This question already has answers here:
Seaborn workaround for hue barplot
(4 answers)
Closed 2 years ago.
I tried to use the Seaborn style bar graph for one of my visualizations. To give more context, I had to group my data w.r.t fiscal quarter and customer, and aggregate the total revenue from each of these customers.
I have attached a .png file, which has the data frame and the code that I have written to plot this using sns.
Problem Statement: The vertical bars are not properly stacked. There is a gap between the second and third bar for 2019-Q2. Similarly, for Q3 and Q4. Can somebody help me understand the reason behind this?
the gaps are perhaps because you dont have any party with any amount for that quarter, So seaborn is reading it as zero. and since these are in an order it appreas as a gap in middle.
Related
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Plot multiple columns of pandas DataFrame using Seaborn
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Convert columns into rows with Pandas
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How to implement seaborn lmplot to get a gridded plot containing each dataframe column?
(2 answers)
Closed yesterday.
I have a dataset with numerous columns: height, width, brand, model, age, etc.
I want to plot two of the variables, height and width, against each other on the X and Y-axis, and then I want to further differentiate the points by colour and shape/style based on their brand and model.
I have been able to do a scatter with different colours for different brands using lmplpt from seaborn.
If anyone knows a way I can differentiate the markers too based on model that will be great!
Also, I have 12 different models in my dataset. Is there any function that will produce 12 scatterplots in one go, one for each model?
Many thanks
I'm working on a dataset of SMS records [datetime_entry, sms_sent] and I was looking to copy a really effective trend visual from a well cited Electricity demand study. Does anyone know the name of this plot, or the implementation of something similar in Python (as I'm not sure this was done in Python).
I know how to subplot the 4 charts after splitting the data by quarter, I'm just stumped on the plot type and stylization.
This is what matplotlib calls an eventplot.
Essentially each vertical line represents an occurance of a Mwh demand during that specific hour. So each row in the plot should have as many vertical lines as there are days in that quarter.
While it works in this plot for these data, relying on the combination of alpha level + data density can be slightly unreliable as the data change as the number of overlapping points is not readily visible. So you can also create a similar visualization using hist2d, where you manually specify your bins.
This question already has answers here:
Make a histogram of a pandas series
(2 answers)
Closed 8 months ago.
I have a csv file having two columns as shown:
The Track_Angle_Ctrl is a column having angles in degrees. The number of rows here is 371.
What is the best way to visualize the distribution of these 371 angles. Is it by using rose diagrams? If so, how to proceed with it.
Python has many options of visualizing a distribution.
You may start with Pandas df["Track_Angle_Ctrl"].describe() to see the general distribution
For graphical presentation, Seaborn provides displot for plotting histogram, which automatically seperate your data into groups and plot the count: sns.displot(df, x="Track_Angle_Ctrl")
You may check here for documentation: https://seaborn.pydata.org/tutorial/distributions.html
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How to set yaxis tick label in a fixed position so that when i scroll left or right the yaxis tick label should be visible?
(2 answers)
Closed 3 years ago.
I have a lot of data series (> 1000 bars). For this reason the plot is scaled very small. Do you know how I can activate a scrollbar in the plt.barh-chart with python? If I can set a fixed height of the bars then I can scroll through the > 1000 bars. Perhaps you have another solution for me?
You have to set position for scrollbar with respect to image size.
Kindly refer a below URL:
Scrollable Bar graph matplotlib
Could you provide a reproducible example? Other than that I can just give you plain advice. First of all, ask you whether you are really in need of Youtube/Netflix/etc. 1-10. Because it makes the chart very confusing. I came across the same problem some time ago too and I figured that the Gantt Chart from Plotly, particularly for Python, is a really good solution to make it clearer.
As for your original question:
fig= plt.figure(figsize=(6,3))
This question already has an answer here:
In matplotlib, what's the difference between title() and suptitle()?
(1 answer)
Closed 5 years ago.
I understand that matplotlib.figure.suptitle() adds a title to a figure.
But what does the "sup" stand for?
It is an abbreviation indicating a "super" title. It is a title which appears at the top of the figure, whereas a normal title only appears above a particular axes. If you only have one axes object, then there's unlikely an appreciable difference, but the difference happens when you have multiple subplots on the same figure and you would like a title at the top of the figure not on each of the axes objects.