I have a data-frame df
like this:
[ Date
: mm/dd/yyyy
]
Date Student_id subject Subject_Scores
11/30/2020 1000101 Math 70
11/25/2020 1000101 Physics 75
12/02/2020 1000101 Biology 60
11/25/2020 1000101 Chemistry 49
11/25/2020 1000101 English 80
12/02/2020 1000101 Sociology 50
11/25/2020 1000102 Physics 80
11/25/2020 1000102 Math 90
12/15/2020 1000102 Chemistry 63
12/15/2020 1000103 English 71
How can I get all the unique Date
s for each of individual Student_id
's.
Output date_df
:
Date Student_id
11/30/2020 1000101
11/25/2020 1000101
12/02/2020 1000101
11/25/2020 1000102
12/15/2020 1000102
12/15/2020 1000103
Also , I need counts of unique Date
s for each of Student_id
:
Student_id unique_date_count
1000101 3
1000102 2
1000103 1
Edits: I cannot drop any rows because of unique subejcts, so how can I get unique dates and its count for each of Student_id
Thanks for the help, in advance!
Use DataFrame.drop_duplicates
:
df1 = df[['Date','Student_id']].drop_duplicates()
print (df1)
Date Student_id
0 11/30/2020 1000101
1 11/25/2020 1000101
2 12/02/2020 1000101
6 11/25/2020 1000102
8 12/15/2020 1000102
9 12/15/2020 1000103
And then Series.value_counts
:
s = df1['Student_id'].value_counts()
print (s)
1000101 3
1000102 2
1000103 1
Name: Student_id, dtype: int64
Last if need DataFrame
add Series.rename_axis
and Series.reset_index
:
df2 = s.rename_axis('Student_id').reset_index(name='unique_date_count')
print (df2)
Student_id unique_date_count
0 1000101 3
1 1000102 2
2 1000103 1
first, you need to do:
df_new=df.drop_duplicates()
Second,you can do value_counts
,
df_new['Student_id'].value_counts()
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