Popularity
5.5
Stable
Activity
0.0
Stable
61
5
8
Monthly Downloads: 2
Programming language: Elixir
License: Apache License 2.0
Latest version: v0.3.1
dataframe alternatives and similar packages
Based on the "Algorithms and Data structures" category.
Alternatively, view dataframe alternatives based on common mentions on social networks and blogs.
-
-
-
matrex
A blazing fast matrix library for Elixir/Erlang with C implementation using CBLAS. -
simple_bayes
A Naive Bayes machine learning implementation in Elixir. -
exconstructor
An Elixir library for generating struct constructors that handle external data with ease. -
erlang-algorithms
Implementations of popular data structures and algorithms -
remodel
:necktie: An Elixir presenter package used to transform map structures. "ActiveModel::Serializer for Elixir" -
MapDiff
Calculates the difference between two (nested) maps, and returns a map representing the patch of changes. -
parallel_stream
A parallelized stream implementation for Elixir -
merkle_tree
:evergreen_tree: Merkle Tree implementation in pure Elixir -
aja
Extension of the Elixir standard library focused on data stuctures, data manipulation and performance -
bloomex
:hibiscus: A pure Elixir implementation of Scalable Bloom Filters -
DeepMerge
Deep (recursive) merge for maps, keywords and others in Elixir -
exmatrix
Elixir library implementing a parallel matrix multiplication algorithm and other utilities for working with matrices. Used for benchmarking computationally intensive concurrent code. -
the_fuzz
String metrics and phonetic algorithms for Elixir (e.g. Dice/Sorensen, Hamming, Jaccard, Jaro, Jaro-Winkler, Levenshtein, Metaphone, N-Gram, NYSIIS, Overlap, Ratcliff/Obershelp, Refined NYSIIS, Refined Soundex, Soundex, Weighted Levenshtein) -
ecto_materialized_path
Tree structure & hierarchy for ecto models -
Conrex
An Elixir implementation of the CONREC algorithm for topographic or isochrone maps. -
-
murmur
:speech_balloon: An implementation of the non-cryptographic hash Murmur3 -
bitmap
Bitmap implementation in Elixir using binaries and integers. Fast space efficient data structure for lookups -
paratize
Elixir library providing some handy parallel processing facilities that supports configuring number of workers and timeout. -
Closure Table
Closure Table for Elixir - a simple solution for storing and manipulating complex hierarchies. It provides in-memory and Ecto adapters.
ONLYOFFICE Docs — document collaboration in your environment
Powerful document editing and collaboration in your app or environment. Ultimate security, API and 30+ ready connectors, SaaS or on-premises
Promo
www.onlyoffice.com
* Code Quality Rankings and insights are calculated and provided by Lumnify.
They vary from L1 to L5 with "L5" being the highest.
Do you think we are missing an alternative of dataframe or a related project?
|
Static code analysis for 29 languages.
sponsored
www.sonarqube.org
|
Popular Comparisons
-
dataframevsmonad
-
dataframevsMappable
-
dataframevsbitmap
-
dataframevsDeepMerge
-
dataframevstinymt
|
Access the most powerful time series database as a service
sponsored
www.influxdata.com
|
README
Dataframe
DataFrame is a library that implements an API similar to Python's Pandas or R's data.frame().
