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pythondev | help | NP | 2019-02-14T14:47:52.640500 | Chiquita | pythondev_help_Chiquita_2019-02-14T14:47:52.640500 | 1,550,155,672.6405 | 8,621 |
pythondev | help | something like that might work | 2019-02-14T14:47:55.640600 | Claudine | pythondev_help_Claudine_2019-02-14T14:47:55.640600 | 1,550,155,675.6406 | 8,622 |
pythondev | help | hmm | 2019-02-14T14:48:00.640900 | Chiquita | pythondev_help_Chiquita_2019-02-14T14:48:00.640900 | 1,550,155,680.6409 | 8,623 |
pythondev | help | i always hit enter before finishing my example | 2019-02-14T14:48:02.641100 | Claudine | pythondev_help_Claudine_2019-02-14T14:48:02.641100 | 1,550,155,682.6411 | 8,624 |
pythondev | help | in short, if you have a list of URLs you can use the `socket` library to do fun things with them | 2019-02-14T14:48:25.641600 | Claudine | pythondev_help_Claudine_2019-02-14T14:48:25.641600 | 1,550,155,705.6416 | 8,625 |
pythondev | help | let me see if understand this.. | 2019-02-14T14:48:40.642200 | Chiquita | pythondev_help_Chiquita_2019-02-14T14:48:40.642200 | 1,550,155,720.6422 | 8,626 |
pythondev | help | in my example you just loop over each url and call `socket.gethostbyname()` on each one and that function will return the IP address of that URL | 2019-02-14T14:48:50.642600 | Claudine | pythondev_help_Claudine_2019-02-14T14:48:50.642600 | 1,550,155,730.6426 | 8,627 |
pythondev | help | and then you just gotta put the domain and the IP together | 2019-02-14T14:49:06.643200 | Claudine | pythondev_help_Claudine_2019-02-14T14:49:06.643200 | 1,550,155,746.6432 | 8,628 |
pythondev | help | you made a new dictionary by iterating through each domain and getting the info.. so if I look at domain_ips it will be all the info. | 2019-02-14T14:49:24.643600 | Chiquita | pythondev_help_Chiquita_2019-02-14T14:49:24.643600 | 1,550,155,764.6436 | 8,629 |
pythondev | help | I think :wink: | 2019-02-14T14:49:35.644000 | Chiquita | pythondev_help_Chiquita_2019-02-14T14:49:35.644000 | 1,550,155,775.644 | 8,630 |
pythondev | help | yea oops i forgot to append `output` to `domain_ips` though, just added that | 2019-02-14T14:49:48.644300 | Claudine | pythondev_help_Claudine_2019-02-14T14:49:48.644300 | 1,550,155,788.6443 | 8,631 |
pythondev | help | There is probably a `dns` module in python | 2019-02-14T14:49:58.644600 | Jimmy | pythondev_help_Jimmy_2019-02-14T14:49:58.644600 | 1,550,155,798.6446 | 8,632 |
pythondev | help | because a hostname could have multiple ip | 2019-02-14T14:50:17.645500 | Jimmy | pythondev_help_Jimmy_2019-02-14T14:50:17.645500 | 1,550,155,817.6455 | 8,633 |
pythondev | help | yea probably, if you're trying to get MX records and stuff that may be better | 2019-02-14T14:50:18.645800 | Claudine | pythondev_help_Claudine_2019-02-14T14:50:18.645800 | 1,550,155,818.6458 | 8,634 |
pythondev | help | there is `dns_python` and `pywhois` for other stuff. | 2019-02-14T14:50:29.646200 | Chiquita | pythondev_help_Chiquita_2019-02-14T14:50:29.646200 | 1,550,155,829.6462 | 8,635 |
pythondev | help | and if you want MX etc, might be easier to directly start with dns | 2019-02-14T14:50:30.646400 | Jimmy | pythondev_help_Jimmy_2019-02-14T14:50:30.646400 | 1,550,155,830.6464 | 8,636 |
pythondev | help | Thanks <@Claudine> <@Jimmy> this makes sense and give me a starting point. TY! | 2019-02-14T14:51:15.647100 | Chiquita | pythondev_help_Chiquita_2019-02-14T14:51:15.647100 | 1,550,155,875.6471 | 8,637 |
