I want to print by moving numbers from 1 to 22 using range (1,23)
Can you help me?
number = 1
numf = driver.find_elements_by_xpath('/html/body/div[1]/div/div/div[5]/div[2]/main/section/ul/li[3]/div[2]/div/div[2]/ul/li[%d]/button' %number)
for i in numf:
print(i.text)```
You can use either of the following solutions:
Using %s:
number = 1
numf = driver.find_elements_by_xpath("/html/body/div[1]/div/div/div[5]/div[2]/main/section/ul/li[3]/div[2]/div/div[2]/ul/li[%s]/button"% str(number))
Using format():
number = 1
numf = driver.find_elements_by_xpath("/html/body/div[1]/div/div/div[5]/div[2]/main/section/ul/li[3]/div[2]/div/div[2]/ul/li[{}]/button".format(str(number)))
Related
My issue is, i don't know how to use the output of a function properly. The output contains multiple lines (j = column , i = testresult)
I want to use the output for some other rules in other functions. (eg. if (i) testresult > 5 then something)
I have a function with two loops. The function goes threw every column and test something. This works fine.
def test():
scope = range(10)
scope2 = range(len(df1.columns))
for (j) in scope2:
for (i) in scope:
if df1.iloc[:,[j]].shift(i).loc[selected_week].item() > df1.iloc[:,[j]].shift(i+1).loc[selected_week].item():
i + 1
else:
print(j,i)
break
Output:
test()
1 0
2 3
3 3
4 1
5 0
6 6
7 0
8 1
9 0
10 1
11 1
12 0
13 0
14 0
15 0
I tried to convert it to list, dataframe etc. However, i miss something here.
What is the best way for that?
Thank you!
A fix of your code would be:
def test():
out = []
scope = range(10)
scope2 = range(len(df1.columns))
for j in scope2:
for i in scope:
if df1.iloc[:,[j]].shift(i).loc[selected_week].item() <= df1.iloc[:,[j]].shift(i+1).loc[selected_week].item():
out.append([i, j])
return pd.DataFrame(out)
out = test()
But you probably don't want to use loops as it's slow, please clarify what is your input with a minimal reproducible example and what you are trying to achieve (expected output and logic), we can probably make it a vectorized solution.
My code uses a column called booking status that is 1 for yes and 0 for no (there are multiple other columns that information will be pulled from dependant on the booking status) - there are lots more no than yes so I would like to take a sample with all the yes and the same amount of no.
When I use
samp = rslt_df.sample(n=298, random_state=1, weights='bookingstatus')
I get the error:
ValueError: Fewer non-zero entries in p than size
Is there a way to do this sample this way?
If our entire dataset looks like this:
print(df)
c1 c2
0 1 1
1 0 2
2 0 3
3 0 4
4 0 5
5 0 6
6 0 7
7 1 8
8 0 9
9 0 10
We may decide to sample from it using the DataFrame.sample function. By default, this function will sample without replacement. Meaning, you'll receive an error by specifying a number of observations larger than the number of observations in your initial dataset:
df.sample(20)
ValueError: Cannot take a larger sample than population when 'replace=False'
In your situation, the ValueError comes from the weights parameter:
df.sample(3,weights='c1')
ValueError: Fewer non-zero entries in p than size
To paraphrase the DataFrame.sample docs, using the c1 column as our weights parameter implies that rows with a larger value in the c1 column are more likely to be sampled. Specifically, the sample function will not pick values from this column that are zero. We can fix this error using either one of the following methods.
Method 1: Set the replace parameter to be true:
m1 = df.sample(3,weights='c1', replace=True)
print(m1)
c1 c2
0 1 1
7 1 8
0 1 1
Method 2: Make sure the n parameter is equal to or less than the number of 1s in the c1 column:
m2 = df.sample(2,weights='c1')
print(m2)
c1 c2
7 1 8
0 1 1
If you decide to use this method, you won't really be sampling. You're really just filtering out any rows where the value of c1 is 0.
I was able to this in the end, here is how I did it:
bookingstatus_count = df.bookingstatus.value_counts()
print('Class 0:', bookingstatus_count[0])
print('Class 1:', bookingstatus_count[1])
print('Proportion:', round(bookingstatus_count[0] / bookingstatus_count[1], 2), ': 1')
# Class count
count_class_0, count_class_1 = df.bookingstatus.value_counts()
# Divide by class
df_class_0 = df[df['bookingstatus'] == 0]
df_class_0_under = df_class_0.sample(count_class_1)
df_test_under = pd.concat([f_class_0_under, df_class_1], axis=0)
df_class_1 = df[df['bookingstatus'] == 1]
based on this https://www.kaggle.com/rafjaa/resampling-strategies-for-imbalanced-datasets
Thanks everyone
I have a basic problem.
If we are to input:
6
1 2 3 4 10 11
The desired outcome should be:
31
Here is the coding, you must simply finish the function and it should work:
#!/bin/python3
import sys
def simpleArraySum(n, ar):
# Complete this function
n = int(input().strip())
ar = list(map(int, input().strip().split(' ')))
result = simpleArraySum(n, ar)
print(result)
We want 1+2+3+4+10+11 = 31
Use sum().
def simpleArraySum(n, ar):
return sum(ar[:n])
The [:n] truncates the array to n elements.
I am using SQL Server and I have an int that is 4 to 5 characters long.
I have a report that cast the first 3 digits as the location and last 1 to 2 digits as a cause.
So this is how they look
5142 = 514 = paint line 2 = paint to thin:
50528 = 505 = machining 28 = oblong hole:
SELECT [Suspect]
,left(Suspect,3) as SuspectOP
,Right(Suspect,2) as SuspectID
This query will return
5142 = SuspectOP = 514 SuspectID = 42
50528 = SuspectOP = 505 SuspectID = 28
So what i want is to read everything after the first 3 digits of the int.
Some of the things I have tried are as follows:
Select Cast(Suspect as Varchar(5)),
Substring(Suspect,3,2)
And
Select Suspect % 514 as SuspectID
Which does work as long as the first 3 digits are always 514 which in my case aren't.
You could use a conditional operators based on the length like this:
SELECT
[Suspect]
, SuspectOP = LEFT(Suspect,3)
, SuspectID = CASE
WHEN LEN(Suspect) = 5 THEN RIGHT(Suspect,2)
ELSE RIGHT(Suspect, 1)
END
Mind you, it's not ideal, you should really keep the values separate if your use case is like the one mentioned.
I currently have two timelines (timeline1 and timeline2), with matching data (data1 and data2). Timelines almost, but not quite match (about 90% of common values).
I'm trying to find values from data1 and data2 that correspond to identical timestamps (ignoring all other values)
My first trivial implementation is as follows (and is obviously terribly slow, given that my timelines contain thousands of values). Any ideas on how to improve this? I'm sure there is a smart way of doing this while avoiding the for loop, or the find operation...
% We expect the common timeline to contain
% 0 1 4 5 9
timeline1 = [0 1 4 5 7 8 9 10];
timeline2 = [0 1 2 4 5 6 9];
% Some bogus data
data1 = timeline1*10;
data2 = timeline2*20;
reconstructedData1 = data1;
reconstructedData2 = zeros(size(data1));
currentSearchPosition = 1;
for t = 1:length(timeline1)
% We only look beyond the previous matching location, to speed up find
matchingIndex = find(timeline2(currentSearchPosition:end) == timeline1(t), 1);
if isempty(matchingIndex)
reconstructedData1(t) = nan;
reconstructedData2(t) = nan;
else
reconstructedData2(t) = data2(matchingIndex+currentSearchPosition-1);
currentSearchPosition = currentSearchPosition+matchingIndex;
end
end
% Remove values from data1 for which no match was found in data2
reconstructedData1(isnan(reconstructedData1)) = [];
reconstructedData2(isnan(reconstructedData2)) = [];
You can use Matlab's intersect function:
c = intersect(A, B)
Couldn't you just call INTERSECT?
commonTimeline = intersect(timeline1,timeline2);
commonTimeline =
0 1 4 5 9
You need to use the indexes returned from intersect.
[~ ia ib] = intersect(timeline1, timeline2);
recondata1 = data1(ia);
recondata2 = data2(ib);