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Seaborn provides an API on top of Matplotlib that offers sane choices for plot style and color defaults, defines simple high-level functions for common statistical plot types, and integrates with the functionality provided by Pandas.By convention, Seaborn is often imported as sns# In[1]%matplotlib inlineimport matplotlib.pyplot as pltimport seaborn as snsimport numpy as npimport pandas as pdsns...

Three-dimensional plots are enabled by importing int mplot3d toolkit, included with the main Matplotlib installation.# In[1]from mpl_toolkits import mplot3dOnce this submodule is imported, a three-dimensional axes can be created by passing the keyword projection='3d' to any of the normal axes creation routines.# In[2]%matplotlib inlineimport numpy as npimport matplotlib.pyplot as plt# In[3]fig=p..

# In[1]import matplotlib.pyplot as pltplt.style.use('classic')import numpy as np%matplotlib inlinePlot Customization by HandYou've seen how it is possible to tweak(변경하다) individual plot settings to end up with something that looks a little nicer than the default.It's also possible to do these customizations for each individual plot.# In[2]x=np.random.randn(1000)plt.hist(x);We can adjust this by ..

Matplotlib's default tick locators and formatters are designed to be generally sufficient in many common situations, but are in no way optimal for every plot.Matplotlib aims to have a Python object representing everything that appears on the plot: for example, recall that the Figure is the bounding box within which plot elements appear.Each Matplotlib object can also act as a container of subobj..

# In[1]%matplotlib inlineimport matplotlib.pyplot as pltimport matplotlib as mplplt.style.use('seaborn-whitegrid')import numpy as npWhen we're visualizing data like this, it is often useful to annotate certain features of the plot to draw the reader's attention.This can be done manually with the plt.text/ax.text functions, which will place text at a particular $x/y$ value.The ax.text method take..

Matplotlib has the concept of subplots: groups of smaller axes that can exist together within a single figure.These subplots might be insets, grids of plots, or other more complicated layouts.# In[1]%matplotlib inlineimport matplotlib.pyplot as pltplt.style.use('seaborn-white')import numpy as npplt.axes: Subplots by HandThe most basic method of creating an axes is to use the plt.axes function.By..

Plot legends identify discrete labels of discrete points.For continuous labels based on the color or points, lines, or regions, a labeled colorbar can be a great tool.In Matplotlib, a colorbar is drawn as a separate axes that can provide a key for the meaning of colors in a plot.# In[1]import matplotlib.pyplot as pltplt.style.use('seaborn-white')%matplotlib inlineimport numpy as npThe simplest c..

# In[1]import matplotlib.pyplot as pltplt.style.use('seaborn-whitegrid')%matplotlib inlineimport numpy as npPlot legends give meaning to a visualization, assigning meaning to the various plot elements.The simplest legend can be created with the plt.legend command, which automatically creates a legend for any labeled plot elements.# In[2]x=np.linspace(0,10,1000)fig,ax=plt.subplots()ax.plot(x,np.s..

Sometimes it is useful to display three-dimensional data in two dimensions using contours(윤곽, 등고선) or color-coded regions.There are three Matplotlib functions that can be helpful for this task: plt.contour for contour plots, plt.contourf for filled contour plots, and plt.imshow for showing images.# In[1]%matplotlib inlineimport matplotlib.pyplot as pltplt.style.use('seaborn-white')import numpy a..

# In[1]%matplotlib inlineimport matplotlib.pyplot as pltplt.style.use('seaborn-whitegrid')import numpy as npScatter Plots with plt.plotWe used plt.plot and ax.plot to produce line plots.It turns out that this same function can produce scatter plots as well.# In[2]x=np.linspace(0,10,30)y=np.sin(x)plt.plot(x,y,'o',color='black');The third argument in the function call is a character that represen..