A short reference for Numpy, Matplotlib and Pandas

NumPy

https://numpy.org/doc/stable/user/quickstart.html

Check the Jupyter notebook: https://github.com/carbonatezero/np_plt_pd_abs_basics/blob/main/quick_starts_numpy.ipynb

Matplotlib

https://matplotlib.org/stable/tutorials/introductory/usage.html

Shortest example:

import matplotlib.pyplot as plt  

fig, ax = plt.subplots() 
ax.plot(a,b) # a and b are NumPy arrays

Figure

• fig = plt.figure() empty figure with no "Axes" (pyplot-style)
• fig, ax = plt.subplots() a figure with a single "Axes" (OO-style)
• fig, ax = plt.subplots(2,2) a figure with a 2x2 grids of "Axes"

Axes

• "The Axes class and its member functions are the primary entry point to working with the OO interface."
• For each Axes graphing method, there is a corresponding function in the matplotlib.pyplot module that performs that plot on the "current" axes.

Change parts of a figure

• ax.set_title()

• ax.legend()

• ax.set_xlabel(), ax.set_ylabel()

• ax.set_yscale('log')

• ax.tick_params(**kwargs) kwargs={'labelsize':14}

• Write a helper function with recommended signature:

  def my_plotter(ax, data1, data2, param_dict):
      out = ax.plot(data1, data2, **param_dict)
      return out
  

• Inputs to plotting functions: Convert everything (e.g. pandas.DataFrame) to numpy.array objects prior to plotting.

• Prefer the OO-style!

Pandas

https://pandas.pydata.org/docs/getting_started/intro_tutorials/index.html

Two primary data structures:

• Series (1-dim); container of scalar
• DataFrame(2-dim); container of Series

Built on top of NumPy

Read and write tabular data

How to Select a subset of a DataFrame

• slect specific column
  • df["Age"] (returns a "Series")
  • df[["Age"]] (returns a "DataFrame")
• filter specific rows
  • df[df["Age"]>35] (df["Age"]>35 returns a pd Series of boolean values)
• select specific rows and columns
  • df.loc[df["Age"]>35, "Name"]
  • df.iloc[9:25, 2:5]