Pandas Dataframe, Skip Index If Condition Is Meet

I want to skip the index of a data frame output only if specific conditions are met. A it's a special id for seller. B it's the selling price. Type is selling type. import pandas

Solution 1:

Use:

import pandas

Data= []
Data+=[{"A": "X1FFFFF", "Special":{"type": "USD"},"B": "0.11",}]
Data+=[{"A": "X2FFFFF", "Special":{"type": "EUR"},"B": "0.122",}]
Data+=[{"A": "X3FFFFF", "Special":{"type": "EUR"},"B": "0.1444",}]

#all data in Special are strings, simulate by astype(str)
Data = pandas.DataFrame(Data).astype(str)

Data = Data.astype({"B": float})
Data = Data.astype({"A": str})

import ast

#convert to dicts if necessary
Data["Special"] = Data["Special"].apply(ast.literal_eval)

#filtering `type` for `EUR`
euro_rows = Data.loc[Data["Special"].str.get('type') == 'EUR']

#get minimal value and index (A) by minimal
lowest_price = euro_rows.set_index('A')['B']

seller = lowest_price.idxmin()
price  = lowest_price.min()

print(seller)
X2FFFFF

print(price)
0.122

Solution 2:

I think you can do something like this:

# gets only the Euro type
euro_rows = Data.loc[Data["Special"] == {'type': 'EUR'}]

# gets the row of the lowest price
lowest_price = euro_rows.loc[euro_rows['B'] == euro_rows['B'].min()]

# gets seller and price
seller, price = lowest_price['A'].item(), lowest_price['B'].item()

print(seller) #prints: IDDDDDDDDDDDprint(price)  #prints: 0.1

Solution 3:

It might be better for you to read your data using pd.json_normalize before filtering to get what you need:

df = pd.json_normalize(Data).astype({"B": float})

#to get the row with the lowest price
>>> df[df["Special.type"]=="EUR"].nsmallest(1, "B")
      A      B Special.type
1  ID_2  0.122          EUR