Various image using pandas and matplotlib

Michael Zippo 18.07.2021

Use these commands to install matplotlib, pandas, and numpy:

 pip install matplotlib pip install pandas pip install numpy

Parcel types:

Basic plotting. In this basic plotting, we can use randomly generated data to plot using series and matplotlib.

       # import libraries  
   import   matplotlib.pyplot as plt 
   import   pandas as pd 
   import   numpy as np 
   
   ts   =   pd.Series (np.random.randn (  1000  ), in dex   =   pd.date_range (
   ’1/1 / 2000’  , periods   =   1000  )) 
   ts   =   ts.cumsum () 
   ts.plot ()  
   
  plt.show ()

Output:

Different data plot: using more than one list of data in a plot.

# importing libraries

import matplotlib.pyplot as plt

import pandas as pd

import numpy as np

ts = pd.Series ( np.random.randn ( 1000 ), index = pd.date_range (

’1/1 / 2000’ , periods = 1000 ))

df = pd.DataFrame (np.random.randn ( 1000 , 4 ),

index = ts.index, columns = list ( ’ABCD’ ))

df = df.cumsum ()

plt.figure ()
df .plot ()
plt.show ()

Output:

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Plot on a given axis: we can explicitly define an axis name and plot data based on that axis.

# importing libraries

import matplotlib.pyplot as plt

import pandas as pd

import numpy as np

ts = pd.Series (np .random.randn ( 1000 ), index = pd.date_range (

’1/1/2000 ’ , periods = 1000 ))

df = pd.DataFrame (np .random.randn ( 1000 , 4 ), index = ts.index,

columns = list ( ’ABCD’ ))

df3 = pd.DataFrame (np.random.randn ( 1000 , 2 ),

columns = [ ’B’ , ’ C’ ]). cumsu m ()

df3 [ ’A’ ] = pd.Series ( list ( range ( len (df))))

df3.plot (x = ’ A’ , y = ’B’ )

plt.show ()

Output:

Histogram using matplotlib: Look for different types of histogram to clearly understand the behavior of the data.

       # importing libraries  
   import   matplotlib.pyplot as plt  
   import   pandas as pd 
   import   numpy as np 
   
   ts   =   pd.Series (np.random.randn (  1000  ), index   =   pd.date_range ( 
   ’1/1 / 2000’  , periods   =   1000  )) 
   
   df   =   pd.DataFrame (np.random.randn (  1000  ,   4  ), index   =   ts.index, 
   columns   =   list   ( ’ ABCD’  )) 
   
   df3   =   pd.DataFrame (np.random.randn (  1000  ,   2  ),  
   columns   =   [  ’B’  ,  ’ C’  ]). cumsum () 
   
   df3 [  ’A’  ]   =   pd.Series (  list   (  range   (  len   (df)))) 
   df3.iloc [  5  ]. plot.bar () 
   plt.axhline (  0  , color   =   ’k ’ ) 
    
  plt.show ()

Output:

Histograms:

       # import libraries  
   import   matplotlib.pyplot as plt 
   import   pandas as pd 
   import   numpy as np 
    
   df4   =   pd.DataFrame ({  ’a’  : np.random.randn (  1000  )   +   1  , 
   ’b’  : np.random.randn (  1000  ), 
   ’c’  : np.random.randn (  1000  )   -   1  },  
   columns   =   [ ’ a’  ,   ’b’  ,   ’c’  ]) 
   plt.figure () 
   
   df4.plot.hist (alpha   =   0.5  )  
   plt.show ()

Exit:

Plot using Series and matplotlib: use a block to plot the data.

# importing libraries

import matplotlib.pyplot as plt

import pandas as pd

import numpy as np

df = pd .DataFrame (np.random.rand ( 10 , 5 ),

columns = [ ’A’ , ’B’ , ’ C’ , ’ D’ , ’E’ ])

df.plot .box ()
plt.show ()

Output:

Plot density:

# importing libraries

import matplotlib.pyplot as plt

import pandas as pd

import numpy as np

df = pd.DataFrame (np.random.rand ( 10 , 5 ),

columns = [ ’A’ , ’ B’ , ’C’ , ’D’ , ’ E’ ])

ser = pd.Series (np.random.randn ( 1000 ))

ser.plot.kde ()

plt.show ()

Output:

Plot area using matplotlib:

# library import

import matplotlib.pyplot as plt

import pandas as pd

import numpy as np

df = pd.DataFrame (np.random.ran d ( 10 , 5 ),

columns = [ ’A’ , ’B’ , ’ C’ , ’D’ , ’ E’ ])

df.plot.area ()
plt.show ()

Output:

Dot plot:

# importing libraries

import matplotlib .pyplot as plt

import pandas as pd

import numpy as np

df = pd.DataFrame (np.random.rand ( 500 , 4 ),

columns = [ ’a’ , ’b’ , ’ c’ , ’d’ ])

df.plot.scatter (x = ’a’ , y = ’ b’ )

plt.show ()

Output:

Hexagonal bunker

       # importing libraries  
   import   matplotlib.pyplot as plt 
   import   pandas as pd 
   import   numpy as np 
   
   df   =   pd.DataFrame (np.random.randn (  1000  ,   2  ) , columns   =   [  ’a’  ,   ’b’  ]) 
   
   df [  ’a’  ]   =   df [ ’ a’  ]   +   np.arange (  1000  ) 
   df.plot.hexbin (x   =  ’ a’  , y   =   ’b’  , gridsize   =   25  ) 
   plt.show ()