Summing specific columns based on a mapping
0
I have a series which contains a mapping:
serm = pd.Series(
data={'ARD1': 53, 'BUL1': 37,
'BUL2': 37, 'BSR1': 49, 'BTR1': 53, 'CR1': 53,
'CRR1': 53, 'CRE3': 53,'TAB1': 52, 'NEP1': 42, 'HAL1': 42})
which maps the asset id (the index) to an area (the value).
I have the the following dataframe where serm index is the columns names.
data=pd.DataFrame(data={'ARD1': {0: 4.0, 1: 2.0, 2: 2.0, 3: 3.0, 4: 2.0},
'BUL1': {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
'BUL2': {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
'BSR1': {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
'BTR1': {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
'CR1': {0: 15.0, 1: 13.0, 2: 13.0, 3: 11.0, 4: 13.0},
'CRR1': {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
'CRE3': {0: 8.0, 1: 10.0, 2: 9.0, 3: 10.0, 4: 11.0},
'TAB1': {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
'NEP1': {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
'HAL1': {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0}})
I would like to sum the columns of data that fall in the same area, according to the mapping of serm. How can I achieve this (the more pandanoic the better)
python-3.x pandas pandas-groupby
asked 17 hours ago
AliAli
846
add a comment |
0
I have a series which contains a mapping:
serm = pd.Series(
data={'ARD1': 53, 'BUL1': 37,
'BUL2': 37, 'BSR1': 49, 'BTR1': 53, 'CR1': 53,
'CRR1': 53, 'CRE3': 53,'TAB1': 52, 'NEP1': 42, 'HAL1': 42})
which maps the asset id (the index) to an area (the value).
I have the the following dataframe where serm index is the columns names.
data=pd.DataFrame(data={'ARD1': {0: 4.0, 1: 2.0, 2: 2.0, 3: 3.0, 4: 2.0},
'BUL1': {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
'BUL2': {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
'BSR1': {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
'BTR1': {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
'CR1': {0: 15.0, 1: 13.0, 2: 13.0, 3: 11.0, 4: 13.0},
'CRR1': {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
'CRE3': {0: 8.0, 1: 10.0, 2: 9.0, 3: 10.0, 4: 11.0},
'TAB1': {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
'NEP1': {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
'HAL1': {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0}})
I would like to sum the columns of data that fall in the same area, according to the mapping of serm. How can I achieve this (the more pandanoic the better)
python-3.x pandas pandas-groupby
asked 17 hours ago
AliAli
846
add a comment |
0
0
0
I have a series which contains a mapping:
serm = pd.Series(
data={'ARD1': 53, 'BUL1': 37,
'BUL2': 37, 'BSR1': 49, 'BTR1': 53, 'CR1': 53,
'CRR1': 53, 'CRE3': 53,'TAB1': 52, 'NEP1': 42, 'HAL1': 42})
which maps the asset id (the index) to an area (the value).
I have the the following dataframe where serm index is the columns names.
data=pd.DataFrame(data={'ARD1': {0: 4.0, 1: 2.0, 2: 2.0, 3: 3.0, 4: 2.0},
'BUL1': {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
'BUL2': {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
'BSR1': {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
'BTR1': {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
'CR1': {0: 15.0, 1: 13.0, 2: 13.0, 3: 11.0, 4: 13.0},
'CRR1': {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
'CRE3': {0: 8.0, 1: 10.0, 2: 9.0, 3: 10.0, 4: 11.0},
'TAB1': {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
'NEP1': {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
'HAL1': {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0}})
I would like to sum the columns of data that fall in the same area, according to the mapping of serm. How can I achieve this (the more pandanoic the better)
python-3.x pandas pandas-groupby
asked 17 hours ago
AliAli
846
I have a series which contains a mapping:
serm = pd.Series(
data={'ARD1': 53, 'BUL1': 37,
'BUL2': 37, 'BSR1': 49, 'BTR1': 53, 'CR1': 53,
'CRR1': 53, 'CRE3': 53,'TAB1': 52, 'NEP1': 42, 'HAL1': 42})
which maps the asset id (the index) to an area (the value).
I have the the following dataframe where serm index is the columns names.
data=pd.DataFrame(data={'ARD1': {0: 4.0, 1: 2.0, 2: 2.0, 3: 3.0, 4: 2.0},
'BUL1': {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
'BUL2': {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
'BSR1': {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
'BTR1': {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
'CR1': {0: 15.0, 1: 13.0, 2: 13.0, 3: 11.0, 4: 13.0},
'CRR1': {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
'CRE3': {0: 8.0, 1: 10.0, 2: 9.0, 3: 10.0, 4: 11.0},
'TAB1': {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
'NEP1': {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
'HAL1': {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0}})
I would like to sum the columns of data that fall in the same area, according to the mapping of serm. How can I achieve this (the more pandanoic the better)
python-3.x pandas pandas-groupby
python-3.x pandas pandas-groupby
asked 17 hours ago
AliAli
846
asked 17 hours ago
AliAli
846
asked 17 hours ago
AliAli
846
asked 17 hours ago
AliAli
846
asked 17 hours ago
AliAli
846
846
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
1
Use Index.map with groupby per columns and aggregate sum:
df = data.groupby(data.columns.map(serm.get), axis=1).sum()
print (df)
37 42 49 52 53
0 0.0 0.0 0.0 0.0 27.0
1 0.0 0.0 0.0 0.0 25.0
2 0.0 0.0 0.0 0.0 24.0
3 0.0 0.0 0.0 0.0 24.0
4 0.0 0.0 0.0 0.0 26.0
Or assign columns back and use sum:
data.columns = data.columns.map(serm.get)
df = data.sum(level=0, axis=1)
answered 17 hours ago
jezraeljezrael
326k23268344
Thank you for your prompt answer. Would you mind explaining whatgetdoes? I couldn't find a good doc on it. Thank you very much in advance.
