Welcome back everyone. So we are going to start our first talk of the day. So it's called consuming government data with Python and D3. I guess that's that would be interesting because data visualization Nowadays is quite booming. So to give an introduction of our speaker. So he's Pratap Vardhan So Pratap is a data scientist at grammar.com a data analytics and visualization company And he has done his BTEC from NIT Bhopal. So before we start the talk just again a few quick announcements Again, I'm reiterating the same facts. Don't create Wi-Fi hotspots. Please So and plus you can check out the open space availability and if you want to propose a talk or something you can do that Make sure you ask for the feedback forms from the volunteers who are there so that we can have the feedback for this talk and Also, the registration for lightning talks are going on outside. So there's a board. You can just clip in your Topic whatever you want to speak during the lightning talks And or get in touch with one of the volunteers. So they'll help you out. So first talk over to you Pratap With Python and D3 and in Python Do I mean in all of all the Python libraries, but no in this talk mostly I'll focus on how do we use pandas This is one of the data crunching libraries and also D3 based Frameworks, which lets you do some interactive visualizations And all this talk is form of a ipython notebook, which is there on github and I might have just tweeted right now and So if you just want to run through the examples with yourself on your system You can just download the notebook and just run it across all the data sets of this talk are up on github And you can just use that now Let me start off with What is it that that is very particularly interesting about about government and data and Rather than me saying it I would just use Calvin. What would he think of why would he need government in a sense? This is one of the reasons which even I particularly think why it's pretty important as in Not that I'm doing any good for the government or with the data But rather I want to have a powerful say or or heck. I just want to run the things Of all the all the things that you do with the government. Which are the couple of things that you think are Pretty much connected to you. Can I just have three four words which connects when you talk about government data? Let's see if we capture that today Just shout out any shout Okay, okay We have sensors Okay, we have a lot of Okay Let's see if we try to answer some of these things But before even getting into the formation of government and all before anyone gets into form the government The first thing they do is launch a political party for the political party to survive the next thing you would need is some money Which is probably like you want to do a some a Funding session right so you want to collect some donations Unfortunately in India the data of political donations is not pretty much open Although we do have some parties or some sources of information where we can do some analysis Interestingly, there was this one particular party which sort of published their donations on their own website However, there was a tricky part the donations were not form of you know a data set rather it was part of a ASP website where you have to scrape the data by clicking next and so on and which is little tricky again because dealing with ASP websites especially for scraping involves some sort of Understanding because they don't store the query parameters that well any guesses which is a party that I'm talking about right so The way we do the way we did it was Take the data keeps scraping the data and after a week or let's say daily update instead of scraping the data again from the bunch Let's just take the Delta of the information So you just have the last updated date when you scrape the information Just take the list of the next hundred records or 200 records Whatever it could be and then start updating the DB So the DB you as you see it will be in the data folder with up donation dot DB which is available in the repo also and fund the The one thing that you need to notice here is let's forget how we how we would usually do it with Python Let's assume the pandas didn't exist You would have number of steps even to read a simple CSS file or to do simple group by or to do some aggregations It involves certain amount of lines of code most of the talk you would see here all the aggregations are all sorts of Any sort of analysis that we do it would involve just a single line of code without any involving any single line of follow I guess you would appreciate why we are not using follow-up in Python at least when you're doing data analysis So the simple thing I've just read the data and the data that I have Currently has name country state and a transaction ID like the moment you make a donation. They give you a transaction ID you have certain amount of The date stamp also is recorded to just to get a feel of the data of The total at least to the period of last month August or something in about 80 crores has been donated to this party and They were about 2 lakh Donations been made on an average amount that has been donated was around 3000 rupees I mean if if you were to think of this like a three-year-old startup We started off and it got crowdsourced funded by two lakh people almost raised close to a billion rupees and But that's a rosy picture. We are looking at a very high level and saying commenting all this But let's let's get into further details. What is that we can actually look into it? I? I Hope the next slides will have a bigger font, but there's nothing I could do because it's just a sponsor this way I'll see what I can do about it So some of the things that we just saw was like which they keep talking about, you know The donations coming in were usually between a certain amount of donation like it's either a thousand rupees or five thousand rupees But not large amounts in a peculiar case. You have Baja's electoral trust which actually donated close to three crore rupees This was a single largest donation made to that party and it was around 2014 and right after the election or around the election time But what's interesting to notice if if we were to consider if you donate money to anyone And if you're donating more than one lakh rupees, that's pretty much like you must be super rich Or that you can afford to donate so much amount of money and if I were to pull out the information The donations which are more than one lakh and what was that some I mean What was that amount of donations which came in which are more than one like so people who have donated so only 400? 400 odd people at least have donated quarter the amount of money Out of the two like that we have been considering it's just that 400 super rich people have donated quarter amount of money and so partners gives you some sort of reasonably good Functions like to deal with any time series data you have resampling methods or to do group buys you have group buy I'll talk about the importance of using resampling especially in the context of when you're dealing with time series data and why it's really fast If you look at this data, what we have simply done is we have taken the data set set the index to date Taken the amount and resampled on month and taken the sum of it So this is sort of month-on-month value that we're getting for our matmi party This is good fine. I'm getting the data, but this doesn't look that good because what do I make sense of it? I just need to read the numbers. I don't know where it is growing over there where it is going down Likewise, we also take in count most number of times we just focus on the absolute figures like what is the sum? but we don't focus on Condent like how many donations were coming at certain periods of time you would actually see it was just because of one or two people There was a spike in the amount or when is a popular perception of public that they were actually donating a lot amount of money Now what do you notice here is? Instead of me doing this in two simple two different functions or two lines of code here I'm trying to do get the amount as well as you count in a single line of code Using the how method where I could actually define the numpy parameters or the functions Which lets you tell either on this column you need to do some or a mean or standard deviation or all sorts of Any aggregation method you want to use? No one thing of that I've noticed like this was the one thing that I sort of skip actually when I did this analysis was When you sort of do any sampling or re-sampling you have something called as At least in machine learning called curse of dimensionality reduction Which sort of tells you that if you have a lot many features your model is sort of not pretty good Probably we could also argue that at least from a visual perception or trying to get the initial insights if we don't sample the data at a Proper frequency we might lose out on some key information Now if you notice the data that's been presented here, it's almost identical It's the same data. It's just some amount money By just that on one I've done it on daily basis aggregation on other I've done it on monthly base aggregation on the daily basis when I did it if you notice in 2014 July there was a huge spike and Actually when I do it on monthly it was generated during the election period where the spike was So the July period was because there was a single donation from Bajaj that actually spiked up this information now All this is good. How do we get into actually doing some interactive? Visualization so one thing or one of the things that pretty much helped me a lot was used to use JavaScript or D3 now How do I combine this JavaScript and Python into this? You would have other frameworks like torn out or flask where you pump in the information and then you can render the information on output But what if I don't want to do all of that? I just want to focus on my ipad notebook I don't want to do any of you know other interactions or any other overhead so Here is one simple way where you can actually include JavaScript into a notebook What so I Python provides you with certain functions where it can inject HTML and it all that I'm doing is I'm just passing the string What that string does is you're initially defining a HTML tag, which is time identified by a time ID All that I'm doing is take a date current date and every half a millisecond Keep updating that value. So you would notice There is some sort of you know the time gets updated right now. It's 1055. I'm almost like 10 minutes past my since I've started my talk now This is good. So there is something that I can dynamically change the value In the in the browser without having to stop my console So I can move on to the next piece of code without having to do that So the next so how do we bring visual into this? To use D3 or to use Other frameworks like C3 or dimple you could use again require config where you can import these libraries So the moment you do this D3's and dimple is sort of like, you know loaded into your browser And you can access these variables or like you can call them as their internal functions to do all all sorts of plotting that you want to do Now one example if you were to use D3, it's a very low-level