So this talk is the top 15 Python tips for data cleaning and understanding. So yeah, so good luck, you're ready to go. Thank you. Okay, yes. So hi everyone, thanks for tuning in. So like for today, for today's talk, I'll be talking quite a bit about like what I do at work like 70, 80% of my time because like, you know, as a data scientist as someone who works on data science project, like 70 to 80% of our time is spent on data cleaning and data understanding. But in a way, like, I feel like this topic is kind of underrated. Like a lot of people don't talk about it. And maybe I guess it's because like it's boring, like you don't see any fantastic product from this process itself. But I feel like it's something that, you know, all of us whenever we are learning how to work with data, how to understand and analyze data, these are the few comments that I feel like are really useful for me. And I hope like at the end of this talk, you'll find this or all these comments and also tasks that I'm going through useful for you. Yeah. So this, you can think of it as like a consolidation of my experience. And of course, like I am also happy to hear from you, like if you have experience working on different projects and if there's anything that you feel that I have missed out, yeah, I'm happy to add them in as well. So a little bit about me, I'm currently working at a global media agency called Essence. And I am actually helping to build a data architecture over there. So we are trying to build a data warehouse and we are laying the foundation basically for more analytics capabilities. And this is where like we do more ETL, extract, transform and loading. And there's a lot of data training involved as well, data understanding, like trying to make sense of what are the valid data and what data do we need to, you know, clean, how do we clean them? Yeah. And outside of work, I run a data science blog called Data Level Confirm. So if there's a little bit of time towards the end, I'll share a bit about my blog. And yeah, basically I actually have been working with data, like since my undergrad days. And back then in school, like I feel like a lot of times, the data sets that we work with are clean and also partly because like the focus of the curriculum is more on statistical analysis and also on modeling. So in a way, like the focus on data cleaning and data understanding is slightly less. And I feel like most of the time, I mean my experience on how we should clean data is related back to my work, like right on the job, I learn about the data itself, like how it's being collected and then all the rules governing the data, like, you know, for example, why this data is being collected at a sudden frequency and why this data is being quoted in a sudden way. So a lot of this is like really dependent on the business context. And I guess this is something that, you know, in schools, we might not be able to work with such data because, you know, they are not really business-centric in a way, like that's why for academic projects, sometimes the data can be much cleaner than the real-world data that we actually work with eventually. So over the seven years of working with real-world data and also with open data, I keep going back to the same stack overflow answers to find out, like, you know, how to do certain tasks, like how to do certain operations, which is why like I eventually came up with this framework, I would say like this process, this guide which I'm gonna talk through later on on the 15 tips, yeah. So without further ado, I will just give a high-level overview first before going down into the code. What these 15 processes, I mean, 15 tasks is, yeah. So mainly like whenever we get the data set, we are interested in knowing like what data variables there are in the data set. So usually we will query to find out what the column name is. And then subsequently we also want to know whether the data that we read in using Python is correct or not, like, you know, whether Python loaded the data correctly. So it can be in various UI, say, for example, if you use Spider or say, in my case, I like to use Jupyter Notebook. So if I read the data in, I want to check that all the data, all the records is being read in, yeah. Then next, I will want to understand like what the data types of my variables are. So, you know, when Python reads the data in, right, like, or to be more specific, like, when we're in using Pandas in the library, then the library itself, like, they will automatically try to detect, you know, what is this particular data type. So it is smart in a way, like, for example, if you have string, like for a particular variable and string format, and then it will detect it as an object. Like, if you have integers of float, yeah, then they would subsequently try to detect it in that way. But in certain cases, like, it can go a bit wrong. So this is why, like, we also want to check whether the data type is correct or not. Yeah. Then next, sometimes we want to understand, like, what are the unique values for each variable that we have, okay. And this is really one way to identify and clean data.  