing rows and columns, and `rename` operation allows us to rename columns to match the previous example.
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3-Data-Visualization/13-meaningful-visualizations/solution/package-lock.json

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3-Data-Visualization/13-meaningful-visualizations/solution/package.json

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"devDependencies": {
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"@vue/cli-plugin-babel": "~4.5.0",
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"@vue/cli-plugin-eslint": "~4.5.0",
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"@vue/cli-service": "~4.5.0",
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"@vue/cli-service": "~5.0.8",
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"babel-eslint": "^10.1.0",
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"eslint": "^6.7.2",
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"eslint-plugin-vue": "^6.2.2",

3-Data-Visualization/13-meaningful-visualizations/starter/package-lock.json

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3-Data-Visualization/13-meaningful-visualizations/starter/package.json

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"devDependencies": {
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"@vue/cli-plugin-babel": "~4.5.0",
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"@vue/cli-plugin-eslint": "~4.5.0",
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"@vue/cli-service": "~4.5.0",
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"@vue/cli-service": "~5.0.8",
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"babel-eslint": "^10.1.0",
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"eslint": "^6.7.2",
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"eslint-plugin-vue": "^6.2.2",

README.md

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# Data Science for Beginners - A Curriculum
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[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://github.com/codespaces/new?hide_repo_select=true&ref=main&repo=344191198)
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[![GitHub license](https://img.shields.io/github/license/microsoft/Data-Science-For-Beginners.svg)](https://github.com/microsoft/Data-Science-For-Beginners/blob/master/LICENSE)
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[![GitHub contributors](https://img.shields.io/github/contributors/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/graphs/contributors/)
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[![GitHub issues](https://img.shields.io/github/issues/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/issues/)
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| 18 | Data Science in the Cloud | [Cloud Data](5-Data-Science-In-Cloud/README.md) | Training models using Low Code tools. |[lesson](5-Data-Science-In-Cloud/18-Low-Code/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) and [Maud](https://twitter.com/maudstweets) |
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| 19 | Data Science in the Cloud | [Cloud Data](5-Data-Science-In-Cloud/README.md) | Deploying models with Azure Machine Learning Studio. | [lesson](5-Data-Science-In-Cloud/19-Azure/README.md)| [Tiffany](https://twitter.com/TiffanySouterre) and [Maud](https://twitter.com/maudstweets) |
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| 20 | Data Science in the Wild | [In the Wild](6-Data-Science-In-Wild/README.md) | Data science driven projects in the real world. | [lesson](6-Data-Science-In-Wild/20-Real-World-Examples/README.md) | [Nitya](https://twitter.com/nitya) |
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## GitHub Codespaces
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Follow these steps to open this sample in a Codespace:
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1. Click the Code drop-down menu and select the Open with Codespaces option.
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2. Select + New codespace at the bottom on the pane.
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For more info, check out the [GitHub documentation](https://docs.github.com/en/codespaces/developing-in-codespaces/creating-a-codespace-for-a-repository#creating-a-codespace).
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## VSCode Remote - Containers
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Follow these steps to open this repo in a container using your local machine and VSCode using the VS Code Remote - Containers extension:
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1. If this is your first time using a development container, please ensure your system meets the pre-reqs (i.e. have Docker installed) in [the getting started documentation](https://code.visualstudio.com/docs/devcontainers/containers#_getting-started).
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To use this repository, you can either open the repository in an isolated Docker volume:
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**Note**: Under the hood, this will use the Remote-Containers: **Clone Repository in Container Volume...** command to clone the source code in a Docker volume instead of the local filesystem. [Volumes](https://docs.docker.com/storage/volumes/) are the preferred mechanism for persisting container data.
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Or open a locally cloned or downloaded version of the repository:
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- Clone this repository to your local filesystem.
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- Press F1 and select the **Remote-Containers: Open Folder in Container...** command.
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- Select the cloned copy of this folder, wait for the container to start, and try things out.
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## Offline access
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You can run this documentation offline by using [Docsify](https://docsify.js.org/#/). Fork this repo, [install Docsify](https://docsify.js.org/#/quickstart) on your local machine, then in the root folder of this repo, type `docsify serve`. The website will be served on port 3000 on your localhost: `localhost:3000`.
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> Note, notebooks will not be rendered via Docsify, so when you need to run a notebook, do that separately in VS Code running a Python kernel.
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## PDF
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A PDF of all of the lessons can be found [here](https://microsoft.github.io/Data-Science-For-Beginners/pdf/readme.pdf)
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## Help Wanted!
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package-lock.json

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