Binning data and plotting - pandas

Question

I have a dataframe of essentially random numbers, (except for one column), some of which are NaNs. 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()
I need to bin the data according to x and plot different columns in each bin, in 3 side-by-side subplots:
x_1 <= 1 plot A-B | 1 < x_2 < 3 plot B+C | 3 < x_3 plot C-D
I've tried to bin the data with
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']
but there's got to be a better way to go about it. What's the best way to produce something like this?

Answer 1

For binning in pandas is used cut, so solution is:
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()