And then somehow, like, during the data collection process, say, for example, it's not collected through system, or it allows, like, manual entry kind of stuff. And then you have people cleaning in, like, M or F, you know. So they are similar in a way, like, you know, male and M is, like, referring to the same thing, but they are treated currently as two separate categories in terms of unique values. So this is where, you know, like, we want to eventually do some correction over there. And then first, this is why we need to know what other unique values there are. So next, similarly, like, so for continuous variables, we want to understand what the range of values we have for them, right? So, like, in certain cases, you only have positive values, like, you can only take positive values, but somehow, this particular variable shows a negative value in terms of when you look at its distribution. So this is probably, like, an incorrect input, or, you know, like, something happened along the way during data collection that resulted in this, yeah. And it's something that we want to investigate further. So which is why we do this task. Okay, so subsequently, next, we will also get the count of values, say, by different groups, like, by different levels. So you overlay, so instead of, like, univariate, like, one level, getting just the unique values you want to get account on. So this is somewhat related to data understanding. And then in certain cases, you want to rename columns before you do any, say, merging or appending, right? So this is why I also included this as a tip. And then as well, there are certain cases where, you know, you have people inputting, say, for example, values in Excel spreadsheet, right? And then they have coded it in as a currency. So you have, like, dollar sign and as well as commerce. And then, you know, eventually, when you read the data set in using pandas, this variable is identified as, like, I'll check, like, it is not identified as integer or float as you would like it to be. So in these cases, like, we want to remove the dollar sign and the commerce, okay, from our variable. Okay, and yeah, relating back to the data types where never, like, we want to convert the data types to the correct format, yeah. So there could be cases where we want to convert string to numeric and string to date. So this is one of the tips that I'm going through as well. And in other cases where, you know, you want to replace values with another values, like, back to the example of the male and female case, like, you want to replace M with male and then F, like, yeah, with female. And I am also including slightly more complex transformation over here where, you know, we want to identify, like, data variables similar or different across data sets. So this is, you know, a way also to understand, like, what are the keys that we can merge on, you know, if we want to. And then, again, like, whether if you want to do, like, a left or right join kind of thing, like, what are the variables, what are the records that are going to drop because they don't appear in both data sets, right? They only appear in one, yeah. And then, similarly, if you have, like, a lot of data that is collected across time, and that's when, you know, you want to append data sets, like, to make your analysis more robust and more meaningful, like, you have basically quarter one, quarter two, quarter three, you know, and then, yeah, each quarter, they collect the same kind of data. And so, every time you have new data set, you want to add them together, yeah. So this is also something that is very common when working on any data science project, when, you know, you have so much data that you can work with, yeah. And similarly, like, if you have a lot of data that, you know, you are, like, pending, then there could be cases of duplication.  So there's different ways where we can choose to drop the earlier record or the late record, you know, kind of thing, yeah. And pandas can handle this very easily, yeah. And next, yeah, similar to a pending, say, for example, you have data from different sources, and then you want to combine them together, right? So for example, if you have, like, demographic data from one database, and then you have transaction data and other database, yeah. Then you want to merge them together, yeah. And then based on, like, a certain key, like a customer key, for example, and this is something that is very useful as well, which, so I'm also going to touch on, yeah. And next, the last tip before the two bonus tips is about recording. So in these cases, like, we want to, say, change, or rather, like, include a new variable based on the existing variables, okay. So we can do some form of recording, and yeah, pandas can allow us to do it very easily as well. So there's two other, I would say, tasks that I do it sometimes, not so often, but I think they are very interesting and they are very useful as well. So I've included them in, yeah. So just to give a bit of context on what we are going to go through later on, there's this particular scenario that I have basically created that is relevant to my work, yeah, what we do at work, for my current work at Essence, where, you know, we want to investigate, say, certain practice behind campaign success, like digital advertising campaign success, or like, yeah, if we want to do some post-campaign analysis, then we have data from different sources, right? We have data that is related to the campaign details set up, like how it's being, like, in terms of what market is being run in, how many days is it run in, you know, this kind of thing. And then we have also viewability matrix, like, for example, related to impressions, clicks, you know, yeah, and then next we also have results from the different platform that help us run Brandleaf, yeah. So we've sold, you know, data from so many different sources, like there's going to be common issues, you know, like related to inconsistent naming of variables or fields across datasets, yeah. So it can be relating to the same thing, but they are just turned differently in different datasets. And then also in appropriate data formats, or like there can be invalid duplicate or missing value. So all these are related to how clean the data can be, yeah. So the materials for today's talk can be found here, yeah. So I will be sharing this link, I mean, I'll be opening up this Google Drive later on, so you can check it out later, yeah. And the two libraries that I'm going to go through today is mainly Pandas and Ampai, and yeah, as you can see, if you search on the web, right, to learn data science, like to learn Python for data science. Mainly, I think these are the two most essential libraries that you need. So before you go into, say, fancy modeling or, you know, like algorithms, then