Matplotlib boxplot mean line color

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Matplotlib boxplot mean line color

Boxplot is probably one of the most common types of graphics. Gives a nice summary of one or more numeric variables. A line that divides the field into 2 parts represents the median of the data. The top and bottom ceramics are shown at the end of the box. Extreme lines show the highest and lowest value without outliers. Note that this field hides the number of values Format 1:1 numeric variable (for y-name) + 1 categorically (gives groups). It's a long or 300-year-long format. Format 2: Multiple numeric variables : one per group. It's a broad format. Seaborn #30 basic boxplot and input format #30 basic boxplot and input format #33 color palette on the boxplot | seaborn #33 Uniform color on seaborn boxplot #33 Specific color for each boxplot group #33 Highlight a group on a boxplot #33 Add transparency on seaborn boxplot #34 Grouped Boxplot #Control order of boxplot #36 Boxplot with Jitter #38 The number of sightings on the boxplot #32 Seaborn boxplot: the width of the line #32 Add the zasjek sea boxplot #32 Contol width sea boxplot Boxplot i hide data Boxplot A boxplot summs the distribution of a numerical variable for one or several groups. This hides the sub-private distribution and the number of points of each group. That makes this map dangerous. This post gives an example of possible errors, and 3 solutions to repair. #39 Boxplot hides data #39 correcting boxplot related VIOLIN DENSITY HISTOGRAM Want to make numerical data? And in a beautiful, interesting and scientifically sound way? And do all this in a few simple lines of code? You're in the right place! A great way to plot numerical data is the matplotlib boxplot. Displays the median, intercovaral range, and output data. How can you visualize data with a box? Get this data into an array-like object -- list, NumPy matrix, pandas series, etc. Download it to plt.boxplot(). Call plt.show(). As a result, matplotlib will draw a nice boxplot for you. import matplotlib.pyplot as plt plt.boxplot(data) plt.show() The box clearly shows the median data (orange line), the upper and lower kertili (top and bottom boxes) and outliers (circles at the top and/or bottom of the mustache plot). We can do quite a few things to improve this plot ? we don't even know what the data represents! ? then dive into a more detailed example. Try it yourself: You can play with a simple example here in our interactive Python shell online. The resulting plot will be stored in the .png file in the online project (just click on the files): Matplotlib Boxplot Example Box is an essential tool that you must use when researching data sets. The matplotlib boxplot feature accepts a lot of keyword arguments and so it can seem quite daunting if you look at docs. So I'm going to cover the most essentials you'll use most often. The boxes show the distribution of numeric data, in particular whether it is filled and whether there are unusual sightings/outliers. They are very useful if you are dealing with a large amount of data and want to see a visual summary ? this is similar to histograms. They give you the option to compare multiple distributions at once, because you can draw a lot of boxplots in one image. This is not really possible with histograms ? more than 3 and starts to look crowded. Since this is an article on how best to work with boxplots, I will not go into detail on how I created the data sets. But to follow, I used the libraries of possible and pandas and the code to generate the data is below. Finally, more information can be found here. import seaborn as sns tips = sns.load_dataset(tips) total_bill = tips.total_bill data = tips.total_bill thu = tips=tips== 'Thur'].total_bill fri = tips='Fri'].total_bill = tips[tips[tips.'day == 'Sat'].total_bill sun = tips[tips.day == 'Sun'].total_bill Suppose you are a waitress/konobnica u restaurantu i you recorded a total invoice in USD for each chair per check from Thursday ? Sunday u last Sunday. You want to visualize this data to understand which days, if any, are best for work. A shared account for all days is stored in total_bill and the total account for each day is stored in variables such as fri, sat and sun. Let's draw an account together and add some information in the axis and address. plt.boxplot(total_bill) plt.title('Total Bill ($) for All Days Last Week') plt.ylabel('Total Bill ($)') plt.show() It looks much better and now it's easy to understand what boxplot shows. We can see that the median account for each table is approximately USD 17 and that the inter-smoking range (upper four- bottom quantitative) is 24 ? 14 = 10 USD. There are about 8 outliers where the account was more than $40 and the lowest was about $3. Matplotlib Boxplot More Boxplot can compare the distribution of different datas. So you will almost always want to draw more than one boxplot on the piece. To do this, as a list of lists, download the data you want to plot in plt.boxplot(). # Create list lists all_days = [thu, fri, sat, sun] # Pass to plt.boxplot() plt.boxplot(all_days) plt.show() Here I merged all individual data sets into the list of all_days lists and downloaded it to plt.boxplot(). Matplotlib automatically puts four boxplots a nice distance apart, but doesn't mark the X-axis for us. Let's do it now. Matplotlib Boxplot Labels To mark each fieldplot, specify a list of stacks of keyword tag strings. If you have multiple tags, I recommend you create it first before you download it to plt.boxplot(). # First, create data and labels all_days = [thu, fri, sat, sun] labels = ['Thu', 'Fri', 'Sun', 'Sun'] # Plot data and labels plt.boxplot(all_days, labels=labels= plt.ylabel('Total Bill($)') plt.show() Great now, we can that each boxplot represents the total bill for each day of each day of and what day is which. Make sure that your tag list is the same as the number of box pins and that you specify them in the order you want them to appear. If you do not want to mark a specific boxplot, specify an empty string ''. Finally, you can also pass the ints and blasts if you want. all_days = [thu, fri, sat, sun] # The second label is an empty string, the fourth is float labels = ['Thu', '', 'Sat', 999.9], plt.boxplot(all_days, labels=labels) plt.show() Your boxplots look much better now, but the default settings are quite boring. It is important that your visualizations are included and one of the best ways to do this is to add some color. Matplotlib Boxplot Fill Color To fill the field color only, you must first set patch_artist=True. Why is that? Under the lid, plt.boxplot() returns a dictionary containing each part of the box and these parts are Line2D objects. However, by definition, they don't have a edge-edge or face color -- the lines have only one color. To color inside a field, you must change it to a patch object that by definition has a face color. To change the field, use the keyword argument fieldprops (field properties). Accepts the dictionary and the pair of key values you need is facecolor and color. # Change the field in the patch so that it has the property facecolor plt.boxplot(total_bill, patch_artist=True, # Set facecolor to red boxprops=dict(facecolor='r')) plt.show() Note that if you do not set patch_artist=True, you will get an error. # Not setting patch_artist=True gives error plt.boxplot(total_bill, # Set facecolor to red boxprops=dict(facecolor='r')) plt.show() --------------------------------------------------------------------------- AttributeError Traceback (last call) in <ipython-input-97-d28bb5a14c71> <module>2 plt.boxplot(total_bill, 3 # Set facecolor to red ----> 4 boxprops=dict(facecolor='r')) 5 plt.show() AttributeError: 'Line2D' object does not have a 'facecolor' #Turn box into a Patch are that it has a facecolor property plt.boxplot(total_bill, patch_artist=True, #Set facecolor and surrounding line to red boxprops=dict(facecolor='r', color='r')) plt.show() Perfect, now you know how to change the box's color, let's look at changing the other parts. Matplotlib Boxplot Color You can change any part of the boxplot to any color you want. </module></ipython-input-97-d28bb5a14c71> There are 6 parts that can be painted: box - main body boxplotmedian - horizontal line showing median distributions ? vertical lines, which extend to the most extreme (non-outlier) data Pointscaps ? horizontal lines at the ends of the moustache ? points above/below the cap representing outliersmean ? horizontal line showing the average distribution (not included after default) In the picture above, I mark the first 5 parts, or it does not turn off u meat, by used frequently. Often. Containers. Each part can be changed by a prop keyword argument similar to the above <part> fields. Available keyword arguments are: boxprops, medianprops, whisperprops, capprops, flierprops, meanprops For example, write this to set the color of the median bar to red medianprops=dict(color='red') Everyone accepts the argument of color keywords and the value can be any matplotlib color string. The only other one is flierprops, which also accepts markeredgecolor for a color line around the outliers. Finally, be sure to set patch_artist=True to change the field fill color. Let's take a