Add column based on different conditions for different columns | python pandas
I have a dataframe with 4 columns:
c1 c2 c3 GName
0.221445 0.300534 5.689 KDD
0.001000 0.969000 15.140 ACC
1.000000 0.094000 -0.245 QETF
And dataframe called file of one column:
GName
Abd
kkoew
KDD
pwqh
ACC
dsewf
I need to add new column call label that based on checking the scores in c1, c2 and c3 and GName
So, if the majority of the 3 scores agreed on their conditions (2 out of the 3 or all the 3) and the value of GName exist in the dataframe file; the label = 1, otherwise the label = 0
The conditions of c1 should be > 0.95
c2 should be > 0.50
c3 should be > 15
The output will be like this:
c1 c2 c3 GName label
0.221445 0.300534 5.689 KDD 0 (because 0 out of 3 and KDD in file)
0.001000 0.969000 15.140 ACC 1 (because 2 out of 3 and ACC in file)
1.000000 0.94060 -0.245 QETF 0 (because 2 out of 3 but QETF not in file)
I'm struggling with those different conditions, any help please?
python pandas dataframe
asked Nov 20 at 23:45
Sara Wasl
817
add a comment |
I have a dataframe with 4 columns:
c1 c2 c3 GName
0.221445 0.300534 5.689 KDD
0.001000 0.969000 15.140 ACC
1.000000 0.094000 -0.245 QETF
And dataframe called file of one column:
GName
Abd
kkoew
KDD
pwqh
ACC
dsewf
I need to add new column call label that based on checking the scores in c1, c2 and c3 and GName
So, if the majority of the 3 scores agreed on their conditions (2 out of the 3 or all the 3) and the value of GName exist in the dataframe file; the label = 1, otherwise the label = 0
The conditions of c1 should be > 0.95
c2 should be > 0.50
c3 should be > 15
The output will be like this:
c1 c2 c3 GName label
0.221445 0.300534 5.689 KDD 0 (because 0 out of 3 and KDD in file)
0.001000 0.969000 15.140 ACC 1 (because 2 out of 3 and ACC in file)
1.000000 0.94060 -0.245 QETF 0 (because 2 out of 3 but QETF not in file)
I'm struggling with those different conditions, any help please?
python pandas dataframe
asked Nov 20 at 23:45
Sara Wasl
817
add a comment |
I have a dataframe with 4 columns:
c1 c2 c3 GName
0.221445 0.300534 5.689 KDD
0.001000 0.969000 15.140 ACC
1.000000 0.094000 -0.245 QETF
And dataframe called file of one column:
GName
Abd
kkoew
KDD
pwqh
ACC
dsewf
I need to add new column call label that based on checking the scores in c1, c2 and c3 and GName
So, if the majority of the 3 scores agreed on their conditions (2 out of the 3 or all the 3) and the value of GName exist in the dataframe file; the label = 1, otherwise the label = 0
The conditions of c1 should be > 0.95
c2 should be > 0.50
c3 should be > 15
The output will be like this:
c1 c2 c3 GName label
0.221445 0.300534 5.689 KDD 0 (because 0 out of 3 and KDD in file)
0.001000 0.969000 15.140 ACC 1 (because 2 out of 3 and ACC in file)
1.000000 0.94060 -0.245 QETF 0 (because 2 out of 3 but QETF not in file)
I'm struggling with those different conditions, any help please?
python pandas dataframe
asked Nov 20 at 23:45
Sara Wasl
817
I have a dataframe with 4 columns:
c1 c2 c3 GName
0.221445 0.300534 5.689 KDD
0.001000 0.969000 15.140 ACC
1.000000 0.094000 -0.245 QETF
And dataframe called file of one column:
GName
Abd
kkoew
KDD
pwqh
ACC
dsewf
I need to add new column call label that based on checking the scores in c1, c2 and c3 and GName
So, if the majority of the 3 scores agreed on their conditions (2 out of the 3 or all the 3) and the value of GName exist in the dataframe file; the label = 1, otherwise the label = 0
The conditions of c1 should be > 0.95
c2 should be > 0.50
c3 should be > 15
The output will be like this:
c1 c2 c3 GName label
0.221445 0.300534 5.689 KDD 0 (because 0 out of 3 and KDD in file)
0.001000 0.969000 15.140 ACC 1 (because 2 out of 3 and ACC in file)
1.000000 0.94060 -0.245 QETF 0 (because 2 out of 3 but QETF not in file)
I'm struggling with those different conditions, any help please?