eventually, I mean, what essentially you need to be able to first process and clean the data, right? So these are the two libraries that lays the groundwork, okay? So Pandas is built on Ampai, and yeah, Ampai basically is a library that helps to handle different kinds of like data structure relating to arrays, yeah. So there's three datasets that I have created, they are all mock data, and this is a preview of the dataset. So main key, these first dataset, hot campaigns, have information relating to the campaigns, like the different advertising campaigns, okay? So you have spans and information relating to which marker and platform they are run on, okay? And then there are also two other datasets that are relating more on durability matrix. So like again, impressions and different kinds of like impressions like this is measurable, and then they are collected across different times. So like H1, you can think of it as being the first half of the year, and then he's shooting second half of the year, but there are more data being collected in the second half of the year. So the thing is that like, there can be different people, different teams collecting the data differently. So in a way, there could be some inconsistencies here and there when it comes to like naming. So if you look at it, right? Like over here, we have campaign name, okay? But in a matrix, we have campaign, for example, but they are similar, referring to the same thing, yeah. So all this kind of like, I would say a reflection of the challenges, like the data challenges I face at work, and in this case, like U2 confidentiality. So I am just creating mock data, like, you know, fake data, because yeah, we can't share the data that we have. So for the rest of my talk today, I'll be using Jupyter to go through the code, okay? So Jupyter notebook is like a very friendly UI. And yeah, if you go to the drive later on, there's like a HTML format as well. So yeah, so mainly, if you just run through, you can see like all the different commands structured type to the task number that I have gone through just now in my presentation, okay?  Then the output will show you exactly, you know, like what are the column names that you have in each data set. And then subsequently, you also can see, like if you just run dot shape, right? So all these are like commands that is type to either numpy or pandas. And then if you just import them at the start, right? Then you can already run it. And by default, like if you install Anaconda, Anaconda has Jupyter notebook and also Python directly within the distribution itself. So it's, yeah, by default already installed. So you don't need to run any, like further pit install to install numpy or pandas, yeah. And then the thing that, yeah, I just want to highlight is like, for example, yeah, all this, you know, all these commands, they are repeated for all the three data sets. So you can kind of like keep seeing the same thing, but really it's like a process of understanding our data. So we want to be very clear, you know, whenever we receive a data set, we go through the same similar process, same treatment for all the data sets. So just one thing to note over here, like I have created this scenario in my data set whereby, you know, people actually do different kinds of coding or rather like do different kinds of recording of the platforms where Facebook and FB, they are actually the same thing. And then YouTube, you can have like, you know, people not doing caps for the tea and also just abbreviating it as YT. So they are referring to the same thing. So this is something that we want to clean as well. Okay, so yeah, over here, we can make use of the described function, you know, to look at variables that are numeric in nature. Okay, yeah. And for variables that are categorical in nature, we can make use of value count, okay? And then similarly, like if you want to look at more detailed breakdown by different levels, okay? So this is by market and by vertical, okay? You can make use of the dot count function.  We can just choose a particular column to output. Yeah, so these numbers over here is the same. That's what you see at the top, okay? So in cases where, you know, we want to rename our column names, okay? So this is how we can do the renaming. Yeah, and this is just directly replacing them in the data set, okay? So again, like to remove symbols in values, we can make use of the replace function. So now the data for spans is clean. Yeah, we have removed those unnecessary symbols. And then similarly, like for converting string to numeric and string to date, yeah, there is this two numeric function in pandas, okay? And then over here, when you see this section, I mean, this part of the code that says errors equals course means like whenever there is a missing value, for example, or like, you know, format that cannot be changed to numerals, there will be output as missing, okay? Yeah, so over here, we want to clean our values for Facebook and YouTube, okay? And then also we've made use of the replace function, okay? So in this particular line of code, what I'm trying to do is to add like underscore between all the values in the different columns. So I'm overwriting this campaign over here because the keys that I want to match on have to be corrected first, okay? Before I can match with the other data sets, okay? So over here, yeah, I'm looking at the variables similar or different across data sets, okay? So the similar column name across all three different data sets is campaign, okay? Yeah, and over here, sorry, just to go back a little bit, yeah. Clips and measurable is present in matrix H2, but not in matrix H1, which you have seen above just now. Yeah, so congecting or appending is very straightforward. Yeah, you just have to make use of the function pan. And then now you can see there is a total of 36. Rows. So cost, in fact, actually when I created these data sets, I made it a case whereby we have some form of duplicates, okay? So I am keeping, yeah, the last, the latest record, okay? So, yeah, I didn't want the earlier record switch unclean to me, okay? So next we do some form of merging, okay? Across the three data sets, okay? And then now I want to add in a new column variable, okay? called days, where it's the number of days between the start and end date. So I want to do some other form of checking or identifying different priority groups, for example, and then I do some form of recording based on the number of days, okay? So this is where I cover the recording, okay? And then this very particular, I mean, this is a new package, okay? Which you have to install. And it's very powerful, I feel. So it gives us an overview of all the variables and its distribution, okay? The unit counts. And from here we can get a sense. Like for example, you see nowadays you have like negative which is something wrong, okay? So yeah, from here you can identify like until data and they actually give you a correlation matrix. So yeah, I think this is really useful if you want to have like some firsthand preliminary understanding of the relationship between variables. So of course, like some might not make sense. Some sort of correlation here might not make sense because it's not data. So in terms of imputing missing values, we can first like, you know, if I'm interested to know which are the growths that have contained missing values, you can, I mean, you can filter it using this particular code, okay? And then next I impute the missing values based on mean proportion, okay? Of the measurable impressions to impressions, okay? And then now after I do the imputation, okay? I have, yeah, all these values now. Yeah, they were originally missing as you can see from the top, yeah? So let me jump back to my side, okay? So just to quickly wrap up, yeah. Mainly there are actually more exercises relating to Python and stuff that you can find on my blog if you're interested. So do check it out, yeah. Thank you. Good.


what to wear this autumn winter with all the latest and most wearable fashion trends for 2021. This is your definitive guide for what to wear this season, how to style it and where you can shop the look without blowing the budget. Welcome back to all my regulars, you know I am always very grateful for all your support and if you're new here thank you so much for finding my channel. Right, new hair, new season and so many beautiful new trends to talk about so let's dive straight in. Dopamine dressing, it's the perfect antidote to those neutral cozy lockdown dressing we've been doing a lot and one fashion trend for this autumn winter sees us switching to all things very bright and beautiful. From coral pink and neon yellow of Versace to orange and very saturated red of Roxander it is the season for a little dopamine dressing. Color is of course very closely associated with our revolutions, I do challenge anyone to pop on a yellow dress and not feel a little bit brighter or red for example always makes me feel very motivated with a healthy dollop of suss. In fact if you take a look at my instagram at the moment I recently painted a wall in my garden bright yellow and it does really lift your spirits so that connection between emotions and color is very true certainly my experience anyway. Saying that I am traditionally a big fan of very neutral dressing, I find it very elegant and effortlessly chic and it's quite easy to put outfits together but I am quite excited to introduce a little bit of this into my own wardrobe after a year and a half of lockdowns I think the idea of adding in a sprinkle of cheerful color here and there is really quite exciting. Now what I will say is I think it will be really easy to integrate this fashion trend into your own capsule wardrobes whatever your budget because the high street is already hot on the heels from the catwalk and it's full to the brim of a rainbow of beautiful colors so there's loads to choose from from all different price points which is good to know and on top of that I'm imagining the selection will just grow and grow as we move further into autumn winter season. As always I shall link all of my favorites in the description box below so you can have a look at those at the end of this video. Luxe layers no big surprises really with this next trend knitwear but the key to making this season slightly different than last year would be the more layers the better. If you think dripping in knitwear and you're on the right track not so great for those of you who live in hotter climates but for those like me who absolutely freeze in winter it is the perfect trend. Now in contrast to dopamine dressing when you look at knitwear as so a standalone trend it does look a lot more neutral. They're two very separate trends so I would say when you're particularly looking for your layers of knit try and keep your eye out for the beiges and creams and camel and grey even. Those are the sort of tones we've been seeing with this look and as a happy bonus they're never going to date in your wardrobe which makes it a bit easier if you're investing a few more of your pennies. And as a side note cohorts are definitely sticking around for another winter and you could really easily integrate those two trends together. So for example find yourself a lovely knitted dress that comes with a co-ord cardigan. Layer a scarf over the top and maybe even add a duster coat in like a lovely knitted wool as your final layer over the top and it's that kind of look with layer upon layer of beautiful knitwear in perhaps slightly different tones that when you look at it as a whole outfit together no matter which kind of wool whether it's quite affordable or really high-end it always ends up looking really effortlessly chic and very luxurious at the same time. Fair Isle. One industry that hasn't really had a look in in the last couple of years would of course be ski resource which might explain why we saw a flurry of ski inspired outfits