language, especially if you're very good with or very imaginative with your Visualizations you could do all sorts of things. So here's simply what we're doing is defining SVG with a width of 600 and 300 And next I'm putting a rectangle which I'm defining it as square at a position 30 and 30 Later if you remember the auto time update that we have written a two slides back if using a similar function But I'm just replacing the X and Y with a random value So it starts as a position 30 30, but slowly after every two seconds it keeps moving around So all this is happening in I put the notebook and I'm not I'm not Stopping it nor I'm controlling it Now you can imagine if I have this control or flexibility that once I have the data I've posted out and now let the JavaScript behave however it wants to it could be me like You know hovering on some element that will give me a tooltip or I could use a brush or a filter to narrow it down Now to give a sense of what sort of visualizations we could do Here is one example. I mean, I've just included the links that we have used Let's walk through some of the features that we used Yeah, so if you notice here all the aggregations the back end crunching is done in pandas and once you visualize it on the browser The way it comes out We are primarily talking about a summary section that sort of updates every time you select any different filters and All this is auto annotated Whatever you want to give like if at all you want to speak about what are the donations coming from certain state or so on? To make it more intuitive with heart like what is it that people okay? Looking at charges fine, but this is something to do with politics and some commenter would be interesting So if you notice here There is some sort of spikes coming at certain periods of time or there was some dip So generally when he began strike or like okay, this was related to the power bills issue there was a spike in the number of donations and This was the day when Yes, the election was announced then there was huge spike then it formed a minority government The moment casual announced that he will take on Modi sort of there was a huge spike And when the moment people got to know okay We bet on a wrong force and actually Modi won and there was collapse in donations, but there was one spike That spike was due to the bhajaj. So we might actually think that no that could be wrong But as we go back in the data and see it was not because of usual public perception But you would notice again in 2015 there was another election called for there was against pike in donations And they will literally it has fallen down and other sort of visuals that you see here This is called some sort of a trim up visualization where the size of the rectangle represents the total amount of donation made from that group it could be from Delhi How many how many donations are coming what's the sum of it and the color sort of represents? What's the average value it might so happen that daily could be donating a lot of amount of money But it might have been donated by a lot many people So the average that they donated could be thousand. So it's actually Maharashtra and NRIs where average donations is much higher than Delhi and So but how do we get to that crux of information that we could actually put a visual all that I need is for every state I need information of what is a total donation amount? What is the average amount? What could be the total number of or the mean of it or the average of it? and One way of doing it is again using group buys So here what I've done is just taken to do a group buy on state Take the amount and write an aggregation function, which does the sum account and mean of it and just sort on count That's it. It's a single line code all that you have to do is call a data frame That's like your tabular data and just do some aggregations all all that you wanted to display in a tree map is right in front of you So it's just single line of piece of code the same thing again could be done for countries I all look might be interesting to see that they did as it sort of classifies India also the content you might have to remove that data So for filtering something all that you have to do is use square brackets and put a condition that I don't want a country Where it is India so all that I want is a set of rows where country is not India and the moment You get that again you chain it with another group by function on country and then you write your aggregate functions And then you do all sorts of either you want to sort it based on count or you want to do it on mean or a total number of donations So in countries if you notice it's the United States that has the highest number of donations coming in and then you have UA UK Singapore Canada Australia this is fine and interesting I mean Probably usually expected because a lot of NRIs are coming from these countries, but if you notice the large amount of you know Less people donating with the donating large amount of money are actually coming from Hong Kong So I don't know what's happening in Hong Kong It could so happen that there have been 161 donations on an average They're sort of donated more than like one and a half lakh or something with the biggest of all where single donation has come from Somalia we keep talking that okay, so some conflict in Somalia But it does seem like at least when it comes to politics of our country people going there working in all that you know All that issues that they're facing with they're still able to save up some money What's rather more interesting is it's not just you don't see any top countries figuring in the average donation countries All you see is tonight and tobacco Turkey Indonesia So you have all sorts of countries that you don't expect where people will be donating in high amount or at least individuals donating in high amount they actually do and The good part is now if I want to see what is actually happening with Hong Kong or what is it that they're doing? Probably I can just click We'll get back to that Now So all that we have done so far is we have used that data as it is we haven't created any data We haven't really seen into what else could we actually deal with the data? We have taken the remands. I've taken the countries and we have taken the cities Is there something that we could actually you know create new data from the existing data? So one such thing that we did was what about you know I'm not interested whether the guy paid two rupees or ten rupees for me anything between one to hundred is the same amount Like he's making a small donation So let's sort of bucket them into bins of one hundred five hundred thousand and then so on so now I've sort of wanted to see the distribution of it you do a histogram you get this sort of values that you want and Okay, one note pandas inherently has NumPy as well so you can all that you need to do is by accessing like PD dot NP So all the NumPy functions that you want to have You could get it from that so the histogram that we used here was for coming from NumPy the next So we sort of created a new column called amount bucket where I'm saying cut the amount on these bins and explicitly mentioning that right is equal to false which means that do not consider the value on the right So whatever is hundred if it is hundred it actually comes to the next bucket not one to hundred So what we just did here was we created matter it or we created a new Information that didn't exist probably it might be interesting to see what is it that we can do with this data? The first thing probably we would be interested to see is from the states that are donating Where is it that people are actually donating in? One rupees and hundred rupees like you take all the pain coming to the internet using your credit card whatever and you donate one rupee I guess probably your internet cost and data data charges would be more than one rupee But you're still investing your time to come and donate one rupee So which are those states where people are actually coming out and voting with those some amounts so again a simple aggregation would let you do you know do it on state get the amount bucket and Get a count of it and you would see you know At least in Andhra Pradesh a lot of amounts are actually coming from thousand to five thousand not bad And if you go to certain other states Especially there was a case where Uttar Pradesh leads in actually donating small amounts people are fascinated with you know giving denomination as a What's more interesting is people have donated every single amount that exists between one to hundred like one two three four five You keep counting the moment they see a sheet of donations that they've made and if they've sorted by mistake They would see okay. Why am I getting? 67 rupees how would someone even donate 69 rupees or 72 rupees in fact? It's not just 1 to 100 if you go from 1 to 200 There are just four values missing people have donated every single amount that exists What's more bizarre actually people are even superstitious in their donations you would see that There are donations like triple one triple two that's fine fancy number, but you have numbers like 2014 2015 Probably auspicious here the thing that you know if you donate on auspicious here, it would be good But there's a mindset probably people think it's just generally people in the low-income groups do this sort of donations But you've seen instances where there were donations coming in actually coming in more than one lakh rupees They've actually given with Thousand something and the date on which they've donated so it's one lakh 23 thousand and 14 something so on so you have all those weird sort of numbers that are coming in and This is interesting as in it at least tries I mean although we can try and extrapolate at least how People react and people sort of say something at least from their donations Can we talk something about the psychology or our psychology? Now All that we're doing so far is to do pivots, but this is not very interesting because if I want to compare Something from an age bucket with other states what I would rather do in excel is called pivot right now the same thing I could do it in Pandas as well all that I need to do is call pivot and do it on which What is the index I'm doing it on state and I'm doing it on columns Which is called amount buckets and take the values of amount of transaction IDs and just do a count of it Now I've got it for all the states for all the amounts I've got amount of values now it might be very easy if I just put a heat grid for every every value that exists here I just need to put a color and then see what is it that I can see now Most of the times you don't want to see all this information all that you want is to focus only on the top five states and Then do this sort of a pivot and then do a heat grid on top of it so To filter that if you remember initially we have taken up states as a variable where we define the top ten states Now I'm saying in the df state Is in it so it's even the syntax is very very much readable that you can see in the up states Take the top ten and check wherever the df state is that and I just want that data frame and do a pivot on it I get it now. I just all that I just need to do is put some sort of a heat grid