Simple, powerful data frames for Ruby
⛰️ Designed for data exploration and machine learning, and powered by Numo
🌲 Uses Vega for visualization
Add this line to your application’s Gemfile:
gem "rover-df"A data frame is an in-memory table. It’s a useful data structure for data analysis and machine learning. It uses columnar storage for fast operations on columns.
Try it out for forecasting by clicking the button below (it can take a few minutes to start):
Use the Run button (or SHIFT + ENTER) to run each line.
From an array
Rover::DataFrame.new([
{a: 1, b: "one"},
{a: 2, b: "two"},
{a: 3, b: "three"}
])From a hash
Rover::DataFrame.new({
a: [1, 2, 3],
b: ["one", "two", "three"]
})From Active Record
Rover::DataFrame.new(User.all)From a CSV
Rover.read_csv("file.csv")
# or
Rover.parse_csv("CSV,data,string")From Parquet (requires the red-parquet gem)
Rover.read_parquet("file.parquet")
# or
Rover.parse_parquet("PAR1...")Get number of rows
df.countGet column names
df.keysCheck if a column exists
df.include?(name)Select a column
df[:a]Note that strings and symbols are different keys, just like hashes. Creating a data frame from Active Record, a CSV, or Parquet uses strings.
Select multiple columns
df[[:a, :b]]Select first rows
df.head
# or
df.first(5)Select last rows
df.tail
# or
df.last(5)Select rows by index
df[1]
# or
df[1..3]
# or
df[[1, 4, 5]]Iterate over rows
df.each_row { |row| ... }Iterate over a column
df[:a].each { |item| ... }
# or
df[:a].each_with_index { |item, index| ... }Filter on a condition
df[df[:a] == 100]
df[df[:a] != 100]
df[df[:a] > 100]
df[df[:a] >= 100]
df[df[:a] < 100]
df[df[:a] <= 100]In
df[df[:a].in?([1, 2, 3])]
df[df[:a].in?(1..3)]
df[df[:a].in?(["a", "b", "c"])]Not in
df[!df[:a].in?([1, 2, 3])]And, or, and exclusive or
df[(df[:a] > 100) & (df[:b] == "one")] # and
df[(df[:a] > 100) | (df[:b] == "one")] # or
df[(df[:a] > 100) ^ (df[:b] == "one")] # xorBasic operations
df[:a] + 5
df[:a] - 5
df[:a] * 5
df[:a] / 5
df[:a] % 5
df[:a] ** 2
df[:a].sqrt
df[:a].cbrt
df[:a].absRounding
df[:a].round
df[:a].ceil
df[:a].floorLogarithm
df[:a].ln # or log
df[:a].log(5)
df[:a].log10
df[:a].log2Exponentiation
df[:a].exp
df[:a].exp2Trigonometric functions
df[:a].sin
df[:a].cos
df[:a].tan
df[:a].asin
df[:a].acos
df[:a].atanHyperbolic functions
df[:a].sinh
df[:a].cosh
df[:a].tanh
df[:a].asinh
df[:a].acosh
df[:a].atanhError function
df[:a].erf
df[:a].erfcSummary statistics
df[:a].count
df[:a].sum
df[:a].mean
df[:a].median
df[:a].percentile(90)
df[:a].min
df[:a].max
df[:a].std
df[:a].varCount occurrences
df[:a].tallyCross tabulation
df[:a].crosstab(df[:b])Group
df.group(:a).countWorks with all summary statistics
df.group(:a).max(:b)Multiple groups
df.group(:a, :b).countAdd Vega to your application’s Gemfile:
gem "vega"And use:
df.plot(:a, :b)Specify the chart type (line, pie, column, bar, area, or scatter)
df.plot(:a, :b, type: "pie")Group data
df.plot(:a, :b, group: :c)Stacked columns or bars
df.plot(:a, :b, group: :c, stacked: true)Add a new column
df[:a] = 1
# or
df[:a] = [1, 2, 3]Update a single element
df[:a][0] = 100Update multiple elements
df[:a][0..2] = 1
# or
df[:a][0..2] = [1, 2, 3]Update all elements
df[:a] = df[:a].map { |v| v.gsub("a", "b") }
# or
df[:a].map! { |v| v.gsub("a", "b") }Update elements matching a condition
df[:a][df[:a] > 100] = 0Clamp
df[:a].clamp!(0, 100)Delete columns
df.delete(:a)
# or
df.except!(:a, :b)Rename columns
df.rename(a: :new_a, b: :new_b)
# or
df[:new_a] = df.delete(:a)Sort rows
df.sort_by! { |r| r[:a] }Clear all data
df.clearAdd rows
df.concat(other_df)Add columns
df.merge!(other_df)Inner join
df.inner_join(other_df)
# or
df.inner_join(other_df, on: :a)
# or
df.inner_join(other_df, on: [:a, :b])
# or
df.inner_join(other_df, on: {df_col: :other_df_col})Left join
df.left_join(other_df)One-hot encoding
df.one_hotDrop a variable in each category to avoid the dummy variable trap
df.one_hot(drop: true)Array of hashes
df.to_aHash of arrays
df.to_hNumo array
df.to_numoCSV
df.to_csvParquet (requires the red-parquet gem)
df.to_parquetYou can specify column types when creating a data frame
Rover::DataFrame.new(data, types: {"a" => :int64, "b" => :float64})Or
Rover.read_csv("data.csv", types: {"a" => :int64, "b" => :float64})Supported types are:
- boolean -
:bool - float -
:float64,:float32 - integer -
:int64,:int32,:int16,:int8 - unsigned integer -
:uint64,:uint32,:uint16,:uint8 - object -
:object
Get column types
df.typesFor a specific column
df[:a].typeChange the type of a column
df[:a].to!(:int32)View the changelog
Everyone is encouraged to help improve this project. Here are a few ways you can help:
- Report bugs
- Fix bugs and submit pull requests
- Write, clarify, or fix documentation
- Suggest or add new features
To get started with development:
git clone https://github.com/ankane/rover.git
cd rover
bundle install
bundle exec rake test