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| QotD |
"To be or not to be" – Shakespeare
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DataFrame
Last updated at 8:42 am UTC on 13 May 2019
https://github.com/PolyMathOrg/DataFrame
What are data frames?
Data frames are the one of the essential parts of the data science toolkit. They are the specialized data structures for tabular data sets that provide us with a simple and powerful API for summarizing, cleaning, and manipulating a wealth of data sources that are currently cumbersome to use.
A data frame is like a database inside a variable. It is an object which can be created, modified, copied, serialized, debugged, inspected, and garbage collected. It allows you to communicate with your data quickly and effortlessly, using just a few lines of code. DataFrame project is similar to pandas library in Python or built-in data.frame class in R.
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What is the definition (code) of a DataFrame?
Is there a Squeak version?
DataFrame booklet
https://github.com/SquareBracketAssociates/Booklet-DataFrame
It describes the complete API of DataFrame and DataSeries data structures, and provides examples for each method.
DataFrame readFromCsv: 'weather.csv'.
DataSeries behave like an OrderedDictionary. You can create it by providing keys, values, and a name:
DataSeries
withKeys: (#('01:10' '01:30' '01:50' '02:10' '02:30') collect: #asTime)
values: #(2.4 0.5 -1.2 -2.3 3.2)
name: #temperature.
weather := DataFrame withColumns: #(
(2.4 0.5 -1.2 -2.3 3.2)
(true true true false true)
(rain rain snow - rain)).
emptyWeather := DataFrame
withRowNames: (#('01:10' '01:30' '01:50' '02:10' '02:30') collect: #asTime)
columnNames: #(temperature precipitation type).