on it and the moment you do that You get some sort of a pattern where you see Delhi has a lot of donations coming from hundred to five hundred There are a lot of values or there's actually an anti pattern that you might see apart from barring out NRI's Karnataka actually donates a lot of you know, thousand to five thousand probably from Bangalore because You know it or whatever fan base they have now So so far we've been talking about, you know the good parts or the good analysis or like you know Which feels as good if you have donated to this party. What if there's something? Mischievous or something that's not missionally wrong But the data is probably pointing out to some funny things like whenever you make a donation You're getting a transaction ID that is supposed to be very unique to you, right? You're not supposed to give Multiple same transaction IDs to multiple people now. It's what happened that When you try to find out I just took on the transaction IDs Give me the value counts and try to find out wherever the transaction IDs is more than one Now you would notice like Anil P Wilson and I okay. I'll be reading out few of the names if lucky enough if you're in the room just raise your hand so We have Anil P Wilson who's donated certain amount of money and on particular date And you would notice that it's actually the same date same amount might have so happened that the bank has actually tried to credit The same amount again. Maybe the database actually recorded it twice I don't know something is happening where people's names are getting duplicated So could be some issue at there and let them figure out what it was But what if they're actually different people you have transaction IDs like the first one if you see Tushar Shah and Harish Kumar Sharma or though they were born at different places. They were connected with the same T IDs and The sort of you sort of see these people like you know It's a weird thing, you know, you and I are connected with this TID and The other thing that you notice is this is some legacy issue probably because all the donations that were made during 2013 had this issue or had this blip where they were giving the same transaction IDs To these people probably later they figured out what might have happened and they probably moved to you know something Better or they must have fixed it Although also while noticing this you might actually have seen if you have the access to the database open the DB and write a simple SQL query where you would notice There's a donations that are missing during 2013 November to December for 15 days or 20 days I'm not sure whether they wiped it off for the database didn't record or something could have happened But for those 10 days because there hasn't been a single day since the inception that there has been I mean There has always been a donation be it even 1 rupee or whatever be the amount but for those 20 days. There was no donation Now Like you know, we all have fun when we're deeply connected with politics Especially if you're in Twitter, you know how people react to things. So I don't know who did this But it seems a lot of people wanted Narendra Modi to donate to Pretty sure he might have not but you see a lot of people actually in the name of Narendra Modi and actually one person even wrote Narendra Modi for up But the interesting thing is they're not donating much 1 rupee 5 rupee the problem trying to make a point or they'll take a receipt And show it to their friends or you know, this is what I did today But that's not it Lot of fans of KG also try to do the same thing But if you have access to the slides, you can go through some of the names, which are pretty funny I don't want to read that out loud Just to give you some sense One 17362 says K. Jiravall Nautanki Then you know, but yeah fine I mean you can't do anything with trolls or they just keep doing this stuff again in the same thing You know the donations are largely less. It's not huge amounts that people are actually donating Then we get international people into our politics. You have Osama Bin Laden. You have Obama Then you have Hitler We just don't leave at Hitler. We just want to make statements like Hitler, Lover, Arvind K. Jiravall and People get real creative, but if you notice again the same thing It's just 1 rupee so I guess a lot of people are actually donating 1 rupee probably they're trying to make a point You know I can fool with the system or I'm just checking before I make a big amount Let me check if this actually goes through you know Fine this all has been fun Like you know you can give along like you know you have this data this is what you're doing But at least good part the data is how you can play around and do something with it. So what if You get elected and you start becoming the prime minister of the country or chief minister You start doing things and media obviously reports for the things that you do What is the most famous thing that you remember of the XPM Manmohan Singh any anything? Pretty good. I guess everyone agrees that he's very silent, right? Let's see if he was really silent or someone cooked up the data Now I'll let you read this later, but let's actually this is more important actually because what it says is I Know more about the private lives of celebrities and I do about any government policy that will actually affect me Right, I'm interested in things that are none of my business and I'm bored by things that are important to know The media aim to please I'll leave that at that point and let you fill in with whatever you like Now the data again the same thing So the PMO had this site called PMO speeches right now It got moved to archive or something and we have to scrape all the speeches like it had a unique ID A link and you have to scrape all that information on all that you get is an ID date on which he gave the speech the place the full content of the speech and the title of the speech and The placement place to all the meta information that we added whether the state or the country and etc So let's let since you anyway studies very silent. I'll not ask the answer. What could be your guess? I'll be silent for a moment. Let you read what this actually says I guess whoever said that he's pretty silent, right? He actually gave a speech every three days. I guess most of us would never do that right giving a speech every three days In all he has given 1200 odd speech in a span of ten years. That's about three speeches. I mean three Every speech every three days. That's pretty high. I mean But it would be interesting to actually see Did those speeches really occur or were they just press statements or wherever they actually given When were they actually given we have the data right so all that we do need to do is you know grew by on time or grew by on date and States and extract some information now all that we have done here is we've just taken the the data frame Did a group I on time? Extracting the ear part now since I'm not working on timestamps directly and I just took you know the count of it You got some values which may not be very revealing because we are not actually trying to compare these What if I just? I've sort of used Dimple J's library where I just sort of injected some information all that I did was pass a Jason object X is based on ears and Y is based on speeches and just put the GM object as a bar, right now so it see it seems like in 2008 he gave almost like 218 speeches that's actually more than Speech almost every one and a half day. I guess right If you remote if you remove Saturdays and Sundays, it's almost like I guess even hot public holidays and other holidays the speech every day Every night probably must be preparing the speech and next day is going in and giving it up But we keep hearing that he doesn't talk right we we haven't heard of him much We don't see him and it's too because we have we have rarely seen him speak But the peculiar thing that to notice here is as when his tenure began in 2004 and from 2008 It's the number of times he spoke or like his speeches kept increasing like 2006 Then I mean sorry five six and seven and you remember there was a second election in 2009 Then it was then I don't know what might have happened They might have sought that you know, it's time to probably you know pull back one more and sing and push some fresh Fresh person in into the party and probably that's the reason they sort of try to push the speech content to the other person now People are best judges to what happened to those speeches by that person But I'll leave it to the point where since 2009 onwards. They've been pretty much stagnant You know the number of speeches have been pretty less and he's been content to himself Now all that we have done is done a group by right I keep mentioning about using re-sampling Now re-sampling is pretty fast like if you're using ipad then you can just use this magic comment time it and Measure the time that it takes so almost it takes 18 microseconds to do if I were using group by if I just do it using re-sampling I would do it at like, you know 6x speed and both the values are actually identical so Take away is generally if you're dealing with any time series data and all that you want to do is you know measure some quantity based on dates Our otherwise or month wise or week wise. It's it's pretty good to use re-sampling Now we have seen that Since when he started speaking and where all he spoke Which were the top places or where where was he speaking the most any guesses again? I guess this will be pretty easy You louder Parliament I guess that will take it as Delhi But do you know what could be the proportion that in Delhi must have spoken X amount outside of Delhi it could be this much any rough idea 80 to 18 Delhi and 20 outside so that's pretty huge right so he's a deli man. He should have been the So it turns out that actually Delhi he gave 760 speeches the second that came closest was actually Maharashtra And then us funny thing is after New Delhi and after like Maharashtra the popular places that he went to was USA Russia Japan and There are few countries, you know some problems his home state from where he know he got himself nominated at least he went there But South Africa was more popular than Some and there were actually few country. There were few places that he hasn't actually visited and So we could do all these sorts of things like how many unique places that he has been to We get some sense of an idea how many places that he has visited in 2006 was the maximum very visited like 50 places That's the best thing today. We keep talking about Modi's been visiting a lot of foreign nations I guess once the data is out we can even compare with this and see which one is going where Now we have text information as well, right? So what is it that we can do with the text string methods or something that even pandas provide So all that we need to do is measure the length of the text and then see what's the How is this variation as you notice again here wise if you go After after 2006 or something the length of the speech also decreased So there were cuts both ends you cannot speak more and the number of times she'll speak also will be less Or it's a self put rule or I don't know from where it came Now the next thing I just want to talk so