100-pandas-puzzles
Pandas
100-pandas-puzzles | Pandas | |
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6 | 397 | |
2,209 | 42,039 | |
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0.0 | 10.0 | |
6 days ago | 1 day ago | |
Jupyter Notebook | Python | |
MIT License | BSD 3-clause "New" or "Revised" 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?
Pandas
- PDEP-13: The Pandas Logical Type System
- PHP Doesn't Suck Anymore
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AWS Serverless Diversity: Multi-Language Strategies for Optimal Solutions
Python is a natural fit for serverless development. It boasts a vast array of libraries, including Powertools for AWS and robust libraries for data engineers. Its versatility and excellent developer experience make it a top choice for serverless projects, offering a seamless and enjoyable development experience.
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Pandas reset_index(): How To Reset Indexes in Pandas
In data analysis, managing the structure and layout of data before analyzing them is crucial. Python offers versatile tools to manipulate data, including the often-used Pandas reset_index() method.
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Deploying a Serverless Dash App with AWS SAM and Lambda
Dash is a Python framework that enables you to build interactive frontend applications without writing a single line of Javascript. Internally and in projects we like to use it in order to build a quick proof of concept for data driven applications because of the nice integration with Plotly and pandas. For this post, I'm going to assume that you're already familiar with Dash and won't explain that part in detail. Instead, we'll focus on what's necessary to make it run serverless.
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Help Us Build Our Roadmap – Pydantic
there is pull request to integrate in both pydantic extra types and into pandas cose [1]
[1]: https://github.com/pandas-dev/pandas/issues/53999
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Stuff I Learned during Hanukkah of Data 2023
Last year I worked through the challenges using VisiData, Datasette, and Pandas. I walked through my thought process and solutions in a series of posts.
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Introducing Flama for Robust Machine Learning APIs
pandas: A library for data analysis in Python
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks.
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Mastering Pandas read_csv() with Examples - A Tutorial by Codes With Pankaj
Pandas, a powerful data manipulation library in Python, has become an essential tool for data scientists and analysts. One of its key functions is read_csv(), which allows users to read data from CSV (Comma-Separated Values) files into a Pandas DataFrame. In this tutorial, brought to you by CodesWithPankaj.com, we will explore the intricacies of read_csv() with clear examples to help you harness its full potential.
What are some alternatives?
numpy-100 - 100 numpy exercises (with solutions)
Cubes - [NOT MAINTAINED] Light-weight Python OLAP framework for multi-dimensional data analysis
tempo - API for manipulating time series on top of Apache Spark: lagged time values, rolling statistics (mean, avg, sum, count, etc), AS OF joins, downsampling, and interpolation
tensorflow - An Open Source Machine Learning Framework for Everyone
pandas_exercises - Practice your pandas skills!
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
idx2numpy_array - Convert data in IDX format in MNIST Dataset to Numpy Array using Python
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
RasgoQL - Write python locally, execute SQL in your data warehouse
Keras - Deep Learning for humans
tempo - Grafana Tempo is a high volume, minimal dependency distributed tracing backend.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration