Groupby, means and std
0
I'm new to python ... thank you in advance!
I need to group, calculate in a column the mean for each nutrient and the corresponding std.
As so:
cod | Nmean | Nstd | etc...for each nutrient
I managed to group and calculate the mean. But I need another column, for example, next to N%, with it's std ... and so on...
report = my_data.groupby(["cod"], as_index = False)[['N%', 'P%','K%', 'Ca%', 'Mg%', 'S%',
'Fe mg/kg', 'Mn mg/kg', 'Zn mg/kg', 'Cu mg/kg',
'B mg/kg', 'Na mg/kg']].mean()
python-3.x pandas jupyter-notebook
asked Nov 13 '18 at 11:16
Patrícia AlmeidaPatrícia Almeida
31
add a comment |
0
I'm new to python ... thank you in advance!
I need to group, calculate in a column the mean for each nutrient and the corresponding std.
As so:
cod | Nmean | Nstd | etc...for each nutrient
I managed to group and calculate the mean. But I need another column, for example, next to N%, with it's std ... and so on...
report = my_data.groupby(["cod"], as_index = False)[['N%', 'P%','K%', 'Ca%', 'Mg%', 'S%',
'Fe mg/kg', 'Mn mg/kg', 'Zn mg/kg', 'Cu mg/kg',
'B mg/kg', 'Na mg/kg']].mean()
python-3.x pandas jupyter-notebook
asked Nov 13 '18 at 11:16
Patrícia AlmeidaPatrícia Almeida
31
add a comment |
0
0
0
I'm new to python ... thank you in advance!
I need to group, calculate in a column the mean for each nutrient and the corresponding std.
As so:
cod | Nmean | Nstd | etc...for each nutrient
I managed to group and calculate the mean. But I need another column, for example, next to N%, with it's std ... and so on...
report = my_data.groupby(["cod"], as_index = False)[['N%', 'P%','K%', 'Ca%', 'Mg%', 'S%',
'Fe mg/kg', 'Mn mg/kg', 'Zn mg/kg', 'Cu mg/kg',
'B mg/kg', 'Na mg/kg']].mean()
python-3.x pandas jupyter-notebook
asked Nov 13 '18 at 11:16
Patrícia AlmeidaPatrícia Almeida
31
I'm new to python ... thank you in advance!
I need to group, calculate in a column the mean for each nutrient and the corresponding std.
As so:
cod | Nmean | Nstd | etc...for each nutrient
I managed to group and calculate the mean. But I need another column, for example, next to N%, with it's std ... and so on...
report = my_data.groupby(["cod"], as_index = False)[['N%', 'P%','K%', 'Ca%', 'Mg%', 'S%',
'Fe mg/kg', 'Mn mg/kg', 'Zn mg/kg', 'Cu mg/kg',
'B mg/kg', 'Na mg/kg']].mean()
python-3.x pandas jupyter-notebook
python-3.x pandas jupyter-notebook
asked Nov 13 '18 at 11:16
Patrícia AlmeidaPatrícia Almeida
31
asked Nov 13 '18 at 11:16
Patrícia AlmeidaPatrícia Almeida
31
asked Nov 13 '18 at 11:16
Patrícia AlmeidaPatrícia Almeida
31
asked Nov 13 '18 at 11:16
Patrícia AlmeidaPatrícia Almeida
31
asked Nov 13 '18 at 11:16
Patrícia AlmeidaPatrícia Almeida
31
31
add a comment |
add a comment |
1 Answer
1
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oldest
votes
0
Write a dict comprehension with each of the column as key and ['mean', 'std'] as value first -
groups_agg = { i:['mean', 'std'] for i in ['N%', 'P%','K%', 'Ca%', 'Mg%', 'S%', 'Fe mg/kg', 'Mn mg/kg', 'Zn mg/kg', 'Cu mg/kg', 'B mg/kg', 'Na mg/kg'] }
It will look like this -
{'B mg/kg': ['mean', 'std'],
'Ca%': ['mean', 'std'],
'Cu mg/kg': ['mean', 'std'],
'Fe mg/kg': ['mean', 'std'],
'K%': ['mean', 'std'],
'Mg%': ['mean', 'std'],
'Mn mg/kg': ['mean', 'std'],
'N%': ['mean', 'std'],
'Na mg/kg': ['mean', 'std'],
'P%': ['mean', 'std'],
'S%': ['mean', 'std'],
'Zn mg/kg': ['mean', 'std']}
Then pass on the object to pd.agg()
my_data.groupby(["cod"], as_index = False).agg(groups_agg)
answered Nov 13 '18 at 11:19
Vivek KalyanaranganVivek Kalyanarangan
5,0361827
Worked very good. Is there any simple way to add a " ± " next to the std ?
