# How to group by multiple data types?
**URL:** https://discourse.julialang.org/t/how-to-group-by-multiple-data-types/76872
**Category:** General Usage
**Tags:** dataframes
**Created:** [February 21, 2022, 11:04pm UTC](https://discourse.julialang.org/t/how-to-group-by-multiple-data-types/76872 "2022-02-21T23:04:16Z")
**Posts on this page:** 5
**Page:** 1
### Author:  [@a.frist](https://discourse.julialang.org/u/a.frist)
#### Post date: [February 21, 2022, 11:04pm UTC](https://discourse.julialang.org/t/how-to-group-by-multiple-data-types/76872/1 "2022-02-21T23:04:16Z")
Hello all,
I’m a bit new to the Julia programming language and haven’t been able to find an answer that solves my problem. I have the following dataframe:
```julia
10×5 DataFrame
Row │ DATE TOPIC_I TOPIC_J JOINT_PROB DOC_COUNT
│ Date String15 String15 Any Any
──┼─────────────────────────────────────────
1 │ 2000-09-01 TOPIC_153 TOPIC_87 0.03806723138 979
2 │ 2000-09-01 TOPIC_81 TOPIC_87 0.01825187194 979
3 │ 2000-09-01 TOPIC_249 TOPIC_87 0.01616933848 979
4 │ 2000-09-01 TOPIC_124 TOPIC_87 0.006607188145 979
5 │ 2000-09-01 TOPIC_140 TOPIC_87 0.0008916937195 979
6 │ 2000-09-01 TOPIC_101 TOPIC_87 0.001341542903 979
7 │ 2000-09-01 TOPIC_89 TOPIC_87 0.07842244991 979
8 │ 2000-09-01 TOPIC_233 TOPIC_87 0.01956784903 979
9 │ 2000-09-01 TOPIC_144 TOPIC_87 0.01501348474 979
10 │ 2000-09-01 TOPIC_201 TOPIC_87 0.007407990334 979
```
I am trying to group by the DATE and TOPIC\_I rows, sum the JOINT\_PROB rows and take the average of the DOC\_COUNT rows. I have implemented the code below:
```julia
# Convert the joint probabilities column and document count column to the correct types.
stuff = [typeof(x) for x in probabilities_data[!, :JOINT_PROB]]
println(unique(stuff))
probabilities_data[!, :JOINT_PROB] = [typeof(x) == String ? tryparse(Float64,x) : x for x in probabilities_data[!, :JOINT_PROB]]
stuff = [typeof(x) for x in probabilities_data[!, :JOINT_PROB]]
println(unique(stuff))
p_i_group = groupby(probabilities_data, [:DATE, :TOPIC_I])
pi_df = combine(p_i_group, :TOPIC_J => sum => :PROB_I)
```
I countinue to get the following error related to the last line of code above:
```julia
TaskFailedException:
MethodError: no method matching +(::String15, ::String15)
```
As far as I can tell my syntax is correct. Can someone help me find what I am missing?
---
### Author:  [@tbeason](https://discourse.julialang.org/u/tbeason)
#### Post date: [February 21, 2022, 11:06pm UTC](https://discourse.julialang.org/t/how-to-group-by-multiple-data-types/76872/2 "2022-02-21T23:06:14Z")
Those are string columns… perhaps you mean to be summing the probabilities?
---
### Author:  [@a.frist](https://discourse.julialang.org/u/a.frist)
#### Post date: [February 21, 2022, 11:12pm UTC](https://discourse.julialang.org/t/how-to-group-by-multiple-data-types/76872/3 "2022-02-21T23:12:24Z")
My apologies. You are correct. I want to sum the “JOINT\_PROB” column. I updated the question to reflect this.
---
### Author:  [@bkamins](https://discourse.julialang.org/u/bkamins)
#### Post date: [February 22, 2022, 6:34am UTC](https://discourse.julialang.org/t/how-to-group-by-multiple-data-types/76872/4 "2022-02-22T06:34:44Z")
Your `JOINT_PROB` and `DOC_COUNT` have both `eltype` `Any` which signals that there is a risk that they do not contain only numbers. If they contain only numbers then the following will work:
```julia
using Statistics
p_i_group = groupby(probabilities_data, [:DATE, :TOPIC_I])
pi_df = combine(p_i_group, :JOINT_PROB => sum => :PROB_I, :DOC_COUNT => mean => :MEAN_COUNT)
```
the easiest way to check if your column contains only numbers is to do e.g. `float.(probabilities_data.JOINT_PROB)`. If this errors this means that you have some bad data in your columns.
---
### Author:  [@a.frist](https://discourse.julialang.org/u/a.frist)
#### Post date: [February 22, 2022, 2:45pm UTC](https://discourse.julialang.org/t/how-to-group-by-multiple-data-types/76872/5 "2022-02-22T14:45:16Z")
It seems there was an issue with the input data. After fixing that and setting the column to “JOINT\_PROB” based on your and @tbeason feedback it looks like the code is working. Thank you both for your help!