Scatter plots in Pandas/Pyplot: How to plot by category [duplicate]

Question

I am trying to make a simple scatter plot in pyplot using a Pandas DataFrame object, but want an efficient way of plotting two variables but have the symbols dictated by a third column (key). I have tried various ways using df.groupby, but not successfully. A sample df script is below. This colours the markers according to 'key1', but Id like to see a legend with 'key1' categories. Am I close? Thanks.

import numpy as np import pandas as pd import matplotlib.pyplot as plt df = pd.DataFrame(np.random.normal(10,1,30).reshape(10,3), index = pd.date_range('2010-01-01', freq = 'M', periods = 10), columns = ('one', 'two', 'three')) df['key1'] = (4,4,4,6,6,6,8,8,8,8) fig1 = plt.figure(1) ax1 = fig1.add_subplot(111) ax1.scatter(df['one'], df['two'], marker = 'o', c = df['key1'], alpha = 0.8) plt.show() 

Answer 1

You can use scatter for this, but that requires having numerical values for your key1, and you won't have a legend, as you noticed.

It's better to just use plot for discrete categories like this. For example:

import matplotlib.pyplot as plt import numpy as np import pandas as pd np.random.seed(1974)  # Generate Data num = 20 x, y = np.random.random((2, num)) labels = np.random.choice(['a', 'b', 'c'], num) df = pd.DataFrame(dict(x=x, y=y, label=labels))  groups = df.groupby('label')  # Plot fig, ax = plt.subplots() ax.margins(0.05) # Optional, just adds 5% padding to the autoscaling for name, group in groups:     ax.plot(group.x, group.y, marker='o', linestyle='', ms=12, label=name) ax.legend()  plt.show() 

If you'd like things to look like the default pandas style, then just update the rcParams with the pandas stylesheet and use its color generator. (I'm also tweaking the legend slightly):

import matplotlib.pyplot as plt import numpy as np import pandas as pd np.random.seed(1974)  # Generate Data num = 20 x, y = np.random.random((2, num)) labels = np.random.choice(['a', 'b', 'c'], num) df = pd.DataFrame(dict(x=x, y=y, label=labels))  groups = df.groupby('label')  # Plot plt.rcParams.update(pd.tools.plotting.mpl_stylesheet) colors = pd.tools.plotting._get_standard_colors(len(groups), color_type='random')  fig, ax = plt.subplots() ax.set_color_cycle(colors) ax.margins(0.05) for name, group in groups:     ax.plot(group.x, group.y, marker='o', linestyle='', ms=12, label=name) ax.legend(numpoints=1, loc='upper left')  plt.show()