Adding a grouped, aggregate nunique column to pandas dataframe
I want to add an aggregate, grouped, nunique column to my pandas dataframe but not aggregate the entire dataframe. I'm trying to do this in one line and avoid creating a new aggregated object and merging that, etc.
my df has track, type, and id. I want the number of unique ids for each track/type combination as a new column in the table (but not collapse track/type combos in the resulting df). Same number of rows, 1 more column.
something like this isn't working:
df['n_unique_id'] = df.groupby(['track', 'type'])['id'].nunique()
nor is
df['n_unique_id'] = df.groupby(['track', 'type'])['id'].transform(nunique)
this last one works with some aggregating functions but not others. the following works (but is meaningless on my dataset):
df['n_unique_id'] = df.groupby(['track', 'type'])['id'].transform(sum)
in R this is easily done in data.table with
df[, n_unique_id := uniqueN(id), by = c('track', 'type')]
thanks!
python pandas dataframe aggregate pandas-groupby
edited Nov 22 '18 at 11:52
jpp
102k2165115
asked May 1 '17 at 21:14
wbartswbarts
142
add a comment |
I want to add an aggregate, grouped, nunique column to my pandas dataframe but not aggregate the entire dataframe. I'm trying to do this in one line and avoid creating a new aggregated object and merging that, etc.
my df has track, type, and id. I want the number of unique ids for each track/type combination as a new column in the table (but not collapse track/type combos in the resulting df). Same number of rows, 1 more column.
something like this isn't working:
df['n_unique_id'] = df.groupby(['track', 'type'])['id'].nunique()
nor is
df['n_unique_id'] = df.groupby(['track', 'type'])['id'].transform(nunique)
this last one works with some aggregating functions but not others. the following works (but is meaningless on my dataset):
df['n_unique_id'] = df.groupby(['track', 'type'])['id'].transform(sum)
in R this is easily done in data.table with
df[, n_unique_id := uniqueN(id), by = c('track', 'type')]
thanks!
python pandas dataframe aggregate pandas-groupby
edited Nov 22 '18 at 11:52
jpp
102k2165115
asked May 1 '17 at 21:14
wbartswbarts
142
Can paste a copy of your source dataframe and what the expected output should look like?
– Scott Boston
May 1 '17 at 21:27
add a comment |
I want to add an aggregate, grouped, nunique column to my pandas dataframe but not aggregate the entire dataframe. I'm trying to do this in one line and avoid creating a new aggregated object and merging that, etc.
my df has track, type, and id. I want the number of unique ids for each track/type combination as a new column in the table (but not collapse track/type combos in the resulting df). Same number of rows, 1 more column.
something like this isn't working:
df['n_unique_id'] = df.groupby(['track', 'type'])['id'].nunique()
nor is
df['n_unique_id'] = df.groupby(['track', 'type'])['id'].transform(nunique)
this last one works with some aggregating functions but not others. the following works (but is meaningless on my dataset):
df['n_unique_id'] = df.groupby(['track', 'type'])['id'].transform(sum)
in R this is easily done in data.table with
df[, n_unique_id := uniqueN(id), by = c('track', 'type')]
thanks!
python pandas dataframe aggregate pandas-groupby
edited Nov 22 '18 at 11:52
jpp
102k2165115
asked May 1 '17 at 21:14
wbartswbarts
142
I want to add an aggregate, grouped, nunique column to my pandas dataframe but not aggregate the entire dataframe. I'm trying to do this in one line and avoid creating a new aggregated object and merging that, etc.
my df has track, type, and id. I want the number of unique ids for each track/type combination as a new column in the table (but not collapse track/type combos in the resulting df). Same number of rows, 1 more column.
something like this isn't working:
df['n_unique_id'] = df.groupby(['track', 'type'])['id'].nunique()
nor is
df['n_unique_id'] = df.groupby(['track', 'type'])['id'].transform(nunique)
this last one works with some aggregating functions but not others. the following works (but is meaningless on my dataset):
df['n_unique_id'] = df.groupby(['track', 'type'])['id'].transform(sum)
in R this is easily done in data.table with
df[, n_unique_id := uniqueN(id), by = c('track', 'type')]
thanks!
python pandas dataframe aggregate pandas-groupby
python pandas dataframe aggregate pandas-groupby
edited Nov 22 '18 at 11:52
jpp
102k2165115
asked May 1 '17 at 21:14
wbartswbarts
142
edited Nov 22 '18 at 11:52
jpp
102k2165115
asked May 1 '17 at 21:14
wbartswbarts
142
edited Nov 22 '18 at 11:52
jpp
102k2165115
edited Nov 22 '18 at 11:52
jpp
102k2165115
edited Nov 22 '18 at 11:52
jpp
102k2165115
102k2165115
asked May 1 '17 at 21:14
wbartswbarts
142
asked May 1 '17 at 21:14
wbartswbarts
142
asked May 1 '17 at 21:14
wbartswbarts
142
142
Can paste a copy of your source dataframe and what the expected output should look like?
