Unexpected behavior of rangebreaks with px.timeline and pattern "hour"
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Mô tả
Symptoms
When using rangebreaks for a plotly.express.timeline-figure with pattern='hour' and bounds=[17.4, 6.4] (i.e., exceeding midnight for excluding non-buisness-hours)
the resulting plot either misses all data (second plot) or data exceeding a certain span (fourth plot).
When doing the very same using values and dvalue kwords in the rangebreaks (in case of multiple days, one would need to provide a rangebreak for every day), the plots are generated as expected.
Environment:
# Name Version Build Channel
_libgcc_mutex 0.1 conda_forge conda-forge
_openmp_mutex 4.5 2_gnu conda-forge
anyio 3.7.1 pyhd8ed1ab_0 conda-forge
argon2-cffi 21.3.0 pyhd8ed1ab_0 conda-forge
argon2-cffi-bindings 21.2.0 py311hd4cff14_3 conda-forge
arrow 1.2.3 pyhd8ed1ab_0 conda-forge
asttokens 2.2.1 pyhd8ed1ab_0 conda-forge
async-lru 2.0.3 pyhd8ed1ab_0 conda-forge
attrs 23.1.0 pyh71513ae_1 conda-forge
babel 2.12.1 pyhd8ed1ab_1 conda-forge
backcall 0.2.0 pyh9f0ad1d_0 conda-forge
backports 1.0 pyhd8ed1ab_3 conda-forge
backports.functools_lru_cache 1.6.5 pyhd8ed1ab_0 conda-forge
beautifulsoup4 4.12.2 pyha770c72_0 conda-forge
bleach 6.0.0 pyhd8ed1ab_0 conda-forge
brotli-python 1.0.9 py311ha362b79_9 conda-forge
bzip2 1.0.8 h7f98852_4 conda-forge
ca-certificates 2023.7.22 hbcca054_0 conda-forge
cached-property 1.5.2 hd8ed1ab_1 conda-forge
cached_property 1.5.2 pyha770c72_1 conda-forge
certifi 2023.7.22 pyhd8ed1ab_0 conda-forge
cffi 1.15.1 py311h409f033_3 conda-forge
charset-normalizer 3.2.0 pyhd8ed1ab_0 conda-forge
comm 0.1.3 pyhd8ed1ab_0 conda-forge
debugpy 1.6.7 py311hcafe171_0 conda-forge
decorator 5.1.1 pyhd8ed1ab_0 conda-forge
defusedxml 0.7.1 pyhd8ed1ab_0 conda-forge
entrypoints 0.4 pyhd8ed1ab_0 conda-forge
exceptiongroup 1.1.2 pyhd8ed1ab_0 conda-forge
executing 1.2.0 pyhd8ed1ab_0 conda-forge
flit-core 3.9.0 pyhd8ed1ab_0 conda-forge
fqdn 1.5.1 pyhd8ed1ab_0 conda-forge
idna 3.4 pyhd8ed1ab_0 conda-forge
importlib-metadata 6.8.0 pyha770c72_0 conda-forge
importlib_metadata 6.8.0 hd8ed1ab_0 conda-forge
importlib_resources 6.0.0 pyhd8ed1ab_1 conda-forge
ipykernel 6.25.0 pyh71e2992_0 conda-forge
ipython 8.14.0 pyh41d4057_0 conda-forge
isoduration 20.11.0 pyhd8ed1ab_0 conda-forge
jedi 0.18.2 pyhd8ed1ab_0 conda-forge
jinja2 3.1.2 pyhd8ed1ab_1 conda-forge
json5 0.9.14 pyhd8ed1ab_0 conda-forge
jsonpointer 2.0 py_0 conda-forge
jsonschema 4.18.4 pyhd8ed1ab_0 conda-forge
jsonschema-specifications 2023.7.1 pyhd8ed1ab_0 conda-forge
jsonschema-with-format-nongpl 4.18.4 pyhd8ed1ab_0 conda-forge
jupyter-lsp 2.2.0 pyhd8ed1ab_0 conda-forge
jupyter_client 8.3.0 pyhd8ed1ab_0 conda-forge
jupyter_core 5.3.1 py311h38be061_0 conda-forge
jupyter_events 0.6.3 pyhd8ed1ab_1 conda-forge
jupyter_server 2.7.0 pyhd8ed1ab_0 conda-forge
jupyter_server_terminals 0.4.4 pyhd8ed1ab_1 conda-forge
jupyterlab 4.0.3 pyhd8ed1ab_0 conda-forge
jupyterlab_pygments 0.2.2 pyhd8ed1ab_0 conda-forge
