Seaborn barplot has three parameters. x, y, hue : names of variables in data or vector data, optional Question What is hue? It seems the attribute to plot but why it is called "hue" because when I googled, the result is about color? Google Hue - Wikipedia Hue is one of the main properties (called color appearance parameters) of a color, defined technically (in the CIECAM02 model)

I would like to plot a landscape spanned by six variables. The numerical target variable is explained by five numerical variables. Ultimately, it is about to get a visual impression for optima and the parameter landscape itself. Any advice how to proceed? I would prefer R or Python but I am open to alternatives.

I have the a dataframe(df) which has the data of a Job being executed at different time intervals. It includes the following details about the execution of a job: Job Start Time (START) Job End Time (END) Time Interval (interval) i.e., END - START. A small part of dataframe is shown below. Dataframe(df): END | START | interval 1423.0 | 1357.0 | 66.0 33277.0 | 33325.0 | -48.0 42284.0 | 42250.0 | 34.0 53466.0 | 53218.0 | 248.0 62158.0 | …

Here is my diagrams, I want to remove the labels of the second bar that is C1,C2,C3,C4,C5, because it is repeating.

I have a dataset with 4 values - amplitude, psi angle, chi angle and class (color). The psi angle and chi angle represent unique points on a . I want plot these psi and chi values on this sphere co-ordinate system as a scatter plot. After that, I'd like to use the amplitude value as the histogram coming out of the sphere at certain height. Each class can be represented by a color. I was trying something in python matplotlib …

I'm trying to generate a seaborn pairplot with the ticklabels roatated. My code is here: sns.set(font_scale = 3,style="whitegrid") h = sns.pairplot(data=dfMViral, hue = 'ID',palette = ['#0000ff','#00ff00','#ff0000'],corner=True,diag_kind="hist") h._legend.remove() for axes in h.axes.flat: axes.set_xlabel(axes.get_xlabel(), rotation=45) plt.show() This is based on the answer here: https://stackoverflow.com/questions/61936040/rotate-ylabel-in-seaborn-pairplot But when I try this I get the following error: AttributeError Traceback (most recent call last) <ipython-input-47-3f18bb9ee92a> in <module> 7 8 for axes in h.axes.flat: ----> 9 axes.set_xlabel(axes.get_xlabel(), rotation=45) 10 11 plt.show() AttributeError: 'NoneType' object has no attribute …

I have the following dataset which I use to plot a line plot. The plot is obtained as the mean of values obtained from the data. I want to add error bars to this plot which shall show the standard deviation. I have looked up to different answers but in most of them they had defined x and y explicitly, but here I calculate the plot directly from the dataframe. How to add error bar to this plot? Dataframe df …

When I plot my data to see its distribution via Swarmplot, these two vertical lines that seems to even overlap occur in the figure. It seems to occur even around values that are not that dense, such as around -2.5. I feel like I might be missing how Swarmplot works but anyone knows the explanation?

I'm trying to do EDA on a dataset and I have found some visualizations someone else was able to produce but I can't seem to figure out how they did it. The dataset looks roughly like this: default Gender Marriage 0 Male Married 0 Female Married 1 Female Single 0 Female Others etc.. you get the idea. These are the visualizations they were able to produce: I have experience with visualizations in python so an example in Seaborn or Matplotlib …

I got this matrix 120 100 80 40 20 10 5 0 120 64.21 58.20 51.20 56.37 47.00 45.61 46.86 2.16 100 62.84 57.80 50.60 51.32 39.43 39.30 42.80 0.89 80 62.62 56.20 51.20 51.61 46.23 37.20 42.20 5.32 40 62.05 52.10 44.20 48.79 42.22 35.16 41.80 1.81 20 61.65 50.90 42.30 46.23 44.83 32.70 41.50 6.24 10 59.69 50.20 40.10 40.20 44.28 32.80 39.90 12.31 5 59.05 49.20 40.60 38.90 44.10 30.80 32.80 9.91 0 56.20 49.10 40.50 38.60 …