python pandas dataframe
python pandas dataframe
asked Nov 20 at 23:45
Sara Wasl
817
asked Nov 20 at 23:45
Sara Wasl
817
asked Nov 20 at 23:45
Sara Wasl
817
asked Nov 20 at 23:45
Sara Wasl
817
asked Nov 20 at 23:45
Sara Wasl
817
817
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
The way I would do it is this:
import pandas as pd
df = pd.DataFrame({'c1':[0.221445, 0.001000, 1.000000],
'c2':[0.300534, 0.969000, 0.094000],
'c3':[5.689, 15.140, -0.245],
'GName':['KDD', 'ACC', 'QETF']})
file = pd.DataFrame({'GName':['KDD', 'ACC']})
conditions = (df['c1'] > 0.95).astype(int) + (df['c2'] > 0.5).astype(int) + (df['c3'] > 15).astype(int)
conditions = (conditions >= 2) & (df['GName'].isin(file['GName']))
df['label'] = 0
df.loc[conditions, 'label'] = 1
>>> df
c1 c2 c3 GName label
0 0.221445 0.300534 5.689 KDD 0
1 0.001000 0.969000 15.140 ACC 1
2 1.000000 0.094000 -0.245 QETF 0
It would be nice if you could include code to generate your dataframe in your question, as well.
answered Nov 21 at 0:12
CJ59
1,2171214
add a comment |
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1 Answer
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active
oldest
votes
1 Answer
1
active
oldest
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active
oldest
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active
oldest
votes
The way I would do it is this:
import pandas as pd
df = pd.DataFrame({'c1':[0.221445, 0.001000, 1.000000],
'c2':[0.300534, 0.969000, 0.094000],
'c3':[5.689, 15.140, -0.245],
'GName':['KDD', 'ACC', 'QETF']})
file = pd.DataFrame({'GName':['KDD', 'ACC']})
conditions = (df['c1'] > 0.95).astype(int) + (df['c2'] > 0.5).astype(int) + (df['c3'] > 15).astype(int)
conditions = (conditions >= 2) & (df['GName'].isin(file['GName']))
df['label'] = 0
df.loc[conditions, 'label'] = 1
>>> df
c1 c2 c3 GName label
0 0.221445 0.300534 5.689 KDD 0
1 0.001000 0.969000 15.140 ACC 1
2 1.000000 0.094000 -0.245 QETF 0
It would be nice if you could include code to generate your dataframe in your question, as well.
answered Nov 21 at 0:12
CJ59
1,2171214
add a comment |
The way I would do it is this:
import pandas as pd
df = pd.DataFrame({'c1':[0.221445, 0.001000, 1.000000],
'c2':[0.300534, 0.969000, 0.094000],
'c3':[5.689, 15.140, -0.245],
'GName':['KDD', 'ACC', 'QETF']})
file = pd.DataFrame({'GName':['KDD', 'ACC']})
conditions = (df['c1'] > 0.95).astype(int) + (df['c2'] > 0.5).astype(int) + (df['c3'] > 15).astype(int)
conditions = (conditions >= 2) & (df['GName'].isin(file['GName']))
df['label'] = 0
df.loc[conditions, 'label'] = 1
>>> df
c1 c2 c3 GName label
0 0.221445 0.300534 5.689 KDD 0
1 0.001000 0.969000 15.140 ACC 1
2 1.000000 0.094000 -0.245 QETF 0
It would be nice if you could include code to generate your dataframe in your question, as well.
answered Nov 21 at 0:12
CJ59
1,2171214
add a comment |
The way I would do it is this:
import pandas as pd
df = pd.DataFrame({'c1':[0.221445, 0.001000, 1.000000],
'c2':[0.300534, 0.969000, 0.094000],
'c3':[5.689, 15.140, -0.245],
'GName':['KDD', 'ACC', 'QETF']})
file = pd.DataFrame({'GName':['KDD', 'ACC']})
conditions = (df['c1'] > 0.95).astype(int) + (df['c2'] > 0.5).astype(int) + (df['c3'] > 15).astype(int)
conditions = (conditions >= 2) & (df['GName'].isin(file['GName']))
df['label'] = 0
df.loc[conditions, 'label'] = 1
>>> df
c1 c2 c3 GName label
0 0.221445 0.300534 5.689 KDD 0
1 0.001000 0.969000 15.140 ACC 1
2 1.000000 0.094000 -0.245 QETF 0
It would be nice if you could include code to generate your dataframe in your question, as well.
answered Nov 21 at 0:12
CJ59
1,2171214
The way I would do it is this:
import pandas as pd
df = pd.DataFrame({'c1':[0.221445, 0.001000, 1.000000],
'c2':[0.300534, 0.969000, 0.094000],
'c3':[5.689, 15.140, -0.245],
'GName':['KDD', 'ACC', 'QETF']})
file = pd.DataFrame({'GName':['KDD', 'ACC']})
conditions = (df['c1'] > 0.95).astype(int) + (df['c2'] > 0.5).astype(int) + (df['c3'] > 15).astype(int)
conditions = (conditions >= 2) & (df['GName'].isin(file['GName']))
df['label'] = 0
df.loc[conditions, 'label'] = 1
>>> df
c1 c2 c3 GName label
0 0.221445 0.300534 5.689 KDD 0
1 0.001000 0.969000 15.140 ACC 1
2 1.000000 0.094000 -0.245 QETF 0
It would be nice if you could include code to generate your dataframe in your question, as well.
answered Nov 21 at 0:12
CJ59
1,2171214
answered Nov 21 at 0:12
CJ59
1,2171214
answered Nov 21 at 0:12
CJ59
1,2171214
answered Nov 21 at 0:12
CJ59
1,2171214
1,2171214
add a comment |
add a comment |
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