CODEWITHSUNDEEP
pythonpandasdictionaryhashmap
kms
kms

Reputation: 2024

Look up from a dictionary to fill pandas DataFrame with a condition

I have a dictionary that's something like this.

d = {
     "p1": ["$0.00", nan, "$25.00"],
     "p2": ["$30.25", nan, "$12.25"],
     "p3": ["$0.00"],
     "p4": [nan, "$15.00"]
    }

I'd like to look up values from the dictionary and assign them to a column in pandas DataFrame. The DataFrame is something like this:

df = pd.DataFrame({
                   'name': ["p1","p2","p3","p4"],
                   'val': [np.nan, np.nan, 4, np.nan] 
                   })

Assignment:

def fun(name):
 
    # look up from dict

    # apply condition

    return 

df['val'] = df.apply(lambda x: func(x['name']), axis=1)

For the assignment, the condition is to find the largest value from the dictionary, if length of the value pair > 0, i.e there is more than 1 element in the list. Note, the elements are stored as strings. So, need the function needs to convert them to float type.

Expected output:

eo

Upvotes: 1

Views: 1259

Answers (1)

mozway
mozway

Reputation: 261900

First rework your dictionary to keep the "highest" value with a custom key. Then perform a simple map:

d2 = {k: max((e for e in v if pd.notna(e)),
             key=lambda x: float(x.strip('$')) if isinstance(x, str) else x)
      for k,v in d.items()}
# {'p1': '$25.00', 'p2': '$30.25', 'p3': '$0.00', 'p4': '$15.00'}

df['val'] = df['name'].map(d2)

output:

  name     val
0   p1  $25.00
1   p2  $30.25
2   p3   $0.00
3   p4  $15.00

Upvotes: 1

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