Using pandas I create a plot of a time series like this:
import numpy as np
import pandas as pd
rng = pd.date_range('2016-01-01', periods=60, freq='D')
ts = pd.Series(np.random.randn(len(rng)), index=rng)
ax = ts.plot()
ax.axhline(y=ts.mean(), xmin=-1, xmax=1, color='r', linestyle='--', lw=2)
I would like to add another horizontal line at the level of the mean using only data from February. The mean is just ts.loc['2016-02']
, but how do I add a horizontal line at that level that doesn't go across the whole figure, but only for the dates in February?
Or you can create a new time series whose values are the mean and index only spans February.
ts_feb_mean = ts['2016-02'] * 0 + ts['2016-02'].mean()
All together it looks like:
import numpy as np
import pandas as pd
rng = pd.date_range('2016-01-01', periods=60, freq='D')
ts = pd.Series(np.random.randn(len(rng)), index=rng)
# Feb mean
ts_fm = ts['2016-02'] * 0 + ts['2016-02'].mean()
ts_fm = ts_fm.reindex_like(ts)
# Total mean
ts_mn = ts * 0 + ts.mean()
# better control over ax
fig, ax = plt.subplots(1, 1)
ts.plot(ax=ax)
ts_mn.plot(ax=ax)
ts_fm.plot(ax=ax)
You can use xmin
and xmax
to control where in the chart the line starts and ends. But this is in percent of the chart.
import numpy as np
import pandas as pd
np.random.seed([3, 1415])
rng = pd.date_range('2016-01-01', periods=60, freq='D')
ts = pd.Series(np.random.randn(len(rng)), index=rng)
ts_feb = ts['2016-02']
# used to figure out where to start and stop
ts_len = float(len(ts))
ts_len_feb = float(len(ts_feb))
ratio = ts_len_feb / ts_len
ax = ts.plot()
ax.axhline(y=ts.mean() * 5, xmin=0, xmax=1, color='r', linestyle='--', lw=2)
ax.axhline(y=ts_feb.mean() * 5, xmin=(1. - ratio), xmax=1, color='g', linestyle=':', lw=2)
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