ax.plot_date((dates, dates), (highs, lows), '-')
I'm currently using this command to plot financial highs and lows using Matplotlib. It works great, but how do I remove the blank spaces in the x-axis left by days without market data, such as weekends and holidays?
I have lists of dates, highs, lows, closes and opens. I can't find any examples of creating a graph with an x-axis that show dates but doesn't enforce a constant scale.
One of the advertised features of scikits.timeseries is "Create time series plots with intelligently spaced axis labels".
You can see some example plots here. In the first example (shown below) the 'business' frequency is used for the data, which automatically excludes holidays and weekends and the like. It also masks missing data points, which you see as gaps in this plot, rather than linearly interpolating them.
Up to date answer (2018) with Matplotlib 2.1.2, Python 2.7.12
The function equidate_ax handles everything you need for a simple date x-axis with equidistant spacing of data points. Realised with ticker.FuncFormatter based on this example.
from __future__ import division
from matplotlib import pyplot as plt
from matplotlib.ticker import FuncFormatter
import numpy as np
import datetime
def equidate_ax(fig, ax, dates, fmt="%Y-%m-%d", label="Date"):
"""
Sets all relevant parameters for an equidistant date-x-axis.
Tick Locators are not affected (set automatically)
Args:
fig: pyplot.figure instance
ax: pyplot.axis instance (target axis)
dates: iterable of datetime.date or datetime.datetime instances
fmt: Display format of dates
label: x-axis label
Returns:
None
"""
N = len(dates)
def format_date(index, pos):
index = np.clip(int(index + 0.5), 0, N - 1)
return dates[index].strftime(fmt)
ax.xaxis.set_major_formatter(FuncFormatter(format_date))
ax.set_xlabel(label)
fig.autofmt_xdate()
#
# Some test data (with python dates)
#
dates = [datetime.datetime(year, month, day) for year, month, day in [
(2018,2,1), (2018,2,2), (2018,2,5), (2018,2,6), (2018,2,7), (2018,2,28)
]]
y = np.arange(6)
# Create plots. Left plot is default with a gap
fig, [ax1, ax2] = plt.subplots(1, 2)
ax1.plot(dates, y, 'o-')
ax1.set_title("Default")
ax1.set_xlabel("Date")
# Right plot will show equidistant series
# x-axis must be the indices of your dates-list
x = np.arange(len(dates))
ax2.plot(x, y, 'o-')
ax2.set_title("Equidistant Placement")
equidate_ax(fig, ax2, dates)
I think you need to "artificially synthesize" the exact form of plot you want by using xticks to set the tick labels to the strings representing the dates (of course placing the ticks at equispaced intervals even though the dates you're representing aren't equispaced) and then using a plain plot.
I will typically use NumPy's NaN (not a number) for values that are invalid or not present. They are represented by Matplotlib as gaps in the plot and NumPy is part of pylab/Matplotlib.
>>> import pylab
>>> xs = pylab.arange(10.) + 733632. # valid date range
>>> ys = [1,2,3,2,pylab.nan,2,3,2,5,2.4] # some data (one undefined)
>>> pylab.plot_date(xs, ys, ydate=False, linestyle='-', marker='')
[<matplotlib.lines.Line2D instance at 0x0378D418>]
>>> pylab.show()
I ran into this problem again and was able to create a decent function to handle this issue, especially concerning intraday datetimes. Credit to @Primer for this answer.
def plot_ts(ts, step=5, figsize=(10,7), title=''):
"""
plot timeseries ignoring date gaps
Params
------
ts : pd.DataFrame or pd.Series
step : int, display interval for ticks
figsize : tuple, figure size
title: str
"""
fig, ax = plt.subplots(figsize=figsize)
ax.plot(range(ts.dropna().shape[0]), ts.dropna())
ax.set_title(title)
ax.set_xticks(np.arange(len(ts.dropna())))
ax.set_xticklabels(ts.dropna().index.tolist());
# tick visibility, can be slow for 200,000+ ticks
xticklabels = ax.get_xticklabels() # generate list once to speed up function
for i, label in enumerate(xticklabels):
if not i%step==0:
label.set_visible(False)
fig.autofmt_xdate()
You can simply change dates to strings:
import matplotlib.pyplot as plt
import datetime
f = plt.figure(1, figsize=(10,5))
ax = f.add_subplot(111)
today = datetime.datetime.today().date()
yesterday = today - datetime.timedelta(days=1)
three_days_later = today + datetime.timedelta(days=3)
x_values = [yesterday, today, three_days_later]
y_values = [75, 80, 90]
x_values = [f'{x:%Y-%m-%d}' for x in x_values]
ax.bar(x_values, y_values, color='green')
plt.show()
scikits.timeseries functionality has largely been moved to pandas, so you can now resample a dataframe to only include the values on weekdays.
>>>import pandas as pd
>>>import matplotlib.pyplot as plt
>>>s = pd.Series(list(range(10)), pd.date_range('2015-09-01','2015-09-10'))
>>>s
2015-09-01 0
2015-09-02 1
2015-09-03 2
2015-09-04 3
2015-09-05 4
2015-09-06 5
2015-09-07 6
2015-09-08 7
2015-09-09 8
2015-09-10 9
>>> s.resample('B', label='right', closed='right').last()
2015-09-01 0
2015-09-02 1
2015-09-03 2
2015-09-04 3
2015-09-07 6
2015-09-08 7
2015-09-09 8
2015-09-10 9
and then to plot the dataframe as normal
s.resample('B', label='right', closed='right').last().plot()
plt.show()
Just use mplfinance https://github.com/matplotlib/mplfinance
import mplfinance as mpf
# df = 'ohlc dataframe'
mpf.plot(df)
