Bogumił Kamiński, Dec 10, 2017
A brief introduction to basic usage of DataFrames
. Tested under DataFrames
master on 2017-12-05.
I will try to keep it up to date as the package evolves. This tutorial covers DataFrames
, CSV
, Missings
and CategoricalArrays
only. It does not show any additional packages that can be used with DataFrames
.
File | Topic |
---|---|
01_constructors.ipynb | Creating DataFrames |
02_basicinfo.ipynb | Getting summary information |
03_missingvalues.ipynb | Handling missing values |
04_loadsave.ipynb | Loading and saving DataFrames |
05_columns.ipynb | Working with columns of DataFrame |
06_rows.ipynb | Working with row of DataFrame |
07_factors.ipynb | Working with categorical data |
08_joins.ipynb | Joining DataFrames |
09_reshaping.ipynb | Reshaping DataFrames |
10_transforms.ipynb | Transforming DataFrames |
11_performance.ipynb | Performance tips |
12_pitfalls.ipynb | Possible pitfalls |
Changelog:
Date | Changes |
---|---|
2017-12-05 | Initial release |
2017-12-06 | Added description of insert! , merge! , empty! , categorical! , delete! , DataFrames.index |
2017-12-09 | Added performance tips |
2017-12-10 | Added pitfalls |
- Constructors:
DataFrame
- Getting summary:
size
,nrow
,ncol
,length
,describe
,showcols
,names
,eltypes
,head
,tail
- Handling missing:
missing
(singleton instance ofMissing
),ismissing
,Missings.T
,skipmissing
,Missings.replace
,allowmissing
,disallowmissing
,allowmissing!
,completecases
,dropmissing
,dropmissing!
- Loading and saving:
CSV
(package),JLD
(package),CSV.read
,CSV.write
,save
(fromJLD
),load
(fromJLD
) - Working with columns:
rename
,rename!
,names!
,hcat
,insert!
,DataFrames.hcat!
,merge!
,empty!
,categorical!
,DataFrames.index
- Working with rows:
sort!
,sort
,append!
,vcat
,push!
,view
,deleterows!
,unique
,nonunique
,unique!
- Working with categorical:
categorical
,cut
,isordered
,ordered!
,levels
,unique
,levels!
,droplevels!
,get
,recode
,recode!
- Joining:
join
- Reshaping:
stack
,melt
,stackdf
,meltdf
,unstack
- Transforming:
groupby
,vcat
,by
,aggregate
,eachcol
,eachrow
,colwise