Matplotlib在所有子图中显示x-ticks和唯一的y标签 [英] Matplotlib show x-ticks on all subplots and unique y label

问题描述

我正在绘制两个共享相同x轴的子图，但是当我绘制时，我仅在第二个子图上看到x轴刻度.如何在两个子图上都显示X刻度?

I am plotting two subplots that share the same x-axis but when I plot I only see the x-axis ticks on the second subplot. How can I make the x-ticks visible on both subplots?

我也想为两个子图设置y标签，但是只有第二个可见.您能帮忙在两个子图上显示y标签吗?

Also I would like to set y-labels for both subplots but only the second is visible. Can you please help in displaying the y-label on both subplots?

下面是我的可复制代码.

Below is my reproducible code.

#!/usr/bin/python3
import pandas as pd
desired_width = 1500
pd.set_option('display.width', desired_width)
import matplotlib.pyplot as plt
import numpy as np


df = pd.DataFrame([{'DATETIME': '2017-09-29 01:00,', 'Population': 1000, 'Temp': 90, 'State': 'California'},
                   {'DATETIME': '2017-09-29 01:00,', 'Population': 2000, 'Temp': 70, 'State': 'Illinois'},
                   {'DATETIME': '2017-09-29 01:00,', 'Population': 3000, 'Temp': 50, 'State': 'Georgia'},
                   {'DATETIME': '2017-09-29 02:00,', 'Population': 2000, 'Temp': 40, 'State': 'California'},
                   {'DATETIME': '2017-09-29 02:00,', 'Population': 6000, 'Temp': 20, 'State': 'Illinois'},
                   {'DATETIME': '2017-09-29 02:00,', 'Population': 4000, 'Temp': 30, 'State': 'Georgia'},
                   {'DATETIME': '2017-09-29 03:00,', 'Population': 3000, 'Temp': 40, 'State': 'California'},
                   {'DATETIME': '2017-09-29 03:00,', 'Population': 4000, 'Temp': 60, 'State': 'Illinois'},
                   {'DATETIME': '2017-09-29 03:00,', 'Population': 2000, 'Temp': 80, 'State': 'Georgia'}])

df.index = df['DATETIME']
df.index = (pd.to_datetime(df.index)).strftime("%m/%d %H:00")

fig, axes = plt.subplots(nrows=2, ncols=1, sharex=True)

df.groupby('State')['Population'].plot(kind='line', linestyle='--', alpha=0.5, marker='o', legend=True, ax=axes[0])
plt.ylabel('Pop')
df.groupby('State')['Temp'].plot(kind='line', linestyle='--', alpha=0.5, marker='o', legend=True, ax=axes[1])
plt.ylabel('Temp')
plt.tick_params(axis='both', which='both', labelsize=7)
plt.tight_layout()
plt.show()

当前图表输出:

推荐答案

您可以做几件事.要么删除sharex = True.或者，如果您想使用它，则sharex将x刻度设置为不可见，即set_visible(False).因此，您可以将它们设置为True来停止此操作.

There are a couple of things you can do. Either remove sharex = True. Or, if you want to use that, sharex sets the x ticks to not be visible i.e. set_visible(False). Therefore, you can set them to True to stop this.

为了使子图的格式相同，您需要通过对两个子图使用axes[0].tick_params(axis='both', which='both', labelsize=7)来设置每个子图的刻度参数(即对axes[1]重复)

In order to have the subplots formatted the same, you need to set the tick params for each subplot by using axes[0].tick_params(axis='both', which='both', labelsize=7) for both subplots (i.e. repeat for axes[1])

注意，我个人更喜欢使用matpotlib面向对象的API，即使用ax.set_ylabel()而不是plt.ylabel()，因为我认为它可以更好地控制您使用的子图和轴.因此，我在这方面也略微修改了您的代码

Note, personally I prefer to use matpotlib object oriented API i.e using ax.set_ylabel() rather than plt.ylabel() as I think it gives more control over which subplots and axes you are using. Therefore I have slightly modified your code in that regards too

df = pd.DataFrame([{'DATETIME': '2017-09-29 01:00,', 'Population': 1000, 'Temp': 90, 'State': 'California'},
                   {'DATETIME': '2017-09-29 01:00,', 'Population': 2000, 'Temp': 70, 'State': 'Illinois'},
                   {'DATETIME': '2017-09-29 01:00,', 'Population': 3000, 'Temp': 50, 'State': 'Georgia'},
                   {'DATETIME': '2017-09-29 02:00,', 'Population': 2000, 'Temp': 40, 'State': 'California'},
                   {'DATETIME': '2017-09-29 02:00,', 'Population': 6000, 'Temp': 20, 'State': 'Illinois'},
                   {'DATETIME': '2017-09-29 02:00,', 'Population': 4000, 'Temp': 30, 'State': 'Georgia'},
                   {'DATETIME': '2017-09-29 03:00,', 'Population': 3000, 'Temp': 40, 'State': 'California'},
                   {'DATETIME': '2017-09-29 03:00,', 'Population': 4000, 'Temp': 60, 'State': 'Illinois'},
                   {'DATETIME': '2017-09-29 03:00,', 'Population': 2000, 'Temp': 80, 'State': 'Georgia'}])

df.index = df['DATETIME']
df.index = (pd.to_datetime(df.index)).strftime("%m/%d %H:00")

fig, axes = plt.subplots(nrows=2, ncols=1, sharex=True)

df.groupby('State')['Population'].plot(kind='line', linestyle='--', alpha=0.5, marker='o', legend=True, ax=axes[0])
axes[0].set_ylabel('Pop')
df.groupby('State')['Temp'].plot(kind='line', linestyle='--', alpha=0.5, marker='o', legend=True, ax=axes[1])
axes[1].set_ylabel('Temp')

# Set the formatting the same for both subplots
axes[0].tick_params(axis='both', which='both', labelsize=7)
axes[1].tick_params(axis='both', which='both', labelsize=7)

# set ticks visible, if using sharex = True. Not needed otherwise
for tick in axes[0].get_xticklabels():
    tick.set_visible(True)

plt.tight_layout()
plt.show()

哪个给:

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