DataFrame is a tabular data structure for data analysis in Pharo.
To install DataFrame v2.0, go to the Playground (Ctrl+OW
) in your Pharo image and execute the following Metacello script (select it and press Do-it button or Ctrl+D
):
Metacello new
baseline: 'DataFrame';
repository: 'github://PolyMathOrg/DataFrame:v2.0/src';
load.
Use this script if you want the latest version of DataFrame:
Metacello new
baseline: 'DataFrame';
repository: 'github://PolyMathOrg/DataFrame/src';
load.
If you want to add a dependency on DataFrame
to your project, include the following lines into your baseline method:
spec
baseline: 'DataFrame'
with: [ spec repository: 'github://PolyMathOrg/DataFrame/src' ].
If you are new to baselines and Metacello, check out the Baselines tutorial on Pharo Wiki.
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.
In this section I show a very simple example of creating and manipulating a little data frame. For more advanced examples, please check the DataFrame Booklet.
weather := DataFrame withRows: #(
(2.4 true rain)
(0.5 true rain)
(-1.2 true snow)
(-2.3 false -)
(3.2 true rain)).
For more information, please read Data Analysis Made Simple with Pharo DataFrame - a booklet that serves as the main source of documentation for the DataFrame project. It describes the complete API of DataFrame and DataSeries data structures, and provides examples for each method.