Matplotlib, чтобы установить частоту на xaxis

Question

Я хотел бы установить частоту оси x моего графика с помощью следующего кода:

fig, ax = plt.subplots()
fig = plt.figure(figsize=(4,3))
plt.plot(vol['BTC'], 'b-', vol['ETH'], 'r-', vol['XRP'], 'g-', vol['BCH'], 'y-')
plt.legend(labels= ['BTC','ETH','XRP','BCH'], fontsize="xx-small")
plt.xticks(rotation=30, horizontalalignment='right')
start, end = ax.get_xlim()
ax.xaxis.set_ticks(np.arange(start, end, 20))
plt.rcParams.update({'font.size': 3})

Однако ничего не меняется, и оно остается как масса чернил, так как метки перекрываются. Я хотел бы снизить частоту показа и уменьшить размер надписей. Любая помощь будет оценена.

РЕДАКТИРОВАТЬ: я пробовал следующее, но диаграмма пуста.

My new code:

years = mdates.YearLocator()   # every year
months = mdates.MonthLocator()  # every month
days = mdates.DayLocator()  # every day
years_fmt = mdates.DateFormatter('%Y-%m-%d')
vol_index = vol.index

fig, ax = plt.subplots()
ax.plot(np.array(vol.index), np.array(vol['BTC']))

# format the ticks
ax.xaxis.set_major_locator(years)
ax.xaxis.set_major_formatter(years_fmt)
ax.xaxis.set_minor_locator(days)

# round to nearest years.
datemin = pd.to_datetime(np.datetime64(vol.index[0], 'D')).strftime('%Y-%m-%d')
datemax = pd.to_datetime(np.datetime64(vol.index[-1], 'D') + np.timedelta64(1, 'D')).strftime('%Y-%m-%d')
ax.set_xlim(datemin, datemax)

# format the coords message box
ax.format_xdata = mdates.DateFormatter('%Y-%m-%d')
ax.format_ydata = lambda x: '$%1.2f' % x  # format the price.
ax.grid(True)

fig.autofmt_xdate()

Этот код использует следующий ввод:

np.array(vol['BTC']):

[0.05498711 0.03454131 0.03454131 0.02293731 0.02191053 0.02191053
 0.01688648 0.02828462 0.02828462 0.02828462 0.03188105 0.03054535
 0.02955679 0.0270898  0.0281818  0.02866918 0.02710031 0.02668099
 0.03404319 0.03670357 0.05311734 0.08663729 0.08663729 0.08339912
 0.08831546 0.08063601 0.07522458 0.06043395 0.06280006 0.06543079
 0.06543079 0.0444152  0.0444152  0.03575803 0.03575803 0.03575803
 0.02870328 0.02870328 0.02870328 0.02870328 0.02835626 0.02263508
 0.02263508 0.02514882 0.0332816  0.02463769 0.0385677  0.0427951
 0.04329788 0.04329788 0.04329788 0.05240083 0.05883257 0.05883257
 0.03162634 0.02906886 0.02765406 0.024522   0.02352016 0.01756594
 0.02030382 0.02030382 0.03326152 0.03601441 0.03601441 0.03250479
 0.04571088 0.06313575 0.07639834 0.07639834 0.0555884  0.05133794
 0.0493664  0.03279575 0.05379169 0.05431873 0.05431873 0.04422756
 0.04649411 0.04825915 0.04584981 0.02741382 0.02741382]

vol.index:

Index(['2019-11-01 23:59:08', '2019-11-02 23:59:08', '2019-11-03 23:59:04',
       '2019-11-04 23:59:12', '2019-11-05 23:59:03', '2019-11-06 23:59:08',
       '2019-11-07 23:59:04', '2019-11-08 23:59:07', '2019-11-09 23:59:06',
       '2019-11-10 23:59:05', '2019-11-11 23:59:03', '2019-11-12 23:59:04',
       '2019-11-13 23:59:06', '2019-11-14 23:59:06', '2019-11-15 23:59:05',
       '2019-11-16 23:59:12', '2019-11-17 23:59:03', '2019-11-18 23:59:10',
       '2019-11-19 23:59:09', '2019-11-20 23:59:06', '2019-11-21 23:59:11',
       '2019-11-22 23:59:05', '2019-11-23 23:59:06', '2019-11-24 23:59:09',
       '2019-11-25 23:59:05', '2019-11-26 23:59:03', '2019-11-27 23:59:04',
       '2019-11-28 23:59:05', '2019-11-29 23:59:04', '2019-11-30 23:59:02',
       '2019-12-01 23:59:06', '2019-12-02 23:59:11', '2019-12-03 23:59:02',
       '2019-12-04 23:59:07', '2019-12-05 23:59:07', '2019-12-06 23:59:08',
       '2019-12-07 23:59:07', '2019-12-08 23:59:06', '2019-12-09 23:59:06',
       '2019-12-10 23:59:06', '2019-12-11 23:59:11', '2019-12-12 23:59:05',
       '2019-12-13 23:59:04', '2019-12-14 23:59:08', '2019-12-15 23:59:07',
       '2019-12-16 23:59:13', '2019-12-17 23:59:10', '2019-12-18 23:59:12',
       '2019-12-19 23:59:09', '2019-12-20 23:59:10', '2019-12-21 23:59:06',
       '2019-12-22 23:59:07', '2019-12-23 23:59:07', '2019-12-24 23:59:13',
       '2019-12-25 23:59:10', '2019-12-26 23:59:11', '2019-12-27 23:59:04',
       '2019-12-28 23:59:05', '2019-12-29 23:59:06', '2019-12-30 23:59:07',
       '2019-12-31 23:59:09', '2020-01-01 23:59:06', '2020-01-02 23:59:02',
       '2020-01-03 23:59:06', '2020-01-04 23:59:03', '2020-01-05 23:59:08',
       '2020-01-06 23:59:07', '2020-01-07 23:59:08', '2020-01-08 23:59:06',
       '2020-01-09 23:59:03', '2020-01-10 23:59:10', '2020-01-11 23:59:02',
       '2020-01-12 23:59:07', '2020-01-13 23:59:04', '2020-01-14 23:59:07',
       '2020-01-15 23:59:04', '2020-01-16 23:59:06', '2020-01-17 23:59:03',
       '2020-01-18 23:59:09', '2020-01-19 23:59:04', '2020-01-20 23:59:04',
       '2020-01-21 23:59:08', '2020-01-22 23:59:04'],
      dtype='object', name='timestamp')

vol:

                      BTC       ETH       XRP       BCH
timestamp                                                  
2019-11-01 23:59:08  0.054987  0.040244  0.029715  0.082026
2019-11-02 23:59:08  0.034541  0.029485  0.026469  0.078933
2019-11-03 23:59:04  0.034541  0.027006  0.026571  0.057731
2019-11-04 23:59:12  0.022937  0.027006  0.026571  0.052057
2019-11-05 23:59:03  0.021911  0.026792  0.023252  0.040931
...                       ...       ...       ...       ...
2020-01-18 23:59:09  0.046494  0.086220  0.066950  0.162209
2020-01-19 23:59:04  0.048259  0.084283  0.066950  0.157251
2020-01-20 23:59:04  0.045850  0.081700  0.064228  0.149919
2020-01-21 23:59:08  0.027414  0.041858  0.042955  0.092721
2020-01-22 23:59:04  0.027414  0.041858  0.042955  0.092721

Answer 1

Это альтернативный подход, когда вы указываете желаемое количество тиков вместо частоты, но вы все равно можете найти это полезным:

import pandas as pd
import matplotlib.pyplot as plt

x = np.arange('2017-01', '2020-01', dtype='datetime64[D]')
y = np.random.random(len(x))

df = pd.DataFrame({'x': x, 'y': y})

plt.scatter(df['x'].values, df['y'].values)
ticks = np.arange(x[0], x[-1], (x[-1] - x[0]).astype('timedelta64[D]') / 15)
plt.xticks(ticks, ticks)
plt.gca().tick_params('x', labelrotation=45, labelsize=10)
plt.show()

, что дает

вместо

---

Edit

Вот полный пример который использует данные,

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

BTC = np.array([0.05498711,0.03454131,0.03454131,0.02293731,0.02191053,0.02191053,
                0.01688648,0.02828462,0.02828462,0.02828462,0.03188105,0.03054535,
                0.02955679,0.0270898, 0.0281818, 0.02866918,0.02710031,0.02668099,
                0.03404319,0.03670357,0.05311734,0.08663729,0.08663729,0.08339912,
                0.08831546,0.08063601,0.07522458,0.06043395,0.06280006,0.06543079,
                0.06543079,0.0444152, 0.0444152, 0.03575803,0.03575803,0.03575803,
                0.02870328,0.02870328,0.02870328,0.02870328,0.02835626,0.02263508,
                0.02263508,0.02514882,0.0332816, 0.02463769,0.0385677, 0.0427951,
                0.04329788,0.04329788,0.04329788,0.05240083,0.05883257,0.05883257,
                0.03162634,0.02906886,0.02765406,0.024522,  0.02352016,0.01756594,
                0.02030382,0.02030382,0.03326152,0.03601441,0.03601441,0.03250479,
                0.04571088,0.06313575,0.07639834,0.07639834,0.0555884, 0.05133794,
                0.0493664, 0.03279575,0.05379169,0.05431873,0.05431873,0.04422756,
                0.04649411,0.04825915,0.04584981,0.02741382,0.02741382])

