Python: create a lag (t-1) data structure of multiple elements
I'm having trouble creating a time lag column for my data. It works fine when I do it for a dataframe with a just a kind of elements, but it doesn't not work fine, when I have different elements. For example, my dataset looks something like this:
when using the command suggested:
data1['lag_t'] = data1['total_tax'].shift(1)
I get a result like this:
As you can see, it just displace all the 'total_tax' value one row. However, I need to do this lag for EACH ONE of the id_inf (as separate items).
My dataset is really huge, so I need to find a way to solve this issue. So I can get as a result a table like this:
python pandas dataframe shift
asked Nov 22 '18 at 22:06
PAstudilloEPAstudilloE
137111
add a comment |
I'm having trouble creating a time lag column for my data. It works fine when I do it for a dataframe with a just a kind of elements, but it doesn't not work fine, when I have different elements. For example, my dataset looks something like this:
when using the command suggested:
data1['lag_t'] = data1['total_tax'].shift(1)
I get a result like this:
As you can see, it just displace all the 'total_tax' value one row. However, I need to do this lag for EACH ONE of the id_inf (as separate items).
My dataset is really huge, so I need to find a way to solve this issue. So I can get as a result a table like this:
python pandas dataframe shift
asked Nov 22 '18 at 22:06
PAstudilloEPAstudilloE
137111
add a comment |
I'm having trouble creating a time lag column for my data. It works fine when I do it for a dataframe with a just a kind of elements, but it doesn't not work fine, when I have different elements. For example, my dataset looks something like this:
when using the command suggested:
data1['lag_t'] = data1['total_tax'].shift(1)
I get a result like this:
As you can see, it just displace all the 'total_tax' value one row. However, I need to do this lag for EACH ONE of the id_inf (as separate items).
My dataset is really huge, so I need to find a way to solve this issue. So I can get as a result a table like this:
python pandas dataframe shift
asked Nov 22 '18 at 22:06
PAstudilloEPAstudilloE
137111
I'm having trouble creating a time lag column for my data. It works fine when I do it for a dataframe with a just a kind of elements, but it doesn't not work fine, when I have different elements. For example, my dataset looks something like this:
when using the command suggested:
data1['lag_t'] = data1['total_tax'].shift(1)
I get a result like this:
As you can see, it just displace all the 'total_tax' value one row. However, I need to do this lag for EACH ONE of the id_inf (as separate items).
My dataset is really huge, so I need to find a way to solve this issue. So I can get as a result a table like this:
python pandas dataframe shift
python pandas dataframe shift
asked Nov 22 '18 at 22:06
PAstudilloEPAstudilloE
137111
asked Nov 22 '18 at 22:06
PAstudilloEPAstudilloE
137111
asked Nov 22 '18 at 22:06
PAstudilloEPAstudilloE
137111
asked Nov 22 '18 at 22:06
PAstudilloEPAstudilloE
137111
asked Nov 22 '18 at 22:06
PAstudilloEPAstudilloE
137111
137111
add a comment |
add a comment |
1 Answer
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You can groupby on index and shift
# an example with random data.
data1 = pd.DataFrame({'id': [9,9,9,54,54,54],'total_tax':[5,6,7,1,2,3]}).set_index('id')
data1['lag_t'] = data1.groupby(level=0)['total_tax'].apply(lambda x: x.shift())
print (data1)
tax lag_t
id
9 5 NaN
9 6 5.0
9 7 6.0
54 1 NaN
54 2 1.0
54 3 2.0
answered Nov 22 '18 at 22:15
AbhiAbhi
2,480320
add a comment |
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1 Answer
1
active
oldest
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1 Answer
1
active
oldest
votes
active
oldest
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active
oldest
votes
You can groupby on index and shift
# an example with random data.
data1 = pd.DataFrame({'id': [9,9,9,54,54,54],'total_tax':[5,6,7,1,2,3]}).set_index('id')
data1['lag_t'] = data1.groupby(level=0)['total_tax'].apply(lambda x: x.shift())
print (data1)
tax lag_t
id
9 5 NaN
9 6 5.0
9 7 6.0
54 1 NaN
54 2 1.0
54 3 2.0
answered Nov 22 '18 at 22:15
AbhiAbhi
2,480320
add a comment |
You can groupby on index and shift
# an example with random data.
data1 = pd.DataFrame({'id': [9,9,9,54,54,54],'total_tax':[5,6,7,1,2,3]}).set_index('id')
data1['lag_t'] = data1.groupby(level=0)['total_tax'].apply(lambda x: x.shift())
print (data1)
tax lag_t
id
9 5 NaN
9 6 5.0
9 7 6.0
54 1 NaN
54 2 1.0
54 3 2.0
answered Nov 22 '18 at 22:15
AbhiAbhi
2,480320
add a comment |
You can groupby on index and shift
# an example with random data.
data1 = pd.DataFrame({'id': [9,9,9,54,54,54],'total_tax':[5,6,7,1,2,3]}).set_index('id')
data1['lag_t'] = data1.groupby(level=0)['total_tax'].apply(lambda x: x.shift())
print (data1)
tax lag_t
id
9 5 NaN
9 6 5.0
9 7 6.0
54 1 NaN
54 2 1.0
54 3 2.0
answered Nov 22 '18 at 22:15
AbhiAbhi
2,480320
You can groupby on index and shift
# an example with random data.
data1 = pd.DataFrame({'id': [9,9,9,54,54,54],'total_tax':[5,6,7,1,2,3]}).set_index('id')
data1['lag_t'] = data1.groupby(level=0)['total_tax'].apply(lambda x: x.shift())
print (data1)
tax lag_t
id
9 5 NaN
9 6 5.0
9 7 6.0
54 1 NaN
54 2 1.0
54 3 2.0
answered Nov 22 '18 at 22:15
AbhiAbhi
2,480320
edited Nov 22 '18 at 22:21
answered Nov 22 '18 at 22:15
AbhiAbhi
2,480320
answered Nov 22 '18 at 22:15
AbhiAbhi
2,480320
answered Nov 22 '18 at 22:15
AbhiAbhi
2,480320
2,480320
add a comment |
add a comment |
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