Pandas Applying For loop with def function to generate other DataFrame?-CodePudding

I have a DataFrame called medal. In medal, there is a column called 'event_gender', which has 4 unique values (men, women, open, and mixed). I tried to wrote function to get groupby by these unique values.

I want to write for loop for these naming process if possible.

Here is what I could do so far and it is working.

def gender(df, sex):

temp_df = df.loc[df['event_gender']==sex]

last_df = temp_df.groupby(['country_3_letter_code', 'medal_type'])['event_gender'].count()

return last_df

Men = gender(medal, 'Men')

Women = gender(medal, 'Women')

Mixed = gender(medal, 'Mixed')

Open = gender(medal, 'Open')

But in the last code here, I am naming every DataFrame seperately, is there easier way to name these for Dataframes? For example:

for item in medal['event_gender'].unique():
item = gender(medal, item)

Any suggestion and advice are highly appreciated!

CodePudding user response：

Yes you could just do:

for item in medal['event_gender'].unique():
   globals()[item] = gender(medal, item)

But why do this? Maintain your dataframe as it is and work on it with groupings. It is easier that way to do same computations on different groups of the same dataframe rather than doing same computation on different dataframes

CodePudding user response：

If you want to work on each unique items in event_gender column. Then you could use aggregation function on your grouped items like so:

gender_grouping = medal.groupby(['event_gender']).agg({'country_3_letter_code': 'value_counts', 
                                                       'medal_type': 'value_counts'})

After this you can retrieve your interested event_gender items simply by:

gender_grouping.loc['Men']