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In [4]:

    
import numpy as np
import pandas as pd



In [6]:

    
%matplotlib inline



In [27]:

    
df = pd.DataFrame()
df['Time'] = range(15);
df['Sig1'] = df['Time'] / 14;
df['Sig2'] = np.sin(df['Time']);
df['Sig3'] = (np.sin(df['Sig1']) + np.cos(df['Sig2'])) / 2;



In [28]:

    
df.plot.line('Time' , ['Sig1', 'Sig2' , 'Sig3'])









    Out[28]:





<matplotlib.axes._subplots.AxesSubplot at 0x7ff13a832b70>



In [29]:

    
df.plot.line('Time' , ['Sig1', 'Sig2', 'Sig3'], color=['b' , 'r' , 'g'])









    Out[29]:





<matplotlib.axes._subplots.AxesSubplot at 0x7ff13a84e400>



In [30]:

    
df.plot.line('Time' , ['Sig1', 'Sig2', 'Sig3'], color=['r' , 'g' , 'b'])









    Out[30]:





<matplotlib.axes._subplots.AxesSubplot at 0x7ff13a6d0470>



In [31]:

    
df.plot.line('Time' , ['Sig1', 'Sig2', 'Sig3'], color=['#FF0000' , '#00FF00' , '#0000FF'])









    Out[31]:





<matplotlib.axes._subplots.AxesSubplot at 0x7ff13a83cb00>

http://matplotlib.org/api/colors_api.html

Commands which take color arguments can use several formats to specify the colors. For the basic built-in colors, you can use a single letter

    b: blue
    g: green
    r: red
    c: cyan
    m: magenta
    y: yellow
    k: black
    w: white



In [32]:

    
df.plot.line('Time' , ['Sig1', 'Sig2', 'Sig3'], color=['c' , 'm' , 'y'])









    Out[32]:





<matplotlib.axes._subplots.AxesSubplot at 0x7ff13a559ba8>



In [33]:

    
df.plot.line('Time' , ['Sig1', 'Sig2', 'Sig3'], color=['#800000' , '#008000' , '#000080'])









    Out[33]:





<matplotlib.axes._subplots.AxesSubplot at 0x7ff13a54bda0>

pre-defined colors

signal => blue

trend =>

trend-residue => red

cycle => yellow

cycle-residue => red

ar =>

ar-residue => red

forecast => green

upper-bound , lower-bound , shaded-area => green, green, light-blue

ticks on left and right sides.



In [ ]:

    
df.plot.line('Time' , ['Sig1', 'Sig2', 'Sig3'], color=['r' , 'g' , 'b'])