The pandas/plotting is the module that takes care of all of the plotting functionalities pandas offers:

• compat.py: This module checks for version compatibility.
• converter.py: This module helps process datetime values for plotting. It helps to execute functions such as autoscaling of time series axes and formatting ticks for datetime axes.
• core.py: This defines classes that help in creating plots, such as bar plots, scatter plots, hex bin plots, and box plots.
• misc.py: This provides a set of plotting functions that take a series or DataFrame as an argument. This module contains the following submodules for performing miscellaneous tasks, such as plotting scatter matries and Andrews curve:
  • scatter_matrix(..): This draws a matrix of scatter plots.
  • andrews_curves(..): This plots multivariate data as curves that are created using samples as coefficients for a Fourier series.
  • parallel_coordinates(..): This is a plotting technique that allows you to see clusters in data and visually estimate statistics.
  • lag_plot(..): This is used to check whether a dataset or a time series is random.
  • autocorrelation_plot(..): This is used for checking randomness in a time series.
  • bootstrap_plot(..): This plot is used to determine the uncertainty of a statistical measure, such as mean or median, in a visual manner
  • radviz(..): This plot is used to visualize multivariate data.
• style.py: This provides a set of styling options for the plot.
• timeseries.py: This defines auxiliary classes for time series plots.
• tools.py: This contains some helper functions that create a table layout from DataFrames and series.