Re-posted from: https://bkamins.github.io/julialang/2021/11/26/clip.html
Introduction
Occasionally I write posts about Julia tools that are often not commonly
known, but are useful in practice. Today I want to talk about
the ClipData.jl package.
The post was written under Julia 1.6.3, DataFrames.jl 1.2.2, and
ClipData.jl 0.2.1.
What is ClipData.jl?
The package does one thing and does it well: it allows you to move
tabular data between your Julia session and the system clipboard both ways.
The to major use cases are:
- You have a table in e.g. Google Sheet, you copy it to the system clipboard,
and want to interactively ingest it in the Julia session as a table
(in my examples I will useDataFrame
). - You have a
DataFrame
in your Julia session and you want to copy it to the
system clipboard so that you can later paste it in e.g. Google Sheet.
Many data scientists need to do both operations virtually every day, and
ClipData.jl comes to the rescue. This package is not only nice, but it
has an excellent visuals explaining how things work. Therefore, since they are
MIT licensed, I just link to the videos prepared by Peter Deffebach here.
Let us get to action.
First you need to know if your data has a header of not. If it has a
header we will work with a DataFrame
, if it does not we will work with a
Matrix
.
Clipping tables
To work with tabular data (having a header) use the cliptable
function. To
copy data from the system clipboard and store it in a DataFrame
called df
just write:
df = cliptable() |> DataFrame
On the other hand if you want to copy your df
data frame to the system
clipboard use:
cliptable(df)
All this is very nicely presented in the following video (in particular notice
that column element types are automatically detected):
Clipping matrices
To work with arrays use the cliparray
function. To copy data from
system clipboard and store it in a Matrix
called mat
just write:
mat = cliparray()
On the other hand if you want to copy your mat
matrix to system clipboard
use:
cliparray(mat)
Here is a video showing the process:
Conclusions
There are several additional features that ClipData.jl provides (like handling
how table cells should be parsed). If you want to know more details please
refer to the ClipData.jl homepage.
I am sure you will find this little package quite useful in your data science
projects!