https://matplotlib.org/gallery/text_labels_and_annotations/date_index_formatter.html
https://matplotlib.org/1.3.1/examples/pylab_examples/date_index_formatter.html
时间不连续
from collections import OrderedDict
def tick_category(frequency, step):
'''
根据频率和频数获取刻度的时间轴
frequency 可取值: 'year','month','day','hour','minute','second'
'''
if frequency=='year':
df['frequency'] = rng_date.strftime('%Y')
elif frequency=='month':
df['frequency'] = rng_date.strftime('%Y-%m')
elif frequency=='day':
df['frequency'] = rng_date.strftime('%Y-%m-%d')
elif frequency=='hour':
df['frequency'] = rng_date.strftime('%Y-%m-%d %H')
elif frequency=='minute':
df['frequency'] = rng_date.strftime('%Y-%m-%d %H:%M')
elif frequency=='second':
df['frequency'] = rng_date.strftime('%Y-%m-%d %H:%M:%S')
else:
df['frequency'] = rng_date.strftime('%Y')
num_date = OrderedDict()
for item in df.groupby('frequency'):
num_date[item[1].index[-1]] = item[0]
nums = list(num_date.keys())[::step]
dates = list(num_date.values())[::step]
return num_date,nums,dates
def show_plot(frequency, step, columns, tick_count=10):
num_date,nums,dates = tick_category(frequency, step)
end_pos = nums[tick_count] if tick_count<len(nums) else nums[-1]
data = df.loc[:,columns][:end_pos]
axes = data.plot()
axes.set_xticks(nums[:end_pos])
axes.set_xticklabels(dates[:end_pos],rotation=45)
plt.show()
def show_plot(frequency, step, columns, tick_count=10):
num_date,nums,dates = tick_category(frequency, step)
end_pos = nums[tick_count] if tick_count<len(nums) else nums[-1]
data = df.loc[:,columns][:end_pos]
axes = data.plot()
axes.set_xticks(nums[:end_pos])
axes.set_xticklabels(dates[:end_pos],rotation=45)
plt.show()
show_plot('month',1, ['open'], tick_count=10)
参考：使用matplotlib绘制不连续时间序列的OHLC数据
方法2
其实官方文档里就提供了方法,这里简单的翻译并记录一下.
11.1.9 Skip dates where there is no data
When plotting time series, e.g., financial time series, one often wants to leave out days on which there is no data, e.g., weekends.
By passing in dates on the x-xaxis, you get large horizontal gaps on periods when there is not data.
The solution is to pass in some proxy x-data, e.g., evenly sampled indices, and then use a custom formatter to format these as dates.
The example below shows how to use an ‘index formatter’ to achieve the desired plot:
解决方案是通过传递x轴数据的代理,比如下标,
然后通过自定义的’formatter’去取到相对应的时间信息
manual内示例代码:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
import matplotlib.ticker as ticker
#读数据
r = mlab.csv2rec('../data/aapl.csv')
r.sort()
r = r[-30:] # get the last 30 days
N = len(r)
ind = np.arange(N) # the evenly spaced plot indices
def format_date(x, pos=None):
#保证下标不越界,很重要,越界会导致最终plot坐标轴label无显示
thisind = np.clip(int(x+0.5), 0, N-1)
return r.date[thisind].strftime('%Y-%m-%d')
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
ax.plot(ind, r.adj_close, 'o-')
ax.xaxis.set_major_formatter(ticker.FuncFormatter(format_date))
fig.autofmt_xdate()
plt.show()
示例:
同样一段数据上为原始,下为去掉无数据间隔区间
import pandas as PD
import numpy as NP
import matplotlib.pyplot as PLT
import matplotlib.ticker as MTK
file = r'vix_series.csv'
df = PD.read_csv(file, parse_dates=[0, 2])
#用下标代理原始时间戳数据
idx_pxy = NP.arange(df.shape[0])
#下标-时间转换func
def x_fmt_func(x, pos=None):
idx = NP.clip(int(x+0.5), 0, df.shape[0]-1)
return df['datetime'].iat[idx]
#绘图流程
def decorateAx(ax, xs, ys, x_func):
ax.plot(xs, ys, color="green", linewidth=1, linestyle="-")
ax.plot(ax.get_xlim(), [0,0], color="blue",
linewidth=0.5, linestyle="--")
if x_func:
#set数据代理func
ax.xaxis.set_major_formatter(MTK.FuncFormatter(x_func))
ax.grid(True)
return
fig = PLT.figure()
ax1 = fig.add_subplot(2,1,1)
ax2 = fig.add_subplot(2,1,2)
decorateAx(ax1, df['datetime'], df['vix_all'], None)
decorateAx(ax2, idx_pxy, df['vix_all'], x_fmt_func)
#优化label显示,非必须
fig.autofmt_xdate()
PLT.show()
很多时候乱翻google还不如好好通读官方manual…
作者：tpoy0099
来源：CSDN
原文：https://blog.csdn.net/tpoy0099/article/details/49511907
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