Plotting multiple lines, in different colors, with pandas dataframe

Another simple way is to use the pandas.DataFrame.pivot function to format the data.

Use pandas.DataFrame.plot to plot. Providing the colors in the 'color' column exist in matplotlib: List of named colors, they can be passed to the color parameter.

# sample datadf = pd.DataFrame([['red', 0, 0], ['red', 1, 1], ['red', 2, 2], ['red', 3, 3], ['red', 4, 4], ['red', 5, 5], ['red', 6, 6], ['red', 7, 7], ['red', 8, 8], ['red', 9, 9], ['blue', 0, 0], ['blue', 1, 1], ['blue', 2, 4], ['blue', 3, 9], ['blue', 4, 16], ['blue', 5, 25], ['blue', 6, 36], ['blue', 7, 49], ['blue', 8, 64], ['blue', 9, 81]], columns=['color', 'x', 'y'])# pivot the data into the correct shapedf = df.pivot(index='x', columns='color', values='y')# display(df)color  blue  redx               0         0    01         1    12         4    23         9    34        16    45        25    56        36    67        49    78        64    89        81    9# plot the pivoted dataframe; if the column names aren't colors, remove color=df.columnsdf.plot(color=df.columns, figsize=(5, 3))

You could use groupby to split the DataFrame into subgroups according to the color:

for key, grp in df.groupby(['color']):

import numpy as npimport pandas as pdimport matplotlib.pyplot as pltdf = pd.read_table('data', sep='\s+')fig, ax = plt.subplots()for key, grp in df.groupby(['color']):    ax = grp.plot(ax=ax, kind='line', x='x', y='y', c=key, label=key)plt.legend(loc='best')plt.show()

yields

If you have seaborn installed, an easier method that does not require you to perform pivot:

import seaborn as snssns.lineplot(data=df, x='x', y='y', hue='color')