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I have a large dataset listing competitor products on sale in different regions across the country. I am looking to split this dataframe into several others based on the region via an iterative process using the column values within the names of those new dataframes, so that I can work with each separately – e.g. to sort information in each region by price to understand what the market looks like in each. I’ve given a simplified version of the data below:
Competitor Region ProductA ProductB Comp1 A £10 £15 Comp1 B £11 £16 Comp1 C £11 £15 Comp2 A £9 £16 Comp2 B £12 £14 Comp2 C £14 £17 Comp3 A £11 £16 Comp3 B £10 £15 Comp3 C £12 £15
I can create a list of the regions using the below:
Which I was hoping to use in an iterative loop that produced a number of dataframes, e.g.
df_A : Competitor Region ProductA ProductB Comp1 A £10 £15 Comp2 A £9 £16 Comp3 A £11 £16
I could do this manually for each region, with the code
but the reality is that this dataset has a large number of areas which would make this code tedious. Is there a way of creating an iterative loop that would replicate this? There is a similar question that asks about splitting dataframes, but the answer does not show how to label outputs based on each column value.
I’m quite new to Python and still learning, so if there is actually a different, more sensible method of approaching this problem I’m very open to suggestions.
Subsetting by distinct values is called a
groupby, if simply want to iterate through the groups with a
for loop, the syntax is:
for region, df_region in df.groupby('Region'): print(df_region) Competitor Region ProductA ProductB 0 Comp1 A £10 £15 3 Comp2 A £9 £16 6 Comp3 A £11 £16 Competitor Region ProductA ProductB 1 Comp1 B £11 £16 4 Comp2 B £12 £14 7 Comp3 B £10 £15 Competitor Region ProductA ProductB 2 Comp1 C £11 £15 5 Comp2 C £14 £17 8 Comp3 C £12 £15