In [69]:
import numpy as np
import pandas as pd
%matplotlib inline
In [70]:
df1 = pd.read_csv('df1',index_col=0)
df2 = pd.read_csv('df2')
In [71]:
df1['A'].hist()
Out[71]:
Call the style:
In [72]:
import matplotlib.pyplot as plt
plt.style.use('ggplot')
Now the plot looks like this
In [73]:
df1['A'].hist()
Out[73]:
In [74]:
plt.style.use('bmh')
df1['A'].hist()
Out[74]:
In [75]:
plt.style.use('dark_background')
df1['A'].hist()
Out[75]:
In [76]:
plt.style.use('fivethirtyeight')
df1['A'].hist()
Out[76]:
In [77]:
plt.style.use('ggplot')
In [78]:
df2.plot.area(alpha=0.4)
Out[78]:
In [79]:
df2.head()
Out[79]:
In [80]:
df2.plot.bar()
Out[80]:
In [81]:
df2.plot.bar(stacked=True)
Out[81]:
In [82]:
df1['A'].plot.hist(bins=50)
Out[82]:
In [83]:
df1.plot.line(x=df1.index,y='B',figsize=(12,3),lw=1)
Out[83]:
In [84]:
df1.plot.scatter(x='A',y='B')
Out[84]:
In [85]:
df1.plot.scatter(x='A',y='B',c='C',cmap='coolwarm')
Out[85]:
In [86]:
df1.plot.scatter(x='A',y='B',s=df1['C']*200)
Out[86]:
In [87]:
df2.plot.box() # Can also pass a by= argument for groupby
Out[87]:
In [88]:
df = pd.DataFrame(np.random.randn(1000, 2), columns=['a', 'b'])
df.plot.hexbin(x='a',y='b',gridsize=25,cmap='Oranges')
Out[88]:
In [89]:
df2['a'].plot.kde()
Out[89]:
In [90]:
df2.plot.density()
Out[90]: