Splitting dataframe based on multiple column values

Question

I have a dataframe with 1M+ rows. A sample of the dataframe is shown below:

df

    ID      Type        File    
 0  123     Phone       1       
 1  122     Computer    2       
 2  126     Computer    1       

I want to split this dataframe based on Type and File. If the total count of Type is 2 (Phone and Computer), total number of files is 2 (1,2), then the total number of splits will be 4.

In short, total splits is as given below:

 total_splits=len(set(df['Type']))*len(set(df['File']))

In this example, total_splits=4. Now, I want to split the dataframe df in 4 based on Type and File.

So the new dataframes should be:

1. df1 (having data of type=Phone and File=1)
2. df2 (having data of type=Computer and File=1)
3. df3 (having data of type=Phone and File=2)
4. df4 (having data of type=Computer and File=2)

The splitting should be done inside a loop.

I know we can split a dataframe based on one condition (shown below), but how do you split it based on two ?

My Code:

data = {'ID' : ['123', '122', '126'],'Type' :['Phone','Computer','Computer'],'File' : [1,2,1]}

df=pd.DataFrame(data) 

types=list(set(df['Type']))
total_splits=len(set(df['Type']))*len(set(df['File']))

cnt=1

for i in range(0,total_splits):
      for j in types:
            locals()["df"+str(cnt)] = df[df['Type'] == j]
            cnt += 1

The result of the above code gives 2 dataframes, df1 and df2. df1 will have data of Type='Phone' and df2 will have data of Type='Computer'.

But this is just half of what I want to do. Is there a way we can make 4 dataframes here based on 2 conditions ?

Note: I know I can first split on 'Type' and then split the resulting dataframe based on 'File' to get the output. However, I want to know of a more efficient way of performing the split instead of having to create multiple dataframes to get the job done.

EDIT

This is not a duplicate question as I want to split the dataframe based on multiple column values, not just one!

Answer 1

You can make do with groupby:

dfs = {}
for k, d in df.groupby(['Type','File']):
    type, file = k

    # do want ever you want here
    # d is the dataframe corresponding with type, file
    dfs[k] = d

You can also create a mask:

df['mask'] = df['File'].eq(1) * 2 + df['Type'].eq('Phone')

Then, for example:

df[df['mask'].eq(0)]

gives you the first dataframe you want, i.e. Type==Phone and File==1, and so on.