I need to dynamically delete a line after counting the number of columns in it. For instance, if the number of columns in the line is less than X, delete the entire line. I have a text file that contains 100+ lines.
Here's what I have so far, which counts the total number of columns in each line of the text file...
Text Sample:
KGAI 2 2 40 50 50 98 75 10 35 40 15 25 15 55
KGED 3 3 15 25 20 60 60 20 50 25 15 25 20 40
KGFL 1 10 3 4 3 85 25
KGHG 15 20 40 20 20 20 20
KGKJ 20 80 25 20 25 97 50 20 60 45 30 30 25 30
KGNR 2 30 4 5 5 25 90 10 35 15 15 15 10 20
KGON 1 1 20 10 5 85 65 5 20 30 15 10 15 25
KGTB
KHEF 2 2 20 30 50 98 60 10 30 25 10 15 10 45
Code:
cat text_data | awk 'BEGIN{FS=" "};{print NF}'
Output:
15
15
8
8
15
15
15
1
15
Do it the other way round: just print those that have at least X columns:
awk -v cols=5 'NF>=cols' file
That is, set the variable cols to the value of minimum amount of cols you want the line to have. Whenever it is true, the line will be printed.
In this case it returns:
KGAI 2 2 40 50 50 98 75 10 35 40 15 25 15 55
KGED 3 3 15 25 20 60 60 20 50 25 15 25 20 40
KGFL 1 10 3 4 3 85 25
KGHG 15 20 40 20 20 20 20
KGKJ 20 80 25 20 25 97 50 20 60 45 30 30 25 30
KGNR 2 30 4 5 5 25 90 10 35 15 15 15 10 20
KGON 1 1 20 10 5 85 65 5 20 30 15 10 15 25
KHEF 2 2 20 30 50 98 60 10 30 25 10 15 10 45
Related
I have a time series as a dataframe. The first column is the week number, the second are values for that week. The first week (22) and the last week (48), are the lower and upper bounds of the time series. Some weeks are missing, for example, there is no week 27 and 28. I would like to resample this series such that there are no missing weeks. Where a week was inserted, I would like the corresponding value to be zero. This is my data:
week value
0 22 1
1 23 2
2 24 2
3 25 3
4 26 2
5 29 3
6 30 3
7 31 3
8 32 7
9 33 4
10 34 5
11 35 4
12 36 2
13 37 3
14 38 10
15 39 5
16 40 7
17 41 10
18 42 11
19 43 15
20 44 9
21 45 13
22 46 5
23 47 6
24 48 2
I am wondering if this can be achieved in Pandas without creating a loop from scratch. I have looked into pd.resample, but can't achieve the results I am looking for.
I would set week as index, reindex with fill_value option:
start, end = df['week'].agg(['min','max'])
df.set_index('week').reindex(np.arange(start, end+1), fill_value=0).reset_index()
Output (head):
week value
0 22 1
1 23 2
2 24 2
3 25 3
4 26 2
5 27 0
6 28 0
7 29 3
8 30 3
I would like to find consecutive numbers in column A and Column B in python, Column A should be ascending but Column B is descending. I am attaching an example file.
Input file
nucleotide
Pos_A
Pos_B
Connection_Pos20_Pos102
20
102
Connection_Pos19_Pos102
19
102
Connection_Pos20_Pos101
20
101
Connection_Pos18_Pos102
18
102
Connection_Pos19_Pos101
19
101
Connection_Pos20_Pos100
20
100
Connection_Pos17_Pos102
17
102
Connection_Pos18_Pos101
18
101
Connection_Pos19_Pos100
19
100
Connection_Pos20_Pos99
20
99
Connection_Pos16_Pos102
16
102
Connection_Pos17_Pos101
17
101
Connection_Pos18_Pos100
18
100
Connection_Pos19_Pos99
19
99
Connection_Pos20_Pos98
20
98
Connection_Pos15_Pos102
15
102
