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There is an input file with values
2 0.2
3 0.3
4 0.4
5 1.0
6 1.1
7 1.2
8 1.3
9 2.0
10 2.1
11 2.2
12 3.0
13 3.1
14 4.0
0 0.0
1 0.1
Which are produces by the part of the code:
BEGIN{
n=4
b=n/10
t=0
for (k=0.0; k<=n; k++){
for (j=0.0; j<=b; j+=0.1){
arr[t]=k+j
t++
}
b=b-0.1
}
for(n in arr){
printf(" %d %.1f\n ",n, arr[n] )}
The question is, how to get an output by separating the floating number.
An expected output has to be:
2 0 2
3 0 3
and so on..
Have you tried:
$ echo "0.1" | awk '{split($0,a,"."); print a[2]; print a[1]}'
This should give the desired output.
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i have a data set like this (file.txt) tab separated
1 a
3 b
5 c
7 d
8 e
12 f
17 g
20 h
When i want to add a new data
6
I want to create new column which includes remaining from new entry.
Desired output is like that:
1 a 5
3 b 3
5 c 1
6 0
7 d 1
8 e 2
12 f 6
17 g 11
20 h 14
I have tried:
awk -v new="6" '
BEGIN {
FS=OFS="\t"
{gsub(new,",\n")}
{print $0"\n"$2,$3=|new-$1|}
' file
$ awk -v new=6 '
BEGIN {
FS=OFS="\t"
}
{
if((new<$1)&&f=="") { # when greater than new values seen
print new # for the first time, act
f=1 # flag to print only once
}
print $1,$2,((v=new-$1)<0?-v:v) # abs with ternary operator
}' file
Output:
1 a 5
3 b 3
5 c 1
6
7 d 1
8 e 2
12 f 6
17 g 11
20 h 14
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Can anyone suggest me how to sort the dates in Pandas? I tried some methods but couldn't able to get desired result
Index Date Confirmed
0 01-01-2020 2
1 01-02-2020 3
2 01-03-2020 1834
3 02-01-2020 23
4 02-02-2020 3
5 02-03-2020 5
First convert column type to datetime using pd.to_datetime then sort using pd.DataFrame.sort_values and then reset index.
df.Date = pd.to_datetime(df.Date, dayfirst=True)
df = df.sort_values('Date').reset_index(drop=True)
df
Date Confirmed
0 2020-01-01 2
1 2020-01-02 23
2 2020-02-01 3
3 2020-02-02 3
4 2020-03-01 1834
5 2020-03-02 5
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I'm very new to coding so I am here seeking some help. I have the table below, and I need to do as follow:
Every time that
[All_SNPs]<[Informative_SNPs]
I need to replace negative numbers or number=0 in [All_SNPs] with the values in [Informative_SNPs]. I have tried with awk but I can't get my head around this. Thank you if you can help.
Input
ID Informative_SNPs All_SNPs
1 13 0
2 29 -27
3 15 18
4 10 0
5 11 -850
6 25 37
Output
ID Informative_SNPs All_SNPs
1 13 13
2 29 29
3 15 18
4 10 10
5 11 11
6 25 37
awk 'NR>1 && ($3<=0 || $3<$2) {$3=$2}1' file
Output:
ID Informative_SNPs All_SNPs
1 13 13
2 29 29
3 15 18
4 10 10
5 11 11
6 25 37
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I have a below dataframe and want to count the number of time the value got changed over time.
input dataframe:
class date value
A 2019-01-02 80
A 2019-02-02 80
A 2019-03-02 90
A 2019-04-02 20
A 2019-05-02 80
A 2019-06-02 Null
A 2019-06-03 70
A 2019-06-04 70
A 2019-06-05 20
B ...
output dataframe as below:
class count_of_val
A 6
reason: (80,90,20,80, null,70, 20)
IIUC, use:
(df.groupby('class', sort=False)['value']
.apply(lambda x: (x != x.shift()).sum()-1)
.reset_index(name='count_of_val'))
[out]
class count_of_val
0 A 6
You can use the diff() function of pandas-DataFrame
df['count_of_val']=np.where((df['value'].diff()).fillna(method="bfill")!=0,1,0)
df['count_of_val'].loc[df['class']=='A'].sum()
Output is:
6
Or if you like DataFrames:
df['count_of_val']=np.where((df['value'].diff()).fillna(method="bfill")!=0,1,0)
desired_class = 'A'
df_count = pd.DataFrame(columns = ['class', 'count_of_val'],
data = [[desired_class, df['count_of_val'].loc[df['class']==desired_class].sum()]])
df_count
Output:
class count_of_val
0 A 6
Compute the rolling difference
diff_kernel = np.array([1,-1])
df['change'] = df.groupby('class', as_index=False)['value'].transform(lambda s: np.array(np.convolve(s, diff_kernel ,'same'), dtype=bool))
Then you can sum it as bool:
change_sum = df.groupby('class')['change'].sum()
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How can I add (numbers on the 2nd column) using for or while loops in an awk script?
While numbers at $1 are random and in increasing order:
1 through 2 in first iteration,
1.1 through 2.1 in 2nd iteration,
1.2 through 2.2 in 3rd iteration,
1.3 through 2.3 in 4th iteration
... up to the end
That means with 0.1 increment at each iteration.
Expected output:
39 for 1st iteration
47 for 2nd iteration...
Input data:
1.0 1
1.1 3
1.2 4
1.3 3
1.4 5
1.5 7
1.6 10
2.0 6
2.1 9
2.2 2
2.3 8
2.4 0
3.0 4
3.2 5
4.0 8
4.1 6
5.0 7
6.0 6
7.0 7
8.7 9
9.8 2
Here, I multiply each $1 by 10 to avoid problems with imprecise decimal numbers.
awk -v max=$(tail -1 data | awk '{print $1*10}') '
{n = $1 * 10}
NR==1 {min = n}
{
for (i=min; i<=(max-10); i++) {
if (i <= n && n <= (i+10)) {
sum[i, i+10] += $2
}
}
}
END {
for (key in sum) {
split(key, a, SUBSEP)
printf "[%.1f,%.1f] = %d\n", a[1]/10, a[2]/10, sum[key]
}
}
' data | sort -n
output
[1.0,2.0] = 39
[1.1,2.1] = 47
[1.2,2.2] = 46
[1.3,2.3] = 50
...
[8.6,9.6] = 9
[8.7,9.7] = 9
[8.8,9.8] = 2