# Equivalent to Pandas "cut" in Julia DataFrames?

**URL:** <https://discourse.julialang.org/t/equivalent-to-pandas-cut-in-julia-dataframes/103036>\
**Category:** New to Julia\
**Tags:** question, dataframes\
**Created:** [August 21, 2023, 3:18pm UTC](https://discourse.julialang.org/t/equivalent-to-pandas-cut-in-julia-dataframes/103036 "2023-08-21T15:18:52Z")\
**Posts on this page:** 5\
**Page:** 1

<div class="post-metadata">

**Author:** ![abehersan](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/abehersan/32/47221_2.png) [@abehersan](https://discourse.julialang.org/u/abehersan)\
**Post date:** [August 21, 2023, 3:18pm UTC](https://discourse.julialang.org/t/equivalent-to-pandas-cut-in-julia-dataframes/103036/1 "2023-08-21T15:18:52Z")

</div>

Pretty self-explanatory.

I have two arrays, `x` and `y`. I would like to bin the values of `x` into linearly-spaced bins and add the corresponding `y` values whose corresponding `x` falls into the same bin.

I haven’t found an analogous function in the `DataFrames.jl` functions glossary [Functions · DataFrames.jl](https://dataframes.juliadata.org/stable/lib/functions/)

---

<div class="post-metadata">

**Author:** ![nilshg](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/nilshg/32/2283_2.png) [@nilshg](https://discourse.julialang.org/u/nilshg)\
**Post date:** [August 21, 2023, 3:23pm UTC](https://discourse.julialang.org/t/equivalent-to-pandas-cut-in-julia-dataframes/103036/2 "2023-08-21T15:23:53Z")

</div>

`CategoricalArrays.cut`:

[https://categoricalarrays.juliadata.org/stable/apiindex/#CategoricalArrays.cut-Tuple{AbstractArray,%20AbstractVector}](https://categoricalarrays.juliadata.org/stable/apiindex/#CategoricalArrays.cut-Tuple%7BAbstractArray,%20AbstractVector%7D)

On a more general note, the philosophy of DataFrames is quite different from pandas. Because of Julia’s composability, DataFrames only implements functionality which is actually directly relevant to a DataFrame (as opposed to, say, any old vector like `cut`), with other functionality coming from relevant packages - CSV reading is not `DataFrames.read_csv` but `CSV.read` from the `CSV` package, computing a rolling mean is `RollingFunctions.rollmean`, imputing missing values by carrying the last nonmissing forward is `Impute.locf` etc. etc.

This takes a bit of getting used to when coming from pandas but tends to have the upside that the specialised packages are much more powerful than some bolted-on functionality that’s not actually core to the DataFrames package.

---

<div class="post-metadata">

**Author:** ![abehersan](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/abehersan/32/47221_2.png) [@abehersan](https://discourse.julialang.org/u/abehersan)\
**Post date:** [August 21, 2023, 3:40pm UTC](https://discourse.julialang.org/t/equivalent-to-pandas-cut-in-julia-dataframes/103036/3 "2023-08-21T15:40:52Z")

</div>

Thanks a ton for taking the time to reply. I will try to solve my problem with `CategoricalArrays.jl` and will post the solution once I have it.

Thanks also for the general comment! In my attempts to write software with the least amount of dependencies I often neglect looking at already-existing modules. Hence why I did not know about `CategoricalArrays.jl` for example…

---

<div class="post-metadata">

**Author:** ![abehersan](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/abehersan/32/47221_2.png) [@abehersan](https://discourse.julialang.org/u/abehersan)\
**Post date:** [August 21, 2023, 4:12pm UTC](https://discourse.julialang.org/t/equivalent-to-pandas-cut-in-julia-dataframes/103036/4 "2023-08-21T16:12:49Z")

</div>

```julia
using DataFrames
using CategoricalArrays

x = [1.2, 2.5, 3.1, 4.8, 5.2, 6.3, 7.7, 2.0, 3.8, 4.2, 5.7, 6.1, 7.4, 8.9]
y = [10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 15.0, 25.0, 35.0, 45.0, 55.0, 65.0, 75.0]

df = DataFrame(x=x, y=y)

# Define bin size and bin edges
bin_size = 2.0
min_x, max_x = extrema(df.x)
bins = min_x:bin_size:max_x

# Create a new column with the bin labels
df.bin_labels = cut(df.x, bins, extend=true)

# Group by the bin labels and calculate the sum of y-coordinates
grouped_df = combine(groupby(df, :bin_labels), :y => sum, renamecols=false)

```

The above yields the expected result:

```julia
4×2 DataFrame
 Row │ bin_labels y
     │ Cat… Float64
─────┼─────────────────────
   1 │ [1.2, 3.2) 75.0
   2 │ [3.2, 5.2) 100.0
   3 │ [5.2, 7.2) 210.0
   4 │ [7.2, 8.9] 210.0

```

A following question would be, how can I now e.g. plot the binned `x` column against the binned `y`?  
I guess one has to choose values within a range for each binned `x`, like e.g. (3.2-1.2)/2 = 1.0 for the first point and so on (choosing the middle of the range/bin for example).

Is there a programmatic way of doing this that I’ve missed in my 30 min. introduction to `CategoricalArrays.jl`? 🫢

---

<div class="post-metadata">

**Author:** ![abehersan](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/abehersan/32/47221_2.png) [@abehersan](https://discourse.julialang.org/u/abehersan)\
**Post date:** [August 21, 2023, 4:26pm UTC](https://discourse.julialang.org/t/equivalent-to-pandas-cut-in-julia-dataframes/103036/5 "2023-08-21T16:26:59Z")

</div>

Naturally, one can do the following:

```julia
using Plots
scatter(df.x, df.y)
scatter!(bins, grouped_df.y)

```

To plot the original and binned dataframes.