– Scott Boston
May 1 '17 at 21:27
add a comment |
Can paste a copy of your source dataframe and what the expected output should look like?
– Scott Boston
May 1 '17 at 21:27
Can paste a copy of your source dataframe and what the expected output should look like?
– Scott Boston
May 1 '17 at 21:27
Can paste a copy of your source dataframe and what the expected output should look like?
– Scott Boston
May 1 '17 at 21:27
add a comment |
1 Answer
1
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oldest
votes
df.groupby(['track', 'type'])['id'].transform(nunique)
Implies that there is a name nunique in the name space that performs some function. transform will take a function or a string that it knows a function for. nunique is definitely one of those strings.
As pointed out by @root, often the method that pandas will utilize to perform a transformation indicated by these strings are optimized and should generally be preferred to passing your own functions. This is True even for passing numpy functions in some cases.
For example transform('sum') should be preferred over transform(sum).
Try this instead
df.groupby(['track', 'type'])['id'].transform('nunique')
demo
df = pd.DataFrame(dict(
track=list('11112222'), type=list('AAAABBBB'), id=list('XXYZWWWW')))
print(df)
id track type
0 X 1 A
1 X 1 A
2 Y 1 A
3 Z 1 A
4 W 2 B
5 W 2 B
6 W 2 B
7 W 2 B
df.groupby(['track', 'type'])['id'].transform('nunique')
0 3
1 3
2 3
3 3
4 1
5 1
6 1
7 1
Name: id, dtype: int64
answered May 1 '17 at 21:27
piRSquaredpiRSquared
158k23154298
4
I was just about to comment with this. It might be useful to mention that the string aliases should usually be preferred if available, as they'll reference optimal functions, e.g..transform(sum)uses pythonsum, whereas.transfrom('sum')would use numpy and be more computationally efficient.
– root
May 1 '17 at 21:31
add a comment |
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df.groupby(['track', 'type'])['id'].transform(nunique)
Implies that there is a name nunique in the name space that performs some function. transform will take a function or a string that it knows a function for. nunique is definitely one of those strings.
As pointed out by @root, often the method that pandas will utilize to perform a transformation indicated by these strings are optimized and should generally be preferred to passing your own functions. This is True even for passing numpy functions in some cases.
For example transform('sum') should be preferred over transform(sum).
Try this instead
df.groupby(['track', 'type'])['id'].transform('nunique')
demo
df = pd.DataFrame(dict(
track=list('11112222'), type=list('AAAABBBB'), id=list('XXYZWWWW')))
print(df)
id track type
0 X 1 A
1 X 1 A
2 Y 1 A
3 Z 1 A
4 W 2 B
5 W 2 B
6 W 2 B
7 W 2 B
df.groupby(['track', 'type'])['id'].transform('nunique')
0 3
1 3
2 3
3 3
4 1
5 1
6 1
7 1
Name: id, dtype: int64
answered May 1 '17 at 21:27
piRSquaredpiRSquared
158k23154298
4
I was just about to comment with this. It might be useful to mention that the string aliases should usually be preferred if available, as they'll reference optimal functions, e.g..transform(sum)uses pythonsum, whereas.transfrom('sum')would use numpy and be more computationally efficient.
– root
May 1 '17 at 21:31
add a comment |
df.groupby(['track', 'type'])['id'].transform(nunique)
Implies that there is a name nunique in the name space that performs some function. transform will take a function or a string that it knows a function for. nunique is definitely one of those strings.
As pointed out by @root, often the method that pandas will utilize to perform a transformation indicated by these strings are optimized and should generally be preferred to passing your own functions. This is True even for passing numpy functions in some cases.
For example transform('sum') should be preferred over transform(sum).
