Matplotlib：在自定义格式的 x 轴上显示所有日期

Question

使用 matplotlib 我想创建一个图表，其中我的数据框 (df) 中的所有日期值都显示在 x 轴上。原则上，代码行plt.gca().xaxis.set_major_locator(matplotlib.dates.DayLocator(interval=1)) 应该可以完成这项工作，但它没有，可能是因为我使用的是自定义格式化程序！？这里需要自定义格式化程序，因为我会阻止 matplotlib 插入不属于我的数据框 (have a look at this question here) 的周末日期值。

我还想使用日期格式“%d.%m.%Y”。

虽然代码适用于 matplotlib 3.3.3/Python 3.8，但我的项目必须使用 matplotlib 3.2.2/Python 3.6，在这些情况下，代码不会返回所需的输出

代码如下：

import matplotlib
import matplotlib.pyplot as plt
from matplotlib.ticker import Formatter
import pandas as pd
import numpy as np


df = pd.DataFrame(data={"col1": [1.000325, 1.000807, 1.001207, 1.000355, 1.001512, 1.003237, 1.000979,
                                 1.000325, 1.000807, 1.001207, 1.000355, 1.001512, 1.003237, 1.000979],
                        "date": ['2018-01-08', '2018-01-09', '2018-01-10', '2018-01-11', '2018-01-12',
                                 '2018-01-15', '2018-01-16', '2018-01-17', '2018-01-18', '2018-01-19',
                                 '2018-01-22', '2018-01-23', '2018-01-24', '2018-01-25',]})
df["date"] = pd.to_datetime(df["date"])

class CustomFormatter(Formatter):

    def __init__(self, dates, fmt='%d.%m.%Y'):
        self.dates = dates
        self.fmt = fmt

    def __call__(self, x, pos=0):
        'Return the label for time x at position pos'
        ind = int(np.round(x))
        if ind >= len(self.dates) or ind < 0:
            return ''

        return self.dates[ind].strftime(self.fmt)

fig = plt.figure()
plt.gca().xaxis.set_major_formatter(CustomFormatter(df["date"]))
plt.plot(np.arange(df.shape[0]), df["col1"])
plt.gcf().autofmt_xdate()
#plt.gca().xaxis.set_major_locator(matplotlib.dates.DayLocator(interval=1)) # <-- this line should do the job, in theory!

使用 matplotlib 3.2.2 输出

预期输出（matplotlib 3.3.3）

感谢您的帮助！

Answer 1

我认为您应该只使用日期工具，但请确保您实际上是在绘制日期，而不是 np.arange(df.shape[0])

import matplotlib
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np


df = pd.DataFrame(data={"col1": [1.000325, 1.000807, 1.001207, 1.000355, 1.001512, 1.003237, 1.000979,
                                 1.000325, 1.000807, 1.001207, 1.000355, 1.001512, 1.003237, 1.000979],
                        "date": ['2018-01-08', '2018-01-09', '2018-01-10', '2018-01-11', '2018-01-12',
                                 '2018-01-15', '2018-01-16', '2018-01-17', '2018-01-18', '2018-01-19',
                                 '2018-01-22', '2018-01-23', '2018-01-24', '2018-01-25',]})
df["date"] = pd.to_datetime(df["date"])

fig, ax = plt.subplots()
ax.xaxis.set_major_locator(matplotlib.dates.DayLocator(interval=1))
ax.xaxis.set_major_formatter(matplotlib.dates.DateFormatter('%d.%m.%Y'))

ax.plot(df["date"], df["col1"])
fig.autofmt_xdate()
plt.show()

Answer 2

由于您不是针对日期而是针对索引进行绘图，因此DateLocator 可能是这里的错误选择：

import matplotlib
import matplotlib.pyplot as plt
from matplotlib.ticker import Formatter, FixedLocator
import pandas as pd
import numpy as np


df = pd.DataFrame(data={"col1": [1.000325, 1.000807, 1.001207, 1.000355, 1.001512, 1.003237, 1.000979,
                                 1.000325, 1.000807, 1.001207, 1.000355, 1.001512, 1.003237, 1.000979],
                        "date": ['2018-01-08', '2018-01-09', '2018-01-10', '2018-01-11', '2018-01-12',
                                 '2018-01-15', '2018-01-16', '2018-01-17', '2018-01-18', '2018-01-19',
                                 '2018-01-22', '2018-01-23', '2018-01-24', '2018-01-25',]})

df["date"] = pd.to_datetime(df["date"])


class CustomFormatter(Formatter):

    def __init__(self, dates, fmt='%d.%m.%Y'):
        self.dates = dates
        self.fmt = fmt

    def __call__(self, x, pos=0):
        'Return the label for time x at position pos'
        ind = int(np.round(x))
        if ind >= len(self.dates) or ind < 0:
            return ''

        return self.dates[ind].strftime(self.fmt)

fig = plt.figure()
plt.plot(np.arange(df.shape[0]), df["col1"])
plt.gca().xaxis.set_major_locator(FixedLocator(np.arange(df.shape[0])))
plt.gca().xaxis.set_major_formatter(CustomFormatter(df["date"]))
plt.gcf().autofmt_xdate()

#print(matplotlib.__version__)
plt.show()

输出：

但是由于无论如何您都针对索引进行绘图，因此您不需要类定义：

import matplotlib
import matplotlib.pyplot as plt
from matplotlib.ticker import FixedLocator
import pandas as pd
import numpy as np


df = pd.DataFrame(data={"col1": [1.000325, 1.000807, 1.001207, 1.000355, 1.001512, 1.003237, 1.000979,
                                 1.000325, 1.000807, 1.001207, 1.000355, 1.001512, 1.003237, 1.000979],
                        "date": ['2018-01-08', '2018-01-09', '2018-01-10', '2018-01-11', '2018-01-12',
                                 '2018-01-15', '2018-01-16', '2018-01-17', '2018-01-18', '2018-01-19',
                                 '2018-01-22', '2018-01-23', '2018-01-24', '2018-01-25',]})

df["date"] = pd.to_datetime(df["date"])


fig = plt.figure()
plt.plot(np.arange(df.shape[0]), df["col1"])
plt.gca().xaxis.set_major_locator(FixedLocator(np.arange(df.shape[0])))
plt.xticks(np.arange(df.shape[0]), df["date"].dt.strftime("%d.%m.%Y"))
plt.gcf().autofmt_xdate()

#print(matplotlib.__version__)
plt.show()

输出：见上文

Answer 3

不涉及类的问题的可能解决方案是这样做（obs！所有导入可能都不是必需的）：

import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.ticker as ticker
import seaborn as sns
from IPython.display import display
from mpl_toolkits.axes_grid1 import make_axes_locatable
from matplotlib.colors import ListedColormap, LinearSegmentedColormap
from matplotlib.lines import Line2D
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec


fig = plt.figure(figsize=[30,30])
gs  = gridspec.GridSpec(100,100)
ax21 = fig.add_subplot(gs[0:100,0:100])

a = df['col1']
ax21 = sns.lineplot(x=df['date'], y=df['col1'].values, palette="Reds", ax=ax21)
ax21.set_xlabel('whatever', c='w', fontsize=16)
ax21.set_ylabel('Other ever', c='w', fontsize=16)
ax21.set_title('This is it', c='w', fontsize=20, weight = 'bold')

plt.show()