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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Plot multiple columns of pandas DataFrame using Seaborn
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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
This question already has answers here:
Plot bar graph from Pandas DataFrame
(2 answers)
A convenient way to plot bar-plot in Python pandas
(1 answer)
Closed 3 months ago.
How do i plot the graph using the dataframe i got?
like that
I tried plotting directly and got this instead
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.
This question already has answers here:
How to draw vertical lines on a given plot
(6 answers)
Color the shaded area under the curve distribution plot different colors
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Closed 5 years ago.
Density Plot from ggplot in r with p-value indicator
I want to make a report with a density plot with a line indicating the p value (or any other arbitrary value), something similar to the image shown above.
However I haven't been able to find a way to do this in Python/Seaborn.
Thanks!
This question already has an answer here:
Combining two matplotlib colormaps
(1 answer)
Closed 7 years ago.
I use matplotlib to plot temperature data for combustion simulations, with temperature in the flame ranging from 3200K - 5500K and temperature outside of the flame ranging from 300K to 1000K. I want to generate projection plots using two different colormaps, one for within the flame and one for outside of it, to show slight variations in both regions. I don't see any temperatures in the intermediate region of 1000K - 3200K, so I waste resolution in my colormap by using one map for the entire 300K - 5500K range. I tried using some of the diverging maps, but they still miss the small variations at the high and low ends.
Does anyone have any suggestions for how to combine two colormaps into one, using one of the colormaps for each temperature range?
EDIT
To make my question more specific: I want to use Matlplotlib's 'hot' colormap for data points in the 3200 - 5500 range and 'cool' for data points in the 300 - 1000 range.
Is there any way to get the source code for these two colormaps, normalize them to their respective start and end points, and combine both into one cmap?
Here's a great write up on creating custom color maps:
http://cresspahl.blogspot.com/2012/03/expanded-control-of-octaves-colormap.html
You can simply modify the color changes around your data set and leave the portion that's not represented unchanged.