直方图处理以删除不需要的数据 [英] Histogram manipulation to remove unwanted data

问题描述

如何在特定频率下从python直方图中删除数据?

说我有10个垃圾箱，第一个垃圾箱有4个，第二个垃圾箱有2个，第三个垃圾箱有1个，第四个垃圾箱有5个，依此类推... 现在，我要删除计数为2或更少的数据.因此，第二个仓位将变为零，第三个仓位也将变为零.

示例:

import numpy as np
import matplotlib.pyplot as plt

gaussian_numbers = np.random.randn(1000)
plt.hist(gaussian_numbers, bins=12)
plt.title("Gaussian Histogram")
plt.xlabel("Value")
plt.ylabel("Frequency")

fig = plt.gcf()

赠予:

我想摆脱频率小于"X"的垃圾箱(例如，频率可以是100).

想要:

谢谢.

解决方案

Une np.where .在有条件的情况下，它会产生一个布尔数组，可用于索引直方图.

import numpy as np
import matplotlib.pyplot as plt

gaussian_numbers = np.random.randn(1000)

# Get histogram
hist, bins = np.histogram(gaussian_numbers, bins=12)

# Threshold frequency
freq = 100

# Zero out low values
hist[np.where(hist <= freq)] = 0

# Plot
width = 0.7 * (bins[1] - bins[0])
center = (bins[:-1] + bins[1:]) / 2
plt.bar(center, hist, align='center', width=width)
plt.title("Gaussian Histogram")
plt.xlabel("Value")
plt.ylabel("Frequency")

(图解部分的灵感来自此处.)

How do I remove data from a histogram in python under a certain frequency count?

Say I have 10 bins, the first bin has a count of 4, the second has 2, the third has 1, fourth has 5, etc... Now I want to get rid of the data that has a count of 2 or less. So the second bin would go to zero, as would the third.

Example:

import numpy as np
import matplotlib.pyplot as plt

gaussian_numbers = np.random.randn(1000)
plt.hist(gaussian_numbers, bins=12)
plt.title("Gaussian Histogram")
plt.xlabel("Value")
plt.ylabel("Frequency")

fig = plt.gcf()

Gives:

and I want to get rid of the bins with fewer than a frequency of say 'X' (could be frequency = 100 for example).

want:

thank you.

解决方案

Une np.histogram to create the histogram.

Then use np.where. Given a condition, it yields an array of booleans you can use to index your histogram.

import numpy as np
import matplotlib.pyplot as plt

gaussian_numbers = np.random.randn(1000)

# Get histogram
hist, bins = np.histogram(gaussian_numbers, bins=12)

# Threshold frequency
freq = 100

# Zero out low values
hist[np.where(hist <= freq)] = 0

# Plot
width = 0.7 * (bins[1] - bins[0])
center = (bins[:-1] + bins[1:]) / 2
plt.bar(center, hist, align='center', width=width)
plt.title("Gaussian Histogram")
plt.xlabel("Value")
plt.ylabel("Frequency")

(Plot part inspired from here.)

这篇关于直方图处理以删除不需要的数据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！