pythondev | help | no problem! | 2019-02-14T14:51:33.647300 | Claudine | pythondev_help_Claudine_2019-02-14T14:51:33.647300 | 1,550,155,893.6473 | 8,638 |
pythondev | help | Hello guys | 2019-02-14T15:22:16.648100 | Misha | pythondev_help_Misha_2019-02-14T15:22:16.648100 | 1,550,157,736.6481 | 8,639 |
pythondev | help | I'm working on project to calculate the mean loan amount and the standard deviation of loan amount of the dataset imworking on | 2019-02-14T15:23:40.648900 | Misha | pythondev_help_Misha_2019-02-14T15:23:40.648900 | 1,550,157,820.6489 | 8,640 |
pythondev | help | sounds fun, have you checked out <#C0JB9ATQV|data_science> ? | 2019-02-14T15:24:18.649900 | Claudine | pythondev_help_Claudine_2019-02-14T15:24:18.649900 | 1,550,157,858.6499 | 8,641 |
pythondev | help | i'm getting errors at some point.... (I'm a newbie that just started using Python few weeks ago, less than a month after self-taught myself...lol) | 2019-02-14T15:24:59.650600 | Misha | pythondev_help_Misha_2019-02-14T15:24:59.650600 | 1,550,157,899.6506 | 8,642 |
pythondev | help | <@Claudine> Thanks... just joined them now | 2019-02-14T15:26:42.651200 | Misha | pythondev_help_Misha_2019-02-14T15:26:42.651200 | 1,550,158,002.6512 | 8,643 |
pythondev | help | :parrot: | 2019-02-14T15:27:22.651400 | Claudine | pythondev_help_Claudine_2019-02-14T15:27:22.651400 | 1,550,158,042.6514 | 8,644 |
pythondev | help | None | 2019-02-14T15:36:16.651600 | Misha | pythondev_help_Misha_2019-02-14T15:36:16.651600 | 1,550,158,576.6516 | 8,645 |
pythondev | help | As of Feb 2019, what's the best visualization package for Python? Something that's intuitive to use, aesthetically appealing and interactive? | 2019-02-14T15:36:24.651800 | Aleshia | pythondev_help_Aleshia_2019-02-14T15:36:24.651800 | 1,550,158,584.6518 | 8,646 |
pythondev | help | None | 2019-02-14T15:36:56.651900 | Misha | pythondev_help_Misha_2019-02-14T15:36:56.651900 | 1,550,158,616.6519 | 8,647 |
pythondev | help | i started getting error on the second line | 2019-02-14T15:38:14.652500 | Misha | pythondev_help_Misha_2019-02-14T15:38:14.652500 | 1,550,158,694.6525 | 8,648 |
pythondev | help | <@Aleshia> It might depend on what you are trying to visualize, personally I like matplot but <http://plot.ly|plot.ly> seems good, `best` might be subjective | 2019-02-14T15:39:10.653400 | Claudine | pythondev_help_Claudine_2019-02-14T15:39:10.653400 | 1,550,158,750.6534 | 8,649 |
pythondev | help | None | 2019-02-14T15:39:16.653500 | Misha | pythondev_help_Misha_2019-02-14T15:39:16.653500 | 1,550,158,756.6535 | 8,650 |
pythondev | help | Pls what am i missing? | 2019-02-14T15:40:46.654100 | Misha | pythondev_help_Misha_2019-02-14T15:40:46.654100 | 1,550,158,846.6541 | 8,651 |
pythondev | help | Well Matplotlib looks little old to me and was wondering if some of the newer packages would make better plots more easily. Like, In R ggplot is clear winner. | 2019-02-14T15:41:44.654900 | Aleshia | pythondev_help_Aleshia_2019-02-14T15:41:44.654900 | 1,550,158,904.6549 | 8,652 |
pythondev | help | is `mywork.groupby('TermDays').mean()` a float? | 2019-02-14T15:41:46.655100 | Claudine | pythondev_help_Claudine_2019-02-14T15:41:46.655100 | 1,550,158,906.6551 | 8,653 |
pythondev | help | The syntax of plotly looked complicated to me , though the graphs look great. | 2019-02-14T15:42:29.656300 | Aleshia | pythondev_help_Aleshia_2019-02-14T15:42:29.656300 | 1,550,158,949.6563 | 8,654 |
