You're so close!
You can specify the colors in the styles list:
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
testdataframe = pd.DataFrame(np.arange(12).reshape(4,3), columns=['A', 'B', 'C'])
styles = ['bs-','ro-','y^-']
linewidths = [2, 1, 4]
fig, ax = plt.subplots()
for col, style, lw in zip(testdataframe.columns, styles, linewidths):
testdataframe[col].plot(style=style, lw=lw, ax=ax)
Also note that the plot
method can take a matplotlib.axes
object, so you can make multiple calls like this (if you want to):
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
testdataframe1 = pd.DataFrame(np.arange(12).reshape(4,3), columns=['A', 'B', 'C'])
testdataframe2 = pd.DataFrame(np.random.normal(size=(4,3)), columns=['D', 'E', 'F'])
styles1 = ['bs-','ro-','y^-']
styles2 = ['rs-','go-','b^-']
fig, ax = plt.subplots()
testdataframe1.plot(style=styles1, ax=ax)
testdataframe2.plot(style=styles2, ax=ax)
Not really practical in this case, but the concept might come in handy later.
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