CODEWITHSUNDEEP
pythonpandasdataframe
Thivagar Moorthy
Thivagar Moorthy

Reputation: 223

How to add one column to pandas dataframe based on values in different columns?

I have a pandas data frame like below:

a     b    c    d   
0.7   0.1  0.2  0.3
0.5   0.2  0.2  0.2

I am writing some nested loops like below to add a column result based on these 4 columns.

def class_decider(df):
    for i in df['a']:
        if i > 0.6:
            a = "class A"
        elif:
            for j in df['b']:
                if j > 0.2:
                    a = "class B"
                elif:
                    for k in df['c']:
                        if j > 0.15:
                            a = "class C"
                        elif:
                            for l in df['d']:
                                if l > 0.10:
                                    a = "class D"
                                else:
                                    a = "null"
    return a

Could anyone please help in optimising the code.

Expected Output:

a     b    c    d     result
0.7   0.1  0.2  0.3   class A
0.5   0.2  0.2  0.2   class C

Upvotes: 2

Views: 100

Answers (2)

user2357448
user2357448

Reputation:

If you're looking for interpretable, flexible, but not necessarily the best performing solutions, here are two approaches:

Approach 1: Using .loc and column comparisons

df = pd.DataFrame({'a':[0.7, 0.5], 'b':[0.1, 0.2], 'c':[0.2, 0.2], 'd':[0.3, 0.2]})
df['result'] = None
df.loc[df['d'] > 0.1, 'result'] = 'class_d'
df.loc[df['c'] > 0.15, 'result'] = 'class_c'
df.loc[df['b'] > 0.2, 'result'] = 'class_b'
df.loc[df['a'] > 0.6, 'result'] = 'class_a'

Approach 2 Using df.iterrows()

df = pd.DataFrame({'a':[0.7, 0.5], 'b':[0.1, 0.2], 'c':[0.2, 0.2], 'd':[0.3, 0.2]})
df['result'] = None

for idx, row in df.iterrows():
    if row['a'] > 0.6:
        df.loc[idx, 'result'] = 'class_a'
    elif row['b'] > 0.2:
        df.loc[idx, 'result'] = 'class_b'
    elif row['c'] > 0.15:
        df.loc[idx, 'result'] = 'class_c'
    elif row['d'] > 0.1:
        df.loc[idx, 'result'] = 'class_d'

Upvotes: 0

Shubham Sharma
Shubham Sharma

Reputation: 71689

IIUC, You can compare the columns a, b, c and d with 0.6, 0.2, 0.15, 0.10 to create a boolean mask, then use idxmax along axis=1 on this mask to get the name of the column where the first True value occur in the mask.

c = ['a', 'b', 'c', 'd']
m = df[c].gt([0.6, 0.2, 0.15, 0.10])
df['Result'] = m.idxmax(1).radd('Class ').mask(~m.any(1), 'Null')

     a    b    c    d   Result
0  0.7  0.1  0.2  0.3  Class a
1  0.5  0.2  0.2  0.2  Class c

Upvotes: 7

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