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LinkedInScraping

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Scraping of LinkedIn Profiles: Creates an Excel file containing the personal data and the last job position of all the provided LinkedIn profiles.

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# LinkedIn Scraping with Python

Create an Excel file containing personal data and last job position of specified people.

Scraping can be currently done only providing the LinkedIn profile url of the target person. If you don't have yet the LinkedIn profile urls, see below how to "Automatically get LinkedIn Profiles Urls".

Doubts? Reach me out on [LinkedIn](https://www.linkedin.com/in/federicohaag/).

## Prerequisites

You must have installed in your machine:
* Google Chrome (last version)
* Python (last version)

## Installing

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

1. Download the whole repository.
2. Place the directory `LinkedinScraping` in your python environment.
3. Navigate to the directory `LinkedinScraping` and run the following:
```
pip install -r requirements.txt
```
4. Run `LinkedinScraping\configurator.py` following the prompted instructions.

If this is the first time, choose the suggested configuration.
In any time in the future you can easily run again the configuration to apply changes.

## Executing

There are two ways you can run the code: headless execution and normal one.

In both cases, be careful (especially when you scrap a lot of profiles) because your computer may enter sleep mode. In sleep mode the scraping could not work. For MacOS I suggest [Amphetamine](https://apps.apple.com/it/app/amphetamine/id937984704?mt=12).

### Normal execution
In this mode the script will do scraping opening a real Chrome window.

**Pros:** In this case you will be able - if prompted - to satisfy the Captcha check and to proceed the scraping: the python script is trained on this situation and will perfectly manage it alerting you.

**Cons:** Be aware that if you choose this mode you can not loose the focus on the window, otherwise no data will be scraped.

To run in normal mode:
```
python do_scraping.py
```

### Headless execution
In this mode the script will do scraping without opening a real Chrome window.

**Pros:** The scraping process is distributed into many threads to speed up to 4 times the performance. Moreover, in this way you can keep on doing your regular business on your computer as you don't have to keep the focus on any specific window.

**Cons:** If you scrap many profiles (more than hundreds) and/or in unusual times (in the night) LinkedIn may prompt a Captcha to check that you are not a human. If this happens, there is no way for you to fill in the Captcha. The script will detect this particular situation and terminate the scraping with an alert: you will have hence to run the script in normal mode, do the captcha, and then you can proceed in scraping the profiles that were left.

To run in headless mode:
```
python do_scraping.py HEADLESS
```

### Examples

LinkedIn URLs:
```
https://www.linkedin.com/in/federicohaag/
https://www.linkedin.com/in/someoneelse/
```

When the Chrome page closes, it means the program ended.
You can find inside the `LinkedInScraping` folder the extracted data in the results file `results_profiles.xlsx`.
The filename will get concatenated to the current timestamp if the configuration was set as suggested.

## Common problems in Running

### Human check freezing the scraping
It may happen that while scraping the script will warn you about the need to perform a Human Check (a Google Captcha) even if it's not prompted for real.

This happens when you inserted in the input file a Profile URL which is not correctly formatted.

Here some tips:
* The profile URL should always end with `/`
* Open a browser window and navigate to such URL. Wait for the page to load. Is the URL currently in the browser navigation bar the same as the one you initially inserted? If not, you should insert in the input file the one you see now at the navigation bar.

## Customizing

You can customize the configurations easily re-running `configurator.py`.

You can also customize the code in many ways:
* The easy one is changing the order how the data is inserted in the excel file, or renaming the excel file headers.
* The harder one is to do scraping of additional data: have a look at the Acknowledgments down here or feel free to reach me out to propose new code.

## Authors

* **Federico Haag** - [LinkedIn](https://www.linkedin.com/in/federicohaag/) - [Medium](https://medium.com/@federicohaag)

## Disclaimer

The repository is intended to be used as a reference to learn more on Python and to perform scraping for personal usage. Every country has different and special regulations on usage of personal information, so I strongly recommend you to check your national legislation before using / sharing / selling / elaborating the scraped information. I decline any responsibility on the usage of scraped information. Cloning this repository and executing the included scripts you declare and confirm the responsibility of the scraped data usage is totally up on you.

LinkedInScraping

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