matplotlib：如何有条件地绘制二维数组中的直方图

Question

I have a 2D array, where I am trying to plot a histogram of all the rows in one column, given a condition in another column. I am trying to select subdata in the plt.hist() command, to avoid making numerous subarrays, which I already know how to do. For example if

a_long_named_array = [1, 5]
                     [2, 6]
                     [3, 7]

I could create a subset of my array such that the 1st column is greater than 5 by writing

a_long_named_subarray = a_long_named_array[a_long_named_array[:,1] > 5]

How do I plot this subdata without making the aforementioned subarray? Please see below.

import numpy as np
import matplotlib.pyplot as plt

#Generate 2D array
arr = np.array([np.random.random_integers(0,10, 10), np.arange(0,10)])

#Transpose it
arr = arr.T

#----------------------------------------------------------------------------
#Plotting a Histogram: This works
#----------------------------------------------------------------------------

#Plot all the rows of the 0'th column
plt.hist(arr[:,0])
plt.show()

#----------------------------------------------------------------------------
#Plotting a conditional Histogram: This is what I am trying to do. This Doesn't work.
#----------------------------------------------------------------------------

#Plot all the rows of the 0th column where the 1st column is some condition (here > 5)
plt.hist(arr[:,0, where 1 > 5])
plt.show()

quit()

Answer 1

You just need to apply the boolean index ( whatever > 5 returns a boolean array) to the first dimension.

You're currently trying to index the array along the third dimension with the boolean mask. The array is only 2D, so you're probably getting an IndexError . (Most likely " IndexError: too many indices ".)

For example:

import numpy as np

# Your example data
arr = np.array([np.random.random_integers(0,10, 10), np.arange(0,10)])
arr = arr.T

# What you want:
print arr[arr[:,1] > 5, 0]

Basically, in place of the : , you just put in the boolean mask ( something > 5 ). You might find it clearer to write:

mask = arr[:,1] > 5
result = arr[mask, 0]

Another way of thinking of this is:

second_column = arr[:,1]
first_column = arr[:,0]
print first_column[second_column > 5]