Daru: Pandas for Ruby
Photo by Bruce Hong
2020 Update: Since writing this article, I created a data frame library called Rover that’s designed for data exploration and machine learning. Check it out as well.
NumPy and Pandas are two extremely popular libraries for machine learning in Python. Last post, we looked at Numo, a Ruby library similar to NumPy. As luck would have it, there’s a library similar to Pandas as well. It’s called Daru, and it’s the focus of this post.
Daru is a data analysis library. Its core data structure is the data frame, which is similar to an in-memory database table. Data frames have rows and columns, and each column has a specific data type. Let’s create a data frame with the most populous countries:
df = Daru::DataFrame.new( country: ["China", "India", "USA"], population: [1433, 1366, 329] # in millions )
Population data from the United Nations, 2019
Here’s what it looks like:
country population 0 China 1433 1 India 1366 2 USA 329
You can get specific columns with:
df[:country] df[:country, :population]
Or specific rows with:
df.first(2) # first 2 rows df.last(2) # last 2 rows df.row # 2nd row df.row[1..2] # 2nd and 3rd row
Filtering, Sorting, and Grouping
Select countries with over 1 billion people.
df.where(df[:population] > 1000)
For equality, use
df.where(df[:country].eq("China")) df.where(df[:country].in(["USA", "India"]))
Negate a condition with
Combine operators with
& (and) and
df.where(df[:country].eq("USA") | (df[:population] < 1400))
Sort the data frame by a column with:
df.sort([:population]) df.sort([:country], ascending: [false])
You can also group data and perform aggregations.
cities = Daru::DataFrame.new( country: ["China", "China", "India"], city: ["Shanghai", "Beijing", "Mumbai"] ) cities.group_by([:country]).count
Combining Data Frames
There are a number of ways to combine data frames. You can add rows:
countries = Daru::DataFrame.new( country: ["Indonesia", "Pakistan"], population: [271, 217] # in millions ) df.concat(countries)
Or add columns:
locations = Daru::DataFrame.new( continent: ["Asia", "Asia", "North America"], planet: ["Earth", "Earth", "Earth"] ) df.merge(locations)
You can also perform joins like in SQL.
cities = Daru::DataFrame.new( country: ["China", "China", "India"], city: ["Shanghai", "Beijing", "Mumbai"] ) df.join(cities, how: :inner, on: [:country])
Reading and Writing Data
Daru makes it easy to load data from a CSV file.
After manipulating the data, you can save it back to a CSV file.
You can also load data directly from Active Record.
relation = Country.where("population > 100") Daru::DataFrame.from_activerecord(relation)
For plotting, use a Jupyter notebook with IRuby. Create a plot with:
df.plot type: :bar, x: :country, y: :population do |plot, diagram| plot.x_label "Country" plot.y_label "Population (millions)" diagram.color(Nyaplot::Colors.Pastel1) end
You can also create line charts, scatter plots, box plots, and histograms.
You’ve now seen how to use Daru to:
- create data frames
- filter, sort, and group data
- combine data frames
- create plots
Try out Daru for your next analysis.