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So I have a DataFrame with several columns, some contain objects (string) and some are numerical. I'd like to create new dataframes which are "filtered" to the combination of the objects available.

To be clear, those are my object type columns:

Index(['OS', 'Device', 'Design', 'Language'], dtype='object')

["Design"] and ["Language"] have 3 options each. I filtered ["OS"] and ["Device"] manually as I needed to match them. However, now I want to create multiple variables each contains a "filtered" dataframe.

For example:

I have "android_fltr1_d1" to represent the next filter:

["OS"]=android, ["Device"]=1,["Design"]=1

and "android_fltr3_d2" to represent:

["OS"]=android, ["Device"]=3,["Design"]=2

I tried the next code (which works perfectly fine).

android_fltr1_d1 = android_fltr1[android_fltr1["Design"]==1].drop(["Design"],axis=1)
android_fltr1_d2 = android_fltr1[android_fltr1["Design"]==2].drop(["Design"],axis=1)
android_fltr1_d3 = android_fltr1[android_fltr1["Design"]==3].drop(["Design"],axis=1)
android_fltr3_d1 = android_fltr3[android_fltr3["Design"]==1].drop(["Design"],axis=1)
android_fltr3_d2 = android_fltr3[android_fltr3["Design"]==2].drop(["Design"],axis=1)
android_fltr3_d3 = android_fltr3[android_fltr3["Design"]==3].drop(["Design"],axis=1)
android_fltr5_d1 = android_fltr5[android_fltr5["Design"]==1].drop(["Design"],axis=1)
android_fltr5_d2 = android_fltr5[android_fltr5["Design"]==2].drop(["Design"],axis=1)
android_fltr5_d3 = android_fltr5[android_fltr5["Design"]==3].drop(["Design"],axis=1)

As you can guess, I don't find it efficient and would like to use a for loop to generate those variables (as I'd need to match each ["Language"] option to each filter I created. Total of 60~ variables). Thought about using something similar to .format() in the loop in order to be some kind of a "place-holder", couldn't find a way to do it. It would be probably the best to use a nested loop to create all the variables, though I'd be content even with a single loop for each column.

I find it difficult to build the for loop to execute it and would be grateful for any help or directions. Thanks!

As suggested I tried to find my answer in:How do I create variable variables? Yet I failed to understand how I use the globals() function in my case. I also found that using '%' is not working anymore.

Omer
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    Creating that many variables is a pretty big code smell in itself. What do you need those 60 variables for? How to create them most efficiently also depends on what you need to do afterwards with them since slicing a DataFrame has several options regarding whether certain subparts of the DF are copied or not. – TToni Apr 13 '21 at 12:41
  • Thank you! Well, I'd like to take each variable (sub-dataframe) and run Kmeans algo (and the elbow method) on it. – Omer Apr 13 '21 at 13:01

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