Description
Code Sample, a copy-pastable example if possible
import pandas as pd
import matplotlib.pyplot as plt
# make a time series
ind = pd.DatetimeIndex(start = '2010', end = '2011', freq='D')
s = pd.Series(data = 1.0, index = ind)
# plottling like this results in integer timestamps on the x-axis
fig, ax = plt.subplots()
ax.plot(s.index, s)
# calling ._mpl_repr() yields proper x-axis timestamps
fig, ax = plt.subplots()
ax.plot(s.index._mpl_repr(), s)
# the original plotting code still results in integer timestamps
fig, ax = plt.subplots()
ax.plot(s.index, s)
# calling the plot() method yields proper timestamps
fig, ax = plt.subplots()
s.plot()
# now the orignal code yields string timestamps!
fig, ax = plt.subplots()
ax.plot(s.index, s)
# now even a whole new series is plotted with string timestamps
ind2 = pd.DatetimeIndex(start = '2014', end = '2015', freq='D')
s2 = pd.Series(data = 2.0, index = ind2)
fig, ax = plt.subplots()
ax.plot(s2.index, s2)Problem description
This bug appears to have been introduced in v.0.21.0, in my testing v.0.20.3 produced the expected behavior.
For a pandas series s the code:
fig, ax = plt.subplots()
ax.plot(s.index, s)produces a plot in which the x-labels are integer timestamps. With previous versions of pandas, this code would yield string timestamps on the x-axis.
However, s.plot() works correctly, and once it is run, all subsequent plots made within the same kernel using the sytax
fig, ax = plt.subplots()
ax.plot(s.index, s)work as expected with string timestamps.
A workaround is:
fig, ax = plt.subplots()
ax.plot(s.index._mpl_repr(), s)but this doesn't affect subsequent plots.
Expected Output
The expected output of:
import pandas as pd
import matplotlib.pyplot as plt
# make a time series
ind = pd.DatetimeIndex(start = '2010', end = '2011', freq='D')
s = pd.Series(data = 1.0, index = ind)
# plottling like this results in integer timestamps on the x-axis
fig, ax = plt.subplots()
ax.plot(s.index, s)is a plot with string representations of timestamps on the x-axis.
Output of pd.show_versions()
[paste the output of pd.show_versions() here below this line]
INSTALLED VERSIONS
commit: None
python: 2.7.14.final.0
python-bits: 64
OS: Darwin
OS-release: 16.7.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.21.0
pytest: 3.2.1
pip: 9.0.1
setuptools: 36.5.0.post20170921
Cython: 0.26.1
numpy: 1.13.3
scipy: 0.19.1
pyarrow: None
xarray: None
IPython: 5.4.1
sphinx: 1.6.3
patsy: 0.4.1
dateutil: 2.6.1
pytz: 2017.3
blosc: None
bottleneck: 1.2.1
tables: 3.4.2
numexpr: 2.6.2
feather: None
matplotlib: 2.1.0
openpyxl: 2.4.8
xlrd: 1.1.0
xlwt: 1.2.0
xlsxwriter: 1.0.2
lxml: 4.1.0
bs4: 4.6.0
html5lib: 0.999999999
sqlalchemy: 1.1.13
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None