Description
Code Sample, a copy-pastable example if possible
# Create sample data
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
p1d = {'2016-08-10 10:00:00': 2.290438,
'2016-10-12 08:20:00': 1.314112,
'2016-11-15 12:45:00': 0.213702,
'2017-04-27 18:30:00': 0.256794,
'2017-05-30 11:10:00': 4.112614,
'2017-07-19 09:18:00': 10.600000}
p1 = pd.Series(p1d)
p1.index=pd.to_datetime(p1.index)
p2d = {'2016-08-09 09:15:00': 1.57970,
'2016-10-11 13:15:00': 0.73000,
'2017-04-27 12:30:00': 0.15900,
'2017-05-31 16:10:00': 1.65440,
'2018-05-24 12:00:00': 0.79260,
'2018-10-25 11:20:00': 0.34500}
p2 = pd.Series(p2d)
p2.index=pd.to_datetime(p2.index)
p3d = {'2016-11-15 09:00:00': 0.094900,
'2017-04-28 11:10:00': 0.055600,
'2017-05-30 16:00:00': 0.659600,
'2017-06-09 17:15:00': 0.300200,
'2018-05-24 16:45:00': 0.329800,
'2018-09-18 15:40:00': 0.200452}
p3 = pd.Series(p3d)
p3.index = pd.to_datetime(p3.index)
ts_index = pd.date_range('2016-01-01','2018-12-31',freq='H')
ts1 = pd.Series(index=ts_index, data=np.random.uniform(low=0.2,high=10,
size=ts_index.size))
ts2 = pd.Series(index=ts_index, data=np.random.uniform(low=0.1,high=3,
size=ts_index.size))
ts3 = pd.Series(index=ts_index, data=np.random.uniform(low=0.05,high=1,
size=ts_index.size))
# plot
fig_ts, axs_ts = plt.subplots(3,1,sharex=False)
ts1.plot(ax=axs_ts[0])
p1.plot(ax=axs_ts[0],style='o')
ts2.plot(ax=axs_ts[1])
p2.plot(ax=axs_ts[1],style='o')
ts3.plot(ax=axs_ts[2])
p3.plot(ax=axs_ts[2],style='o')
fig_ts.tight_layout()
Problem description
Data on the second axis is not plotted correctly on the X axis. The irregular point data is shifted relative to the other data. It seems this is caused by the uncommon timestamp between the first entry of the irregular timeseries and the regular timeseries.
The following changes causes the data to be plotted correctly, I assume due to having matching timestamps of the first entries:
- Changing the first datetime p2 to full hour (e.g. '2016-08-09 09:15:00' to '2016-08-09 09:00:00'
- Changing frequency of ts2 to '15T', 15 minutes
It does not matter if later data points in the irregular series have timestamps that are not common with the regular series
Changing the plot order for the second axis also causes the data to be plotted on the correct place along the X axis, so plotting the irregular timeseries before the regular:
p2.plot(ax=axs_ts[1],style='o', zorder=10)
ts2.plot(ax=axs_ts[1], zorder=1)
This does however cause other issues such as different X axis labels, and using this method it will also fail if using sharex=True.
Possibly related issues:
#11574 - Misaligned x axis using shared axis (one series plotted per axis), not when plotted on the same axis as here.
#18571 - Misaligned x axis using twinx(), possibly same issue as here?
Expected Output
Plotting the data at the correct x axis coordinates.
Output of pd.show_versions()
INSTALLED VERSIONS
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en
LOCALE : None.None
pandas : 0.25.3
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 41.6.0.post20191030
Cython : 0.29.13
pytest : 5.2.4
hypothesis : None
sphinx : 2.2.1
blosc : None
feather : None
xlsxwriter : 1.2.6
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.9.0
pandas_datareader: None
bs4 : 4.8.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.1
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.11
tables : 3.6.1
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.6