matplotlib中具有字符串数组的散点图 [英] scatter plots with string arrays in matplotlib

问题描述

这似乎应该很简单，但我不知道.我有一个pandas数据框，想用3列做一个3D散点图. X和Y列不是数字，而是字符串，但是我不认为这应该是一个问题.

this seems like it should be an easy one but I can't figure it out. I have a pandas data frame and would like to do a 3D scatter plot with 3 of the columns. The X and Y columns are not numeric, they are strings, but I don't see how this should be a problem.

X= myDataFrame.columnX.values #string
Y= myDataFrame.columnY.values #string
Z= myDataFrame.columnY.values #float

fig = pl.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(X, Y, np.log10(Z), s=20, c='b')
pl.show()

没有简单的方法可以做到这一点吗?谢谢.

isn't there an easy way to do this? Thanks.

推荐答案

您可以使用 np.unique(...，return_inverse = True)以获得每个字符串的代表性整数.例如，

You could use np.unique(..., return_inverse=True) to get representative ints for each string. For example,

In [117]: uniques, X = np.unique(['foo', 'baz', 'bar', 'foo', 'baz', 'bar'], return_inverse=True)

In [118]: X
Out[118]: array([2, 1, 0, 2, 1, 0])

请注意，X具有dtype int32，因为np.unique最多可以处理2**31个唯一字符串.

Note that X has dtype int32, as np.unique can handle at most 2**31 unique strings.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d.axes3d as axes3d

N = 12
arr = np.arange(N*2).reshape(N,2)
words = np.array(['foo', 'bar', 'baz', 'quux', 'corge'])
df = pd.DataFrame(words[arr % 5], columns=list('XY'))
df['Z'] = np.linspace(1, 1000, N)
Z = np.log10(df['Z'])
Xuniques, X = np.unique(df['X'], return_inverse=True)
Yuniques, Y = np.unique(df['Y'], return_inverse=True)

fig = plt.figure()
ax = fig.add_subplot(1, 1, 1, projection='3d')
ax.scatter(X, Y, Z, s=20, c='b')
ax.set(xticks=range(len(Xuniques)), xticklabels=Xuniques,
       yticks=range(len(Yuniques)), yticklabels=Yuniques) 
plt.show()

这篇关于matplotlib中具有字符串数组的散点图的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！