6
The shaded area likely shows the dark green line plus or minus some error/uncertainty estimate. Common error estimates may be based on the standard deviation, a confidence interval, or the interquartile range depending on the data and the analysis being done. Without more information, we cannot know what the shaded area represents.
5
It might be plus or minus one standard deviation. But it could be anything really. Without more context you can't be sure.
3
You can have a look at the kaggle stock dataset.
https://www.kaggle.com/borismarjanovic/price-volume-data-for-all-us-stocks-etfs
This questions are normally done in OpenData stack exchange.
https://opendata.stackexchange.com/
3
Without more information it is hard to say for certain, but my best guess is that the shaded region is a confidence interval (say ± 1 standard deviation) around the predicted values which are represented by the line. For example, have a read through this article where the author creates a time series prediction for stock market prices with a confidence ...
3
If you want to see the distribution of the data that is hidden in the bottom portion, you can add a histogram or probability plot, or even a violin plot. Each will show the distribution of the data more clearly than this boxplot does, and you can still see the true value directly. You can also add some jitter to the boxplot to see more of the overlapping ...
2
If the values are greater than 0, you can apply the logarithm to Value and you should be able to compare the distributions much more. Another thing you can do is cropping at some value (let's say Value = 10) but you are going to lose some information.
If your values are not greater than 0 but have a lower bound (let's say -t), you can apply the transform log(...
1
Clearly there's no way to have the names of the drugs.
Assuming the relation between the two columns is important, a scatter plot with units prescribed as X and number of patients as Y might work. You could even add the name of the drug for a few isolated points. Transparency/opacity can be used to show the dense areas.
In case the relation between the ...
1
So the problem is how to visualise your box plots so that they appear in the same plot (axes).
To do this, it is simply a minor alteration to your code.
f, axes = plt.subplots(1, 2)
sns.boxplot(x="status",y="assets" ,data=df1, palette="Set3",ax=axes[0])
sns.boxplot(x="status",y="assets" ,data=df2, palette=&...
1
Whenever you give percentages, you need a very clear statement about the denominator. I can deduce it here, but in general one could give the percentage of bike only accidents of all accidents in the medium sized cities or the percentage of bike only accidents happening in medium sized cities of all bike-only accidents. Or...
The kind of percentage (or ...
1
Looking into so many plots may miss the bigger picture.
We should keep the initial plot in "One Consolidated plot"
e.g.
Can add few other consolidated views - Pie on aggregated data on region and Vehicle
1
One option is to reframe it as a word embedding problem. Emojis can be embedded in a vector space along with comments and hashtags. Then distance measures and clustering can be used to find the emojis that are associated with different sentiments.
1
Plotly is an excellent tool for 2D and 3D data. It is quite simple to use with python as well.
https://plotly.com/python/
1
Your example is a star schema, it's just a star with three points (dimensions).
It's OK to have star schemas with more dimensions. Some large OLAP schemas can have tens of dimensions.
The star schema holds the underlying data. The cube is a convenient set of pre-aggregated values that make our run-time faster. The "cube" name is a handy visual and ...
1
Shiny could be an interesting option: it's an R library which lets the programmer generate interactive web pages from an R program.
All the R libraries for data manipulation/visualization can be used (e.g. the great ggplot2 library for graphs)
The interactive pages are rendered with very little effort required on the programmer side
Very flexible, allows ...
1
How about a small multiples style visualization? A nice 2 x 3 grid would cover the six categories here. Size on X, frequency on Y.
This approach is one of the clearest ways to show this kind of data. The stacked histogram is difficult to interpret because the bars of the same color do not have common starting points. If you make six histograms arranged in a ...
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