Installation
Add dataframe to your list of dependencies in mix.exs:
def deps do
[{:dataframe, "~> 0.1.0"}]
end
Usage
Tutorials
- [Lesson 1](tutorial/lesson1.md)
Creation
data = DataFrame.new(DataFrame.Table.build_random(6,4), [1,3,4,5], DataFrame.DateRange.new("2016-09-12", 6))
output:
1 3 4 5
2016-09-12 0.3216495192 0.3061978162 0.5240627861 0.3014870998
2016-09-13 0.7085624128 0.1027917034 0.0274851281 0.4999253931
2016-09-14 0.5409299230 0.7234486655 0.0902951353 0.9265397862
2016-09-15 0.8144437609 0.7566869039 0.5943981962 0.4555049347
2016-09-16 0.0228473208 0.9033617026 0.6984988237 0.9858222366
2016-09-17 0.6401066584 0.2700256640 0.4256911712 0.1085587668
Exploring
DataFrame.head(data, 2)
1 3 4 5
2016-09-12 0.3216495192 0.3061978162 0.5240627861 0.3014870998
2016-09-13 0.7085624128 0.1027917034 0.0274851281 0.4999253931
DataFrame.tail(data, 1)
1 3 4 5
2016-09-17 0.6401066584 0.2700256640 0.4256911712 0.1085587668
DataFrame.describe(data)
1 3 4 5
count 6 6 6 6
mean 0.6465539263 0.5159964091 0.3872831261 0.3932447202
std 0.1529956837 0.3280592207 0.1795171140 0.3121805879
min 0.4016542004 0.0206350637 0.0337014209 0.0177659020
25% 0.6282734986 0.5048574951 0.3799407685 0.2747983874
50% 0.7006870983 0.6401629955 0.4141661547 0.4043847826
75% 0.7412280866 0.6620905719 0.4517382532 0.4916518963
max 0.8024114094 0.9682031054 0.6199458675 0.8934404147
Transposing
DataFrame.transpose(data)
2016-09-12 2016-09-13 2016-09-14 2016-09-15 2016-09-16 2016-09-17
1 0.3216495192 0.7085624128 0.5409299230 0.8144437609 0.0228473208 0.6401066584
3 0.3061978162 0.1027917034 0.7234486655 0.7566869039 0.9033617026 0.2700256640
4 0.5240627861 0.0274851281 0.0902951353 0.5943981962 0.6984988237 0.4256911712
5 0.3014870998 0.4999253931 0.9265397862 0.4555049347 0.9858222366 0.1085587668
Sorting
Sorting index (defaults bigger to smaller)
DataFrame.sort_index(data)
1 3 4 5
2016-09-17 0.6401066584 0.2700256640 0.4256911712 0.1085587668
2016-09-16 0.0228473208 0.9033617026 0.6984988237 0.9858222366
2016-09-15 0.8144437609 0.7566869039 0.5943981962 0.4555049347
2016-09-14 0.5409299230 0.7234486655 0.0902951353 0.9265397862
2016-09-13 0.7085624128 0.1027917034 0.0274851281 0.4999253931
2016-09-12 0.3216495192 0.3061978162 0.5240627861 0.3014870998
Sorting by a column (false to sort smaller to bigger)
DataFrame.sort_values(data, 4, false)
1 3 4 5
2016-09-13 0.7085624128 0.1027917034 0.0274851281 0.4999253931
2016-09-14 0.5409299230 0.7234486655 0.0902951353 0.9265397862
2016-09-17 0.6401066584 0.2700256640 0.4256911712 0.1085587668
2016-09-12 0.3216495192 0.3061978162 0.5240627861 0.3014870998
2016-09-15 0.8144437609 0.7566869039 0.5943981962 0.4555049347
2016-09-16 0.0228473208 0.9033617026 0.6984988237 0.9858222366
Selecting
By name:
DataFrame.loc(data, DataFrame.DateRange.new("2016-09-15", 2), [3,4])
3 4
2016-09-15 0.5417848216 0.5546980818
2016-09-16 0.6621771048 0.5763923325
A specific data by name:
DataFrame.at(data, "2016-09-15", 4)
0.5546980818725673
By position:
DataFrame.iloc(data, 4..6, 2..4)
4 5
2016-09-16 0.6984988237 0.9858222366
2016-09-17 0.4256911712 0.1085587668
DataFrame.iat(data, 0, 0)
0.31553155828919915
The library is in very early stages of development. No effort has been made to optimize its performance. Expect it to be slow.
Plotting
If you have Python and Matplotlib you can plot the data in your Dataframe. Check out the Explot package for installation details.
Let's plot the cummulative sum of the values:
data |> DataFrame.cumsum |> DataFrame.plot
Will give us this graph: [](readme_example.png)
Development
Run tests
mix test
TODO
- Deal with exceptions (negative numbers as input, etc.)
- Setting of subtable data
- Types of columns (no stat data on text, etc)