Connection_Pos16_Pos101
16
101
Connection_Pos17_Pos100
17
100
Connection_Pos18_Pos99
18
99
Connection_Pos19_Pos98
19
98
Connection_Pos20_Pos97
20
97
Connection_Pos14_Pos102
14
102
Connection_Pos15_Pos101
15
101
Connection_Pos16_Pos100
16
100
Output:
nucleotide
Pos_A
Pos_B
Consecutive ID
Consecutive Number (Size)
Connection_Pos20_Pos102
20
102
101
1
Connection_Pos19_Pos102
19
102
100
2
Connection_Pos20_Pos101
20
101
100
2
Connection_Pos18_Pos102
18
102
99
3
Connection_Pos19_Pos101
19
101
99
3
Connection_Pos20_Pos100
20
100
99
3
Connection_Pos17_Pos102
17
102
98
4
Connection_Pos18_Pos101
18
101
98
4
Connection_Pos19_Pos100
19
100
98
4
Connection_Pos20_Pos99
20
99
98
4
Connection_Pos16_Pos102
16
102
97
5
Connection_Pos17_Pos101
17
101
97
5
Connection_Pos18_Pos100
18
100
97
5
Connection_Pos19_Pos99
19
99
97
5
Connection_Pos20_Pos98
20
98
97
5
Connection_Pos15_Pos102
15
102
96
6
Connection_Pos16_Pos101
16
101
96
6
Connection_Pos17_Pos100
17
100
96
6
Connection_Pos18_Pos99
18
99
96
6
Connection_Pos19_Pos98
19
98
96
6
Connection_Pos20_Pos97
20
97
96
6
Connection_Pos14_Pos102
14
102
95
7
Connection_Pos15_Pos101
15
101
95
7
Connection_Pos16_Pos100
16
100
95
7
Connection_Pos17_Pos99
17
99
95
7
Connection_Pos18_Pos98
18
98
95
7
Connection_Pos19_Pos97
19
97
95
7
Connection_Pos20_Pos96
20
96
95
7
For Consecutive ID, if Pos_B's shifted difference != 1, then we want to subtract 1, so we mark those indexes as -1 with mul(-1) and cumsum them:
df['ID'] = df.Pos_B.shift().sub(df.Pos_B).ne(1).mul(-1).cumsum() + df.Pos_B[0]
For Consecutive Number, if Pos_A's shifted difference != -1, then we want to add 1, so we mark those indexes as 1 and cumsum again:
df['Number'] = df.Pos_A.shift().sub(df.Pos_A).ne(-1).mul(1).cumsum()
Result:
nucleotide Pos_A Pos_B ID Number
0 Connection_Pos20_Pos102 20 102 101 1
1 Connection_Pos19_Pos102 19 102 100 2
2 Connection_Pos20_Pos101 20 101 100 2
3 Connection_Pos18_Pos102 18 102 99 3
4 Connection_Pos19_Pos101 19 101 99 3
5 Connection_Pos20_Pos100 20 100 99 3
6 Connection_Pos17_Pos102 17 102 98 4
7 Connection_Pos18_Pos101 18 101 98 4
8 Connection_Pos19_Pos100 19 100 98 4
9 Connection_Pos20_Pos99 20 99 98 4
10 Connection_Pos16_Pos102 16 102 97 5
11 Connection_Pos17_Pos101 17 101 97 5
12 Connection_Pos18_Pos100 18 100 97 5
13 Connection_Pos19_Pos99 19 99 97 5
14 Connection_Pos20_Pos98 20 98 97 5
15 Connection_Pos15_Pos102 15 102 96 6
16 Connection_Pos16_Pos101 16 101 96 6
17 Connection_Pos17_Pos100 17 100 96 6
18 Connection_Pos18_Pos99 18 99 96 6
19 Connection_Pos19_Pos98 19 98 96 6
20 Connection_Pos20_Pos97 20 97 96 6
21 Connection_Pos14_Pos102 14 102 95 7
22 Connection_Pos15_Pos101 15 101 95 7
23 Connection_Pos16_Pos100 16 100 95 7
Do it one by one then groupby with ngroup
s1 = df.Pos_A.diff().le(0).cumsum()
s2 = df.Pos_B.diff().ge(0).cumsum()
df['out'] = df.groupby([s1,s2]).ngroup()+1
Out[452]:
0 1
1 2
2 2
3 3
4 3
5 3
6 4
7 4
8 4
9 4
10 5
11 5
12 5
13 5
14 5
15 6
16 6
17 6
18 6
19 6
20 6
21 7
22 7
23 7
24 7
25 7
26 7
27 7
dtype: int64
Trying to let the user input a number, and print a table according to the square of its size. Here's an example.
