Question

我有一个本质上是随机数的数据帧（除了一列），其中一些是NaN。 MWE：

import numpy as np
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import pandas as pd

randomNumberGenerator = np.random.RandomState(1000)
z = 5 * randomNumberGenerator.rand(101)
A = 4 * z - 3+ randomNumberGenerator.randn(101)
B = 4 * z - 2+ randomNumberGenerator.randn(101)
C = 4 * z - 1+ randomNumberGenerator.randn(101)
D = 4 * z - 4+ randomNumberGenerator.randn(101)

A[50] = np.nan
A[:3] = np.nan
B[12:20] = np.nan

sources= pd.DataFrame({'z': z})
sources['A'] = A
sources['B'] = B
sources['C'] = C
sources['D'] = D
#sources= sources.dropna()
x = sources.z
y1 = sources.A
y2 = sources.B
y3 = sources.C
y4 = sources.D

for i in [y1, y2, y3, y4]:
    count = np.count_nonzero(~np.logical_or(np.isnan(x), np.isnan(i)))
    label = 'Points plotted: %d'%count
    plt.scatter(x, i, label = label)

plt.legend()

我需要根据x对数据进行分类，并在3个并排的子图中将每个列中的不同列绘制出来：

x_1 <= 1 plot A-B  |  1 < x_2 < 3 plot B+C  |  3 < x_3 plot C-D

我尝试将数据与

进行合并

x1 = sources[sources['z']<1]      # z < 1
x2 = sources[sources['z']<3]
x2 = x2[x2['z']>=1]               # 1<= z < 3
x3 = sources[sources['z']<max(z)] 
x3 = x3[x3['z']>=3]               # 3 <= z <= max(z)
x1 = x1['z']
x2 = x2['z']
x3 = x3['z']

但是必须有一个更好的方法来解决它。产生这样的东西的最好方法是什么？

Answer 1

对于在大熊猫中进行分箱使用的是cut，因此解决方案是：

sources= pd.DataFrame({'z': z})
sources['A'] = A
sources['B'] = B
sources['C'] = C
sources['D'] = D
#sources= sources.dropna()

bins = pd.cut(sources['z'], [-np.inf, 1, 3, max(z)], labels=[1,2,3])

m1 = bins == 1
m2 = bins == 2
m3 = bins == 3

x11 = sources.loc[m1, 'A']
x12 = sources.loc[m1, 'B']

x21 = sources.loc[m2, 'B']
x22 = sources.loc[m2, 'C']

x31 = sources.loc[m3, 'C']
x32 = sources.loc[m3, 'D']

y11 = sources.loc[m1, 'A']
y12 = sources.loc[m1, 'B']

y21 = sources.loc[m2, 'B']
y22 = sources.loc[m2, 'C']

y31 = sources.loc[m3, 'C']
y32 = sources.loc[m3, 'D']

tups = [(x11, x12, y11, y12), (x21, x22,y21, y22),(x31, x32, y31, y32)]

fig, ax = plt.subplots(1,3)
ax = ax.flatten()

for k, (i1, i2, j1, j2) in enumerate(tups):

    count1 = np.count_nonzero(~np.logical_or(np.isnan(i1), np.isnan(j1)))
    count2 = np.count_nonzero(~np.logical_or(np.isnan(i2), np.isnan(j2)))

    label1 = 'Points plotted: %d'%count1
    label2 = 'Points plotted: %d'%count2

    ax[k].scatter(i1, j1, label = label1)
    ax[k].scatter(i2, j2, label = label2)

    ax[k].legend()

合并数据并绘图

1 个答案: