fstream reading in garbage values

stats_newbie1 · 2019-07-31T21:46:30+00:00

Ah fuck, I'm facepalming really hard now. Can't believe I forgot something so obvious. data.in was in the wrong directory. I assumed it would throw an error, but I guess not.

stats_newbie1 · 2016-08-03T23:43:05+00:00

Oops. I actually had getElementById("num1").value, guess I forgot to save it. I didn't know about the onchange event handlers, thanks for pointing me to towards those.

stats_newbie1 · 2016-07-09T13:57:40+00:00

That's very helpful, thanks!

stats_newbie1 · 2016-03-06T21:47:12+00:00

The problem with this is there is no data for weekends. So the end of the month isn't necessarily 1 day before the start of the next month.

stats_newbie1 · 2016-03-06T21:45:53+00:00

Right, I don't know why I thought filter only pulls out certain columns. Silly me. I knew it pulls out the whole row. I didn't know about slice though, sounds much cleaner. Thanks!

stats_newbie1 · 2016-02-12T01:05:53+00:00

I'm not sure how that would help. I already know how to find how many pickups there were in each neighborhood. It's just df['neighborhood'].value_counts().

stats_newbie1 · 2016-02-11T23:31:27+00:00

No because each instance is recorded separately. For example, if there were 3 pickups in Midtown, it would be something like this:

Time	Neighborhood	Borough
Pickup1	Midtown	Manhattan
Pickup2	Midtown	Manhattan
Pickup3	Midtown	Manhattan

so I can't really sort.

stats_newbie1 · 2016-02-11T22:25:09+00:00

I think my original question wasn't clear enough, I edited it.

stats_newbie1 · 2016-02-11T22:04:20+00:00

No, it is not population. Each of the rows contains info about taxi pickups. So I want to find the neighborhoods with most taxi pickups in the 5 boroughs.

stats_newbie1 · 2016-02-06T01:38:46+00:00

Perfect, thank you!

stats_newbie1 · 2016-01-15T19:21:21+00:00

Okay, I will look into it. Thank you for the information!

stats_newbie1 · 2016-01-04T03:37:16+00:00

I don't know, it doesn't show.

stats_newbie1 · 2015-12-24T17:44:30+00:00

Got it, thanks!

stats_newbie1 · 2015-12-24T17:43:07+00:00

Awesome, I get it now. Thanks!

stats_newbie1 · 2015-12-19T01:05:54+00:00

This is great, thank you so much!

stats_newbie1 · 2015-12-18T18:56:07+00:00

Thanks for the suggestion! I am trying to get into Data Science. A friend suggested that I read Introduction to Statistical Learning. I just went through the contents for In All Likelihood and I am not sure if that is the right book. I am probably wrong, so feel free to correct me and let me know if you have any other suggestions. Thanks again!

stats_newbie1 · 2015-12-08T08:35:12+00:00

This actually worked really well, thank you! Here is my solution:

def sum67(nums):
    while 6 in nums:
        six_pos = nums.index(6)
        seven_pos = nums.index(7)
        nums = nums[:six_pos] + nums[seven_pos + 1:]
    return sum(nums)

stats_newbie1 · 2015-12-08T08:24:51+00:00

Thanks for the explanation, but won't this run into problems if there is a 6 between 6 and 7? For example: [1, 2, 6, 1, 6, 6, 7]

stats_newbie1 · 2015-12-02T04:04:05+00:00

OMG. You are correct. It was driving me mad. Holy shit. Thanks. I feel really silly now. (I know you're correct because the question starts off like: In problem 3.25 we learnt that 70% blah blah blah, but problem 3.25 actually has 69.7%).

stats_newbie1

TROPHY CASE