How can I format multiple thousands of date-dependant data points in a readable graph with Matplotlib?
I have two corresponding lists, one of the dates and another of its respective price. The lists are 30,000 data points long.
plot
Changing the “tick frequency” on x or y axis in matplotlib
I am trying to fix how python plots my data.
Say:
Save plot to image file instead of displaying it using Matplotlib
I am writing a quick-and-dirty script to generate plots on the fly. I am using the code below (from Matplotlib documentation) as a starting point:
Add missing dates to pandas dataframe
My data can have multiple events on a given date or NO events on a date. I take these events, get a count by date and plot them. However, when I plot them, my two series don’t always match.
Annotate bars with values on Pandas bar plots
I was looking for a way to annotate my bars in a Pandas bar plot with the rounded numerical values from my DataFrame.
When to use cla(), clf() or close() for clearing a plot in matplotlib?
Matplotlib offers these functions:
Plotting time in Python with Matplotlib
I have an array of timestamps in the format (HH:MM:SS.mmmmmm) and another array of floating point numbers, each corresponding to a value in the timestamp array.
Matplotlib different size subplots
I need to add two subplots to a figure. One subplot needs to be about three times as wide as the second (same height). I accomplished this using GridSpec and the colspan argument but I would like to do this using figure so I can save to PDF. I can adjust the first figure using the figsize argument in the constructor, but how do I change the size of the second plot?
Create own colormap using matplotlib and plot color scale
I have the following problem, I want to create my own colormap (red-mix-violet-mix-blue) that maps to values between -2 and +2 and want to use it to color points in my plot.
The plot should then have the colorscale to the right.
Is there a way to detach matplotlib plots so that the computation can continue?
After these instructions in the Python interpreter one gets a window with a plot: