Comments (3)
will investigate which ramda function call is taking up so much time.
from zebras.
Unsurprisingly, mapping over the rows is what's taking so much time.
To parse this (65mb) file: https://s3.amazonaws.com/nyc-tlc/trip+data/green_tripdata_2018-01.csv
- the ramda implementation takes 6 seconds
- using the csv-parse npm package actually takes twice as long (12.5 seconds).
from zebras.
pandas.read_csv() actually takes about the same time as the ramda implementation (4.5s) so I'm going to close this.
from zebras.
Related Issues (20)
- Implement merge
- Write project roadmap document
- Build out groupBy functionality
- Implement resampling
- Add instructions for setting up a Jupyter notebook with JS kernel + zebras + vega-lite
- Add different types of joins and ability to join on multiple columns to merge function
- Why check `node_modules` into the repo? HOT 1
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- Make zebras browser friendly HOT 5
- Add https to zebrasjs.com HOT 1
- Add tests HOT 1
- Typescript type definitions HOT 14
- Implement map for groupby objects
- Does not handle multiline column headers
- Does not import data with a comma in them HOT 2
- gbCount, gbMean etc. not curried HOT 1
- (latest npm package) Does not import data with a comma in them HOT 2
- (string) numbers with commas are not parsed correctly HOT 8
- [fix] - fix wrong function declaration mistake on toCSV.js.html & index.d.ts
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from zebras.