Plotting

In [46]:

    

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from IPython.display import display_html
%matplotlib inline

ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))
ts = ts.cumsum()
ts.plot()


    

    
Out[46]:





<matplotlib.axes.AxesSubplot at 0x7f5564a7cad0>






    

In [27]:

    

df = pd.DataFrame(ts, columns=['A'])
df['B'] = df['A'] ** 2
df['C'] = np.exp(df['B'])
df.cumsum().plot(x='A', y='B', figsize=(6, 4))


    

    
Out[27]:





<matplotlib.axes.AxesSubplot at 0x7f55653e5dd0>






    

In [29]:

    

df.cumsum().hist(figsize=(20, 8))


    

    
Out[29]:





array([[<matplotlib.axes.AxesSubplot object at 0x7f5565185cd0>,
        <matplotlib.axes.AxesSubplot object at 0x7f556517e110>],
       [<matplotlib.axes.AxesSubplot object at 0x7f55650cd490>,
        <matplotlib.axes.AxesSubplot object at 0x7f5564df01d0>]], dtype=object)






    

In [44]:

    

df = pd.DataFrame({
'B': np.random.randint(0, 100, 5),
'C': np.random.randint(50, 150, 5),
'D': np.random.randint(0, 100, 5),
}, index=list('ABCDE'))
# plt.figure()
df['B'].plot(kind='bar')
df.plot(kind='bar')
df.plot(kind='bar', stacked=True)
df.plot(kind='barh', stacked=True)


    

    
Out[44]:





<matplotlib.axes.AxesSubplot at 0x7f5564601fd0>






    













    













    













    

In [45]:

    

df.plot(kind='hist')


    

    
Out[45]:





<matplotlib.axes.AxesSubplot at 0x7f55649e10d0>






    

In [47]:

    

df = pd.DataFrame(np.random.rand(10, 5), columns=list('ABCDE'))
display_html(df)
df.plot(kind='box')


    

    







  

    

      

      
A
      
B
      
C
      
D
      
E
    
  
  

    

      
0
      
 0.861996
      
 0.854195
      
 0.169300
      
 0.163941
      
 0.383768
    
    

      
1
      
 0.086928
      
 0.274264
      
 0.918127
      
 0.873760
      
 0.840835
    
    

      
2
      
 0.248209
      
 0.963460
      
 0.145096
      
 0.654023
      
 0.579490
    
    

      
3
      
 0.437722
      
 0.161625
      
 0.444892
      
 0.065964
      
 0.329682
    
    

      
4
      
 0.128290
      
 0.105836
      
 0.315159
      
 0.714923
      
 0.473313
    
    

      
5
      
 0.808679
      
 0.970344
      
 0.937924
      
 0.406341
      
 0.173013
    
    

      
6
      
 0.114899
      
 0.359807
      
 0.410558
      
 0.225799
      
 0.141491
    
    

      
7
      
 0.081415
      
 0.796789
      
 0.621102
      
 0.789584
      
 0.305951
    
    

      
8
      
 0.141955
      
 0.072683
      
 0.246083
      
 0.321504
      
 0.832740
    
    

      
9
      
 0.466192
      
 0.674512
      
 0.252660
      
 0.106329
      
 0.401946
    
  








    
Out[47]:





<matplotlib.axes.AxesSubplot at 0x7f5564507150>






    

In [57]:

    

df = pd.DataFrame(np.random.rand(10,3), columns=['Col1', 'Col2', 'Col3'] )
df['X'] = pd.Series(['A','A','A','A','A','B','B','B','B','B'])
df['Y'] = pd.Series(['A','B','A','B','A','B','A','B','A','B'])
display_html(df)
df.boxplot(column=['Col1', 'Col2'], by=['X', 'Y'], figsize=(20, 5))


    

    







  

    

      

      
Col1
      
Col2
      
Col3
      
X
      
Y
    
  
  

    

      
0
      
 0.763797
      
 0.760341
      
 0.905555
      
 A
      
 A
    
    

      
1
      
 0.105471
      
 0.878515
      
 0.746531
      
 A
      
 B
    
    

      
2
      
 0.876214
      
 0.471943
      
 0.663997
      
 A
      
 A
    
    

      
3
      
 0.704899
      
 0.994817
      
 0.547028
      
 A
      
 B
    
    

      
4
      
 0.293411
      
 0.676280
      
 0.508604
      
 A
      
 A
    
    

      
5
      
 0.118772
      
 0.576163
      
 0.756079
      
 B
      
 B
    
    

      
6
      
 0.259740
      
 0.085938
      
 0.551347
      
 B
      
 A
    
    

      
7
      
 0.332336
      
 0.495598
      
 0.108375
      
 B
      
 B
    
    

      
8
      
 0.782794
      
 0.268654
      
 0.190788
      
 B
      
 A
    
    

      
9
      
 0.996578
      
 0.176642
      
 0.735227
      
 B
      
 B
    
  








    
Out[57]:





array([<matplotlib.axes.AxesSubplot object at 0x7f5563d96390>,
       <matplotlib.axes.AxesSubplot object at 0x7f5563ca83d0>], dtype=object)






    

In [72]:

    

df = pd.DataFrame(np.random.rand(50, 4), columns=['a', 'b', 'c', 'd'])
ax = df.plot(kind='scatter', x='a', y='d', figsize=(8, 8), label='group 1', color='green')
df.plot(kind='scatter', x='b', y='c', figsize=(8, 8), ax=ax, label='group 2', color='red')
df.plot(kind='scatter', x='a', y='b', c='c', figsize=(11, 8), color='purple')
df.plot(kind='scatter', x='a', y='b', s=df['c'] * 200, figsize=(8, 8))


    

    
Out[72]:





<matplotlib.axes.AxesSubplot at 0x7f55631d2250>






    













    













    

In [85]:

    

df = pd.DataFrame(np.random.randn(1000, 2), columns=['a', 'b'])
df['b'] = df['b'] + np.arange(1000)
df.plot(kind='hexbin', x='a', y='b', gridsize=10)
df.plot(kind='hexbin', x='a', y='b', gridsize=50)


    

    
Out[85]:





<matplotlib.axes.AxesSubplot at 0x7f5561109e90>






    













    

In [93]:

    

df = pd.DataFrame(np.random.randn(1000, 2), columns=['a', 'b'])
df['b'] = df['b'] + np.arange(1000)
df['z'] = np.random.uniform(0, 3, 1000)
df.plot(kind='hexbin', x='a', y='b', C='z', reduce_C_function=np.mean, gridsize=25, figsize=(10, 5))
df.plot(kind='hexbin', x='a', y='b', C='z', reduce_C_function=np.max, gridsize=25, figsize=(10, 5))


    

    
Out[93]:





<matplotlib.axes.AxesSubplot at 0x7f5560cc7810>






    













    

In [108]:

    

df = pd.DataFrame(np.random.rand(5, 2), columns=['X', 'Y'])
# display_html(df)
df.plot(kind='pie', subplots=True, figsize=(12, 6), labels=list('ABCDE'), autopct='%.1f%%', fontsize=16, table=True)


    

    
Out[108]:





array([<matplotlib.axes.AxesSubplot object at 0x7f55601a8f10>,
       <matplotlib.axes.AxesSubplot object at 0x7f556003f0d0>], dtype=object)






    

In [ ]: