Dynamically allocate values
2
I have a dataframe with columns like Name, cash, date. In the dataframe b I want to fill the xnpv values dynamically
def xnpv(rate, values, dates):
if rate <= -1.0:
return float('inf')
d0 = dates.min() # or min(dates)
return sum([ vi / (1.0 + rate)**((di - d0).days / 365.0) for vi, di in zip(values, dates)])
for cl in range(2,ctr_max+1,1):
grouped = b.groupby('Name')
b["XNPV"+str(cl)]=grouped.apply(lambda x: xnpv(0.1,
x[str(cl)+"cash"], x['Value Date']))
With the above code I want to dynamically fill the values like xnpv1, xnpv2,xnpv3 with the values 1cash, 2cash, 3cash. The result is coming to be NaN with the above code , but it do generate column xnpv1, xnpv2, xnpv3 but with NaN values. How can i solve this?
python pandas
asked Nov 28 '18 at 6:18
Chetan PChetan P
7010
add a comment |
2
I have a dataframe with columns like Name, cash, date. In the dataframe b I want to fill the xnpv values dynamically
def xnpv(rate, values, dates):
if rate <= -1.0:
return float('inf')
d0 = dates.min() # or min(dates)
return sum([ vi / (1.0 + rate)**((di - d0).days / 365.0) for vi, di in zip(values, dates)])
for cl in range(2,ctr_max+1,1):
grouped = b.groupby('Name')
b["XNPV"+str(cl)]=grouped.apply(lambda x: xnpv(0.1,
x[str(cl)+"cash"], x['Value Date']))
With the above code I want to dynamically fill the values like xnpv1, xnpv2,xnpv3 with the values 1cash, 2cash, 3cash. The result is coming to be NaN with the above code , but it do generate column xnpv1, xnpv2, xnpv3 but with NaN values. How can i solve this?
python pandas
asked Nov 28 '18 at 6:18
Chetan PChetan P
7010
2
Can you add some sample data?
– jezrael
Nov 28 '18 at 6:28
add a comment |
2
2
2
I have a dataframe with columns like Name, cash, date. In the dataframe b I want to fill the xnpv values dynamically
def xnpv(rate, values, dates):
if rate <= -1.0:
return float('inf')
d0 = dates.min() # or min(dates)
return sum([ vi / (1.0 + rate)**((di - d0).days / 365.0) for vi, di in zip(values, dates)])
for cl in range(2,ctr_max+1,1):
grouped = b.groupby('Name')
b["XNPV"+str(cl)]=grouped.apply(lambda x: xnpv(0.1,
x[str(cl)+"cash"], x['Value Date']))
With the above code I want to dynamically fill the values like xnpv1, xnpv2,xnpv3 with the values 1cash, 2cash, 3cash. The result is coming to be NaN with the above code , but it do generate column xnpv1, xnpv2, xnpv3 but with NaN values. How can i solve this?
python pandas
asked Nov 28 '18 at 6:18
Chetan PChetan P
7010
I have a dataframe with columns like Name, cash, date. In the dataframe b I want to fill the xnpv values dynamically
def xnpv(rate, values, dates):
if rate <= -1.0:
return float('inf')
d0 = dates.min() # or min(dates)
return sum([ vi / (1.0 + rate)**((di - d0).days / 365.0) for vi, di in zip(values, dates)])
for cl in range(2,ctr_max+1,1):
grouped = b.groupby('Name')
b["XNPV"+str(cl)]=grouped.apply(lambda x: xnpv(0.1,
x[str(cl)+"cash"], x['Value Date']))
With the above code I want to dynamically fill the values like xnpv1, xnpv2,xnpv3 with the values 1cash, 2cash, 3cash. The result is coming to be NaN with the above code , but it do generate column xnpv1, xnpv2, xnpv3 but with NaN values. How can i solve this?
python pandas
python pandas
asked Nov 28 '18 at 6:18
Chetan PChetan P
7010
asked Nov 28 '18 at 6:18
Chetan PChetan P
7010
edited Nov 28 '18 at 6:31
Chetan P
asked Nov 28 '18 at 6:18
Chetan PChetan P
7010
asked Nov 28 '18 at 6:18
Chetan PChetan P
7010
asked Nov 28 '18 at 6:18
Chetan PChetan P
7010
7010
2
Can you add some sample data?
– jezrael
Nov 28 '18 at 6:28
add a comment |
2
Can you add some sample data?
– jezrael
Nov 28 '18 at 6:28
2
2
Can you add some sample data?
– jezrael
Nov 28 '18 at 6:28
Can you add some sample data?
– jezrael
Nov 28 '18 at 6:28
add a comment |
1 Answer
1
active
oldest
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0
I believe you need custom function:
b = pd.DataFrame({"Name":['a','a','a','a','b','b','c','c'],
"2cash":[1,1,3,4,1,2,4,5],
"3cash":[4,5,3,2,4,5,7,9],
"4cash":[1,1,2,4,5,1,3,4],
"Value Date":['2017-01-01','2017-02-01','2017-03-01','2017-04-01',
'2017-01-01','2017-02-01','2017-03-01','2017-04-01']
})
b["Value Date"] = pd.to_datetime(b["Value Date"])
def xnpv(rate, values, dates):
if rate <= -1.0:
return float('inf')
d0 = dates.min() # or min(dates)
return sum([ vi / (1.0 + rate)**((di - d0).days/ 365.0) for vi, di in zip(values, dates)])
ctr_max = 4
def f(x):
for cl in range(2,ctr_max+1,1):
x["XNPV{}".format(cl)] = xnpv(0.1, x["{}cash".format(cl)], x['Value Date'])
return x
df = b.groupby('Name').apply(f)
print (df)
Name 2cash 3cash 4cash Value Date XNPV2 XNPV3 XNPV4
0 a 1 4 1 2017-01-01 8.853165 13.867370 7.868453
1 a 1 5 1 2017-02-01 8.853165 13.867370 7.868453
2 a 3 3 2 2017-03-01 8.853165 13.867370 7.868453
3 a 4 2 4 2017-04-01 8.853165 13.867370 7.868453
4 b 1 4 5 2017-01-01 2.983876 8.959689 5.991938
5 b 2 5 1 2017-02-01 2.983876 8.959689 5.991938
6 c 4 7 3 2017-03-01 8.959689 15.927441 6.967751
7 c 5 9 4 2017-04-01 8.959689 15.927441 6.967751
answered Nov 28 '18 at 6:30
jezraeljezrael
347k25304379
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
I believe you need custom function:
b = pd.DataFrame({"Name":['a','a','a','a','b','b','c','c'],
"2cash":[1,1,3,4,1,2,4,5],
"3cash":[4,5,3,2,4,5,7,9],
"4cash":[1,1,2,4,5,1,3,4],
"Value Date":['2017-01-01','2017-02-01','2017-03-01','2017-04-01',
'2017-01-01','2017-02-01','2017-03-01','2017-04-01']
})
b["Value Date"] = pd.to_datetime(b["Value Date"])
def xnpv(rate, values, dates):
if rate <= -1.0:
return float('inf')
d0 = dates.min() # or min(dates)
return sum([ vi / (1.0 + rate)**((di - d0).days/ 365.0) for vi, di in zip(values, dates)])
ctr_max = 4
def f(x):
for cl in range(2,ctr_max+1,1):
x["XNPV{}".format(cl)] = xnpv(0.1, x["{}cash".format(cl)], x['Value Date'])
return x
df = b.groupby('Name').apply(f)
print (df)
Name 2cash 3cash 4cash Value Date XNPV2 XNPV3 XNPV4
0 a 1 4 1 2017-01-01 8.853165 13.867370 7.868453
1 a 1 5 1 2017-02-01 8.853165 13.867370 7.868453
2 a 3 3 2 2017-03-01 8.853165 13.867370 7.868453
3 a 4 2 4 2017-04-01 8.853165 13.867370 7.868453
4 b 1 4 5 2017-01-01 2.983876 8.959689 5.991938
5 b 2 5 1 2017-02-01 2.983876 8.959689 5.991938
6 c 4 7 3 2017-03-01 8.959689 15.927441 6.967751
7 c 5 9 4 2017-04-01 8.959689 15.927441 6.967751
answered Nov 28 '18 at 6:30
jezraeljezrael
347k25304379
add a comment |
0
I believe you need custom function:
b = pd.DataFrame({"Name":['a','a','a','a','b','b','c','c'],
"2cash":[1,1,3,4,1,2,4,5],
"3cash":[4,5,3,2,4,5,7,9],
"4cash":[1,1,2,4,5,1,3,4],
"Value Date":['2017-01-01','2017-02-01','2017-03-01','2017-04-01',
'2017-01-01','2017-02-01','2017-03-01','2017-04-01']
})
b["Value Date"] = pd.to_datetime(b["Value Date"])
def xnpv(rate, values, dates):
if rate <= -1.0:
return float('inf')
d0 = dates.min() # or min(dates)
return sum([ vi / (1.0 + rate)**((di - d0).days/ 365.0) for vi, di in zip(values, dates)])
ctr_max = 4
def f(x):
for cl in range(2,ctr_max+1,1):
x["XNPV{}".format(cl)] = xnpv(0.1, x["{}cash".format(cl)], x['Value Date'])
return x
df = b.groupby('Name').apply(f)
print (df)
Name 2cash 3cash 4cash Value Date XNPV2 XNPV3 XNPV4
0 a 1 4 1 2017-01-01 8.853165 13.867370 7.868453
1 a 1 5 1 2017-02-01 8.853165 13.867370 7.868453
2 a 3 3 2 2017-03-01 8.853165 13.867370 7.868453
3 a 4 2 4 2017-04-01 8.853165 13.867370 7.868453
4 b 1 4 5 2017-01-01 2.983876 8.959689 5.991938
5 b 2 5 1 2017-02-01 2.983876 8.959689 5.991938
6 c 4 7 3 2017-03-01 8.959689 15.927441 6.967751
7 c 5 9 4 2017-04-01 8.959689 15.927441 6.967751
answered Nov 28 '18 at 6:30
jezraeljezrael
347k25304379
add a comment |
0
0
0
I believe you need custom function:
b = pd.DataFrame({"Name":['a','a','a','a','b','b','c','c'],
"2cash":[1,1,3,4,1,2,4,5],
"3cash":[4,5,3,2,4,5,7,9],
"4cash":[1,1,2,4,5,1,3,4],
"Value Date":['2017-01-01','2017-02-01','2017-03-01','2017-04-01',
'2017-01-01','2017-02-01','2017-03-01','2017-04-01']
})
b["Value Date"] = pd.to_datetime(b["Value Date"])
def xnpv(rate, values, dates):
if rate <= -1.0:
return float('inf')
d0 = dates.min() # or min(dates)
return sum([ vi / (1.0 + rate)**((di - d0).days/ 365.0) for vi, di in zip(values, dates)])
ctr_max = 4
def f(x):
for cl in range(2,ctr_max+1,1):
x["XNPV{}".format(cl)] = xnpv(0.1, x["{}cash".format(cl)], x['Value Date'])
return x
df = b.groupby('Name').apply(f)
print (df)
Name 2cash 3cash 4cash Value Date XNPV2 XNPV3 XNPV4
0 a 1 4 1 2017-01-01 8.853165 13.867370 7.868453
1 a 1 5 1 2017-02-01 8.853165 13.867370 7.868453
2 a 3 3 2 2017-03-01 8.853165 13.867370 7.868453
3 a 4 2 4 2017-04-01 8.853165 13.867370 7.868453
4 b 1 4 5 2017-01-01 2.983876 8.959689 5.991938
5 b 2 5 1 2017-02-01 2.983876 8.959689 5.991938
6 c 4 7 3 2017-03-01 8.959689 15.927441 6.967751
7 c 5 9 4 2017-04-01 8.959689 15.927441 6.967751
answered Nov 28 '18 at 6:30
jezraeljezrael
347k25304379
I believe you need custom function:
b = pd.DataFrame({"Name":['a','a','a','a','b','b','c','c'],
"2cash":[1,1,3,4,1,2,4,5],
"3cash":[4,5,3,2,4,5,7,9],
"4cash":[1,1,2,4,5,1,3,4],
"Value Date":['2017-01-01','2017-02-01','2017-03-01','2017-04-01',
'2017-01-01','2017-02-01','2017-03-01','2017-04-01']
})
b["Value Date"] = pd.to_datetime(b["Value Date"])
def xnpv(rate, values, dates):
if rate <= -1.0:
return float('inf')
d0 = dates.min() # or min(dates)
return sum([ vi / (1.0 + rate)**((di - d0).days/ 365.0) for vi, di in zip(values, dates)])
ctr_max = 4
def f(x):
for cl in range(2,ctr_max+1,1):
x["XNPV{}".format(cl)] = xnpv(0.1, x["{}cash".format(cl)], x['Value Date'])
return x
df = b.groupby('Name').apply(f)
print (df)
Name 2cash 3cash 4cash Value Date XNPV2 XNPV3 XNPV4
0 a 1 4 1 2017-01-01 8.853165 13.867370 7.868453
1 a 1 5 1 2017-02-01 8.853165 13.867370 7.868453
2 a 3 3 2 2017-03-01 8.853165 13.867370 7.868453
3 a 4 2 4 2017-04-01 8.853165 13.867370 7.868453
4 b 1 4 5 2017-01-01 2.983876 8.959689 5.991938
5 b 2 5 1 2017-02-01 2.983876 8.959689 5.991938
6 c 4 7 3 2017-03-01 8.959689 15.927441 6.967751
7 c 5 9 4 2017-04-01 8.959689 15.927441 6.967751
answered Nov 28 '18 at 6:30
jezraeljezrael
347k25304379
edited Nov 28 '18 at 6:51
answered Nov 28 '18 at 6:30
jezraeljezrael
347k25304379
answered Nov 28 '18 at 6:30
jezraeljezrael
347k25304379
answered Nov 28 '18 at 6:30
jezraeljezrael
347k25304379
347k25304379
add a comment |
add a comment |
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2
Can you add some sample data?
– jezrael
Nov 28 '18 at 6:28