• We will start by setting up the notebook for plotting and importing the packages we will use

# In[1]
%matplotlib  inline
import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
import numpy as np

• For all Matplotlib plots, we start by creating a figure and axes.

# In[2]
fig=plt.figure()
ax=plt.axes()

• In Matplotlib, the figure can be thought of as a single container that contains all the objects representing axes, graphics, text, and labels.
• The axes is what we see above: a bounding box with ticks, grids, and labels, which will eventually contain the plot elements that make up our visualization
  
  
• Once we have created an axes, we can use the ax.plot method to plot some data.

# In[3]
fig=plt.figure()
ax=plt.axes()

x=np.linspace(0,10,100)
ax.plot(x,np.sin(x));

• The semicolon at the end of the last line is intentional: it suppresses(숨기다, 참다) the textual representation of the plot from the output.
  
  
• We can use the Pylab interface and let the figure and axes be created for us in the background; the result is the same

# In[4]
plt.plot(x,np.sin(x))

• If we want to create a single figure with multiple lines, we can simply call the plot function multiple times.

# In[5]
plt.plot(x,np.sin(x))
plt.plot(x,np.cos(x))

Adjusting the Plot: Line Colors and Styles

• The plt.plot function takes additional arguments that can be used to specify the line colors and styles.
• To adjust the color, you can use the color keyword, which accpets a string argument representing virtually any imaginable color.

# In[6]
plt.plot(x,np.sin(x-0), color='blue')        # specify color by name
plt.plot(x,np.sin(x-1), color='g')           # short color code(rgbmyk)
plt.plot(x,np.sin(x-2), color='0.75')        # grayscale between 0 and 1
plt.plot(x,np.sin(x-3), color='#FFDD44')     # hex code(RRGGBB, 00 to FF)
plt.plot(x,np.sin(x-4), color=(1.0,0.2,0.3)) # RGB tuple, values 0 to 1
plt.plot(x,np.sin(x-5), color='chartreuse')  # HTML color names supported

• If no color is specified, Matplotlib will automatically cycle through a set of default colors for multiple lines.
• The line style can be adjusting using the linestyle keyword.

# In[7]
plt.plot(x,x+0, linestyle='solid')
plt.plot(x,x+1, linestyle='dashed')
plt.plot(x,x+2, linestyle='dashdot')
plt.plot(x,x+3, linestyle='dotted')

# For short, you can use the following codes
plt.plot(x,x+4, linestyle='-')
plt.plot(x,x+5, linestyle='--')
plt.plot(x,x+6, linestyle='-.')
plt.plot(x,x+7, linestyle=':')

• Though it may be less clear to someone reading your code, you can save some key-strokes by combining these linestyle and color codes into a single non-keyword argument to the plt.plot function.

# In[8]
plt.plot(x,x+0,'-g')  # solid green
plt.plot(x,x+1,'--c') # dashen cyan
plt.plot(x,x+2,'-.k') # dashdot black
plt.plot(x,x+3,':r')  # dotted red

• These single-character color codes reflect the standard abbreviations in the RGB and CMYK color system, commonly used for digital color graphics.

Adjusting the Plot: Axes Limits

• Matplotlib does a decent job of choosing default axes limits for your plot, but sometimes it's nice to have finer control.
• The most basic way to adjust the limits is to use the plt.xlim and plt.yilm functions.

# In[9]
plt.plot(x,np.sin(x))

plt.xlim(-1,11)
plt.ylim(-1.5,1.5);

• If for some reason you'd like either axis to be displayed in reverse, you can simply reverse the order of the arguments.

# In[10]
plt.plot(x,np.sin(x))

plt.xlim(10,0)
plt.ylim(1.2,-1.2);

• A useful related method is plt.axis, which allows more qualitative specifications of axis limits.

# In[11]
plt.plot(x,np.sin(x))
plt.axis('tight');

• Or you can specify that you want an equal axis ratio, such that one unit in x is visually equivalent to one unit in y.

# In[12]
plt.plot(x,np.sin(x))
plt.axis('equal');

• Other axis options include on, off, square ,image, and more. For more information on these, refer to the plt.axis documentation

Labeling Plots

• Titles and axis labels are the simplest such labels-there are methods that can be used to quickly set them.

# In[13]
plt.plot(x,np.sin(x))
plt.title("A Sine Curve")
plt.xlabel("x")
plt.ylabel("sin(x)");

• The position, size, and style of these labels can be adjusted using optional arguments to the functions, described in the docstrings.
  
  
• When multiple lines are being shown within a single axes, it can be useful to create a plot legend that labels each line type.
• Matplotlib has a bulit-in way of quickly creating such a legend; it is done via the plt.legend method.

# In[14]
plt.plot(x,np.sin(x),'-g',label='sin(x)')
plt.plot(x,np.cos(x),':b',label='cos(x)')
plt.axis('equal')

plt.legend();

• The plt.legend function keeps track of the line style and color, and matches these with the correct label.

For more information about plt.legend, refer to this url :
plt.legend documentation

Matplotlib Gotchas

• While most plt functions translate directly to ax methods, this is not the case for all commands.
• In particular, functions to set limits, labels, and titles are slightly modified.
• For transitioning between MATLAB-style functions and object-oriented methods, make the following changes.
  
  
• plt.xlabel => ax.set_xlabel
• plt.ylabel => ax.set_ylabel
• plt.xlim => ax.set_xlim
• plt.ylim => ax.set_ylim
• plt.title => ax.set_title
  
  
• If the object-oriented interface to plotting, rather than calling these functions individually, it is often more convenient to use the ax.set method to set all these properties at once.

# In[15]
ax=plt.axes()
ax.plot(x,np.sin(x))
ax.set(xlim=(0,10),ylim=(-2,2),
xlabel='x',ylabel='sin(x)',
title='A simple plot');