– Patrícia Almeida
Nov 13 '18 at 11:29
I don't think so :(. But that can be achieved easily by having another column -my_data2['Mean_STD'] = my_data2['mean'] + (2*my_data2['std']). The negative can have another column
– Vivek Kalyanarangan
Nov 13 '18 at 11:35
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
0
Write a dict comprehension with each of the column as key and ['mean', 'std'] as value first -
groups_agg = { i:['mean', 'std'] for i in ['N%', 'P%','K%', 'Ca%', 'Mg%', 'S%', 'Fe mg/kg', 'Mn mg/kg', 'Zn mg/kg', 'Cu mg/kg', 'B mg/kg', 'Na mg/kg'] }
It will look like this -
{'B mg/kg': ['mean', 'std'],
'Ca%': ['mean', 'std'],
'Cu mg/kg': ['mean', 'std'],
'Fe mg/kg': ['mean', 'std'],
'K%': ['mean', 'std'],
'Mg%': ['mean', 'std'],
'Mn mg/kg': ['mean', 'std'],
'N%': ['mean', 'std'],
'Na mg/kg': ['mean', 'std'],
'P%': ['mean', 'std'],
'S%': ['mean', 'std'],
'Zn mg/kg': ['mean', 'std']}
Then pass on the object to pd.agg()
my_data.groupby(["cod"], as_index = False).agg(groups_agg)
answered Nov 13 '18 at 11:19
Vivek KalyanaranganVivek Kalyanarangan
5,0361827
Worked very good. Is there any simple way to add a " ± " next to the std ?
– Patrícia Almeida
Nov 13 '18 at 11:29
I don't think so :(. But that can be achieved easily by having another column -my_data2['Mean_STD'] = my_data2['mean'] + (2*my_data2['std']). The negative can have another column
– Vivek Kalyanarangan
Nov 13 '18 at 11:35
add a comment |
0
Write a dict comprehension with each of the column as key and ['mean', 'std'] as value first -
groups_agg = { i:['mean', 'std'] for i in ['N%', 'P%','K%', 'Ca%', 'Mg%', 'S%', 'Fe mg/kg', 'Mn mg/kg', 'Zn mg/kg', 'Cu mg/kg', 'B mg/kg', 'Na mg/kg'] }
It will look like this -
{'B mg/kg': ['mean', 'std'],
'Ca%': ['mean', 'std'],
'Cu mg/kg': ['mean', 'std'],
'Fe mg/kg': ['mean', 'std'],
'K%': ['mean', 'std'],
'Mg%': ['mean', 'std'],
'Mn mg/kg': ['mean', 'std'],
'N%': ['mean', 'std'],
'Na mg/kg': ['mean', 'std'],
'P%': ['mean', 'std'],
'S%': ['mean', 'std'],
'Zn mg/kg': ['mean', 'std']}
Then pass on the object to pd.agg()
my_data.groupby(["cod"], as_index = False).agg(groups_agg)
answered Nov 13 '18 at 11:19
Vivek KalyanaranganVivek Kalyanarangan
5,0361827
Worked very good. Is there any simple way to add a " ± " next to the std ?
