For 循环以创建多个直方图 png 文件

Question

我不确定为什么会这样。也许这只是一个我看不到的简单错误，但是通过使用此代码：

for filename in glob.glob('/Users/jacob/Desktop/MERS/new/NOT COAL/gensets/statistics_per_lgu/per_lgu_files/*.csv'):
base = os.path.basename(filename)
name = os.path.splitext(base)[0]
df = pd.read_csv(filename)
# Show 4 different binwidths
for i, binwidth in enumerate([10, 20, 30, 40]):
# Set up the plot
ax = plt.subplot(2, 2, i + 1)
plt.subplots_adjust( wspace=0.5, hspace=0.5)
# Draw the plot
ax.hist(df['New Capacity based on 0.8 PF'], bins=binwidth,
color='red', edgecolor='black',alpha=0.5)
# Title and labels
ax.set_title('Histogram with Binwidth = %d' % binwidth, size=10)
ax.set_xlabel('Capacity', size=11)
ax.set_ylabel('Frequency count', size=11)
ax.axvline(x=df['New Capacity based on 0.8 PF'].median(), linestyle='dashed', alpha=0.3, color='blue')
min_ylim, max_ylim = plt.ylim()
ax.text(x=df['New Capacity based on 0.8 PF'].median(),y= max_ylim*0.9, s='Median', alpha=0.7, color='blue',fontsize = 12)
ax.axvline(x=df['New Capacity based on 0.8 PF'].mean(), linestyle='dashed', alpha=0.9, color='green')
min_ylim, max_ylim = plt.ylim()
ax.text(x=df['New Capacity based on 0.8 PF'].mean(),y= max_ylim*0.5, s='Mean', alpha=0.9, color='green',fontsize = 12)
plt.tight_layout()
plt.grid(True)
plt.savefig('/Users/jacob/Documents/Gensets_gis/historgrams/per_lgu_files/{}.png'.format(name))

我在这里获得像这张附照片一样创建的所有文件。

关于我做错了什么的任何想法？

提前谢谢。 附上一个直方图输出的照片

我想要的结果是这样的。

期望的输出

Answer 1

它不会创建新的子图，但它使用以前的子图，然后在旧图上绘制新图，因此在绘制新直方图之前必须使用清晰的子图。

ax = plt.subplot(2, 2, i + 1)
ax.clear()

---

示例代码。它提供了所需的输出，但是如果您删除'ax.clear((，那么第一个图像就可以了，但是您会在第二张和第三张图像上获得带有旧图的新图。

import os
import pandas as pd
import matplotlib.pyplot as plt
import random
for n in range(3):
filename = f'example_data_{n}.csv'
base = os.path.basename(filename)
name = os.path.splitext(base)[0]
df = pd.DataFrame({'New Capacity based on 0.8 PF': random.choices(list(range(1000)), k=100)})
data = df['New Capacity based on 0.8 PF']
median = data.median()
mean = data.mean()

# Show 4 different binwidths
for i, binwidth in enumerate([10, 20, 30, 40]):
# Set up the plot
ax = plt.subplot(2,2,i+1)
ax.clear()  # <--- it removes previous histogram 
plt.subplots_adjust( wspace=0.5, hspace=0.5)
# Draw the plot
ax.hist(data , bins=binwidth, color='red', edgecolor='black',alpha=0.5)
# Title and labels
ax.set_title('Histogram with Binwidth = %d' % binwidth, size=10)
ax.set_xlabel('Capacity', size=11)
ax.set_ylabel('Frequency count', size=11)
min_ylim, max_ylim = plt.ylim()
ax.axvline(x=median, linestyle='dashed', alpha=0.3, color='blue')
ax.text(x=median, y= max_ylim*0.9, s='Median', alpha=0.7, color='blue',fontsize = 12)
ax.axvline(x=mean, linestyle='dashed', alpha=0.9, color='green')
ax.text(x=mean, y= max_ylim*0.5, s='Mean', alpha=0.9, color='green',fontsize = 12)
plt.tight_layout()
plt.grid(True)
plt.savefig('{}.png'.format(name))