DataFrame is a library that implements an API similar to Python's Pandas or R's data.frame().
Add dataframe to your list of dependencies in mix.exs:
def deps do
[{:dataframe, "~> 0.1.0"}]
end
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
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
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 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
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.
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:
Run tests
mix test
- Deal with exceptions (negative numbers as input, etc.)
- Setting of subtable data
- Types of columns (no stat data on text, etc)