Interesting article by Karolina Alexiou.

Regarding mistake #1, I disagree. I do it all the time, and it's faster than finding, understanding, and fine-tuning a piece of code that will work for you, unless you are looking for something basic such as computing correlations for weighted observations. If you are good, your reinvented wheel will be better, implemented faster, more robust, and more customized than existing ones.

Actually, these mistakes below apply to any language, not just Python.

Here's the original list:

1. Reinventing the wheel Not tuning for performance
2. Not tuning for performance
3. Not understanding time and timezones
4. Manual integration with heavier technologies or other scripts
5. Not keeping track of data types & schemata
6. No data provenance tracking
7. No (regression) testing

I would also add:

1. Not refreshing lookup tables with the correct frequency
2. Writing obfuscated code
3. Ignoring special characters and formatting, especially for imported data, in NLP applications
4. Running in collisions with hash tables indexes
5. Generated files used by multiple users at the same time, but with no file lock
6. Not creating a data dictionary
7. Creating too big, too small, or non-optimized lookup tables
8. Creating hash tables that mix many small values with a few extremely long ones
9. Writing code that can't easily be restarted (with one click) when crashing, with little or no data loss
10. Poor error handling
11. Poor encapsulation
12. Poor load balance when splitting a task via a Map-Reduce framework
13. Poor joins, too many joins, joins that are not optimized
14. Not out-putting a detailed log (with time stamps) showing progress when code is run (to help with debugging and fine tuning performance)
15. Meaningless or poor variables or file names 
16. System calls (to be avoided), and poor process / scheduling management
17. Poor memory management (not de-allocating all memory as needed)
18. Errors with pointers

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