How to create dataframes iterating over a set?

Question

I have this dataframe:

d = {'city':['Barcelona','Madrid','Rome','Torino','London','Liverpool','Manchester','Paris'],
'country': ['ES','ES','IT','IT','UK','UK','UK','FR'],
'revenue': [1,2,3,4,5,6,7,8],
'amount': [8,7,6,5,4,3,2,1]
df = pd.DataFrame(d)

I want to obtain this for each country:

españa = {'city':['Barcelona','Madrid']
          'revenue':[1,2]
          'amount':[8,7]}
 ES = pd.DataFrame(españa)

So that in the end I will have 4 dataframes named ES,IT,UK and FR.

I have tried this so far:

a = set(df.loc[:]["country"])
for country in a:
    country = df.loc[(df["country"]== country),['date','sum']]

But that only gave me one dataframe with one value.

Answer 1

The loop gave you all four data frames, but you threw the first three into the garbage.

You iterate through a with the variable country, but then destroy that value in the next statement, country = .... Then you return to the top of the loop, reset country to the next two-letter abbreviation, and continue this conflict through all four nations.

If you need four data frames, you need to keep each one in a separate place. For instance:

a = set(df.loc[:]["country"])
df_dict = {}

for country in a:
    df_dict[country] = df.loc[(df["country"]== country),['date','sum']]

Now you have a dictionary with four data frames, each one indexed by its country code. Does that help?

Answer 2

You can use a dictionary comprehension with groupby:

res = {k: v.drop('country', 1) for k, v in df.groupby('country')}

print(res)

{'ES':    amount       city  revenue
       0       8  Barcelona        1
       1       7     Madrid        2,
 'FR':    amount   city  revenue
       7       1  Paris        8,
 'IT':    amount    city  revenue
       2       6    Rome        3
       3       5  Torino        4,
 'UK':    amount        city  revenue
       4       4      London        5
       5       3   Liverpool        6
       6       2  Manchester        7}

Answer 3

Country is an iterator variable that is being over written.

In order to generate 4 different dataframes, try using a generator function.

def country_df_generator(data): for country in data['country']unique(): yield df.loc[(df["country"]== country), ['date','sum']] countries = country_df_generator(data)