python - 如何获得主要和次要刻度标签

Question

我有一个 group-by 的输出，表示每周的日期总和。

Date
2008-10-28    20.0
2008-11-04    25.0
2008-11-11    20.0
2008-11-18    40.0
2008-11-25    35.0
2008-12-02    35.0
2008-12-09     NaN
2008-12-16     NaN
2008-12-23     NaN
2008-12-30     NaN
Freq: W-TUE, Name: Count, dtype: float64

我正在尝试使用plot_date

fig, ax = plt.subplots(figsize=(2, 4))
# ax = plt.gca()
line = ax.plot_date(a.index, a.values, '.', label='a', alpha=0.5, linewidth=1)
ax.tick_params('y', colors='k')
ax.set_xlabel('Date')
ax.set_ylabel('Frequency')
ax.set_title('Daily Games')
ax.tick_params('y', colors='k')
ax.grid(b=True, which='major', color='w', linewidth=1.0)
ax.grid(b=True, which='minor', color='w', linewidth=0.5)
ax.yaxis.grid(True)
ax.get_xaxis().set_minor_locator(mpl.ticker.AutoMinorLocator())
ax.set_xticklabels(ax.xaxis.get_majorticklabels(),
                    rotation=70)
ax.set_xticklabels(ax.xaxis.get_minorticklabels(),
                    rotation=70)
plt.xticks(rotation=70)
plt.show()

这产生了一个像这样的图表：

我已经尝试了各种重新排列方式，但我无法同时获得要绘制的日期的次要标签和主要标签。

我希望每个月都标记为 70 度。我该如何调整我必须这样做？

标签： pythonmatplotlibaxis

Answer 1

您可以使用AutoDateLocator()如下：

import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt
import pandas as pd

sns.set()

a = pd.DataFrame([
    ('2008-10-28', 20.0), ('2008-11-04', 25.0), ('2008-11-11', 20.0), 
    ('2008-11-18', 40.0), ('2008-11-25', 35.0), ('2008-12-02', 35.0)], columns=['Date', 'Frequency'])

a['Date'] = pd.to_datetime(a['Date'], format='%Y-%m-%d')

fig, ax = plt.subplots(figsize=(5, 5))
# ax = plt.gca()
line = ax.plot_date(a.Date, a.Frequency, '.', label='a', alpha=0.5, linewidth=1)
ax.tick_params('y', colors='k')
ax.set_xlabel('Date')
ax.set_ylabel('Frequency')
ax.set_title('Daily Games')
ax.tick_params('y', colors='k')
ax.grid(b=True, which='major', color='w', linewidth=1.0)
ax.grid(b=True, which='minor', color='w', linewidth=0.5)
ax.yaxis.grid(True)

xtick_locator = mpl.dates.AutoDateLocator()
xtick_formatter = mpl.dates.AutoDateFormatter(xtick_locator)

ax.xaxis.set_major_locator(xtick_locator)
ax.xaxis.set_major_formatter(xtick_formatter)

fig.subplots_adjust(bottom=0.24)
plt.xticks(rotation=70)
plt.show()

这将显示为：