100-pandas-puzzles
100-pandas-puzzles | github_innovation_graph_analysis | |
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6 | 1 | |
2,209 | 0 | |
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0.0 | 4.7 | |
6 days ago | 7 months ago | |
Jupyter Notebook | Jupyter Notebook | |
MIT License | - |
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100-pandas-puzzles
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What are the best Python libraries to learn for beginners?
#1: Welcome to df[pandas]! #2: 100 data puzzles for pandas, ranging from short and simple to super tricky | 3 comments #3: Happy Halloween, Pandas! 🎃🤓 | 0 comments
- 100 data puzzles for pandas, ranging from short and simple to super tricky
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pandas practice resources?
I remember someone sharing this with me earlier: https://github.com/ajcr/100-pandas-puzzles Let me know if you think it's comprehensive and a good resource.
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how important are learning the data manipulation libraries?
If you want to get better with pandas specifically you could work through the 100 pandas puzzles repo in your spare time, https://github.com/ajcr/100-pandas-puzzles
- Can anyone recommend resources to prepare for Pandas and Numpy interview questions?
- Is there anything AoC-like for Machine Learning or Data Science?
github_innovation_graph_analysis
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GitHub Innovation Graph analysis
If you want access to a Jupyter Notebook with a more detailed analysis, you can take a look at this repository.
What are some alternatives?
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Linear-Algebra-With-Python - Lecture Notes for Linear Algebra Featuring Python. This series of lecture notes will walk you through all the must-know concepts that set the foundation of data science or advanced quantitative skillsets. Suitable for statistician/econometrician, quantitative analysts, data scientists and etc. to quickly refresh the linear algebra with the assistance of Python computation and visualization.
machine_learning_complete - A comprehensive machine learning repository containing 30+ notebooks on different concepts, algorithms and techniques.
idx2numpy_array - Convert data in IDX format in MNIST Dataset to Numpy Array using Python
ML-Workspace - 🛠All-in-one web-based IDE specialized for machine learning and data science.
RasgoQL - Write python locally, execute SQL in your data warehouse
Data-Science-For-Beginners - 10 Weeks, 20 Lessons, Data Science for All!
tempo - Grafana Tempo is a high volume, minimal dependency distributed tracing backend.
pyjanitor - Clean APIs for data cleaning. Python implementation of R package Janitor