Try this instead
df.groupby(['track', 'type'])['id'].transform('nunique')
demo
df = pd.DataFrame(dict(
track=list('11112222'), type=list('AAAABBBB'), id=list('XXYZWWWW')))
print(df)
id track type
0 X 1 A
1 X 1 A
2 Y 1 A
3 Z 1 A
4 W 2 B
5 W 2 B
6 W 2 B
7 W 2 B
df.groupby(['track', 'type'])['id'].transform('nunique')
0 3
1 3
2 3
3 3
4 1
5 1
6 1
7 1
Name: id, dtype: int64
answered May 1 '17 at 21:27
piRSquaredpiRSquared
158k23154298
4
I was just about to comment with this. It might be useful to mention that the string aliases should usually be preferred if available, as they'll reference optimal functions, e.g..transform(sum)uses pythonsum, whereas.transfrom('sum')would use numpy and be more computationally efficient.
– root
May 1 '17 at 21:31
add a comment |
df.groupby(['track', 'type'])['id'].transform(nunique)
Implies that there is a name nunique in the name space that performs some function. transform will take a function or a string that it knows a function for. nunique is definitely one of those strings.
As pointed out by @root, often the method that pandas will utilize to perform a transformation indicated by these strings are optimized and should generally be preferred to passing your own functions. This is True even for passing numpy functions in some cases.
For example transform('sum') should be preferred over transform(sum).
Try this instead
df.groupby(['track', 'type'])['id'].transform('nunique')
demo
df = pd.DataFrame(dict(
track=list('11112222'), type=list('AAAABBBB'), id=list('XXYZWWWW')))
print(df)
id track type
0 X 1 A
1 X 1 A
2 Y 1 A
3 Z 1 A
4 W 2 B
5 W 2 B
6 W 2 B
7 W 2 B
df.groupby(['track', 'type'])['id'].transform('nunique')
0 3
1 3
2 3
3 3
4 1
5 1
6 1
7 1
Name: id, dtype: int64
answered May 1 '17 at 21:27
piRSquaredpiRSquared
158k23154298
df.groupby(['track', 'type'])['id'].transform(nunique)
Implies that there is a name nunique in the name space that performs some function. transform will take a function or a string that it knows a function for. nunique is definitely one of those strings.
As pointed out by @root, often the method that pandas will utilize to perform a transformation indicated by these strings are optimized and should generally be preferred to passing your own functions. This is True even for passing numpy functions in some cases.
For example transform('sum') should be preferred over transform(sum).
Try this instead
df.groupby(['track', 'type'])['id'].transform('nunique')
demo
df = pd.DataFrame(dict(
track=list('11112222'), type=list('AAAABBBB'), id=list('XXYZWWWW')))
print(df)
id track type
0 X 1 A
1 X 1 A
2 Y 1 A
3 Z 1 A
4 W 2 B
5 W 2 B
6 W 2 B
7 W 2 B
df.groupby(['track', 'type'])['id'].transform('nunique')
0 3
1 3
2 3
3 3
4 1
5 1
6 1
7 1
Name: id, dtype: int64
answered May 1 '17 at 21:27
piRSquaredpiRSquared
158k23154298
edited May 1 '17 at 21:31
answered May 1 '17 at 21:27
piRSquaredpiRSquared
158k23154298
answered May 1 '17 at 21:27
piRSquaredpiRSquared
158k23154298
answered May 1 '17 at 21:27
piRSquaredpiRSquared
158k23154298
158k23154298
4
I was just about to comment with this. It might be useful to mention that the string aliases should usually be preferred if available, as they'll reference optimal functions, e.g..transform(sum)uses pythonsum, whereas.transfrom('sum')would use numpy and be more computationally efficient.
– root
May 1 '17 at 21:31
add a comment |
4
I was just about to comment with this. It might be useful to mention that the string aliases should usually be preferred if available, as they'll reference optimal functions, e.g..transform(sum)uses pythonsum, whereas.transfrom('sum')would use numpy and be more computationally efficient.
– root
May 1 '17 at 21:31
4
4
I was just about to comment with this. It might be useful to mention that the string aliases should usually be preferred if available, as they'll reference optimal functions, e.g.
.transform(sum) uses python sum, whereas .transfrom('sum') would use numpy and be more computationally efficient.– root
May 1 '17 at 21:31
I was just about to comment with this. It might be useful to mention that the string aliases should usually be preferred if available, as they'll reference optimal functions, e.g.
.transform(sum) uses python sum, whereas .transfrom('sum') would use numpy and be more computationally efficient.– root
May 1 '17 at 21:31
add a comment |
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Can paste a copy of your source dataframe and what the expected output should look like?
– Scott Boston
May 1 '17 at 21:27