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Pandas is a Python library for data manipulation and analysis, e.g


Seaborn is a Python data visualization library based on matplotlib



There are just so many features that you can control with the plotting capabilities of pandas which leverages matplotlib;i found that seaborn is a lot easier to produce pretty charts and you have a lot more control over the parameters of your plots

from question  

Change y-axis scaling fontsize in pandas dataframe.plot()

Seaborn is more for making the plots more readable with less direct intervention in the script and generally gets the most mileage when dealing with pandas dataframes for example yields as to how to set the styles the way you want for the variables you re trying to show that i m not sure how to handle

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How to plot a dashed line on seaborn lineplot?

For similar functionality that speaks pandas but has more flexible features you could use the facetgrid object from seaborn

from question  

Is there a way to pass different vertical lines to each subplot when using pandas histogram with "by=somevar"?

I also think working with seaborn is much easier using pandas dataframes and not numpy arrays

from question  

How to decrease the density of x-ticks in seaborn

So if you are using pandas for basic plot you can use matplotlib for plot customization;however i propose an alternative method here using seaborn which allows more customization of the plot while not going into the basic level of matplotlib

from question  

Add x and y labels to a pandas plot

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