CODEWITHSUNDEEP
juliahistogramplots.jldataframes.jl
nboogerz
nboogerz

Reputation: 31

Manually setting bin sized for grouped histogram bins in a DataFrame Julia

I've been using the following code to generate histograms from binning one column of a Dataframe and using that bin to calculate a median from another column.

using Plots, Statistics, DataFrames
df = DataFrame(x=repeat(["a","b","c"], 5), y=1:15)
res = combine(groupby(df, :x, sort=true), :y => median)
bar(res.x, res.y_median, legend=false)

The code point selects values for the bins and I would like to apply a bin range of values manually, if possible?

Row  │ A        B_median 
     │ Any      Float64  
─────┼───────────────────
   1 │ 1515.74     0.09
   2 │ 1517.7      0.81
   3 │ 1527.22    10.23
   4 │ 1529.88     2.95
   5 │ 1530.72    17.32
   6 │ 1530.86    15.22
   7 │ 1532.26     1.45
   8 │ 1532.68    18.51
   9 │ 1541.08     1.32
  10 │ 1541.22    15.78
  11 │ 1541.36     0.12
  12 │ 1541.5     13.55
  13 │ 1541.92    11.99
  14 │ 1542.06    21.14
  15 │ 1542.34    10.645
  16 │ 1542.62    19.95
  17 │ 1542.76    21.0
  18 │ 1543.32    20.91

For example, instead of calculating a median for rows 9->17 individually. Could these rows be bunched together automatically i.e. 1542.7+/-0.7 and a total median value be calculated for this range? Many thanks!

Upvotes: 2

Views: 158

Answers (1)

Bogumił Kamiński
Bogumił Kamiński

Reputation: 69939

I assume you want something like this:

julia> using DataFrames, CategoricalArrays, Random, Statistics

julia> Random.seed!(1234);

julia> df = DataFrame(A=rand(20), B=rand(20));

julia> df.A_group = cut(df.A, 4);

julia> res = combine(groupby(df, :A_group, sort=true), :B => median)
4×2 DataFrame
 Row │ A_group                            B_median
     │ Cat…                               Float64
─────┼─────────────────────────────────────────────
   1 │ Q1: [0.014908849285099945, 0.532…  0.134685
   2 │ Q2: [0.5323651749779272, 0.65860…  0.347995
   3 │ Q3: [0.6586057536399257, 0.81493…  0.501756
   4 │ Q4: [0.8149335702852593, 0.97213…  0.531899

Upvotes: 2

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