Vectorized look-up of Pandas dataframe column values in a separate list

Question

I'm looking for a quick (vectorized) way to perform calculations using the contents of a Pandas dataframe.

My dataframe contains 2 labels for each row and I want to look up values corresponding to each label (from a dictionary / list) and perform a calculation, returning the result to a new column in the dataframe.

I include my working example below making use of loops.

label1s = np.array(['A', 'A', 'A', 'B', 'B', 'B', 'C', 'C', 'C'], dtype=str)
label2s = np.array(['A', 'B', 'C', 'A', 'B', 'C', 'A', 'B', 'C'], dtype=str)
data = np.column_stack([label1s, label2s])

label_values = {'A':1, 'B':2, 'C':3}

df = pd.DataFrame(data=data, columns=['Label1', 'Label2'])

new_col = np.zeros_like(label1s, dtype=float)

for index, row in df.iterrows():
    val1 = label_values[row['Label1']]
    val2 = label_values[row['Label2']]
    new_col[index] = val1 - val2

df['result'] = new_col
df

However, for large datasets, the loop is highly undesirable and slow.

Is there a way to optimize this please?

I've explored some of the pandas functionality like "Lookup", but this seems to want each sized arrays, whereas in my case, I need to lookup values from a list external and different sized to the dataframe.

Answer 1

您可以将字典map到所需的列，即

df['result'] = df.Label1.map(label_values) - df.Label2.map(label_values)