from the likes of Louis Vuitton Chanel and Mu Mu. Think Fair Isle sweaters, stirrup leggings and padding just about anywhere you want to wear it but the most wearable fashion trend for me at least within this ski resort genre would be a feral jumper. You can pick up some really affordable versions on the high street or maybe support some of your small independence who often have some of the most original pieces. Don't forget to let me know in the comment section below as we go along which of these fashion trends you think you will be wearing this autumn winter. Maybe you want to rock the stirrup leggings trends or you're going to try the neon knitwear or possibly even one that we're going to get to a full length bodysuit I know. The thought of that one fills me with dread but it's always lovely to have a good chit chat with you all in the comment section below. Super-sized jeans. So last season it was all about loose fitting jeans. This season like a lot of fashion trends the designers have taken it that step further and made it even more extreme. Super-sized baggy jeans were all the rage on the catwalk and although at first thought you might be having mild panic at the idea when you think about all the other fashion trends that we'll be seeing this season you can see how it will work really nicely together. And when it comes to the tone of denim you go for Vogue advises that dark indigo is where it's at. Although honestly I don't think anyone's going to pull you up if you've got a light wash pair of oversized baggy jeans. Getting the shape and fit right is the most important factor. The tone of them is a bit of a brusy bonus if you wish. Now although we say oversized jeans there are also variations of that that you can pull on. You could opt for a slightly tailored version that gives the waistband a bit more shape opt for pleats for more detailing. Loose long, high-waisted, cropped, flared or straight are all on the list so there's loads of options to choose from. Micro-minis. Now Tom Ford, Valentino along with a variety of other designers paved the way for the micro mini this autumn winter. So this might be finally the season that I've put my favourite mini skirts to one side and opt for something a little bit shorter. As you might have noticed on my channel I very rarely wear mini skirts or skirts at all for that matter. But when I do wear them I've traditionally preferred a pencil skirt, a midi or a maxi. But in winter or more specifically when it gets colder and I compare it with tights a mini skirt does suddenly become a bit of a possibility for me in my own wardrobe. And I actually quite like that look as you can see here I've paired one I bought recently from Mango so really affordable with a pair of black tights and my little ankle boots. So this one has been made from faux leather and it retails under £30 so really affordable and I do like the look of this paired with black tights I do feel comfortable and I think that will mix and match with the other tops I've got in my wardrobe already and what I've got my eye on to buy this season. The high street's gone all out on this trend as you can imagine they would so there's pretty much a mini skirt for every single budget. Now I must have lusted it after this bestseller at Cezanne for a couple of seasons it's one of their essential pieces so they bring it out season after season and perhaps this year might finally be the year that I actually decide to go for it. Faux fur. Faux fur coats, gelets, hats and bags even were seen all over the runway for this autumn winter. Now I must admit the hats reminded me of some sort of mid 90s rave so I'm not sure I personally want to recreate that particular look but on the flip side a faux fur coat for winter can be super cozy and look very chic while you're doing it if you find the right one of course. A cropped version can look great worn over evening outfits for example keeping you super toasty and adding a sprinkle of glam and I do like the longer versions for more everyday wear. Puffer jackets. Gone are the days when they're solely reserved for the side of the football pitch. This 2021 the humble puffer has had an upgrade and you'd be hard pushed to find a brand out there at the moment without their own interpretation of it. I actually found this one recently at ASOS which came in under a hundred pounds I do really like it I'm definitely wanting to go for either beige white cream etc but I do think this one's coming up a little bit big. Kate had a stunning version but sadly that is far too out of budget for me as much as I love it. I also like this one from La Mer but my standout favorite was this absolute stunner from Isabel Marron. At the time of filming this video that actual one hadn't been released to buy just yet on her website I'd only seen it on the runway but I'm hoping by the time this one actually comes on sale that I wouldn't need to remortgage my house to afford it and I could actually put it on my shopping list. Failing that I will be looking at my usual favorites from the high street to see what their take on the trend is. Mango always do great coats, H&M often replicate the runway in a super affordable fashion, Reese always put a great spin on the trends which I like and other stories equally bring something new to the table which is often very original. You get extra points for styling your puffer jacket with your extreme high low outfit underneath and if you're feeling adventurous your fluffy faux-faux hat to boot. Extreme high low, it's cool, it looks effortless, full of fun and I think that's exactly what we need this autumn winter. Seen in all its glory at Celine with their big sparkly lamb shade style skirts starred with a simple bummer jacket and boots. Though obviously that one's not quite suited for the supermarket or school run I think I might need to tame that one down a little bit to make it truly wearable. But what you can do and what I tend to do a lot is look at the essence of that