far. We've been talking about you know The data that we can scrape on somewhere where it is not readily available But we have some nice initiatives like data.gov does provide some good data sets. What is it that we can do with? other datasets one of the datasets that is working this week was exports and imports So all that I just came to some of these slides you can just look look them up how to read these The interesting things that we noticed was wherever your major exports and you know imports coming from obviously exports were exporting more to USA China and the imports were actually coming from China anyway But how do I merge these two then I would probably sort of need to do merge or a join of these two operations And then try and see what is it that I can compare This is how you would merge two datasets doing a concatenation of the two because both are series and I could join them on those keys But so where is it that my exports are pretty high? And if I were to sort on that ratio of exports by imports you'd get its UAE where you know you're exporting a lot But it's almost in the one is ratio USA. We actually export more than we import But there are certain countries China for example, we actually import more than we export now all this is good you have certain methods to do it and And What's interesting what I found more interesting was you have information for what commodities are we actually trading? So we would assume that any commodity we import or we export we won't do the both right I mean if I'm importing petrol, why would I export it again because I need it. Why would I actually buy and sell it again? If you notice some of these examples Cashews we actually export a lot of cashews, but there is some sort of a shell cashew that we actually import I don't know what that special variety is that people want to import But there are some glaring examples where we actually import and export as well for example petrol we all know that we actually Import a lot of petrol. That's the reason for the fluctuation dollar as well But why would we export could be to the could it be a reason that you are actually buying someone and selling it to someone I'll do a higher price. We just need to look at all these policies interesting thing at the bottom if you see wheat We'd be would assume that we are exporting at a very rarest of case if you're importing wheat, which means that We have made some policy that we thought we have surplus of our wheat and we have actually Exported it and later realized that our country doesn't have enough feed and we import now this data sort of you know Let's us look at more more meaningful way that if our policies are working or not now This was again a simple representation I just came through this and I'll try and touch up on crops bit a bit because We keep talking about you know Also far in the media we are very less about farmers or what's that may concern? So I just want to touch up on two topics which are more about crops and rainfall So in the crops area similar sort of analysis. We just carried out try to do some aggregations if at all you can find some patterns What we noticed was the data was incomplete All that you required to find out How is it I can extract there are anomalies in the data was you just try to extract how many unique crops I'm actually getting in the data set for every year for some reason In the year 2002 and 2003 there were a lot of crops the other other data sets were not coming in so there was a mismatch in this data and I'll skim through this and I guess I'm a bit out of time so I'll skim through this and just take it up When you have the notebook in your hands, I'll just touch up on the last point Which is more about telling data stories So once we have the data and once we have the visuals that we want to tell I just can't put them simply in form of you know data and then a visual and describe it There must be some intuitive way that people can understand it could be form of if you're hard publishing in a newspaper You can't do much about it But when you have a powerful medium like web where you have all sorts of things that you could do in terms of interactivity One thing that we tried to do was sort of how do you block this? All that we have was a census information The data points we had we had a state What is a percentage of households who are having bikes who are having smartphones? Who had land water all sorts of different variables we had so? The data story that we wanted to tell was you select your x-axis you select your y-axis and Radius and color also varies based on that all that you just need to do is select anything So if I were to change this to percentage of four wheelers The data sort of moves so you do see which are the states that are actually moving this is fine But I don't know anything to start with could you actually tell me a story? Fine, let's write a story. We'll start with some of the data points or insights that we initially identified I'll talk about okay though We may think that irrigation equipments would be higher where we're having a lot of irrigated land But it turns out there are a lot of exceptions So you do see this a regression I mean sort of a regression pattern But there are some outliers in the east and the west right so and like you could tell your story Insert your concept over there and once the person comes over to that piece of text The visual is sort of to be refreshed So he sort of knows that you know for some reason it's especially if you're working in a telecom sector This must be a good interesting data point The belt specifically Madhya Pradesh that this girl and Orissa has a lowest mobile penetration Irrespective rest of India Yeah, so in all So we have a lot of data coming out from the government some could be very messy. You just need to find means to Find out how is it that I can do you could marry some data sets It could be crop or rainfall and combine them to tell some stories But we would come at a point that some information is incomplete We might have to deal with you know taking help of some methods Like how do you extrapolate or interpolate or fill these values with some of the methods that we have walked through The important part however Would be we have all sorts of tools that we would like to play with we are actually having good Hold of data at least in the recent years. We are getting good amount of data. It's being made public probably we just need some imagination and concern for The data that's coming out that we could tell some stories so that we are not blinded by the media and we can do something Thanks. I'll just have some few minutes if you have any questions I can just take Which tool you are using like any packages or Okay, I guess I've mentioned in the beginning for some of the charts that you've seen in the ipad notebook Well done using D3 or dimple. Yes. Okay. Those were JavaScript based libraries. Otherwise, you do have matplot lab in python itself Okay, this is pretty good for exploratory in it, but for interactivity and stuff you might need, you know to use these Things like the amount of text that is highlighted. It will show the amount means that part only to visualize Second paragraph we are highlighting so the data rated only second part will be highlighted So how can we do that? Basically, if you notice all the filters on the top, it's URL driven Okay, the moment I come to any section Yeah, the URL gets changed and based on the URL the visual gets refreshed So I sort of push new data once the URL changes. Okay, and that data gets refreshed. Thank you Hello. Yeah Hi, I'm one see I've been working on similar problem set Basically, I'm dealing with healthcare data. So I have a small flask server which just serves Jason content To angular front end there. I'm using a D3 and cross filters to you know, just display the data One of the bottleneck I've been hitting is the amount of data which I need to push out So there should be some form of streamable That Jason should be streamable and one such thing I've come across was bokeh Bokeh plots. Have you ever used it? And if you have what are the? Advantages or disadvantages? Okay, I went to use bokeh because I used D3 So pretty much solved my problem But at a high level when it principle is again the same behind both the both these things You just instead of passing all the data that you want for the amount of visuals that you need to represent You just pass that amount of data. The thing is if I put a scatter plot all that information that I need is just the state name The x value y value and the radius, right? Even if you pass like hundred rows of information, I cannot display hundred rows of data because you cannot even consume that As much as possible probably when you're working with you know real-time streaming You would only pass aggregates which would come into the browser that would actually save so that you can actually have control on the data Just to interrupt you guys, can you take these questions outside or you sure offline because We are ready ready for the next stop. So sorry to interrupt, but yeah, so can we have a round of applause? Thank you for such a fantastic talk by Ashutosh


Welcome to the Endless Knot! With the new Captain Marvel movie coming out soon, not to mention some other big developments in the Marvel Cinematic Universe, we thought we'd take a look at the word Marvel itself. Content Warning, Brief Mention of Sexual Violence At the heart of Captain Marvel and the Marvel Cinematic Universe, or MCU, is the word Marvel, expressing the idea of something astonishing or something to be wondered at, appropriate enough for the fantasy world of comic books.  As the word can be traced back through old French merveille, a wonder, surprise, miracle, to Latin marabolous, wonderful, marvelous, strange, mirari, to wonder at, admire, and mirris, wonderful or amazing, which are also the roots of words such as admire, miracle, and mirror. The current name of the comic book company comes from the title of the first comic book it published. The company was originally called Timely Comics, and its first publication, Marvel Comics Number One, came out in 1939, featuring the superheroes, the human torch, and Namor the Submariner. Timely Comics changed names a few times, to Atlas Comics, and eventually to Marvel Comics, with the resurgence of superhero comic books at the beginning of the 1960s, after superheroes had become unpopular during the 1950s. Stan Lee, who was born Stan Lee Lieber, but wanted to use a pseudonym in the comic books industry, instead keeping his real name for more serious work as a novelist, recruited at Timely Comics as an assistant in 1939, and was soon appointed interim editor and then editor-in-chief. With the return to superhero titles in the 1960s, he changed the comic book world by creating more naturalistic characters who, despite their extraordinary powers, often had very ordinary foibles and emotions of everyday people, such as the Fantastic Four, with their bickering amongst themselves, and Spider-Man, with Peter Parker's teenage angst. The Marvel Company changed owners a number of times, eventually ending up in 2009 with the Disney Corporation, under whose aegis the new Captain Marvel film is being released. As for Captain Marvel herself, well, the character has a complex history. First of all, she wasn't the first comic book superhero named Captain Marvel. The original Captain Marvel was created in 1939, appearing in Wiz Comics No. 2 from Fawcett Publications. Through the magic of the ancient wizard named Shazam, the 12-year-old boy Billy Batson is able to transform into the adult superhero Captain Marvel whenever he speaks the wizard's name. In the name Shazam is an acronym made up of the sources of Captain Marvel's superpowers. Solomon, a wise king of Israel from the book of Samuel, provides him with wisdom. Hercules, the Greco-Roman demigod strongman hero, provides his strength.  