Pandas / Pyplot scatter plots: how to build by category

Question

I am trying to make a simple scatter plot in pyplot using a Pandas DataFrame object, but I want to have an efficient way to build two variables, but have characters dictated by the third column (key). I tried various ways using df.groupby, but not successfully. Below is an example df script. This colors the markers according to "key1", but Id loves to see a legend with the categories "key1". I am close? Thank.

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 for this you need to have numerical values ​​for key1 , and you will not see the legend, as you noticed.

Better to just use plot for such discrete categories. 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 want everything to look like the default pandas style, just update rcParams the pandas stylesheet and use its color generator. (I also tweak the legend a bit):

 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() 

Answer 2

This is easy to do with Seaborn ( pip install seaborn ) as an insert

sns.pairplot(x_vars=["one"], y_vars=["two"], data=df, hue="key1", size=5) :

 import seaborn as sns import pandas as pd import numpy as np np.random.seed(1974) 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) sns.pairplot(x_vars=["one"], y_vars=["two"], data=df, hue="key1", size=5) 

Here is the data for reference:

Since there are three column variables in your data, you can build all pairwise dimensions with:

 sns.pairplot(vars=["one","two","three"], data=df, hue="key1", size=5) 

https://rasbt.imtqy.com/mlxtend/user_guide/plotting/category_scatter/ is another option.

Answer 3

With plt.scatter I can only think of one plt.scatter : use a proxy executor:

 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) x=ax1.scatter(df['one'], df['two'], marker = 'o', c = df['key1'], alpha = 0.8) ccm=x.get_cmap() circles=[Line2D(range(1), range(1), color='w', marker='o', markersize=10, markerfacecolor=item) for item in ccm((array([4,6,8])-4.0)/4)] leg = plt.legend(circles, ['4','6','8'], loc = "center left", bbox_to_anchor = (1, 0.5), numpoints = 1) 

And the result:

Answer 4

You can use df.plot.scatter and pass the array to the c = argument, which determines the color of each dot:

 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) colors = np.where(df["key1"]==4,'r','-') colors[df["key1"]==6] = 'g' colors[df["key1"]==8] = 'b' print(colors) df.plot.scatter(x="one",y="two",c=colors) plt.show() 

Answer 5

You can also try Altair or ggpot , which are focused on declarative rendering.

 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)) 

Altair Code

 from altair import Chart c = Chart(df) c.mark_circle().encode(x='x', y='y', color='label') 

ggplot code

 from ggplot import * ggplot(aes(x='x', y='y', color='label'), data=df) +\ geom_point(size=50) +\ theme_bw() 

Answer 6

These are pretty hacks, but you can use one1 as Float64Index to do it all in one go:

 df.set_index('one').sort_index().groupby('key1')['two'].plot(style='--o', legend=True) 

Note that with 0.20.3, sorting the index is necessary , and the legend is a bit inconvenient .

Answer 7

Starting with matplotlib 3.1 you can use .legend_elements() . An example is shown in Automatic Legend Creation . The advantage is that you can use one disparate call.

In this case:

 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) fig, ax = plt.subplots() sc = ax.scatter(df['one'], df['two'], marker = 'o', c = df['key1'], alpha = 0.8) ax.legend(*sc.legend_elements()) plt.show() 

If the keys were not indicated as numbers, it would look like

 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'] = list("AAABBBCCCC") labels, index = np.unique(df["key1"], return_inverse=True) fig, ax = plt.subplots() sc = ax.scatter(df['one'], df['two'], marker = 'o', c = index, alpha = 0.8) ax.legend(sc.legend_elements()[0], labels) plt.show()