fashion trend and then make it work for your own lifestyle. So for example I'd find a glitzy top like this and I'd probably style that with a pair of jeans maybe a nice pair of boots but that just makes it much more wearable possibly even everyday wear but you're still giving a nod to that trend which is what I like. You'd have that extreme high low shining through but in a much more wearable fashion say for the local pub. High low dressing has that wonderful knack of pretty much always making your outfits feel effortlessly chic and classy and elegant but with that cool up-to-date twist so I do keep hoping on about that style but it really does work. Polar neck tops on a similar train of thought but a much milder option shall we say would be the humble polar neck top or roll neck top making a comeback for this autumn winter. Now I spotted most of these layered underneath this season's trouser suit or micro mini skirts for everyday style I'd probably wear mine with loose fitting jeans maybe adding a chunky cardigan over the top but when it comes to really easy trends like this the options of where to buy the colors the fabric content is vast. You could go with a fine marino knit a cotton or cashmere if it's within budget and this could also be where you introduce a sprinkle of dopamine dressing and maybe opt for a pop of color with your roll neck so you've subtly introduced that trend as well. The key points for this trend would be to make sure that your roll neck fits pretty snug. You'll miss a trick if it's too loose and it would just look like you've half heartedly attempted this look. The trouser suit. One hugely wearable trend for this 2021 has got to be the trouser suit. Perhaps having been out of the office for so long designers and us for that matter are all hankering after some work wear for our wardrobes. Whatever the reason I personally love this trend and I don't even work in an office. Now I actually found so many trouser suits that I would be more than happy to add into my own wardrobe so here's a little selection of the ones that are topping my list. Now they're sourced from all different price points really so there should definitely be something for all different budgets within there and I've linked them all in the description box below. And as a happy bonus if you pick yours wisely not only will it look great styled together but you should also have some great separate pieces that you can mix and match with the other items in your wardrobe. Victoria Beckham actually had a great interpretation of the trend while Stella McCartney's version fell a little more menswear inspired with that boxy cut and a nod to the 70s with those flares as well. Now for me personally I think I'm looking for a straight leg possibly a crop um so slimmer on the bottom half paired with perhaps an oversized blazer for the top that way I know I'm going to get the best use out of those pieces as individual elements with the other items I've got in my wardrobe and that's the way you should try and think about it not only as the suit as one outfit but what will the trouser portion look like with the other tops in your wardrobe will you wear that blazer with all the other bottoms you've got and if you can tick loads of those boxes you know your cost per wear will come right down. Craft work I would say the 70s are one of my favourite fashion eras so I never mind adding a sprinkle of it into my outfit ideas and thanks to the ever-spinning carousel of fashion we only have to wait a season or two before it comes back round again. This autumn winter the trends hark back to craft work, crochet and a bit of patchwork denim so more of a homespun vibe to it really. Again it's a pretty easy one to integrate into your outfit ideas without it feeling like you're playing dress up. I would add something like this top into an outfit perhaps paired with a denim skirt maybe knee-high boots depending on the weather it gives a nod to the trend without it feeling like fancy dress which I know is a fine line to get right but totally possible if you find the right pieces all in one body suits. Now even though some of these fashion trends are not particularly for me it's definitely worth giving them a mention especially when I think they would look super cool on other people which seamlessly brings me to the all-in-one body suit. For some reason I've got 60s icon Jean Shrimpton in my head when I think of this fashion trend I'm not sure she's ever worn one but I'm imagining she would look amazing in it.  I've seen quite a few second skin style tops about recently obviously a trend from last season but definitely still hanging around for another few months yet and quite probably a much more wearable way to wear this trend. A quick mention for some other new and noteworthy fashion trends we will see this season cutouts back again for another winter. Coles particularly have some great options on this look. Florals are back again but with a much more vintage feel to the print. You can never go wrong with a beautiful trench coat and although they never really go out of fashion they are particularly coming back with a vengeance for this autumn winter. And super-sized handbags are another big hitter which I personally love because it means I can cart around everything but the kitchen sink. A lot of these fashion trends for 2021 are kind of pushing us out of our comfort zone and encouraging us to have a bit of fun with our styling and just throw caution to the wind and try something new and I think after the year and half we've all had what's the worst that can happen? I'm going to have a bit of fun with my styling this season and hopefully I've encouraged you with some of these fashion trends to do the same. I really hope you've enjoyed today's video and it's given you lots of inspiration about not only what the trends are for this autumn winter but how you can interpret them in a really wearable fashion into your own capsule wardrobe. I've really enjoyed taking a deep dive into all the fashion trends coming up and I'm even more excited about starting to style them into my outfit ideas. If you liked this video I would be very grateful if you would show your support with a thumbs up and if you're not already subscribed that would be really lovely too and greatly appreciate it. As always I really look forward to having a good chitchat with you in the comment section below so don't forget to let me know your favorite trends the ones you're going to give a wide berth and those ones that you think you will end up wearing on repeat. It's always really nice to hear from you all. Have a lovely week everyone and I will see you next Sunday.


