I want to create an input file for one code. It looks like this
SITE FREQ DATA TYPE DATUM ERROR
1 1 1 2.01562 0.217000E-01
1 1 2 44.8114 2.86600
1 1 5 2.02486 0.217000E-01
1 1 6 44.0423 2.86600
1 2 1 2.03421 0.217000E-01
1 2 2 53.5181 2.86600
1 2 5 2.01103 0.217000E-01
1 2 6 43.6452 2.86600
1 3 1 1.88711 0.217000E-01
1 3 2 51.5582 2.86600
1 3 5 2.00536 0.217000E-01
1 3 6 43.4296 2.86600
1 4 1 1.85939 0.217000E-01
1 4 2 49.8675 2.86600
1 4 5 2.04246 0.217000E-01
1 4 6 41.5948 2.86600
1 5 1 1.86721 0.217000E-01
1 5 2 42.6603 2.86600
1 5 5 2.02059 0.217000E-01
1 5 6 44.6032 2.86600
1 6 1 1.90233 0.217000E-01
1 6 2 34.9367 2.86600
1 6 5 2.02904 0.217000E-01
1 6 6 45.5312 2.86600
2 1 1 2.02998 0.217000E-01
2 1 2 46.3565 2.86600
2 1 5 2.07089 0.217000E-01
2 1 6 47.8481 2.86600
2 2 1 1.94406 0.217000E-01
2 2 2 52.9107 2.86600
2 2 5 1.94073 0.217000E-01
2 2 6 47.7353 2.86600
2 3 1 1.77228 0.217000E-01
2 3 2 53.3664 2.86600
2 3 5 1.93717 0.217000E-01
I have thought of something like this
do i=1,74
do j=1,4
write(50,)num1,s1(i),dt(j),v1(i),er1
end do
end do
But the data type takes value 1,2,5,6 not 1 to 4. How to solve this?
Use one loop as you normally would (drop the loop over j) and print corresponding dt values (1,2,5,6) as
merge( dt(mod(i,4)), dt(size(dt)), mod(i,4)/=0 )
This is Fortran's ternary, producing either dt(mod(i,4)) if mod(i,4)/=0 or dt(size(dt)) when mod returns zero. The merge is F95. This does run mod every time through. Alternatively you can make a 74-long vector with repeating 1,2,5,6 in which case you have an extra vector.
I'm not sure that I fully understand the question but unless I'm wide of the mark I'd frame a solution along these lines:
integer, dimension(4) :: aux = [1,2,5,6]
...
do i=1,74
do j=1,4
write(50,)num1,s1(i),dt(aux(j)),v1(i),er1
end do
end do
Related
can next_permutation avoid the duplication as what I want is to skip 2th and 4th as only change in first 2 character is important to me.
do {
//Do something
} while(next_permutation(s.begin(), s.end()));
this will get 4! = 24 solution, while I only wanted 4P2 = 12 solution.
The above coding will give me.
1 2 3 4
1 2 4 3
1 3 2 4
1 3 4 2
1 4 2 3
1 4 3 2
2 1 3 4
2 1 4 3
2 3 1 4
2 3 4 1
2 4 1 3
2 4 3 1
3 1 2 4
3 1 4 2
3 2 1 4
3 2 4 1
3 4 1 2
3 4 2 1
4 1 2 3
4 1 3 2
4 2 1 3
4 2 3 1
4 3 1 2
4 3 2 1
While actually I only want
1 2
1 3
1 4
2 1
2 3
2 4
3 1
3 2
3 4
4 1
4 2
4 3
do {
// Do something with the first two entries of `s`.
std::prev_permutation(s.begin()+2, s.end());
} while(std::next_permutation(s.begin(), s.end()));
This will essentially skip all permutations of the last two items, so it won't iterate all permutations unnecessarily and be relatively efficient even if you change the length of the vector or the number of items you are interested in (the magic number 2 in the code above).
