Aggregate numpy array dynamically (aggregation function is parameterized)
Aggregate a set of values with a parameterized aggregation function.
I have a numpy array of numeric values, and an aggregation function which is a parameter (options are sum, count, avg, max, min, etc.). How can I apply this aggregation function dynamically in the code ?
combined_vals_list = [1,2,3]
vals_df = pd.DataFrame({'values': combined_vals_list , 'group': [1]*len(combined_vals_list)})
agg_col_query_value =
vals_df.groupby('group').aggregate(agg).iloc[0,0]
the expected result is 6 and my code returns 6 , but this way of using pandas seem cumbersome.
Any better and more efficient simpler way ?
python-3.x pandas numpy
asked Jan 3 at 11:42
RNNRNN
52
add a comment |
Aggregate a set of values with a parameterized aggregation function.
I have a numpy array of numeric values, and an aggregation function which is a parameter (options are sum, count, avg, max, min, etc.). How can I apply this aggregation function dynamically in the code ?
combined_vals_list = [1,2,3]
vals_df = pd.DataFrame({'values': combined_vals_list , 'group': [1]*len(combined_vals_list)})
agg_col_query_value =
vals_df.groupby('group').aggregate(agg).iloc[0,0]
the expected result is 6 and my code returns 6 , but this way of using pandas seem cumbersome.
Any better and more efficient simpler way ?
python-3.x pandas numpy
asked Jan 3 at 11:42
RNNRNN
52
2
You can do something like adictof strings to functions, but in Pandas you can also just write it likepd.Series([1, 2, 3]).aggregate('sum').
– jdehesa
Jan 3 at 12:58
add a comment |
Aggregate a set of values with a parameterized aggregation function.
I have a numpy array of numeric values, and an aggregation function which is a parameter (options are sum, count, avg, max, min, etc.). How can I apply this aggregation function dynamically in the code ?
combined_vals_list = [1,2,3]
vals_df = pd.DataFrame({'values': combined_vals_list , 'group': [1]*len(combined_vals_list)})
agg_col_query_value =
vals_df.groupby('group').aggregate(agg).iloc[0,0]
the expected result is 6 and my code returns 6 , but this way of using pandas seem cumbersome.
Any better and more efficient simpler way ?
python-3.x pandas numpy
asked Jan 3 at 11:42
RNNRNN
52
Aggregate a set of values with a parameterized aggregation function.
I have a numpy array of numeric values, and an aggregation function which is a parameter (options are sum, count, avg, max, min, etc.). How can I apply this aggregation function dynamically in the code ?
combined_vals_list = [1,2,3]
vals_df = pd.DataFrame({'values': combined_vals_list , 'group': [1]*len(combined_vals_list)})
agg_col_query_value =
vals_df.groupby('group').aggregate(agg).iloc[0,0]
the expected result is 6 and my code returns 6 , but this way of using pandas seem cumbersome.
Any better and more efficient simpler way ?
python-3.x pandas numpy
python-3.x pandas numpy
asked Jan 3 at 11:42
RNNRNN
52
asked Jan 3 at 11:42
RNNRNN
52
asked Jan 3 at 11:42
RNNRNN
52
asked Jan 3 at 11:42
RNNRNN
52
asked Jan 3 at 11:42
RNNRNN
52
52
2
You can do something like adictof strings to functions, but in Pandas you can also just write it likepd.Series([1, 2, 3]).aggregate('sum').
– jdehesa
Jan 3 at 12:58
add a comment |
2
You can do something like adictof strings to functions, but in Pandas you can also just write it likepd.Series([1, 2, 3]).aggregate('sum').
– jdehesa
Jan 3 at 12:58
2
2
You can do something like a
dict of strings to functions, but in Pandas you can also just write it like pd.Series([1, 2, 3]).aggregate('sum').– jdehesa
Jan 3 at 12:58
You can do something like a
dict of strings to functions, but in Pandas you can also just write it like pd.Series([1, 2, 3]).aggregate('sum').– jdehesa
Jan 3 at 12:58
add a comment |
1 Answer
1
active
oldest
votes
it seems that pandas apparently the best way to achieve dynamic parameterized aggregation
pd.Series([1, 2, 3]).aggregate('sum') per jdehesa comment
answered Jan 7 at 8:23
RNNRNN
52
add a comment |
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1 Answer
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1 Answer
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active
oldest
votes
active
oldest
votes
active
oldest
votes
it seems that pandas apparently the best way to achieve dynamic parameterized aggregation
pd.Series([1, 2, 3]).aggregate('sum') per jdehesa comment
answered Jan 7 at 8:23
RNNRNN
52
add a comment |
it seems that pandas apparently the best way to achieve dynamic parameterized aggregation
pd.Series([1, 2, 3]).aggregate('sum') per jdehesa comment
answered Jan 7 at 8:23
RNNRNN
52
add a comment |
it seems that pandas apparently the best way to achieve dynamic parameterized aggregation
pd.Series([1, 2, 3]).aggregate('sum') per jdehesa comment
answered Jan 7 at 8:23
RNNRNN
52
it seems that pandas apparently the best way to achieve dynamic parameterized aggregation
pd.Series([1, 2, 3]).aggregate('sum') per jdehesa comment
answered Jan 7 at 8:23
RNNRNN
52
answered Jan 7 at 8:23
RNNRNN
52
answered Jan 7 at 8:23
RNNRNN
52
answered Jan 7 at 8:23
RNNRNN
52
52
add a comment |
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You can do something like a
dictof strings to functions, but in Pandas you can also just write it likepd.Series([1, 2, 3]).aggregate('sum').– jdehesa
Jan 3 at 12:58