pythondev | help | python has a ggplot module :slightly_smiling_face: | 2019-02-14T15:42:52.657100 | Claudine | pythondev_help_Claudine_2019-02-14T15:42:52.657100 | 1,550,158,972.6571 | 8,655 |
pythondev | help | is not | 2019-02-14T15:43:36.658000 | Misha | pythondev_help_Misha_2019-02-14T15:43:36.658000 | 1,550,159,016.658 | 8,656 |
pythondev | help | it's int | 2019-02-14T15:43:43.658400 | Misha | pythondev_help_Misha_2019-02-14T15:43:43.658400 | 1,550,159,023.6584 | 8,657 |
pythondev | help | but don't know how to go about it | 2019-02-14T15:43:53.659100 | Misha | pythondev_help_Misha_2019-02-14T15:43:53.659100 | 1,550,159,033.6591 | 8,658 |
pythondev | help | Yeah, didn't see it getting used very often though...not sure why | 2019-02-14T15:43:56.659200 | Aleshia | pythondev_help_Aleshia_2019-02-14T15:43:56.659200 | 1,550,159,036.6592 | 8,659 |
pythondev | help | looks like your error says it needs a float, you may just be able to cast it to one | 2019-02-14T15:44:04.659700 | Claudine | pythondev_help_Claudine_2019-02-14T15:44:04.659700 | 1,550,159,044.6597 | 8,660 |
pythondev | help | <@Aleshia> I’d second matplotlib. The learning curve can be tough depending on what type of visualizations you’re making. The examples/docs you’ll find will sometimes be OO version and sometimes not which can be annoying. I’ve used it quite a bit for a couple years now though and the level of control you have over it i... | 2019-02-14T15:44:14.660300 | Cherish | pythondev_help_Cherish_2019-02-14T15:44:14.660300 | 1,550,159,054.6603 | 8,661 |
pythondev | help | `float(mywork.groupby('TermDays').mean())` | 2019-02-14T15:44:18.660400 | Claudine | pythondev_help_Claudine_2019-02-14T15:44:18.660400 | 1,550,159,058.6604 | 8,662 |
pythondev | help | ok | 2019-02-14T15:44:29.660700 | Misha | pythondev_help_Misha_2019-02-14T15:44:29.660700 | 1,550,159,069.6607 | 8,663 |
pythondev | help | let me give it a try | 2019-02-14T15:44:35.661000 | Misha | pythondev_help_Misha_2019-02-14T15:44:35.661000 | 1,550,159,075.661 | 8,664 |
pythondev | help | yea I made my own wrapper class for matplotlib that automates the setup & colors and stuff | 2019-02-14T15:44:46.661300 | Claudine | pythondev_help_Claudine_2019-02-14T15:44:46.661300 | 1,550,159,086.6613 | 8,665 |
pythondev | help | now it's super fast to make charts | 2019-02-14T15:44:54.661600 | Claudine | pythondev_help_Claudine_2019-02-14T15:44:54.661600 | 1,550,159,094.6616 | 8,666 |
pythondev | help | just a simple `line(x,y)` or `bar(x,y` and im good to go | 2019-02-14T15:45:15.662700 | Claudine | pythondev_help_Claudine_2019-02-14T15:45:15.662700 | 1,550,159,115.6627 | 8,667 |
pythondev | help | i cant say i've done more complicated visualization though | 2019-02-14T15:45:38.663500 | Claudine | pythondev_help_Claudine_2019-02-14T15:45:38.663500 | 1,550,159,138.6635 | 8,668 |
pythondev | help | geomapping would be my next trick, one day | 2019-02-14T15:45:47.664000 | Claudine | pythondev_help_Claudine_2019-02-14T15:45:47.664000 | 1,550,159,147.664 | 8,669 |
pythondev | help | I’m doing that right now actually. Which leads to a question of my own: is there a good resource explaining how to get such a module onto PyPi so other people within my company can grab it with pip? I want to make sure for some standardized data visualizations the output is the same regardless of who generates the plot... | 2019-02-14T15:46:55.666000 | Cherish | pythondev_help_Cherish_2019-02-14T15:46:55.666000 | 1,550,159,215.666 | 8,670 |