Zeus, the Greek god of lightning and ruler of the Olympians, provides him with magic power. Achilles, Greek hero of Homer's Trojan War epic, the Iliad, provides him with courage. And Mercury, the Roman messenger god, provides him with speed. It's an oddly mixed bunch. Solomon stands out as the only figure not from Greco-Roman mythology. And as for the rest, they're an odd mixture of Greek and Roman equivalents. Hercules is the Roman form of Greek Heracles. Zeus is the Greek god who later became associated with Roman, Jove, or Jupiter. And Mercury is a Roman god who became associated with Greek Hermes. The character's creators based Captain Marvel on the face of actor Fred McMurray, best known for the film noir Double Indemnity, numerous Disney films such as The Absent Minded Professor, and the 1960s TV show My Three Sons. The problem was that following up on the recently created Superman character, Captain Marvel was kind of a rip-off. And for a while, Captain Marvel started to become more popular than Superman and was outselling him at the newsstands. So Superman's publisher, National Comics Publications, which later became known as DC Comics, filed a lawsuit and Captain Marvel ceased publication. Since Fawcett's Captain Marvel was out of the picture, Marvel Comics decided to grab the trademark for obvious reasons and created their own Captain Marvel, the alien Captain Marvel of the Kree Empire, who only appeared sporadically in print, mainly to preserve the trademark. Later on, DC acquired the rights to Fawcett's Captain Marvel character and wanted to revive him. But since they could no longer use the trademark Captain Marvel, they rebranded the character as Shazam. Interestingly, Shazam is set to make his entrance into the DC Extended Universe, or DCEU, the DC equivalent of Marvel's MCU, with the film Shazam to open in April 2019, which Captain Marvel will win. So that's two Captain Marvels and neither is the titular character of the new MCU movie. And in fact, there have been a number of other Marvel characters who have used the name as well. But the current Captain Marvel, both in the comic books and in the movie, is Carol Danvers, a U.S. Air Force officer who, in the comic books anyways, was caught in the explosion of a Kree device with the Kree Captain Marvel causing her DNA to meld with the alien, turning her into a human Kree hybrid and giving her superpowers. In an effort to join in on 1970s feminism, Marvel Comics gave her the superhero name Ms. Marvel, but the character's storyline wasn't all smooth sailing. Ms. Marvel's own comic book lasted for just a few years before being canceled, and afterwards the character appeared in a number of other titles with groups such as The Avengers and The X-Men. During this time, the Captain at various points had her memory erased, lost her powers, and had been raped and impregnated by a supervillain, with her Avengers colleagues, including the female ones, failing to really recognize or acknowledge her ordeal and assuming she'd at least be happy with the child. Subsequently, the character was made an alcoholic and became negligent in her duty, leading to a storyline in which Captain America was going to court-martial her, whereupon she quit the Avengers. The rape storyline was criticized by comic book historian Carol Strickland as well as then fan Gail Simone, who compiled a much-circulated list entitled Women in Refrigerators, in reference to a Green Lantern story in which the hero's girlfriend is killed, dismembered, and stuffed into a refrigerator. The list contained all the female comic book characters who had been killed, maimed, or depowered, often just as a plot device for male characters. A trope often referred to now as fridging, and it's a long list. Simone later went on to become a comic book writer herself, doing much to raise the status of female characters in the genre, and as for Carol Danvers, her character's having something of a resurgence, having rejoined the Avengers and eventually, after the death of Cree Captain Marvell, taking on the name Captain Marvel in his honour, and now becoming the title character of the new MCU movie and also being brought into the upcoming Avengers Endgame. Now the faucet Captain Marvel wasn't the only, or even the first attempt, to rip off the Superman character. Fox Comics only released one issue of Wonder Man in 1939, which featured a hero with basically the exact same powers as Superman, though given to him by a magic ring from a Tibetan monk, before a lawsuit put an end to it. Interestingly, Wonder Man is also the name of a later Marvel character, one of the Avengers, and sometimes romantic interest of Ms. Marvel. Of course the word Wonder is a perfect synonym for Marvel, so I guess that also connects the two Superman rip-offs, Wonder Man and Captain Marvel. The word Wonder comes from the proto-Germanic root Wundron, miracle, or wonder, with many cognates in the Germanic languages, but its earlier etymology is uncertain. It might come from proto-Indo-European when to desire strive for, also the source of the words win, wish, and venerate, and the name of the Roman goddess of love, Venus. Of course the most famous superhero with the moniker Wonder is Wonder Woman, a DC character and the most successful and famous female superhero. Wonder Woman was created in 1941 by psychologist William Moulton Marston, who believed in the superiority of women. Marston thought that comic books had an untapped potential and wanted to create his own superhero. And at the insistence of his wife Elizabeth Halloway Marston, also a psychologist as well as an attorney, he made his character a woman based not only on his wife, but also on their polyamorous partner Olive Byrne, who had also been a psychology student. She was created to have all the strength of Superman, but triumphed as much through peace and love as through brute strength. Marston's storylines often featured Wonder Woman being chained up as he was a defender of the practice of bondage and submission. But the freeing of herself undercut the usual trope of the damsel in distress needing to be rescued by the hero. Her lasso of truth was a reflection of Marston's development of the lie detector test, and her bracelets reflected those of Olive Byrne who wore them in lieu of a wedding ring. Olive, by the way, was the daughter of Ethel Byrne who, along with her sister Margaret Sanger, was a radical feminist and advocate of birth control. It is therefore fitting that the Wonder Woman movie was widely acclaimed as a breakthrough for female representation in the world of superhero movies, not only by being the first to center on a woman, but also by being the first superhero movie from an American studio directed by a woman, Patty Jenkins. And now the MCU is following in the DCEU's footsteps, with its first movie starring a female superhero. And Captain Marvel is also co-directed by a woman, Anna Bowden. So that's the Marvel part of Marvel Comics. What about the comics part? Well, the comic book was a development of the comic strip published in newspapers, so called because before the introduction of the adventure hero into the medium, comic strips were generally humorous, and were also referred to as the funny pages, or simply the funnies. The word comic comes through Latin from Greek komos, revel, carousel, or merry-making. The etymology beyond that is uncertain, but it might come either from the proto-Indo-European root ka to like or desire, which also gives us the words caress and cherish, or from the root kems to proclaim or speak solemnly. The style of illustration used in comic books is usually referred to as cartoon. The cartoon in this sense goes back to the 19th century satirical magazine Punch. You see, the original sense of the word cartoon was a preliminary sketch of a painting or fresco made on cardboard or stiff paper, and thus we refer to Leonardo da Vinci's famous sketches as his cartoons. The words cartoon, card, and chart can all be traced back ultimately to the Greek word cartes, meaning papyrus, a word that is probably of Egyptian origin along with papyrus itself. By the way, one particular sketch by Leonardo of one of his flying machine ideas was the inspiration for artist and co-creator of Batman, Bob Kane, in designing the look of the character and his distinctive cape. Well, Punch magazine, in order to satirize the self-aggrandizing politicians when the preparatory cartoons for the murals in the newly built houses of parliament were put on display, appropriated the term cartoon to refer to their satirical sketches about the event, and the names stuck with these kinds of humorous or satirical illustrations on any topic. Now, in those Punch magazine cartoons, as well as other cartoons of the period, the text was included below as captions. The speech balloons that we now associate with the cartoon genre came a little later with the first comic strip Hogan's Alley by Richard F. Outcult, which was first published in 1895 in the New York World newspaper. One of the lead characters in this strip, called the Yellow Kid because of his oversized yellow night shirt, had his speech initially rendered on his shirt, but soon Outcult started using speech balloons for the dialogue in the strip. By the way, the term comic book was first used in 1897 in reference to Outcult's McFadden's Row of Flats supplement in the New York Journal. There were of course precursors to the speech balloons, for instance, speech scrolls, which were independently developed in pre-Columbian Mesoamerican art and in European art in the Middle Ages, sometimes referred to as bandaroles, which appear as actual scrolls in the paintings with text written on them. The thought balloon was developed in another comic strip from around the same time, the Katzenjammer kids, created by Rudolph Dirks, who was also a pioneer in the use of graphic symbols such as a log being sought in half to indicate loud snoring and sparkling stars to show pain. In later superhero comic books, the use of thought bubbles was pushed even further by Stan Lee, who used them to include more complex interior monologues, giving a deeper sense of the psychology of his characters. There were also precursors to the use of sequential art in comic books in order to tell a narrative, such as Greek freezes and Trajan's Column, which depicts the Roman Emperor Trajan's victory in the Dacian Wars in a 190 meter strip of images that spiral around the length of the 30 meter high column. There was also Mayan art, which represented a narrative in sequential images, and in the 11th century, the Bayeux Tapestry depicted the Norman invasion of England on the 70 meter long strip of embroidered cloth. The