idx = np.array(['2019-11-01 23:59:08', '2019-11-02 23:59:08', '2019-11-03 23:59:04',
                '2019-11-04 23:59:12', '2019-11-05 23:59:03', '2019-11-06 23:59:08',
                '2019-11-07 23:59:04', '2019-11-08 23:59:07', '2019-11-09 23:59:06',
                '2019-11-10 23:59:05', '2019-11-11 23:59:03', '2019-11-12 23:59:04',
                '2019-11-13 23:59:06', '2019-11-14 23:59:06', '2019-11-15 23:59:05',
                '2019-11-16 23:59:12', '2019-11-17 23:59:03', '2019-11-18 23:59:10',
                '2019-11-19 23:59:09', '2019-11-20 23:59:06', '2019-11-21 23:59:11',
                '2019-11-22 23:59:05', '2019-11-23 23:59:06', '2019-11-24 23:59:09',
                '2019-11-25 23:59:05', '2019-11-26 23:59:03', '2019-11-27 23:59:04',
                '2019-11-28 23:59:05', '2019-11-29 23:59:04', '2019-11-30 23:59:02',
                '2019-12-01 23:59:06', '2019-12-02 23:59:11', '2019-12-03 23:59:02',
                '2019-12-04 23:59:07', '2019-12-05 23:59:07', '2019-12-06 23:59:08',
                '2019-12-07 23:59:07', '2019-12-08 23:59:06', '2019-12-09 23:59:06',
                '2019-12-10 23:59:06', '2019-12-11 23:59:11', '2019-12-12 23:59:05',
                '2019-12-13 23:59:04', '2019-12-14 23:59:08', '2019-12-15 23:59:07',
                '2019-12-16 23:59:13', '2019-12-17 23:59:10', '2019-12-18 23:59:12',
                '2019-12-19 23:59:09', '2019-12-20 23:59:10', '2019-12-21 23:59:06',
                '2019-12-22 23:59:07', '2019-12-23 23:59:07', '2019-12-24 23:59:13',
                '2019-12-25 23:59:10', '2019-12-26 23:59:11', '2019-12-27 23:59:04',
                '2019-12-28 23:59:05', '2019-12-29 23:59:06', '2019-12-30 23:59:07',
                '2019-12-31 23:59:09', '2020-01-01 23:59:06', '2020-01-02 23:59:02',
                '2020-01-03 23:59:06', '2020-01-04 23:59:03', '2020-01-05 23:59:08',
                '2020-01-06 23:59:07', '2020-01-07 23:59:08', '2020-01-08 23:59:06',
                '2020-01-09 23:59:03', '2020-01-10 23:59:10', '2020-01-11 23:59:02',
                '2020-01-12 23:59:07', '2020-01-13 23:59:04', '2020-01-14 23:59:07',
                '2020-01-15 23:59:04', '2020-01-16 23:59:06', '2020-01-17 23:59:03',
                '2020-01-18 23:59:09', '2020-01-19 23:59:04', '2020-01-20 23:59:04',
                '2020-01-21 23:59:08', '2020-01-22 23:59:04'], dtype='datetime64[s]')

df = pd.DataFrame({'BTC': BTC})
df.index = pd.DatetimeIndex(idx)

x = df.index.values.astype('datetime64[D]')
y = df['BTC']

plt.figure(figsize=(8,4))
plt.plot(x, y)
ticks = np.arange(x[0], x[-1], (x[-1] - x[0]).astype('timedelta64[D]') / 15)
plt.xticks(ticks, ticks)
plt.gca().tick_params('x', labelrotation=90, labelsize=10)
plt.show()

Это даст вам

Answer 2

Вам нужен тиковый локатор :

import matplotlib.ticker as ticker

# ...
ax.xaxis.set_major_locator(ticker.MultipleLocator(20))

И вы можете сделать это, если хотите, вместо изменения rcParams:

ax.tick_params(axis='x',which='major',labelsize=3)