Python data analysis—Pandas visualization

Series.plot main parameters and usage instructions

parameter	illustrate
label	Labels for charts
style	Style string, such as 'g - -
alpha	The fill opacity of the image (0, 1)
kind	Image type (bar, line, hist, kde, etc.)
xticks	Set the X-axis scale value
yticks	Set Y-axis scale value
xlim，ult	Set axis limits
grid	Show axis gridlines
rot	Rotate tick labels
use_index	Use the object's index as tick labels
logy	Use a logarithmic scale on the Y-axis

The main parameters of DataFrame.plot and their descriptions

parameter	illustrate
subplot	Plot each DataFrame column into a separate subplot
sharex，sharey	Share X,Y axis
title	image caption
legend	Add legend, displayed by default
sort_colunms	Plots columns in alphabetical order, using the current order by default

Import module

import numpy as np
import pandas as pd
import matplotlib.pyplot as plot

1: Linear graph

Linear tables are generally used to describe trends between two sets of data.

Single curve:

s = pd.Series(np.random.normal(size = 50))
s.plot()

Multiple curves:

df = pd.DataFrame({
    
    'one':np.random.normal(size = 20),
                   'two':np.random.normal(size = 20),
                  'three':np.random.normal(size = 20)})
df.plot()