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Yeah, it was good just to get my opportunities. Todd gave me a great opportunity. Ryan threw me open. It wasn't any magical route running that I did. Just Ryan's such a great quarterback. He understands everything. He's just a good little spot where only I can make it. So it's credit to him and the play corner.  He kind of caught it with your wrist. Yeah. It was kind of a broken down play. We're doing something quick. Kind of broke down. And then I kind of peeled up through the middle of the field. Probably couldn't have just sat it down. What made it like getting hit by the safety. But found a way to get it done. You mentioned great team win. I mean, home court was it today that everybody stepped up? Especially with the offense struggling a little bit? Yeah, it was awesome. I mean, this is the NFL. I mean, we can't light it up. You know, 300 in the air, 300 on the ground every week. I mean, sometimes, you know, you need these tough 4-4 gritty wins that just builds character, helps the team work through adversity. The group like this. Four straight wins now since you went to start. I mean, what does it say about this team to kind of bounce back? Yeah, I mean, it's crazy to say that I know that bomb was forgot. I mean, we're just so focused on the process here. And that's what you can do. As soon as you start smelling the roses, that's where you get taken out in this league. So Braves, J-Rob, coaches have to do a great job of just, you know, nose to the grindstone every single day. Thanks, who? How did the Dominos fall on that? I think they just want to put, you know, the best, you know, 11 guys that give them the best chance of winning. And whatever they do, I'm going to do my job and I just want to help the team win. So wherever they put me, I'm going to go play it to the best of my ability. Can you talk us through the pick six? Oh, yeah. So we were in an empty formation and we had a little defensive check and that allowed me to get my eyes on a quarterback. So I was key in the quarterback the whole way. So when I saw him look to my right, I broke to my right. He threw the ball, picked it. Rest was history. What was the hardest part? Making the easy catch or outrunning everybody for 76? I would say the hardest part was making sure I didn't get caught. I was kind of checking back and behind me, making sure nobody was coming. And then once I saw I was clear, it felt good. I know you haven't been here this long, but that's this team. You go for 76. Terrence was the big punch out at the end. The guys just come in the door and seem to step up. Yeah, I mean, I think it's the culture in the building.  It was interesting as well because we didn't know what the Colts were going to do after last week if they were going to revert to running it or throwing it. They still went out there and tried to chuck it around. Right, right. Yeah, I know, you know, last week we hit through the ball 58 times, but like they said, it's hard to win like that, you know, a lot of games. So we were thinking, you know, Taylor was back that they were going to give him some carries, but we knew DB's had to play well. So this one of those games where it kind of took a village, I mean, everybody contributed. Definitely. But we preached that and we knew that and we knew it was going to come down to four quarters. It's always like that with the Colts, the divisional game. We knew everybody was going to have to put their hand in the pile, which they did, which is, you know, the best feeling. No, because I knew he had to get rid of the ball based on the defense. So I knew the ball had to come out quick. So I knew if I get my eyes and break and get a good break, I can make a play. Just to kneel down and pray when you got to the end. Yeah. No dance, no break. No, that was my first pick six of the career. I have other interceptions, but I mean just all glory to God. I wouldn't be here if it wasn't for him. So I had to give him the glory first and then celebrate with my teammates. All right, if we won the game, like, we have to be on the same page. I think that's where we can so much better on defense, especially up front. Whoever, you know, whoever on the field we communicate so much and we just working so good together. Able to hold Jonathan Taylor just 58 yards on their ground.  I mean, just like the first game, you know, we're here at all week that Jonathan Taylor and Heinz are going to be back. But I think the first game, collectively they only had like, what, 48 yards in the first game. So, you know, around here, we stopped the run. Now, no matter who in the backfield, you know, I hate to say, if we played against their hearing, that's all mindset, stop the run. So, but the same time, you know, that's all model on defense, you know, stop the run. And we're going to affect the quarterback once we stop the run. He's over five against you guys now, in the 100 yard department. That's what we expect. We expect, you know, no matter if it's Jonathan Taylor or whoever else in the NFL, when we line up, I mean, to go back to that first game, that's when I'll stand it. We're safe one. So, I mean, we have gotten way better by stopping the run. And we've talked about it. You know, we have to get back to stopping the run right here. In practice, you think you were taking this one kind of personally. You see, kind of fired up. Did this game mean a little more to you? No, I think it just, you know, especially coming off the boundary.  