so-called Popper's Bible of the later Middle Ages were basically cartoon strip versions of biblical narratives for the benefit of those who were illiterate. In the 18th century, painter and engraver William Hogarth used a series of images to depict the moralizing lesson of a used decline and fall in a harlot's progress and a rake's progress, and in a particular anticipation of superhero comic books, the 1847 Traveau d'Arcule, or Labors of Hercules, by Gustave Doré, depicts the story of an ancient hero. By the early 20th century, newspaper comic books had become wildly popular, and soon enough, they were used for other purposes than humor. And adventure strips like Tarzan and Buck Rogers started appearing in the 1920s, which told longer narrative threads over many strips, each ending with a cliffhanger. The concept of the cliffhanger may have its origins in the 1872 serialized novel A Pair of Blue Eyes by Thomas Hardy, in which one installment ends with protagonist Henry Knight literally hanging off of a cliff. And supposedly the term was invented in the silent film era for serials such as The Pair of Pauline from 1914, in which the titular character is also left at the end of each installment in some sort of peril. A good example of that damsel in distress cliché that Wonder Woman was working against, though the phrase doesn't seem to have occurred in print until the 1930s. Supposedly, the phrase to be continued was also coined in 1903 for a comic strip. The comic book started out as reprints of syndicated newspaper comic strips, such as The Funnies in 1929, Funnies on Parade in 1933, and Famous Funnies in 1934. Of course, this is the American comic book we're talking about here, as they were separate and independent traditions of comic books such as the Franco-Belgian comics like The Adventures of Tintin, British comics like Bino, and Japanese manga, which dates all the way back to the 18th century. And all of these traditions have their own conventions, but it's in the American tradition that the superhero first appears, so that's the one we're following. In addition to Dirks' innovation of using graphic symbols in the Katzenjammer kids, other symbols have been used in comics, most famously the Grolicks, that is the use of typographical symbols as stand-ins for profanity, which go back to both Dirks and a contemporary of his Jean Carr, creator of the Lady Bountiful strips, which seems to be the first strip to feature a female protagonist. Though the name for this practice wasn't invented until 1964 when Beatle Bailey cartoonist Mort Walker came up with it as a kind of joke for a satirical piece for the National Cartoonist Society. Linguist and lexicographer Ben Zimmer has since suggested the more appropriate name Obsenicon for this typographical phenomenon, which is a portmanteau of obscenity and icon, mirroring the portmanteau emoticon from emotion and icon. It has just recently been found that Carr's use of the obsenicon just slightly predates the earliest known use in Dirks' comic strip. And speaking of emoticons, the use of typographical symbols to depict facial expressions in order to express emotion in online communication, their invention is generally credited to computer scientist Scott Thalman, who proposed it on a message board at Carnegie Mellon University in 1982. Though there is an earlier antecedent of the use of typographical symbols to show facial expressions in 1881 in the pages of Puck Magazine, which is basically the American version of the British Punch Magazine. More recently in online communication, the emoticon is often being crowded out by the use of emoji, actual graphical pictures to convey facial expressions and emotions, though of course there are pictures of far more than just faces available in emoji. The word emoji, by the way, only coincidentally resembles the word emotion, in fact coming from the Japanese elements A, picture, and emoji, character. The instantly recognizable yellow smiley face used in emoji was originally designed by American commercial artist Harvey Ball for State Mutual Life Assurance Company of Worcester, Massachusetts in 1963 in an attempt to raise employee morale by giving them smiley pins to encourage them to smile more. And this attempt to influence emotions via facial expressions, odd as it may sound, might actually have some basis in science. The role of smiling and other facial gestures has been studied for a long time, at least as far back as the Greek philosopher Aristotle, and it became a particular focus of scientific study from Darwin onwards. Typically, words for face, at least in Indo-European languages, came from words meaning to see, such as the usual French word visage, like our English word visage, which can be traced back to Latin wedora and the Proto-Indo-European root wade, meaning to see. Old English had its own native word, andlita, face or countenance, from the verb glitan, to see or look. But the word face comes from a different semantic source, coming through Old French from Latin facies, appearance, form or face, from the notion of a form imposed on something, and is related to the verb facora, to make or do, from the Proto-Indo-European root de, to set or put. So, faces are both something that is seen, but also something that we act on and make. And this is why the connection between facial expressions and emotions is so important and has attracted so much speculation and research. One of Charles Darwin's lesser known works is the expression of the emotions in man and animals, which he intended as support for his theory of evolution. He wanted to show that the expressions of emotion had biological origins and therefore tried to demonstrate their universality, both across human communities and across species. He also argued that facial expression had evolved complexity with faces becoming increasingly mobile as one moves up the phylogenetic ladder, with primates having a greater range of expressions than other animals. So, the basic assumption was that faces display emotions, and both facial expressions and the basic emotions they expressed were therefore in some sense universal. Scientists since Darwin have demonstrated that this is at least partially true, identifying some six or so basic emotional categories in nearly all cultures around the world. Happiness, sadness, anger, fear, disgust, and possibly surprise and contempt depending on who you ask. Even babies born blind are able to make at least some recognizable facial expressions without the capacity to have learned and mimic them. Nevertheless, the rules of how and when we use facial expressions of emotion differ from culture to culture, and are learned and dependent on context. For instance, when it is acceptable to show grief in public versus in private. What expressions are appropriate in certain occupations, such as politician or depending on status or gender? We can also be deceptive in our facial expressions and can attempt to hide our true feelings, such as faking an emotion when making a polite smile to a customer or trying not to hurt a friend's feelings. The face is also a tool of self-presentation, and we use it to create an image of ourselves in the eyes of others. As well, the face is one of the ways we define our own sense of personal identity. So, facial expressions are both an automatic readout of internal emotional states and consciously controlled methods of non-verbal communication serving social functions. So, like visage and face, both something that is seen and something we do. Surprisingly, expressing emotions on our face may also be connected with our ability to perceive emotions in others. There is some evidence that we use the same mechanisms for perception and production of facial expressions. If our ability to produce facial expressions is impaired, such as through birth defect or through cosmetic Botox injections, which can paralyze some of the muscles in the face, there is some evidence that this can affect our ability to read emotion. For instance, one study showed that we are slow at reading emotion-invoking sentences when we can't make the facial expressions implied by the sentences. However, studies have also shown that people who can't produce facial expressions are able to judge the expressions of others unimpeded, perhaps using workarounds, so facial mimicry is useful but not necessary for facial perception. The other important job of facial expressions, as we've already seen, is non-verbal communication. In fact, the face can even affect our verbal communication, as in the McGurk effect, in which we see a visual input from a face producing one particular sound while hearing an audio input from the voice of a different sound and actually perceive a third sound, which is neither the sound we hear nor the sound we see. In fact, researchers have found that face perception is second in importance to speech in terms of communicative information. The face also plays an important role in interactive management and can be used as a kind of visible punctuation. It can open and close channels of verbal communication, compliment or qualify speech, and even replace speech. For instance, in a conversation, an open mouth can indicate that we wish to take our turn in the interaction, and a smile can indicate a number of different things. For example, that we wish to engage in an interaction or a smile of appeasement can end an interaction. And although we typically think of smiles as indicating happiness, they can instead function in a conversation as a listener response or back-channeling, indicating attentiveness or agreement, regardless of the emotional content of the conversation.  