PythonDataScienceHandbook
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PythonDataScienceHandbook | Pandas | |
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98 | 393 | |
41,182 | 41,678 | |
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1.0 | 10.0 | |
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Jupyter Notebook | Python | |
MIT License | BSD 3-clause "New" or "Revised" License |
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PythonDataScienceHandbook
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About Data analyst, data scientist and data engineer, resources and experiences
Python Data Science Handbook
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Other programing options?
Python Data Science Handbook by Jake VanderPlas (https://jakevdp.github.io/PythonDataScienceHandbook/)
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Mastering Data Science: Top 10 GitHub Repos You Need to Know
7. Data Science Handbook Are you looking for a comprehensive guide to data science with Python? Look no further than the Data Science Handbook by Jake VanderPlas. This repository contains the entire book, which introduces essential tools and techniques used in data science, including IPython, NumPy, Pandas, Matplotlib, and Scikit-Learn. Itβs a fantastic resource for anyone looking to deepen their understanding of data science concepts and best practices.
- Resources for Current DE Interested in Learning Data Science
- What are the best Python libraries to learn for beginners?
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Best Websites For Coders
Data Science course : Python Data Science Handbook
- What are some good useful libraries I can get the hang of?
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I learnt all the basics about python and had interest in learning about "AI development with python". and now I'm stuck cuz I don't know where to start. Can anyone give some advice to me ?
Something that helped me get started is this git repo: https://github.com/jakevdp/PythonDataScienceHandbook/tree/master/notebooks It covers all the basics of data analysis using numpy, pandas and matplotlib.
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Learn Python with CFB tutorial
Thanks! Will do. I work full time on data engineering/geospatial big data analytics, so I haven't had the energy to do this in the evenings or weekends yet. I do plenty of work with regression (but not in an MLOps sense) and dimensionality reduction (we do PCA). So in my mind my gap is (1) actual neural network work and (2) familiarity with workflows using e.g. pytorch or scikit-learn or something similar. Any pointers on where to get started resource-wise? Been thinking of starting with Ch.5 here and moving on from that: https://jakevdp.github.io/PythonDataScienceHandbook/. I have some projects in mind (including some predictive CFB model) so will start that up on the side while doing some of these tutorials.
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What programming language should I learn and what skills should I learn
Yeah! Ofc. So for R: https://r4ds.had.co.nz Python: https://jakevdp.github.io/PythonDataScienceHandbook/ SQL: I had to get a book, but this would be a good place to start: https://datascience.foundation/sciencewhitepaper/sql-for-data-science
Pandas
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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 β Pydantic
there is pull request to integrate in both pydantic extra types and into pandas cose [1]
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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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What Would Go in Your Dream Documentation Solution?
So, what I'd like to do is write a documentation package in Python to recreate what I've lost. I plan to build upon the fantastic python-docx and docxtpl packages, and I'll probably rely on pandas from much of the tabular stuff. Here are the features I intend to include:
- Read files from s3 using Pandas/s3fs or AWS Data Wrangler?
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10 Github repositories to achieve Python mastery
Explore here.
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Interacting with Amazon S3 using AWS Data Wrangler (awswrangler) SDK for Pandas: A Comprehensive Guide
AWS Data Wrangler is a Python library that simplifies the process of interacting with various AWS services, built on top of some useful data tools and open-source projects such as Pandas, Apache Arrow and Boto3. It offers streamlined functions to connect to, retrieve, transform, and load data from AWS services, with a strong focus on Amazon S3.
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How to Build and Deploy a Machine Learning model using Docker
Pandas
What are some alternatives?
Cubes - [NOT MAINTAINED] Light-weight Python OLAP framework for multi-dimensional data analysis
tensorflow - An Open Source Machine Learning Framework for Everyone
orange - π :bar_chart: :bulb: Orange: Interactive data analysis
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Keras - Deep Learning for humans
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
pyexcel - Single API for reading, manipulating and writing data in csv, ods, xls, xlsx and xlsm files
SymPy - A computer algebra system written in pure Python
Dask - Parallel computing with task scheduling
NumPy - The fundamental package for scientific computing with Python.
blaze - NumPy and Pandas interface to Big Data
Scrapy - Scrapy, a fast high-level web crawling & scraping framework for Python.