How to create different dataframes (with filters) from a list in Python

Question

I have two dataframes:

One is called data, with several columns including one called bank with different banks names.
The other is banks and contains a list with each bank from data.

I need to create an individual dataframe for each bank from the original dataframe ("data"), iterating from the other dataframe ("banks").

I tried with something like this, but it doesn't work

for ind in banks.bank: 
     [ind] = data[(data['bank'] == [ind])]

These are samples of the data

I must divide the first dataframe for each bank of the second dataframe through the "bank" column

So you want to match dataframes bancos and datos using the index? — Marios Nikolaou
– Marios Nikolaou, Commented Nov 29, 2019 at 11:38
not exactly, I must filter the first dataframe by the bank column for each row of the second dataframe — Lucas Tossolini
– Lucas Tossolini, Commented Nov 29, 2019 at 12:00
so you want to match the 2 dataframes based on bank column and create new dataframe for each bank, right? — Marios Nikolaou
– Marios Nikolaou, Commented Nov 29, 2019 at 12:02

Marios Nikolaou · Accepted Answer · 2019-11-29 13:22:51Z

1

I hope it helps you.

#inner join 2 dataframes based on bank name
merged_inner = pd.merge(datos,bancos, on=['bank','bank'])

#dictionary of banks, key-value, key = bank name, value=details of each bank     
bank_dict = {k: v for (k, v) in merged_inner.groupby('bank')}

#print them
for i in bank_dict:
   print(bank_dict[i])

edited Nov 29, 2019 at 13:22

answered Nov 29, 2019 at 12:58

Marios Nikolaou

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Comments

FBruzzesi · Accepted Answer · 2019-11-29 11:36:18Z

0

You can store them in a dictionary and access them using the key value:

# Initialize empty dictionary
banks_dict={}

# Add each bank by looping over bank column
for b in banks['bank'].unique(): 
     banks_dict[b] = data[data['bank']==b]

then you can access a bank by its name: banks_dict[<bank_name>]

answered Nov 29, 2019 at 11:36

FBruzzesi

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