根据条件熔化多列 pandas dataframe

Question

I have a pandas dataframe as follows:

dataframe = pd.DataFrame(
    {
    'ID': [1,2,3,4],
    'Gender': ['F','F','M','M'],
    'Language': ['EN', 'ES', 'EN', 'EN'],
    'Year 1': [2020,2020,2020,2020],
    'Score 1': [93,97,83,86],
    'Year 2': [2020,2020,None,2018],
    'Score 2': [85,95,None,55],
    'Year 3': [2020,2018,None,None],
    'Score 3': [87,86,None,None]
    }
)

ID	Gender	Language	Year 1	Score 1	Year 2	Score 2	Year 3	Score 3
1	F	EN	2020	93	2020	85	2020	87
2	F	ES	2020	97	2020	95	2018	86
3	M	EN	2020	83
4	M	EN	2020	86	2018	55

And I would like to melt based on the year and the corresponding scores, for example if any year equals 2020 then the following would be generated:

ID	Gender	Language	Year	Score
1	F	EN	2020	93
1	F	EN	2020	85
1	F	EN	2020	87
2	F	ES	2020	97
2	F	ES	2020	95
3	M	EN	2020	83
4	M	EN	2020	86

I have tried using pd.melt but am having trouble filtering by the year across the columns and keeping the corresponding entries.

Answer 1

From what i understand, you may try:

out = (pd.wide_to_long(dataframe,['Year','Score'],['ID','Gender','Language'],'v',' ')
                                               .dropna().droplevel(-1).reset_index())

---

print(out)

   ID Gender Language    Year  Score
0   1      F       EN  2020.0   93.0
1   1      F       EN  2020.0   85.0
2   1      F       EN  2020.0   87.0
3   2      F       ES  2020.0   97.0
4   2      F       ES  2020.0   95.0
5   2      F       ES  2018.0   86.0
6   3      M       EN  2020.0   83.0
7   4      M       EN  2020.0   86.0
8   4      M       EN  2018.0   55.0

Answer 2

long_df = (pd.wide_to_long(dataframe, stubnames=["Year","Score"],i="ID", j="Repetition",
                           sep = " ").reset_index())
df_2020 = (long_df[long_df["Year"] == 2020].drop("Repetition",
                                                 axis=1).sort_values("ID").reset_index(drop=True))

print(df_2020)

   ID Gender Language    Year  Score
0   1      F       EN  2020.0   93.0
1   1      F       EN  2020.0   85.0
2   1      F       EN  2020.0   87.0
3   2      F       ES  2020.0   97.0
4   2      F       ES  2020.0   95.0
5   3      M       EN  2020.0   83.0
6   4      M       EN  2020.0   86.0