CODEWITHSUNDEEP
pythonpandasdataframe
Ray
Ray

Reputation: 53

How to use pd.melt in Python pandas

This dataframe from csv:

id  name    A   B   C   gpa
0   1111    Phineas NaN B   NaN 3.0
1   1113    Tilly   NaN NaN C   2.5
2   1110    Andres  A   NaN NaN 3.8
3   1112    Jax NaN B   NaN 3.2
4   1114    Ray NaN B   NaN 3.1
5   1115    Koda    NaN NaN C   2.4
6   1120    Bruno   A   NaN NaN 3.7
7   1134    Davis   NaN NaN C   2.6
8   1102    Cassie  A   NaN NaN 4.0

I want the output:

id  name    grade   gpa
0   1111    Phineas B   3.0
1   1113    Tilly   C   2.5
2   1110    Andres  A   3.8
3   1112    Jax     C   3.2
4   1114    Ray     B   3.1
5   1115    Koda    C   2.4
6   1120    Bruno   A   3.7
7   1134    Davis   C   2.6
8   1102    Cassie  A   4.0

what is code for that?

Upvotes: 1

Views: 402

Answers (2)

Space Impact
Space Impact

Reputation: 13255

Use combine_first with drop, you don't need melt in this case:

df['grade'] = df['A'].combine_first(df['B']).combine_first(df['C'])
df.drop(['A','B','C'], axis=1, inplace=True)

Or:

df['grade'] = df[['A','B','C']].values[df[['A','B','C']].notnull()]
df.drop(['A','B','C'], axis=1, inplace=True)

print(df)
     id     name  gpa grade
0  1111  Phineas  3.0     B
1  1113    Tilly  2.5     C
2  1110   Andres  3.8     A
3  1112      Jax  3.2     B
4  1114      Ray  3.1     B
5  1115     Koda  2.4     C
6  1120    Bruno  3.7     A
7  1134    Davis  2.6     C
8  1102   Cassie  4.0     A

Upvotes: 2

David
David

Reputation: 654

If you don't have your heart set on using melt, this solution might work for you: Because each student has either an A,B or C exclusively, you can first turn all the NaN values in these columns into empty strings, and then concatenate your A,B and C columns together using the + operator

Import statements and starting DataFrame:

import pandas as pd
import numpy as np

df = pd.DataFrame({'id':[1111,1113],
'name':['Phineas','Tilly'],
'A':[np.NaN,np.NaN],
'B':['B',np.NaN],
'C':[np.NaN,'C'],
'gpa':[3.0,2.5]
})
#     id      name    A   B   C   gpa
# 0   1111    Phineas NaN B   NaN 3.0
# 1   1113    Tilly   NaN NaN C   2.5

Column-wise string concatenation and output:

df.fillna('',inplace=True) #replaces all NaN's with ""-empty strings
df['letter_grades'] = df['A'] + df['B'] + df['C'] #concatenate
df = df[['id','name','letter_grades','gpa']] #reassign dataframe identifier
print(df)

#     id     name letter_grades  gpa
#0  1111  Phineas             B  3.0
#1  1113    Tilly             C  2.5

Upvotes: 1

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