– Ali
8 hours ago
@Ali You can check this, but if use last version of pandas get should be omit.
– jezrael
6 hours ago
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
1
Use Index.map with groupby per columns and aggregate sum:
df = data.groupby(data.columns.map(serm.get), axis=1).sum()
print (df)
37 42 49 52 53
0 0.0 0.0 0.0 0.0 27.0
1 0.0 0.0 0.0 0.0 25.0
2 0.0 0.0 0.0 0.0 24.0
3 0.0 0.0 0.0 0.0 24.0
4 0.0 0.0 0.0 0.0 26.0
Or assign columns back and use sum:
data.columns = data.columns.map(serm.get)
df = data.sum(level=0, axis=1)
answered 17 hours ago
jezraeljezrael
326k23268344
Thank you for your prompt answer. Would you mind explaining whatgetdoes? I couldn't find a good doc on it. Thank you very much in advance.
– Ali
8 hours ago
@Ali You can check this, but if use last version of pandas get should be omit.
– jezrael
6 hours ago
add a comment |
1
Use Index.map with groupby per columns and aggregate sum:
df = data.groupby(data.columns.map(serm.get), axis=1).sum()
print (df)
37 42 49 52 53
0 0.0 0.0 0.0 0.0 27.0
1 0.0 0.0 0.0 0.0 25.0
2 0.0 0.0 0.0 0.0 24.0
3 0.0 0.0 0.0 0.0 24.0
4 0.0 0.0 0.0 0.0 26.0
Or assign columns back and use sum:
data.columns = data.columns.map(serm.get)
df = data.sum(level=0, axis=1)
answered 17 hours ago
jezraeljezrael
326k23268344
Thank you for your prompt answer. Would you mind explaining whatgetdoes? I couldn't find a good doc on it. Thank you very much in advance.
– Ali
8 hours ago
@Ali You can check this, but if use last version of pandas get should be omit.
– jezrael
6 hours ago
add a comment |
1
1
1
Use Index.map with groupby per columns and aggregate sum:
df = data.groupby(data.columns.map(serm.get), axis=1).sum()
print (df)
37 42 49 52 53
0 0.0 0.0 0.0 0.0 27.0
1 0.0 0.0 0.0 0.0 25.0
2 0.0 0.0 0.0 0.0 24.0
3 0.0 0.0 0.0 0.0 24.0
4 0.0 0.0 0.0 0.0 26.0
Or assign columns back and use sum:
data.columns = data.columns.map(serm.get)
df = data.sum(level=0, axis=1)
answered 17 hours ago
jezraeljezrael
326k23268344
Use Index.map with groupby per columns and aggregate sum:
df = data.groupby(data.columns.map(serm.get), axis=1).sum()
print (df)
37 42 49 52 53
0 0.0 0.0 0.0 0.0 27.0
1 0.0 0.0 0.0 0.0 25.0
2 0.0 0.0 0.0 0.0 24.0
3 0.0 0.0 0.0 0.0 24.0
4 0.0 0.0 0.0 0.0 26.0
Or assign columns back and use sum:
data.columns = data.columns.map(serm.get)
df = data.sum(level=0, axis=1)
answered 17 hours ago
jezraeljezrael
326k23268344
answered 17 hours ago
jezraeljezrael
326k23268344
answered 17 hours ago
jezraeljezrael
326k23268344
answered 17 hours ago
jezraeljezrael
326k23268344
326k23268344
Thank you for your prompt answer. Would you mind explaining whatgetdoes? I couldn't find a good doc on it. Thank you very much in advance.
– Ali
8 hours ago
@Ali You can check this, but if use last version of pandas get should be omit.
– jezrael
6 hours ago
add a comment |
Thank you for your prompt answer. Would you mind explaining whatgetdoes? I couldn't find a good doc on it. Thank you very much in advance.
– Ali
8 hours ago
@Ali You can check this, but if use last version of pandas get should be omit.
– jezrael
6 hours ago
Thank you for your prompt answer. Would you mind explaining what
get does? I couldn't find a good doc on it. Thank you very much in advance.– Ali
8 hours ago
Thank you for your prompt answer. Would you mind explaining what
get does? I couldn't find a good doc on it. Thank you very much in advance.– Ali
8 hours ago
@Ali You can check this, but if use last version of pandas get should be omit.
– jezrael
6 hours ago
@Ali You can check this, but if use last version of pandas get should be omit.
– jezrael
6 hours ago
add a comment |
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