I'm looking to create a variable based on this data sample:
Video Subject Pre_post Pre_Post_ID
1 1 0 1
1 2 0 1
1 2 0 1
1 3 0 1
1 3 0 1
2 1 1 1
2 1 1 1
2 2 1 1
2 2 1 1
2 3 1 1
4 1 0 2
4 2 0 2
4 2 0 2
4 3 0 2
4 3 0 2
5 1 1 2
5 1 1 2
5 2 1 2
5 2 1 2
5 3 1 2
The goal of the variable will be to create an ID that links the pre_post variable to the subject on the condition that the pre_post_id is the same:
Video Subject Pre_post Pre_Post_ID Subject_P_P_ID
1 1 0 1 1
1 2 0 1 2
1 2 0 1 2
1 3 0 1 3
1 3 0 1 3
2 1 1 1 1
2 1 1 1 1
2 2 1 1 2
2 2 1 1 2
2 3 1 1 3
4 1 0 2 4
4 2 0 2 5
4 2 0 2 5
4 3 0 2 6
4 3 0 2 6
5 1 1 2 4
5 1 1 2 4
5 2 1 2 5
5 2 1 2 5
5 3 1 2 6
Thank you in advance for the help!
You will want to track the pairs (<pre_post_id>,<subject>) as a composite key and increment the Subject_P_P_ID every time a new pair (or key) is encountered.
To simplify the discussion, call the two items in the pair item1 and item2
Here are two ways:
Sort by item1 item2, step through BY item1 item2 and track pair count using logic based on an automatic first. variable -- pair_id + (first.item2), or
Track pairs as keys of a hash and assign new id as <hash>.num_items + 1 when key lookup fails.
Sort + Data Step + Revert Sort
proc sort data=have out=have_sorted;
by item1 item2;
run;
data have_sequenced;
set have_sorted;
by item1 item2;
item1_item2_pair_id + (first.item2);
run;
proc sort data=have_sequenced out=want;
by video subject pre_post pre_post_id item1_item2_pair_id;
run;
Hash
data want;
set have;
if _n_=1 then do;
declare hash lookup();
lookup.defineKeys('item1', 'item2');
lookup.defineData('item1_item2_pair_id');
lookup.defineDone();
end;
if lookup.find() ne 0 then do;
item1_item2_pair_id = lookup.num_items+1;
lookup.add();
end;
end;
I have a dataframe that looks like:
subgroup value
0 1 0
1 1 1
2 1 1
3 1 0
4 2 0
5 2 0
6 2 0
7 3 0
8 3 1
9 3 0
10 3 0
I need to add a column that add 1 whenever there is at least one value different than 0 in the different subgroups. Please, note that if the value 1 is repeated more than once in the same subgroup, it doesn't affect the count.
The result should be:
subgroup value count
0 1 0 1
1 1 1 1
2 1 1 1
3 1 1 1
4 2 0 1
5 2 0 1
6 2 0 1
7 3 0 2
8 3 1 2
9 3 0 2
10 3 0 2
Thank you in advance for your help!
Using shift with -1 and 1 and cumsum the result
mask=(df.value.ne(df.value.shift()))&(df.value.ne(df.value.shift(-1)))
mask.cumsum()
Out[18]:
0 1
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 2
9 2
10 2
Name: value, dtype: int32
Using merge and groupby
df.merge(df.groupby('subgroup').value.sum().gt(0).cumsum().reset_index(name='out'))
subgroup value out
0 1 0 1
1 1 1 1
2 1 1 1
3 1 0 1
4 2 0 1
5 2 0 1
6 2 0 1
7 3 0 2
8 3 1 2
9 3 0 2
10 3 0 2
I have dataframe look like this:
a b c d e
0 0 1 2 1 0
1 3 0 0 4 3
2 3 4 0 4 2
3 4 1 0 4 3
4 2 1 3 4 3
5 3 2 0 3 3
6 2 1 1 1 0
7 1 1 0 3 3
8 3 3 3 3 4
9 2 3 4 2 2
I do following command:
df.groupby('A').sum()
And i get:
b c d e
a
0 1 2 1 0
1 1 0 3 3
2 5 8 7 5
3 9 3 14 12
4 1 0 4 3
And after that I want to access
labels = df['A']
But I have an error that there are no such column.