You know, I would take it by, you know, a lot of, I guess, I hold myself to a higher standard. You know, when we come in off a boundary, it's got to always be the best week of practice. You know, that was our motto in the defensive room. Even in the walkthrough, let's have energy, let's communicate. You know, just not me, but I always say every game personally. But also, you know, I self-match up to it. You know, when you got to say Clint Nelson, whoever it may be, or if I line up against it, I'm trying to take it here. You guys took Adams and put them on the field and they all put a money in the nickel. And it looked like it really was a huge difference between you guys. I mean, it was great to have a hook back on the field.  And like I said, I told him we get some shirts made. Me, Nico, and Bud, and mama, there they go. And when you feel like you three guys are on the field, does anybody can stop you? We don't feel like no one can stop us. And that's what we're trying to get and, you know, because we want to keep gaining a trust from our coaches.  Let us just go get the quarterback. You play this game to win and you don't have to feel good when you play. And no matter what, we came out on top today. It's a big win for at home versus the vision of home it and whatever it takes. I'm just glad we won. There was a play at the end of the game where it looked like Derek Henry was going to come up short of the first down and then you and the rest of the line kind of picked him up and pushed him to get the first down. What was going on in the play? Yeah, it was 11 guys. It takes all 11 to run the ball. We had 11 guys pushing the power right there and that's what it takes to win games. It's not easy. It's not pretty. But when you want to win, you find a way. How personal do you take it when people say Jonathan Taylor is the best running back in the late? We got Derek Henry and everybody knows the real answer to that. We love that guy and how much he cares how hard he works and it's no other back I've blocked for. 30 yards, I think he had 30 more carries today. That's total rock. That's a load. Yeah, that's what he wants. That's what we want. It's an offensive line. It's a physical game and that's how we want to play. The quarterbacks hurt. It's obvious and everybody knows you're going to try to run the ball and you're able to do it. How big of a move forward is that for this group? Yeah. We haven't put together second half in the past but we're on the right track and we come off the ball. We had a mindset and you got a guy like 2-2 in the backfield who's special and he wants it as bad as us so whatever it takes. 5-0 against this team. Love it. Love it. And now 4-0, 4 straight wins. How do you continue this in Houston next week? Yeah. You got to put this in the bed just next two or three days and it's another big division opponent and you got to win these. These are the ones that come up at the end of the year and you got to take care of the division. The last drive was huge to make it a two-score game. Kind of put it out of reach and then of course that turn over at the end was huge also. This is a Colts team that they're continuing as a team, as a Titans team to dominate a 5-strike win just for you guys to be kind of a hold of the division right now. Yeah, it's really special. They're a great team. They always play us really well. It always kind of comes down to the wire so each week we play them. We're going into it with that mentality. I don't know if you realize if you hit a mark that not many players are able to do that you want to field goals made were you aware of that coming into the team? Yeah, actually I wasn't aware of that. I had a couple guys come over and congratulate me. That's pretty special, pretty cool but I didn't know that was a milestone until somebody had told me that. In a game where the team doesn't score and all kinds of touchdowns, how good does it feel for you when you can kind of carry the weight? Yeah, it feels great to just go out there and do your job, help the team win, get points on the board and that's the biggest thing every point matters in these games. I know there's a couple of times where you realize, wow, my kick is going to make a difference and put this a little bit out of reach. Is it any more pressure or you just kick? To be honest, it's one of those things where every kick matters. All these games are so tight.  So it's not anything different for any other opportunity. Well, David Long, Jr., they don't come easy in this league but it's not how, it's how many, right? It definitely is. I think it was the team effort. We got some pressure right there. Matt ain't seen me, man. I just made a play on the ball. Yeah, the defense making plays all day and really the special teams as well on a day where the offense often struggle a little bit. My boy, Monty over there makes plays. I think it was all three sides of the ball, you know, playing off each other. The energy, you know, as long as we play together, man, it's hard to beat. Another great effort against their running game, you know, you held Heinz and Taylor and Czech at least on the ground. How much pride do you guys take in that and stopping the run? I think we take a lot of pride in it. I think, you know, throughout the week, that was, that's our key.  So coming in again, we know we got a good front seven. You know, we just play our game.  Four and two now after an 0-1-2 start, what does it say about the character and resilience of this football team? I just think, I think it speaks to who we are.  You know, put the team first. A lot of those things, you know, go a long way. I think it's not showing. Definitely off to Houston next week. I know it's not West Virginia almost. Good luck. Appreciate it. Congrats on the win. Thank you.