– Patrícia Almeida
Nov 13 '18 at 11:29
I don't think so :(. But that can be achieved easily by having another column -my_data2['Mean_STD'] = my_data2['mean'] + (2*my_data2['std']). The negative can have another column
– Vivek Kalyanarangan
Nov 13 '18 at 11:35
add a comment |
0
0
0
Write a dict comprehension with each of the column as key and ['mean', 'std'] as value first -
groups_agg = { i:['mean', 'std'] for i in ['N%', 'P%','K%', 'Ca%', 'Mg%', 'S%', 'Fe mg/kg', 'Mn mg/kg', 'Zn mg/kg', 'Cu mg/kg', 'B mg/kg', 'Na mg/kg'] }
It will look like this -
{'B mg/kg': ['mean', 'std'],
'Ca%': ['mean', 'std'],
'Cu mg/kg': ['mean', 'std'],
'Fe mg/kg': ['mean', 'std'],
'K%': ['mean', 'std'],
'Mg%': ['mean', 'std'],
'Mn mg/kg': ['mean', 'std'],
'N%': ['mean', 'std'],
'Na mg/kg': ['mean', 'std'],
'P%': ['mean', 'std'],
'S%': ['mean', 'std'],
'Zn mg/kg': ['mean', 'std']}
Then pass on the object to pd.agg()
my_data.groupby(["cod"], as_index = False).agg(groups_agg)
answered Nov 13 '18 at 11:19
Vivek KalyanaranganVivek Kalyanarangan
5,0361827
Write a dict comprehension with each of the column as key and ['mean', 'std'] as value first -
groups_agg = { i:['mean', 'std'] for i in ['N%', 'P%','K%', 'Ca%', 'Mg%', 'S%', 'Fe mg/kg', 'Mn mg/kg', 'Zn mg/kg', 'Cu mg/kg', 'B mg/kg', 'Na mg/kg'] }
It will look like this -
{'B mg/kg': ['mean', 'std'],
'Ca%': ['mean', 'std'],
'Cu mg/kg': ['mean', 'std'],
'Fe mg/kg': ['mean', 'std'],
'K%': ['mean', 'std'],
'Mg%': ['mean', 'std'],
'Mn mg/kg': ['mean', 'std'],
'N%': ['mean', 'std'],
'Na mg/kg': ['mean', 'std'],
'P%': ['mean', 'std'],
'S%': ['mean', 'std'],
'Zn mg/kg': ['mean', 'std']}
Then pass on the object to pd.agg()
my_data.groupby(["cod"], as_index = False).agg(groups_agg)
answered Nov 13 '18 at 11:19
Vivek KalyanaranganVivek Kalyanarangan
5,0361827
answered Nov 13 '18 at 11:19
Vivek KalyanaranganVivek Kalyanarangan
5,0361827
answered Nov 13 '18 at 11:19
Vivek KalyanaranganVivek Kalyanarangan
5,0361827
answered Nov 13 '18 at 11:19
Vivek KalyanaranganVivek Kalyanarangan
5,0361827
5,0361827
Worked very good. Is there any simple way to add a " ± " next to the std ?
– Patrícia Almeida
Nov 13 '18 at 11:29
I don't think so :(. But that can be achieved easily by having another column -my_data2['Mean_STD'] = my_data2['mean'] + (2*my_data2['std']). The negative can have another column
– Vivek Kalyanarangan
Nov 13 '18 at 11:35
add a comment |
Worked very good. Is there any simple way to add a " ± " next to the std ?
– Patrícia Almeida
Nov 13 '18 at 11:29
I don't think so :(. But that can be achieved easily by having another column -my_data2['Mean_STD'] = my_data2['mean'] + (2*my_data2['std']). The negative can have another column
– Vivek Kalyanarangan
Nov 13 '18 at 11:35
Worked very good. Is there any simple way to add a " ± " next to the std ?
– Patrícia Almeida
Nov 13 '18 at 11:29
Worked very good. Is there any simple way to add a " ± " next to the std ?
– Patrícia Almeida
Nov 13 '18 at 11:29
I don't think so :(. But that can be achieved easily by having another column -
my_data2['Mean_STD'] = my_data2['mean'] + (2*my_data2['std']). The negative can have another column– Vivek Kalyanarangan
Nov 13 '18 at 11:35
I don't think so :(. But that can be achieved easily by having another column -
my_data2['Mean_STD'] = my_data2['mean'] + (2*my_data2['std']). The negative can have another column– Vivek Kalyanarangan
Nov 13 '18 at 11:35
add a comment |
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