And finally, we can use facial expressions as emblems which replace spoken messages. Emblems are gestures and expressions that replace or modify words, like making the okay sign or the peace sign with your hand, or like a police officer's badge or a medical cross. And even superhero emblems, like Superman's S and Batman's Bat, are communicative emblems. The smiley emblem is used ironically in the Watchmen comic books with a drop of blood running from the forehead. Facial emblems include lowering our jaw to indicate we find a speaker's statement is surprising or the widening of our eyes as a nonverbal wow. We can make a smile to indicate that what we just said is not to be taken seriously and a return smile shows you got the message. The fact that we can't communicate through these facial expressive means in social media interactions can often cause a lot of trouble, which is the reason behind all those emoticons and emoji we looked at earlier. They're a replacement for those important expressions we can't make with our faces. Getting back to the role of facial expressions in emotions, there have been some who have suggested that not only is the face not a simple readout of emotional experience, but it may actually determine emotions, an idea now known as the facial feedback hypothesis. The notion goes at least as far back as the 18th century philosopher and playwright Gotthold F.R.I.M. Lessing, who suggested that if an actor has properly imitated the external signs of an emotion, the resulting sense impressions will induce those emotions in the actor. Darwin too picks up on this notion stating that if one freely expresses the outward signs of an emotion, it will become intensified, but if one represses the outward signs, the emotion will be softened. But it was the American philosopher and psychologist William James who took this idea to the furthest extremes. James had already developed a theory of emotion in which physiological responses are the emotions. As James famously explained, it is not that we see a bear, fear it, and run, we see a bear and run, consequently, we fear the bear. It's our mind's perception of our physiological responses, like a spike in adrenaline and a racing heart, that is the emotion. So James believed that if we wanted to terminate an emotion, all we had to do was produce the outward expression of its opposite. Maybe you really can turn that frown upside down. Of course, these sorts of effects have been studied, argued over, and tested ever since, including studies with Botox injections, which suggest that removing the ability to frown might be an effective treatment for depression. It has also been shown that people who suffer from depression will, when shown humorous video clips, actively try to suppress their smiles. As a side note on William James, by the way, he also coined the word multiverse, though he used it in a different way than we do now, as an alternative to universe in which there is no single order or ruling power. The idea of the multiverse in the modern sense is an integral part of the comic book world, with both DC and Marvel having their own complex multiverses that contain many different realities. The idea was first introduced all the way back in the 1961 Flash storyline, Flash of Two Worlds, in which the new rebooted version of the Flash encounters the original 1940s version of the Flash. And as a final comic book coincidence, William James was very influential as a university professor on his student, Learned Hand. Yes, that's his actual name, and his cousin was named August Noble Hand, who would later go on to be the judge in the legal case of DC against faucet publications over the original Captain Marvel. But since we've been talking about smiling and frowning, it's worth taking a quick final look at their etymologies and what they really mean, and because they're part of those categories of universal facial expressions indicating basic emotions. But are they, though? Pause for a moment and answer this question. What part of your face do you use to frown? If you're British, you probably said the eyebrows, contracting them in either displeasure or deep thought. But if you're North American, you may have said the mouth. Remember a moment ago I said, turn that frown upside down? That's what it means. It's when you pull the corners of your mouth down in a curve that's opposite of the curve of a smile. Consider the emoticon or emoji frowny faces. Funny thing is, both those answers are wrong. At least, etymologically speaking, it should be the nose. The word frown comes from old French fronnier, to frown or scowl, snort, or turn up one's nose, from a Gaulish word, frogna, nostril, which probably comes through the proto-Celtic root, srogna, ultimately, from the proto-Indo-European root, shrink, to snore. See if that changes your mood. As for the smile, well, as we've seen, there are different kinds of smiles, and they don't always mean the same thing. French neurologist Guillaume Duchen studied facial expressions and emotions and used electrical charges to stimulate the muscles of the face, and thereby reproduced various expressions. In doing so, he found that genuine smiles require not only the muscles that raise the corner of the mouth, but also the muscles that raise the cheek and produce crow's feet around the eyes. This kind of smile, incorporating the eyes, has become known as the Duchenne Smile, whereas fake smiles are sometimes referred to as the Pan Am Smile because of the polite smiles flashed by Pan Am flight attendants, or the Botox Smile, because Botox injections are used to reduce wrinkles around the eyes, thus inhibiting the Duchenne Smile. And indeed, the different types and uses of smiles are important. It's often been noted that women smile more often than men, at least in Western society, and it has been suggested that this may be the result of the larger phenomenon of dominant status, with people with lower status needing to make polite smiles or smiles of appeasement for social communicative purposes. Many women know all too well the experience of being told repeatedly to smile more by men around them, from strangers in the street to patronizing bosses, the word smile comes from the Germanic branch of languages, probably through a Scandinavian loanword, displacing the old English word smerkean, which became modern English smirk, developing the more specific negative connotation of smiling smugly or maliciously. Both words are related, however, coming from the Proto-Indo-European root smei, or mei, to laugh or smile. Interestingly, this word seems to be what's called an S-mobile root, that is to say that S at the beginning seems to come and go unpredictably, and we don't know why this feature exists in Proto-Indo-European. The S-mobile always appears at the beginning of roots in certain specific consonant clusters, like SP, ST, SN, and in this case, SM. As these roots pass into various Indo-European languages, that initial S is sometimes dropped, but with no apparent pattern. So for instance, smei comes into Old Irish as meof, which means honor, dignity, rank, or status, relevant to that status-related smiling. This root comes into Latin without the initial S2, though in that case, the S would have disappeared anyway, as S regularly did before resonant consonants like M, N, and L in Latin. But that's why Proto-Indo-European smei became Latin mirris, wonderful or amazing, which, as we've already seen, eventually gave us the English word marvel. Just don't try telling Captain Marvel to smile more. Thanks for watching. There's so much to say about the history of comics and superheroes that I wasn't able to fit everything into this video. So I'm working on a future video more focused on superheroes themselves, which will fill in some of what I had to leave out here. If you enjoyed these etymological explorations and cultural connections, please subscribe and click the little bell to be notified of every new episode. And check out our Patreon, where you can make a contribution to help me make more videos. I'm at alliterative on Twitter, and you can visit our website alliterative.net for more language and connections in our podcast, blog, and more.


We have something called severity softening and lack of detail. There's tons of minute, perfect little details about the intricate process he's going through with these sunflower seeds. Our sunflower seeds got sprayed and died. Then what's the detail on the crime scene? Drove up and saw and called. That's the difference between sunflower seeds versus dead family members here.


Okay, James P. Madonna Megalife 21 progressive discussions. I have now just arrived at the Royal Hibachi buffet rule 46 West out of Brooklyn, New Jersey. Look at that new awning that new Warning they have now. Isn't that lovely? Look at that Excellent excellent excellent beautiful beautiful. I'm gonna enter now All right, let's see what else is in store for me here No, no hardcore sushi at just the California You know California Rolls styles. I Wish they all can be California Rolls was at the Beach Boys, uh, Jeff Sandbell. I wish they yo can be California Rolls This is not bad to know what avocado To narrow avocado, that's not too bad. Maybe I can make this a surf and turf after all We got here. We got the seaweed salad and some kimchi No, so Jeff's in bellow if I take Like two of these with that you legally make it a surf and turf What do you think? I wish they your can be California Rolls This is weird never did this before I Love avocado. I love raw tuna All right, let's see. Let's see if this soup the sought after soup No crab soup No, no, I got the seafood soup, but no crab soup What do we got here roast lemon chicken breast teriyaki chicken See we got is another angle from the bouillabaisse seafood medley Let's see what we got going down here at the Royal Hibachi Nope, not this time no coconut chicken. I Mean I know coconut shrimp at the Platonus midurals is the fried fish Very good very good Very good you want to be on YouTube now say hi to everybody Say hi to all your family and friends Okay buffalo chicken wings here we are that was just one of the very nice waiters I've had He's he's he's scheduled to work on the other side of the building though Here's the buffalo chicken wings a very finest Buffalo chicken hot wings that you can get you can come in and buy it in bulk for your Sporting event party It could be any sporting event party Doesn't matter what kind of sport it is if you're having that party at home You can come in and buy the chicken wings in bulk and ahead of time and take them home Royal Hibachi buffet the best buffalo chicken wings going way to get my they got mild ones It's sweet and sour chicken So it doesn't matter what sporting event it is could be baseball football soccer professional wrestling boxing MMA Any pay-per-view sporting event get the very finest in Buffalo chicken hot wings to go at Royal Hibachi U46 West out of book, New Jersey See what we got going here Ah Not yet. Not yet. That's some mangoes going. I don't see any papayas The ice cream station looks pretty damn good Looks good so far until the fatty our buckles come in the little boys and they they mess it all up There's my favorite radioactive Chernobyl ice cream Jeff Zanbello.  I Bring in everybody Every time I come here. They come pouring it is a steak to knife Jeff Zanbello steak to knife I'll deal with the napkin situation I'm gonna need extra napkins with the with the lamb and the duck. That's for sure. That's all right Okay, now it's time now. It's time for the lemons. Oh, excuse me. I dropped the napkin I'm a very clean person Very meticulous You would think this is turf and turf, but it's actually turf and turf with a little bit of surf And let me grab some lemons right, you know, I'm gonna go for the cobalt or yeah, whatever you want to call it cobalt blue Okay, see we got going Stuff great leads Beautiful beautiful All right Look at this New Zealand green-lip muscles both male and female Jeff Zanbello the pink ones are female by the way The gray ones are male Okay, until the codfish comes I'm gonna have to deal with For my water and one more one more One more Commodore Jeff Zanbello one more.  All right now.  Everything is food. I want to give a shout out to mr. Ken Thieson Character actor commercial model Former professional wrestling superstar and Personal trainer extraordinaire in Boca Raton, Florida. Mr. Ken Thieson Ken Thieson I am at the Royal Hibachi buffet I'm rule 46 West and Saddle Brook, New Jersey and Very good friend of mine now they finally put out the cod Scrub cod fillet now I'm before I only Had to settle for Turf and turf with a tiny bit of surf now I get to have all Surf all your base mixture here Muscle shrimp calamari some scallops Okay, I'm going to get some of this succulent roasted eggplant Commodore Jeff Zanbello and Mr. Ken Thieson As well as exorcist Bishop Ryan Delicious marinated Roasted eggplant, I always loved eggplant Even though it does not resemble an egg at all gentlemen They also love mushrooms. That's why I'm a fun guy over here. We have cauliflower Zucchini and yellow squash. I call it squish squash when you start chewing on it. Okay getting some lemons for the thick succulent scarred cod fillet Now getting the female New Zealand green Trying to get a hold of it New Zealand green-lip muscles should do it then after this I get dessert. Okay We got marinated roasted eggplant Middle Eastern style. We got New Zealand green-lip muscles three females actually and we have thick succulent scarred cod fillet This is worthy of a photo Okay, there's the Mongolian barbecue the king of Mongolian barbecue That beef over there in the corner He's been helping a beef all the way in the corner. That's the vapors of life Commodore Jeff Zanbello now This has been a MegaLife 21 production


