sheet2dict
datatable
| sheet2dict | datatable | |
|---|---|---|
| 3 | 9 | |
| 218 | 1,790 | |
| - | 0.5% | |
| 4.1 | 6.1 | |
| 9 months ago | 5 months ago | |
| Python | C++ | |
| MIT License | Mozilla Public License 2.0 |
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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.
sheet2dict
- Pytlicek/sheet2dict A simple XLSX/CSV reader - to dictionary converter
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Show HN: Sheet2dict – simple Python XLSX/CSV reader/to dictionary converter
Correct, it's a wrapper around csv.DictReader
https://github.com/Pytlicek/sheet2dict/blob/main/sheet2dict/...
datatable
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Cheat Sheets for data.table to Python's pandas syntax?
Aside from that, there is a Python translation of data.table (see documentation here), which might be worth looking into. However, it hasn't had any major updates in a while: the last release 2 years ago ...
- Any advice on using Pandas as a data analyst?
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Alternative to Pandas
There's datatable. I haven't used it much, but the R version (data.table) is phenomenal.
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Need advice on whether to store data set for regression model in SQL database or by using Python modules like Pickle or Parquet
just use HDF5 or Parquet, or CSV + https://github.com/h2oai/datatable to speed up the file reading.
- Massive R analysis of Data Science Language and Job Trends 2022
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Scikit-Learn Version 1.0
> For me I had with pandas the most issues using it's multiindex.
Yessss. I loathe indices, and have never been in a situation where I was better off with them than without them.
> Regarding fast you have something like Vaex on python sid
I've never used Vaex, but I've used datatable (https://github.com/h2oai/datatable) and polars (https://github.com/pola-rs/polars). Polars is my favorite API, but datatable was faster at reading data (Polars was faster in execution). I'll have to give Vaex a try at some point.
- Show HN: Sheet2dict – simple Python XLSX/CSV reader/to dictionary converter
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Hey Reddit, here's my comprehensive course on Python Pandas, for free.
Yep. I think this is the downside to a package being entirely maintained by volunteers. In any case, Pandas is still the leading data wrangling package for Python. (I'm excited to see how datatable evolves.)
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Ditching Excel for Python in a Legacy Industry (Reinsurance)
h2o's data.table clone is fine
https://github.com/h2oai/datatable
What are some alternatives?
ExportTools.bundle - Export tools for Plex
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
pydfql - Wireshark-like display filter for various data formats, including Python dictionaries, lists, objects, and SQL databases.
DataFrame - C++ DataFrame for statistical, Financial, and ML analysis -- in modern C++ using native types and contiguous memory storage
DataProfiler - What's in your data? Extract schema, statistics and entities from datasets
db-benchmark - reproducible benchmark of database-like ops
Arduino-Python-Cross-platform-Data-logging-to-CSV-file- - A low cost multi channel data logging system using Python and Arduino UNO board that will log and save data to a CSV (Comma Separated Values) file on the disk.The system can monitor temperature data from 4 independent sources at the same time and log the data to the disk.
scientific-visualization-book - An open access book on scientific visualization using python and matplotlib
OnePasswordConverter - Convert 1Password .1pux to Bitwarden .csv
sktime - A unified framework for machine learning with time series
Excelize - Excelize is a small app that will allow users to easily manipulate Excel files, by merging, converting and splitting them as needed. It support multiple file selection with the aim to simplify the process
vinum - Vinum is a SQL processor for Python, designed for data analysis workflows and in-memory analytics.