df = @linq df |>
groupby(:PetalLength) |>
transform(cs = cumsum(:PetalLength))
You can also use the @linq macro to pipe the output from one operation to
the next, which often reads more clearly than nesting the function calls
and is a little closer to the Pandas syntax.
On Wednesday, May 4, 2016 at 9:23:26 AM UTC-4, Cedric St-Jean wrote:
>
> That's way better, thank you!
>
> I never thought I'd say this, but I miss pandas. I could write
>
> df['cs'] = df.groupby('PetalLength').transform(cumsum)
>
> That's not possible in Julia because DataFrames don't have a row index.
>
> On Wednesday, May 4, 2016 at 9:04:21 AM UTC-4, tshort wrote:
>>
>> Here's another way with DataFramesMeta [1]:
>>
>> using DataFrames, DataFramesMeta, RDatasets
>> df = dataset("datasets", "iris")@transform(groupby(df, :Species), cs =
>> cumsum(:PetalLength))
>>
>>
>>
>> [1] https://github.com/JuliaStats/DataFramesMeta.jl/
>>
>>
>>
>> On Wed, May 4, 2016 at 8:09 AM, Cedric St-Jean <[email protected]>
>> wrote:
>>
>>> "Do blocks" are one of my favourite things about Julia, they're
>>> explained in the docs
>>> <http://docs.julialang.org/en/release-0.4/manual/functions/#do-block-syntax-for-function-arguments>.
>>>
>>> Basically it's just a convenient way of defining and passing a function
>>> (the code that comes after `do`) to another function (in this case, `by`).
>>> `by` goes over the dataframe, splits it into 3 subdataframes (one for each
>>> Species in the iris dataset), and calls the do-block for each of them. Then
>>> their return values (the last line in the do-block) gets concatenated
>>> together to form the final result. The code I really wanted to write is:
>>>
>>> using RDatasets
>>> df = dataset("datasets", "iris")
>>> # For each species
>>> df2 = by(df, :Species) do sub_df
>>> sub_df = copy(sub_df) # don't modify the original dataframe
>>> # Add a :cumulative_PetalLength column
>>> sub_df[:cumulative_PetalLength] = cumsum(sub_df[:PetalLength])
>>> # Return the new sub-dataframe
>>> sub_df
>>> end
>>>
>>> but unfortunately, this code doesn't work with DataFrames.jl
>>>
>>>
>>> On Wednesday, May 4, 2016 at 4:42:41 AM UTC-4, Ben Southwood wrote:
>>>>
>>>> Thanks Cedric, that worked very well. I'm having a little trouble
>>>> following the documentation as to how the "by ... do ..." structure
>>>> actually works. Would you mind explaining what the code is doing?
>>>>
>>>> On Tuesday, May 3, 2016 at 10:07:10 PM UTC-4, Cedric St-Jean wrote:
>>>>>
>>>>> Something like
>>>>>
>>>>> using RDatasets
>>>>> df = dataset("datasets", "iris")
>>>>> df[:cumulative_PetalLength] = 0.0
>>>>> by(df, :Species) do sub_df
>>>>> sub_df[:cumulative_PetalLength] = cumsum(sub_df[:PetalLength])
>>>>> sub_df
>>>>> end
>>>>>
>>>>> though I hope someone can provide a more elegant solution. `sub_df` a
>>>>> SubDataFrame, and those objects can neither have a new column nor be
>>>>> converted to DataFrame.
>>>>>
>>>>> On Tuesday, May 3, 2016 at 4:22:29 PM UTC-4, Ben Southwood wrote:
>>>>>>
>>>>>> I have the following dataframe with values of the form
>>>>>>
>>>>>> date1,label1,qty1_1
>>>>>> date2,label1,qty1_2
>>>>>> date3,label1,qty1_3
>>>>>> ....
>>>>>> dateN,label1,qty1_N
>>>>>> date1,label2,qty2_1
>>>>>> date2,label2,qty2_2
>>>>>> date3,label2,qty2_3
>>>>>> ....
>>>>>> dateN,label2,qty1_N
>>>>>> ....
>>>>>>
>>>>>>
>>>>>>
>>>>>> I would like to cumulative sum the qtys such that the value of the
>>>>>> cumulative sum only increases for each label. And then i'd have
>>>>>>
>>>>>> date1,label1,cuml1_1
>>>>>> date2,label1,cuml1_2
>>>>>> date3,label1,cuml1_3
>>>>>> ....
>>>>>> dateN,label1,cuml1_N
>>>>>> date1,label2,cuml2_1
>>>>>>
>>>>>>
>>>>>>
>>>>>> This way I can use gadfly and run the following plot
>>>>>>
>>>>>>
>>>>>> plot(x=grouped[:date],y=grouped[:cuml_sum],color=grouped[:label],Geom.line)
>>>>>>
>>>>>>
>>>>>> and have each cuml sum have it's own colouring by date. I'm stuck on
>>>>>> how to do this simply without creating lookups. Any help? Thanks!
>>>>>>
>>>>>>
>>>>>>
>>
