Python 年月日格式x轴matplotlib

我有一个包含累积降雨数据的熊猫数据框

这些列是

“年度日期”、“1981”、“1982”…'2019’

我使用以下代码绘制数据：

fig, ax = plt.subplots(1, 1, figsize=(10,10))

ax.set_title('Cummulative rainfall in Chennai hydrological basin')
ax.set_xlabel('day of the year')
ax.set_ylabel('total rainfall in mm')
ax.yaxis.tick_right()
ax.yaxis.set_label_position("right")


df.plot(ax=ax,
        x='dayofyear',
        y=years_string,
        colormap='gray',
        legend=False,
        alpha=0.2)

df.plot(ax=ax,
        x='dayofyear',
        y=['2015','2016','2017','2018'],
        alpha=0.7,
        colormap='plasma')

df.plot(ax=ax,
        x='dayofyear',
        y='2019',
        color='red',
        linewidth=3)

fig.savefig('test.jpg')

结果看起来真的很好

然而，一年中的哪一天可能很难理解，如果可能的话，我想在每个月的哪一天加上主要的记号。我发现了这个，并试图让它工作，但没有结果。有没有一种简单的方法可以在不转换数据的情况下更改xaxis刻度

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完整代码

您的问题很清楚，您应该发布一个数据帧示例以及您正在处理的内容。您可以使用这行代码在x轴上选择记号的位置

plt.xticks(np.arange(min(x), max(x)+1, 1.0))

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回答我自己的问题

最简单的解决方案是转换为datetime。使用设置轴的打印

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您可以将dayofyear列转换为true dates。这里有一个指向完整脚本的链接，其中包括数据和示例

# -*- coding: utf-8 -*-
"""Y2019M07D31_RH_Chennai_v01.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/gist/rutgerhofste/666c1a01e9f2724de1451ee9b27d9cdd/y2019m07d31_rh_chennai_v01.ipynb
"""

import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib.dates as dates

df =pd.read_csv('https://gist.githubusercontent.com/rutgerhofste/94b9035bdcaf1163ff910a08ad3239a3/raw/db2efe21aa980418558010695db08af5c27b616c/cummulative.csv')

df.head()

df['date'] =  pd.to_datetime(df['dayofyear'], format='%j')

years = list(range(1981,2014+1))

def string(year):
  return str(year)

years_string = list(map(string,years))

fig, ax = plt.subplots(1, 1, figsize=(10,10))

ax.set_title('Cummulative rainfall in Chennai hydrological basin')

df.plot(ax=ax,
        x='date',
        y=years_string,
        colormap='gray',
        legend=False,
        alpha=0.2)

df.plot(ax=ax,
        x='date',
        y=['2015','2016','2017','2018'],
        alpha=0.7,
        colormap='viridis')

df.plot(ax=ax,
        x='date',
        y='2019',
        color='red',
        linewidth=3)

ax.set_xlabel('Month')
ax.set_ylabel('total rainfall in mm')
ax.yaxis.tick_right()
ax.yaxis.set_label_position("right")

major_format = mdates.DateFormatter('%b')

ax.xaxis.set_major_formatter(major_format)


ax.xaxis.grid(linestyle=':')
fig.savefig('test.pdf')