pythondev | help | i see matplotlib referenced the most so I assume it's sort of a defacto standard | 2019-02-14T15:47:45.667100 | Claudine | pythondev_help_Claudine_2019-02-14T15:47:45.667100 | 1,550,159,265.6671 | 8,671 |
pythondev | help | <@Claudine> I got this error `TypeError: float() takes at most 1 argument (2 given)` | 2019-02-14T15:48:40.668800 | Misha | pythondev_help_Misha_2019-02-14T15:48:40.668800 | 1,550,159,320.6688 | 8,672 |
pythondev | help | i dont have a lof of experience with them, but seaborn and bokeh are things to check out | 2019-02-14T15:50:51.669000 | Jorge | pythondev_help_Jorge_2019-02-14T15:50:51.669000 | 1,550,159,451.669 | 8,673 |
pythondev | help | huh, what's `mywork.groupby('TermDays').mean()` do if you print 'er on out? | 2019-02-14T15:51:17.669500 | Claudine | pythondev_help_Claudine_2019-02-14T15:51:17.669500 | 1,550,159,477.6695 | 8,674 |
pythondev | help | I want to calculate *the mean* loan amount in 30-day loans and what is *the standard deviation* of loan amount in 90-day loans? and my answers to the nearest two decimal places. | 2019-02-14T15:52:43.670500 | Misha | pythondev_help_Misha_2019-02-14T15:52:43.670500 | 1,550,159,563.6705 | 8,675 |
pythondev | help | i was just looking at seaborn and it looks cool, built on top of matplotlib but with modern aesthetics | 2019-02-14T15:53:00.670600 | Claudine | pythondev_help_Claudine_2019-02-14T15:53:00.670600 | 1,550,159,580.6706 | 8,676 |
pythondev | help | sorry poor wording, i meant what is output of printing `mywork.groupby('TermDays').mean()` | 2019-02-14T15:53:27.671000 | Claudine | pythondev_help_Claudine_2019-02-14T15:53:27.671000 | 1,550,159,607.671 | 8,677 |
pythondev | help | if `float()` thinks `mywork.groupby('TermDays').mean()` has two values | 2019-02-14T15:53:52.671600 | Claudine | pythondev_help_Claudine_2019-02-14T15:53:52.671600 | 1,550,159,632.6716 | 8,678 |
pythondev | help | None | 2019-02-14T15:55:52.671700 | Misha | pythondev_help_Misha_2019-02-14T15:55:52.671700 | 1,550,159,752.6717 | 8,679 |
pythondev | help | oh yea i see, i may have lead you astray, `round(float(mywork.groupby('TermDays').mean(), 2)).iloc[1,1]` maybe | 2019-02-14T15:57:51.672700 | Claudine | pythondev_help_Claudine_2019-02-14T15:57:51.672700 | 1,550,159,871.6727 | 8,680 |
pythondev | help | well | 2019-02-14T15:58:07.673200 | Claudine | pythondev_help_Claudine_2019-02-14T15:58:07.673200 | 1,550,159,887.6732 | 8,681 |
pythondev | help | no | 2019-02-14T15:58:09.673400 | Claudine | pythondev_help_Claudine_2019-02-14T15:58:09.673400 | 1,550,159,889.6734 | 8,682 |
pythondev | help | `mywork.groupby('TermDays').mean()` is that whole table, if you want to round one of the values you have to call `round` on a single value | 2019-02-14T15:58:40.674500 | Claudine | pythondev_help_Claudine_2019-02-14T15:58:40.674500 | 1,550,159,920.6745 | 8,683 |
pythondev | help | if `.iloc[1,1]` is intended to grab that one value you'll have to move that inside your `round()` call | 2019-02-14T15:59:29.676300 | Claudine | pythondev_help_Claudine_2019-02-14T15:59:29.676300 | 1,550,159,969.6763 | 8,684 |
pythondev | help | Ok | 2019-02-14T15:59:41.676900 | Misha | pythondev_help_Misha_2019-02-14T15:59:41.676900 | 1,550,159,981.6769 | 8,685 |
