Scatter plots in Pandas/Pyplot: How to plot by cat

问题:

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()

回答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()

回答2:

This is simple to do with Seaborn (pip install seaborn) as a oneliner

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 dataframe for reference:

Since you have three variable columns in your data, you may want to plot all pairwise dimensions with:

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

https://rasbt.github.io/mlxtend/user_guide/plotting/category_scatter/ is another option.

回答3:

With plt.scatter, I can only think of one: to use a proxy artist:

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 is:

回答4:

You can use df.plot.scatter, and pass an array to c= argument defining the color of each point:

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()

回答5:

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

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()

回答6:

It\'s rather hacky, but you could use one1 as a Float64Index to do everything in one go:

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

Note that as of 0.20.3, sorting the index is necessary, and the legend is a bit wonky.