look at the case where I change the whole box of red. Since there are so many keyword arguments, I will first create a dictionary and use operator ** to unpack in my plt.boxplot() call. # Set color to red c = 'r' # Create dictionary of keyword aruments to pass to plt.boxplot red_dict = {'patch_artist': True, 'boxprops': dict(color=c, facecolor=c), 'capprops': dict(color=c), 'flierprops': dict(color=c, markeredgecolor=c), 'medianprops': dict(color=c), 'whiskerprops': dict(color=c)} # Pass dictionary to boxplot using ** operator to unpack it plt.boxplot(total_bill, **red_dict) plt.show() First I created a variable c to hold the color string in. This means that if I want to change the color to green, I only need to change one code line ? c = 'g' ? and this will change the color everywhere. Then I created a red_dict where the key values are set and dictionary. The first key patch_artists=True and the other keys are the key <part>prop words. Finally, I created a boxplot total_bill and painted it red with unpacking red_dict with the a** operator. To brush on your dictionary knowledge, see my article's ultimate guide to dictionaries. The red plot is much more interesting than the standard matplotlib colors. However, since the median line was the same color as everything else, you lost some of the information it showed. One way to do this is to set the median line to black with medianprops: dict(color='k') in red_dict. The result is shown above. Matplotlib Boxplot Width To change the width of the box, give a float to the keyword width argument in plt.boxplot(). It represents a fraction of the space the box takes over in the picture. If you have one box, the scalar represents the percentage of the parcel that the field takes over. plt.boxplot(total_bill, widths=1) plt.show() Here the field ingests 100% width as the width=1. Plt.boxplot(total_bill, widths=0.1) plt.show() Here the field ingests only 10% of the space as the width=0.1. # Boxes take over 100% of their allocated space plt.boxplot(all_days, widths=1) plt.show() Here each box ingests 100% of the space allocated as width=1. Boxes capture 80% of the allocated space.</part> </part>widths=0.8) plt.show() Here each box draws 80% of the space allocated to them as width=0.8. The width of each box can be adjusted separately by transferring the list to width instead of scalar. In [83]: plt.boxplot(all_days, widths=[0.1, 0.9, 0.5, 0.8], stickers=['10%', '90%', '50%', '80%']) plt.show() Here I marked the amount of horizontal space that each field takes. Although it can be done, I do not recommend it. Adds another dimension to this field, but does not display any new information. Personally, I think width=0.8 looks best, but you are free to choose any size you want. Just make sure your boxes are the same width so as not to confuse the reader. Matplotlib Boxplot Horizontal To create a horizon boxplot in matplotlib, set the vertical keyword argument to False. plt.boxplot(total_bill, vert=False) plt.show() Conclusion This is it, now you know all the basics of boxplots in matplotlib! You have learned to draw singles and multiple boxes on one number. You can mark them what you want and change the color of any of the 6 parts to everything you can imagine. Finally, you have also learned to adjust the width of the flats and horizontal. You still need to learn more about containers, such as changing an absent marker, adding legends, grouping and even working with them and a library of pandas. But I'm going to leave that for another article. Where to go from here? Do you wish you could be a full-time programmer but don't know how to start? Check out the pure webinar, packed with value, where Chris ? creator of ? teaches you to become a Python freelancer in 60 days or your money back! It doesn't matter if you're a Python beginner or a Python pro. If you don't do six numbers/year with Python right now, you'll learn something from this webinar. These are proven, no-BS methods that give you results quickly. The webinar won't be online forever. Click the link below before the seats are filled and learn how to become a Python freelancer, guaranteed. Expert Writer & Content Creator ? Data Science & Machine Learning. --? I help educational companies create attractive online and video content that teach data science to beginners. Unlike my competitors, I learn new concepts every day, and that's how I understand what it's like to be a student. My articles are easy to understand, effective and a pleasure to read. My videos are appealing and detailed. --? If you want to work with me, contact the upwork

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