pandas
pandas-chat
pandas | pandas-chat | |
---|---|---|
1 | 1 | |
33,264 | 2 | |
- | - | |
10.0 | 4.4 | |
about 2 years ago | about 1 year ago | |
Python | Python | |
BSD 3-clause "New" or "Revised" License | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
pandas
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Does pandas iterrows have performance issues?
This discussion on GitHub led me to believe it is caused when mixing dtypes in the dataframe, however the simple example below shows it is there even when using one dtype (float64). This takes 36 seconds on my machine:
pandas-chat
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Introducing pandas-chat: a python library that uses LLM prompts to analyze and process pandas data in a conversational way.
torshind/pandas-chat: pandas-ai is a python library that uses ChatGPT prompts to analyze and process pandas data in a conversational way. (github.com)
What are some alternatives?
pandas-datareader - Extract data from a wide range of Internet sources into a pandas DataFrame.
datasets - 🤗 The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools
gurobipy-pandas - Convenience wrapper for building optimization models from pandas data
Dask - Parallel computing with task scheduling
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
tqdm - :zap: A Fast, Extensible Progress Bar for Python and CLI
datasloth - Natural language Pandas queries and data generation powered by GPT-3
AWS Data Wrangler - pandas on AWS - Easy integration with Athena, Glue, Redshift, Timestream, Neptune, OpenSearch, QuickSight, Chime, CloudWatchLogs, DynamoDB, EMR, SecretManager, PostgreSQL, MySQL, SQLServer and S3 (Parquet, CSV, JSON and EXCEL).
data-science-ipython-notebooks - Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
30-Days-Of-Python - 30 days of Python programming challenge is a step-by-step guide to learn the Python programming language in 30 days. This challenge may take more than100 days, follow your own pace. These videos may help too: https://www.youtube.com/channel/UC7PNRuno1rzYPb1xLa4yktw