count frequency of element in dataframe on row wise

Question

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my data frame is like this EMPLOYEE_ID 2020-02-26 2020-02-27 2020-02-28 2020-02-29 2020-03-01 100000074 Absent Absent Absent Present Absent 100000086 No Match No Match Absent Present Absent

and want to be like

EMPLOYEE_ID     Absent  Present  No Match 
100000074       4         1        0

Plz help

Stack Overflow is not a coding service. Please show some effort and request for some help, not code! — Peyman Mohamadpour
– Peyman Mohamadpour, Commented Mar 16, 2020 at 8:28

jezrael · Accepted Answer · 2020-03-16 08:29:08Z

1

Use DataFrame.melt with DataFrame.pivot_table:

df1 = (df.melt('EMPLOYEE_ID')
         .pivot_table(index='EMPLOYEE_ID', columns='value', aggfunc='size', fill_value=0)
         )
print (df1)
value        Absent  No Match  Present
EMPLOYEE_ID                           
100000074         4         0        1
100000086         2         2        1

Another solution with Series.value_counts per rows in DataFrame.apply:

df1 = (df.set_index('EMPLOYEE_ID').apply(lambda x: x.value_counts(), axis=1)
         .fillna(0)
         .astype(int))
print (df1)
             Absent  No Match  Present
EMPLOYEE_ID                           
100000074         4         0        1
100000086         2         2        1

edited Mar 16, 2020 at 8:29

answered Mar 16, 2020 at 8:23

jezrael

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1 Comment

jezrael Over a year ago

@SanjayGhosh - ya, maybe in future is better add some code, what you try to question for avoid downvotes.

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