So does pandas have some syntax to get something like this?
a b c d e
0 0 1 2 1 0
1 1 1 0 3 3
2 2 5 8 7 5
3 3 9 3 14 12
4 4 1 0 4 3
I need to sum all values of columns b, c, d, e to column a with the relevant index
You can just access the index with df.index, and add it back into your dataframe as another column.
grouped_df = df.groupby('A').sum()
grouped_df['A'] = grouped_df.index
grouped_df.sum(axis=1)
Alternatively, groupby has 'as_index' option to keep the column 'A'
groupby('A', as_index=False)
or, after groupby, you can use reset_index to put the column 'A' back.
I would like to create a dummy variable that will look at the variable "count" and label the rows as 1 starting from the last row of each id. As an example ID 1 has count of 3 and the last three rows of this id will have such pattern: 0,0,1,1,1 Similarly, ID 4 which has a count of 1 will have 0,0,0,1. The IDs have different number of rows. The variable "wish" shows what I want to obtain as a final output.
input byte id count wish str9 date
1 3 0 22sep2006
1 3 0 23sep2006
1 3 1 24sep2006
1 3 1 25sep2006
1 3 1 26sep2006
2 4 1 22mar2004
2 4 1 23mar2004
2 4 1 24mar2004
2 4 1 25mar2004
3 2 0 28jan2003
3 2 0 29jan2003
3 2 1 30jan2003
3 2 1 31jan2003
4 1 0 02dec1993
4 1 0 03dec1993
4 1 0 04dec1993
4 1 1 05dec1993
5 1 0 08feb2005
5 1 0 09feb2005
5 1 0 10feb2005
5 1 1 11feb2005
6 3 0 15jan1999
6 3 0 16jan1999
6 3 1 17jan1999
6 3 1 18jan1999
6 3 1 19jan1999
end
For future questions, you should provide your failed attempts. This shows that you have done your part, namely, research your problem.
One way is:
clear
set more off
*----- example data -----
input ///
byte id count wish str9 date
1 3 0 22sep2006
1 3 0 23sep2006
1 3 1 24sep2006
1 3 1 25sep2006
1 3 1 26sep2006
2 4 1 22mar2004
2 4 1 23mar2004
2 4 1 24mar2004
2 4 1 25mar2004
3 2 0 28jan2003
3 2 0 29jan2003
3 2 1 30jan2003
3 2 1 31jan2003
4 1 0 02dec1993
4 1 0 03dec1993
4 1 0 04dec1993
4 1 1 05dec1993
5 1 0 08feb2005
5 1 0 09feb2005
5 1 0 10feb2005
5 1 1 11feb2005
6 3 0 15jan1999
6 3 0 16jan1999
6 3 1 17jan1999
6 3 1 18jan1999
6 3 1 19jan1999
end
list, sepby(id)
*----- what you want -----
bysort id: gen wish2 = _n > (_N - count)
list, sepby(id)
I assume you already sorted your date variable within ids.
One way to accomplish this would be to use within-group row numbers using 'bysort'-type logic:
***Create variable of within-group row numbers.
bysort id: gen obsnum = _n
***Calculate total number of rows within each group.
by id: egen max_obsnum = max(obsnum)
***Subtract the count variable from the group row count.
***This is the number of rows where we want the dummy to equal zero.
gen max_obsnum_less_count = max_obsnum - count
***Create the dummy to equal one when the row number is
***greater than this last variable.
gen dummy = (obsnum > max_obsnum_less_count)
***Clean up.
drop obsnum max_obsnum max_obsnum_less_count