Size--> 3
0 1 2
3 4 5
6 7 8
Size--> 4
0 1 2 3
4 5 6 7
8 9 10 11
12 13 14 15
Size--> 6
0 1 2 3 4 5
6 7 8 9 10 11
12 13 14 15 16 17
18 19 20 21 22 23
24 25 26 27 28 29
30 31 32 33 34 35
Size--> 9
0 1 2 3 4 5 6 7 8
9 10 11 12 13 14 15 16 17
18 19 20 21 22 23 24 25 26
27 28 29 30 31 32 33 34 35
36 37 38 39 40 41 42 43 44
45 46 47 48 49 50 51 52 53
54 55 56 57 58 59 60 61 62
63 64 65 66 67 68 69 70 71
72 73 74 75 76 77 78 79 80
Here's is the code that i have tried.
length=int(input('Size--> '))
size=length*length
biglist=[]
for i in range(size):
biglist.append(i)
biglist = [str(i) for i in biglist]
for i in range(0, len(biglist), length):
print(' '.join(biglist[i: i+length]))
but instead here's what i got
Size--> 3
0 1 2
3 4 5
6 7 8
Size--> 4
0 1 2 3
4 5 6 7
8 9 10 11
12 13 14 15
Size--> 6
0 1 2 3 4 5
6 7 8 9 10 11
12 13 14 15 16 17
18 19 20 21 22 23
24 25 26 27 28 29
30 31 32 33 34 35
As you can see the rows are not aligned properly like the example.
What's the simplest way of presenting it in a proper alignment? Thx :)
Using .format on string with right aligning.
And strlen is the number of characters required for each number.
length = int(input('Size--> '))
size = length*length
biglist = []
for i in range(size):
biglist.append(i)
biglist = [str(i) for i in biglist]
strlen = len(str(length**2-1))+1
for i in range(0, len(biglist), length):
# print(' '.join(biglist[i: i+length]))
for x in biglist[i: i+length]:
print(f"{x:>{strlen}}", end='')
print()
I have a list of 1 column and 50 rows.
I want to divide it into 5 segments. And each segment has to become a column of a dataframe. I do not want the NAN to appear (figure2). How can I solve that?
Like this:
df = pd.DataFrame(result_list)
AWA=df[:10]
REM=df[10:20]
S1=df[20:30]
S2=df[30:40]
SWS=df[40:50]
result = pd.concat([AWA, REM, S1, S2, SWS], axis=1)
result
Figure2
You can use numpy's reshape function:
result_list = [i for i in range(50)]
pd.DataFrame(np.reshape(result_list, (10, 5), order='F'))
Out:
0 1 2 3 4
0 0 10 20 30 40
1 1 11 21 31 41
2 2 12 22 32 42
3 3 13 23 33 43
4 4 14 24 34 44
5 5 15 25 35 45
6 6 16 26 36 46
7 7 17 27 37 47
8 8 18 28 38 48
9 9 19 29 39 49
This question already has answers here:
Calculate Percentile in Excel 2010
(3 answers)
Closed 9 years ago.
I am trying to calculate how many calls came back in 95 percentile of time. Below is my Result Set. I am working with Excel 2010
Milliseconds Number
0 1702
1 15036
2 14262
3 13190
4 9137
5 5635
6 3742
7 2628
8 1899
9 1298
10 963
11 727
12 503
13 415
14 311
15 235
16 204
17 140
18 109
19 83
20 72
21 55
22 52
23 35
24 33
25 25
26 15
27 18
28 14
29 15
30 13
31 19
32 23
33 19
34 21
35 20
36 25
37 26
38 13
39 12
40 10
41 17
42 6
43 7
44 8
45 4
46 7
47 9
48 11
49 12
50 9
51 9
52 9
53 8
54 10
55 10
56 11
57 3
58 7
59 7
60 2
61 5
62 7
63 5
64 5
65 2
66 3
67 2
68 1
70 1
71 2
72 1
73 4
74 1
75 1
76 1
77 3
80 1
81 1
85 1
87 2
93 1
96 1
100 1
107 1
112 1
116 1
125 1
190 1
356 1
450 1
492 1
497 1
554 1
957 1
Just some background what does above information means-
1702 calls came back in 0 milliseconds
15036 calls came back in 1 milliseconds
14262 calls came back in 2 milliseconds
etc etc
So to calculate the 95th percentile from the above data, I am using this formula in excel 2010-
=PERCENTILE.EXC(IF(TRANSPOSE(ROW(INDIRECT("1:"&MAX(H$2:H$96))))<=H$2:H$96,A$2:A$96),0.95)
Can anyone help me whether the way I am doing in Excel 2010 is right or not?
I am getting 95th percentile as 10 by using the above scenario.
Thanks for the help.
that's essentially the same question you asked here and the formula I suggested. As per my last comments in that question - that formula should work OK as long as you use CTRL+SHIFT+ENTER correctly. I get 10 as the answer for this example using that formula.
I think you can verify manually that that is indeed the correct answer. If you have a running total in an adjacent column then you can see where the 95th percentile is reached......