pythondev | help | `round(float(mywork.groupby('TermDays').mean().iloc[1,1]), 2)` maybe | 2019-02-14T15:59:55.677300 | Claudine | pythondev_help_Claudine_2019-02-14T15:59:55.677300 | 1,550,159,995.6773 | 8,686 |
pythondev | help | Ok | 2019-02-14T16:00:02.677600 | Misha | pythondev_help_Misha_2019-02-14T16:00:02.677600 | 1,550,160,002.6776 | 8,687 |
pythondev | help | Oh, also touching on your point about interactive plots <@Aleshia> -- I recently found the `mpld3` library. This is super convenient for sharing visualizations (2D support only) that are still interactive. You can save a `matplotlib` `Figure` to an HTML file using `mpld3.save_html(figure, 'filename.html')`, that others... | 2019-02-14T16:00:33.678300 | Cherish | pythondev_help_Cherish_2019-02-14T16:00:33.678300 | 1,550,160,033.6783 | 8,688 |
pythondev | help | oh by the way it'd probably be best to keep this in one channel, i see we're splitting this across here and <#C0JB9ATQV|data_science> . No need to repeat question in multiple channels | 2019-02-14T16:00:47.678700 | Claudine | pythondev_help_Claudine_2019-02-14T16:00:47.678700 | 1,550,160,047.6787 | 8,689 |
pythondev | help | Ok <@Claudine>....Thanks it worked... YIPPEE | 2019-02-14T16:02:32.679300 | Misha | pythondev_help_Misha_2019-02-14T16:02:32.679300 | 1,550,160,152.6793 | 8,690 |
pythondev | help | nice | 2019-02-14T16:02:56.679500 | Claudine | pythondev_help_Claudine_2019-02-14T16:02:56.679500 | 1,550,160,176.6795 | 8,691 |
pythondev | help | hit me with a taco :pacman: | 2019-02-14T16:03:04.679900 | Claudine | pythondev_help_Claudine_2019-02-14T16:03:04.679900 | 1,550,160,184.6799 | 8,692 |
pythondev | help | <@Claudine> :pacman: | 2019-02-14T16:03:36.680700 | Misha | pythondev_help_Misha_2019-02-14T16:03:36.680700 | 1,550,160,216.6807 | 8,693 |
pythondev | help | :taco: <@Claudine> like this <@Misha> | 2019-02-14T16:03:48.681100 | Cherish | pythondev_help_Cherish_2019-02-14T16:03:48.681100 | 1,550,160,228.6811 | 8,694 |
pythondev | help | lol | 2019-02-14T16:03:48.681200 | Misha | pythondev_help_Misha_2019-02-14T16:03:48.681200 | 1,550,160,228.6812 | 8,695 |
pythondev | help | ha thanks | 2019-02-14T16:03:54.681400 | Claudine | pythondev_help_Claudine_2019-02-14T16:03:54.681400 | 1,550,160,234.6814 | 8,696 |
pythondev | help | yea just @ my name and then add a taco emoji | 2019-02-14T16:04:02.681700 | Claudine | pythondev_help_Claudine_2019-02-14T16:04:02.681700 | 1,550,160,242.6817 | 8,697 |
pythondev | help | i do it all for the taco | 2019-02-14T16:04:10.681900 | Claudine | pythondev_help_Claudine_2019-02-14T16:04:10.681900 | 1,550,160,250.6819 | 8,698 |
pythondev | help | :pacman:<@Claudine> | 2019-02-14T16:05:54.682300 | Misha | pythondev_help_Misha_2019-02-14T16:05:54.682300 | 1,550,160,354.6823 | 8,699 |
pythondev | help | novice me :disappointed: | 2019-02-14T16:06:38.682600 | Misha | pythondev_help_Misha_2019-02-14T16:06:38.682600 | 1,550,160,398.6826 | 8,700 |
pythondev | help | lol no no :taco: not :pacman: | 2019-02-14T16:06:48.682900 | Claudine | pythondev_help_Claudine_2019-02-14T16:06:48.682900 | 1,550,160,408.6829 | 8,701 |
pythondev | help | dont worry haha | 2019-02-14T16:07:06.683600 | Claudine | pythondev_help_Claudine_2019-02-14T16:07:06.683600 | 1,550,160,426.6836 | 8,702 |
pythondev | help | it's just internet points for fun | 2019-02-14T16:07:14.683800 | Claudine | pythondev_help_Claudine_2019-02-14T16:07:14.683800 | 1,550,160,434.6838 | 8,703 |