Hey, this is Brett and this 2018 Ram 3500 Megacab Short Box Dual-Rewheel is stock number 13251Z. I am here at Summit Automotive in Fond du Lac, Wisconsin, your new and used heavy-duty truck and Ram headquarters. This 2018 Ram 3500 has the 6.7 liter Cummins diesel engine. This truck has been fully-safetyed and inspected by our service shop, has a fresh oil and filter change. All the fluids have been checked and topped off, and this truck is 100% ready to go. I'm going to go all the way around in this video inside, underneath, start it up, and take a look under the hood and give you the most accurate representation of the vehicle as possible. Brilliant black crystal pearl is the color and I shoot all my videos in 4K, so if you have HD capabilities on your computer, tablet, smartphone, or television, I highly recommend turning them on now because it is your best way to check out the quality, condition, options, and cleanliness of the vehicle before seeing it in person. Going down the driver's side duly tub, pretty nice shape.  Cab and doors all look really good on this truck. Paint is very reflective, front fender, no denser dings on there, and if you like the video, you can subscribe to my YouTube channel, which is youtube.com slash Summit Auto. Click the bell notification so you'll get updates on the videos I do each and every day here at Summit Automotive. This one comes with the steel rims with the chrome covers, and the front brakes look pretty new on there. I would suspect we put those on in our safety inspection. It has Sumitomo encounter AT tires, these are LT 235 ADR 17s, and I would say they have probably about 70% of the tread left on them. Frame and underbody is in excellent shape. This is a one owner out of Indiana, I believe. Once again, that front fender, excellent condition. Headlight lenses are nice and clear, and it does come with the factory fog lights. The chrome front bumper, no major dents or dings on there, looks pretty good. You get the chrome trimmed grille, and the hood is in very nice clean condition as well. You do get the cab lights up top, and the passenger side front fender is in pretty nice condition. Passenger side wheel cover looks good, and brilliant black crystal pearl. Got a lot of metal flake too, you can see just how nice that paint is, and really a cool looking color, especially in the sunlight. As you go down this side of this 2018 Ram 3500, take note of how clean the body is, how reflective and mirror like that paint is. Take these HD videos so if you are far away, or even if you're close by and you just cannot make the trip down but you're still interested in purchasing the vehicle, you can see the truck, hear the truck, and have confidence in the vehicle that you're looking at before you even get here. So when you do get here, there's absolutely no surprises, and you can make a smart and informed buying decision from wherever you're at. If this video helps you make that buying decision, let your salesman know that you saw the video that it was helpful, and that Brett sent you. This dually tub looks really good, back wheel cover is in nice shape as well, and the back tires have just as much tread as those front tires, maybe even a little bit more. Your underbody is in excellent shape, it's like that all the way underneath, has all the remaining factory exhaust, so it hasn't been altered in any way and the lower rockers all look pretty good, I didn't see any corrosion or anything like that on the quarters. Pastner side box, couple little small dings but nothing terrible. Coming around to the back, rear bumper is in very nice shape, didn't see any denser dings in there, it does have the full towing package of course with the receiver hitch, four pin and seven pin wiring, and the tailgate is in pretty nice condition as well, does have just a little bit of a dent right there, little spot we touched up to, other than that not too bad, comes with a WeatherTech bed mat, gooseneck hitch, seven pin wiring back here, that WeatherTech mat goes on to the tailgate, which is nice as well, and for full disclosure this back rim cover is in nice shape as well, cab looks really good, does come with the heated fold up tow mirrors, they have built in directional signals, they fold up like so, and in like that, inside the big horn package gives you the gray cloth interior, in the front here you get the 40, 20, 40 split bench seating, no rips or tears on those seats, they're in very nice condition, comes with the all weather floor mats, those are factories, auto headlamps you can see it definitely is the six speed manual, power windows, power locks, power mirrors, we'll take a quick look at the back seats, then we'll check out the radio and everything that this truck has to offer in the front, back seats are in nice shape as well, it does have the latch child safety system for any child car seats you may have, power sliding rear window, these seats go down two different ways, the first way is that the backrest goes down like so, there is a storage area back here, get a bin underneath there, you can see just how nice and flat that those seats go, now you can put both sides down if you need to take a nap back here, or if you have pets or something they have a nice flat place to lay, and the other way that this seat goes down is just the backrest goes down, that gives you a higher load floor, it also gives you a confined space back there to store things, which is pretty nice, you get the all weather floor mat back here as well, child safety locks on the back doors and the bottoms of the doors all look really good, we'll hop inside check out the miles, the radio and everything that this truck has to offer and then we'll start it up, take a look under the hood, see that this one has 59,616 miles, instrument cluster is pretty nice and clean, just a little bit of a tire shine on there, digital speedometer, compass outside temperature display and comes with the heated leather wrap steering wheel, cruise controls on the right, Bluetooth and information center controls on the left, audio controls on the back of the steering wheel, this one has the 8.4 for C radio, you get AM FM and Sirius XM radio capabilities that you can see right there, you also have all your different apps on here, including projection measure, we can project your cell phone to the screen, so while this one doesn't have the factory navigation system, project your cell phone to the screen via Android Auto or Apple CarPlay and if you have Waze or Google Maps or some other NAV program, put it right up on the screen that's like you have NAV right there, you also have your heated seats, heated steering wheel controls and your backup camera is right there as well, that is working nicely, down here your climb controls, your factory brake controller, more heated seat and steering wheel controls, stability control, factory exhaust brake turned out four-wheel drive, get a 12 volt power point, a USB down there and then a 115 volt, 150 watt plug-in and there is your six-speed manual transmission, passenger side format and seat are in excellent condition, no rips or tears on there, I don't think this truck's ever been smoked in and the headliner is in great shape as well, you do get your map lights right there and your power sliding rear window button, assist and SOS buttons in the rear view mirror, we'll start it up and take a look under the hood, starts right up, other than no check engine lights or anything like that, other than the parking brake, you can see that when I don't have it on it's not flashing, we'll take a quick look under the hood and I would personally like to thank you for checking out the video today and hopefully from this HD video you've been able to verify the quality condition options and cleanliness of this truck all the way around, inside and out, under the hood we have the 6.7 liter Cummins diesel engine, engine bay is very clean, runs very smooth, once again this truck has been fully safe and inspected by our server shop, has a fresh oil and filter change, all the fluids have been tracked and topped off, this truck is 100% ready to go, there is your emissions sticker, did want to show you the door stickers here real quick, this one has the tire and moding information and then your ving stickers right there, once again thanks for checking out the video and I would highly recommend this truck from a quality and condition and just a rare standpoint, pretty rare truck right here with the mega cab and the 6 speed with the diesel and to see more pictures of this truck or one of our other 550 new and used cars, trucks, SUVs, minivans, wranglers, halftons, recorder tons, one tons, you name it we got to go to that website right there, summitauto.com full pictures and descriptions of every single vehicle from two locations, all at summitauto.com and if you'd like to check out more HD videos you can go to youtube.com slash summitautom, remember to like, subscribe and share on this video and all the videos that you see there, click the bell notification so you get updates on the videos I do each and every day here at Summit Automotive, in fact in a second you will see a link to subscribe to my YouTube channel on the upper left to link to more Ram 3500 truck videos, like this one the upper right link to this vehicle on our website on the lower left, and a link to one of our latest YouTube videos on the lower right with those checks out, we're super excited to help you with this ultra clean 2018 Ram 3500 mega cab, duly, short box, big horn, Cummins diesel with the 6 speed manual transmission in brilliant black crystal, thanks again for checking out the video, remember to like, subscribe and share on the YouTube channel, I really appreciate it, thanks again.


What type of glue should I use to mount paper to a surface? There are lots of different types and qualities of glue available. You need to pick a glue that will dry clear and doesn't have much water in it so that it won't ripple the paper that you're using. A spray adhesive or even a glue stick works really well.  Then very carefully roll it out bit by bit onto your substrate making sure that no air pockets are trapped underneath. Then you can take a clean piece of paper and a brayer and roll on top of your piece making sure once again all those bubbles are out and that the piece is adhered securely. And then just allow enough time for that glue to dry thoroughly before you apply your art resin.