I'm working on a dataset of Uber Rides from Kaggle. Of the important variables there are pickup and drop-off coordinates, passenger count, datetime of pickup, distance and the final price. I'm currently in the exploration phase and just about to begin feature engineering. When I'm plotting the different potential correlations, some of them just feel odd to plot fare against something. For example, fare vs passenger count or fare vs hour doesn't make much sense to me, as the average …

I am two columns Age(discrete), and Purchase(binary). I want to visualize it to understand which age group is most interested in purchasing things. I was thinking of plotting something like a line graph on top of the histogram, where the line graph will show the trend of purchasing over the age group. I am not sure if it will be a good chart. Can you help me with its code or do you have any other good ideas for this …

So I draw a pairplot/heatmap from the feature correlations of a dataset and see a set of features that bears Zero-correlations both with: every other feature and also with the target/label .Reference code snippet in python is below: corr = df.corr() sns.heatmap(corr) # Visually see how each feature is correlate with other (incl. the target) Can I drop these features to improve the accuracy of my classification problem? Can I drop these features to improve the accuracy of my classification …

I have a dataframe called df['ProgressStep'] I would like to get overlaid CDF plot in histogram. Have tried 2 methods, neither one meet my target perfectly. please help to fine tune the code, either method is fine for me. how can I do the following things: (1) add/edit plot title and Y axis title; (2) add/edit primary X axis title, for example, I want more granularity here; (3) for overlapped plots, add secondary X axis against histogram ; (4) show …

I'm looking at telecom customers data. Two of the variables I'm looking at currently are: Monthly Charges - The total amount charged to the customer monthly. Is Senior Citizen - Whether the customer is a senior citizen. I'm trying to plot two histograms to see if the distributions for non-senior and senior citizens is different. If I use seaborn's distplot then I get the following result And if I use pyplot hist then I get the following result In the …

I have a column in my dataframe which has 'True' as a value and all other values are NaNs (so there are no 'false' values). I want to plot a countplot for the said data in seaborn but want to include the NaNs as well. Basically, I want to convert the NaNs to 'false' values and plot a graph then but I dont want to make any changes to my original column. Is there a way I can create a …

So I have a lot of discrete variables in my dataset and want to visualize them (univariate for now). I went through various articles over the internet and it is suggested that histograms and count plots are apt choices for plotting discrete data. Many of the discrete variables in my dataset have 500+ unique discrete values and when I plot them on a histogram it is taking a lot of time to show my any output. So is my approach …

I'm in trouble creating a heatmap using a CSV file. csv data is in a format like below here is a code years = np.array(datadf.PublicationYear) sns.set(font_scale=2) wordlist = ['greenhouse_gas', 'pollution', 'resilience', 'urban','city', 'environmental_impacts', 'climate_change', 'adaptation','mitigation','carbon', 'ghg_emissions','sustainable','sustainability','lca'] word_tuples = [('urban','city','urban'), ('greenhouse_gas','ghg_emissions','greenhouse_gas'),('sustainable','sustainability','sustainability')] use_wordlist = True word_number = 30 freqdata = [] agg_keys = [] for i in np.arange(len(abstrct)): ngram_model = Word2Vec(ngram[[abstrct[i]]], size=100, min_count=1) ngram_model_counter = Counter() for key in ngram_model.wv.vocab.keys(): if key not in stoplist: if use_wordlist: if key in wordlist: if …

I'm going through this YouTube series on simulation by The Coding Train. I'm trying to graph some filtered random numbers, but seaborn is leaving an odd gap in the very middle of the histogram. My data is filtered by collecting random numbers bigger than the output of some function, like $y = x^2$. I also tested graphing the output from random integers from 0 to 100 and did not get the same gap in the histogram, so I thought the …

There are 2 sns.pairplot, tell me how to interpret them. As I understand it, sns.pairplot allows us to look at the diagonal distribution of these signs, and on the non-diagonal linear relationship between the signs, i.e. it is possible to identify in which space (a pair of signs) the classes will be well separated from each other. If you look at the picture, I understand that, on the first there are very few dependencies, unlike the second. By the way, …