pythondev | help | <@Claudine> :taco: | 2019-02-14T16:07:27.684100 | Ashley | pythondev_help_Ashley_2019-02-14T16:07:27.684100 | 1,550,160,447.6841 | 8,704 |
pythondev | help | or you can also react with :taco: on the person's message | 2019-02-14T16:07:56.684600 | Ashley | pythondev_help_Ashley_2019-02-14T16:07:56.684600 | 1,550,160,476.6846 | 8,705 |
pythondev | help | welp, nevermind on the reacting bit. could have sworn I read that somewhere haha | 2019-02-14T16:08:40.685300 | Ashley | pythondev_help_Ashley_2019-02-14T16:08:40.685300 | 1,550,160,520.6853 | 8,706 |
pythondev | help | <@Claudine> :taco: | 2019-02-14T16:09:30.686200 | Misha | pythondev_help_Misha_2019-02-14T16:09:30.686200 | 1,550,160,570.6862 | 8,707 |
pythondev | help | <@Misha> we give tacos as rewards for help or general awesomeness, and those who receive them consume them with :pacman: (usually as a reaction on the message that gave the taco). And you have 5 tacos to give out every day | 2019-02-14T16:10:11.686900 | Ashley | pythondev_help_Ashley_2019-02-14T16:10:11.686900 | 1,550,160,611.6869 | 8,708 |
pythondev | help | exactly | 2019-02-14T16:10:19.687100 | Ashley | pythondev_help_Ashley_2019-02-14T16:10:19.687100 | 1,550,160,619.6871 | 8,709 |
pythondev | help | Pls how do i go about the *the standard deviation* of loan amount in 90-day loans? and my answers to the nearest two decimal places. | 2019-02-14T16:10:50.687400 | Misha | pythondev_help_Misha_2019-02-14T16:10:50.687400 | 1,550,160,650.6874 | 8,710 |
pythondev | help | that is more of a statistics question, than a python one. I would hit up khan academy. they probably have some good video tutorials on how to calculate that | 2019-02-14T16:11:48.688200 | Ashley | pythondev_help_Ashley_2019-02-14T16:11:48.688200 | 1,550,160,708.6882 | 8,711 |
pythondev | help | Thanks <@Ashley> | 2019-02-14T16:12:33.688800 | Misha | pythondev_help_Misha_2019-02-14T16:12:33.688800 | 1,550,160,753.6888 | 8,712 |
pythondev | help | :+1: | 2019-02-14T16:12:41.689000 | Ashley | pythondev_help_Ashley_2019-02-14T16:12:41.689000 | 1,550,160,761.689 | 8,713 |
pythondev | help | pls i still need to solve this | 2019-02-14T16:12:56.689400 | Misha | pythondev_help_Misha_2019-02-14T16:12:56.689400 | 1,550,160,776.6894 | 8,714 |
pythondev | help | is this a homework question? | 2019-02-14T16:13:32.689700 | Hiroko | pythondev_help_Hiroko_2019-02-14T16:13:32.689700 | 1,550,160,812.6897 | 8,715 |
pythondev | help | yes | 2019-02-14T16:13:39.689900 | Misha | pythondev_help_Misha_2019-02-14T16:13:39.689900 | 1,550,160,819.6899 | 8,716 |
pythondev | help | heh | 2019-02-14T16:13:44.690300 | Claudine | pythondev_help_Claudine_2019-02-14T16:13:44.690300 | 1,550,160,824.6903 | 8,717 |
pythondev | help | ok, we’ll be happy to help you when you show something you’ve tried | 2019-02-14T16:13:54.690700 | Hiroko | pythondev_help_Hiroko_2019-02-14T16:13:54.690700 | 1,550,160,834.6907 | 8,718 |
pythondev | help | but actually doing the work, no | 2019-02-14T16:14:01.691100 | Hiroko | pythondev_help_Hiroko_2019-02-14T16:14:01.691100 | 1,550,160,841.6911 | 8,719 |
pythondev | help | I have work 1-3 by myself | 2019-02-14T16:14:17.691700 | Misha | pythondev_help_Misha_2019-02-14T16:14:17.691700 | 1,550,160,857.6917 | 8,720 |
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