jupyterlab_server 2.24.0 pyhd8ed1ab_0 conda-forge
kaleido 0.2.1 pypi_0 pypi
ld_impl_linux-64 2.40 h41732ed_0 conda-forge
libblas 3.9.0 17_linux64_openblas conda-forge
libcblas 3.9.0 17_linux64_openblas conda-forge
libexpat 2.5.0 hcb278e6_1 conda-forge
libffi 3.4.2 h7f98852_5 conda-forge
libgcc-ng 13.1.0 he5830b7_0 conda-forge
libgfortran-ng 13.1.0 h69a702a_0 conda-forge
libgfortran5 13.1.0 h15d22d2_0 conda-forge
libgomp 13.1.0 he5830b7_0 conda-forge
liblapack 3.9.0 17_linux64_openblas conda-forge
libnsl 2.0.0 h7f98852_0 conda-forge
libopenblas 0.3.23 pthreads_h80387f5_0 conda-forge
libsodium 1.0.18 h36c2ea0_1 conda-forge
libsqlite 3.42.0 h2797004_0 conda-forge
libstdcxx-ng 13.1.0 hfd8a6a1_0 conda-forge
libuuid 2.38.1 h0b41bf4_0 conda-forge
libzlib 1.2.13 hd590300_5 conda-forge
markupsafe 2.1.3 py311h459d7ec_0 conda-forge
matplotlib-inline 0.1.6 pyhd8ed1ab_0 conda-forge
mistune 3.0.0 pyhd8ed1ab_0 conda-forge
nbclient 0.8.0 pyhd8ed1ab_0 conda-forge
nbconvert-core 7.7.3 pyhd8ed1ab_0 conda-forge
nbformat 5.9.1 pyhd8ed1ab_0 conda-forge
ncurses 6.4 hcb278e6_0 conda-forge
nest-asyncio 1.5.6 pyhd8ed1ab_0 conda-forge
notebook 7.0.0 pyhd8ed1ab_0 conda-forge
notebook-shim 0.2.3 pyhd8ed1ab_0 conda-forge
numpy 1.25.1 py311h64a7726_0 conda-forge
openssl 3.1.1 hd590300_1 conda-forge
overrides 7.3.1 pyhd8ed1ab_0 conda-forge
packaging 23.1 pyhd8ed1ab_0 conda-forge
pandas 2.0.3 py311h320fe9a_1 conda-forge
pandocfilters 1.5.0 pyhd8ed1ab_0 conda-forge
parso 0.8.3 pyhd8ed1ab_0 conda-forge
pexpect 4.8.0 pyh1a96a4e_2 conda-forge
pickleshare 0.7.5 py_1003 conda-forge
pip 23.2.1 pyhd8ed1ab_0 conda-forge
pkgutil-resolve-name 1.3.10 pyhd8ed1ab_0 conda-forge
platformdirs 3.9.1 pyhd8ed1ab_0 conda-forge
plotly 5.15.0 pyhd8ed1ab_0 conda-forge
prometheus_client 0.17.1 pyhd8ed1ab_0 conda-forge
prompt-toolkit 3.0.39 pyha770c72_0 conda-forge
prompt_toolkit 3.0.39 hd8ed1ab_0 conda-forge
psutil 5.9.5 py311h2582759_0 conda-forge
ptyprocess 0.7.0 pyhd3deb0d_0 conda-forge
pure_eval 0.2.2 pyhd8ed1ab_0 conda-forge
pycparser 2.21 pyhd8ed1ab_0 conda-forge
pygments 2.15.1 pyhd8ed1ab_0 conda-forge
pysocks 1.7.1 pyha2e5f31_6 conda-forge
python 3.11.4 hab00c5b_0_cpython conda-forge
python-dateutil 2.8.2 pyhd8ed1ab_0 conda-forge
python-fastjsonschema 2.18.0 pyhd8ed1ab_0 conda-forge
python-json-logger 2.0.7 pyhd8ed1ab_0 conda-forge
python-tzdata 2023.3 pyhd8ed1ab_0 conda-forge
python_abi 3.11 3_cp311 conda-forge
pytz 2023.3 pyhd8ed1ab_0 conda-forge
pyyaml 6.0 py311hd4cff14_5 conda-forge
pyzmq 25.1.0 py311h75c88c4_0 conda-forge
readline 8.2 h8228510_1 conda-forge
referencing 0.30.0 pyhd8ed1ab_0 conda-forge
requests 2.31.0 pyhd8ed1ab_0 conda-forge
rfc3339-validator 0.1.4 pyhd8ed1ab_0 conda-forge
rfc3986-validator 0.1.1 pyh9f0ad1d_0 conda-forge
rpds-py 0.9.2 py311h46250e7_0 conda-forge
send2trash 1.8.2 pyh41d4057_0 conda-forge
setuptools 68.0.0 pyhd8ed1ab_0 conda-forge
six 1.16.0 pyh6c4a22f_0 conda-forge
sniffio 1.3.0 pyhd8ed1ab_0 conda-forge
soupsieve 2.3.2.post1 pyhd8ed1ab_0 conda-forge
stack_data 0.6.2 pyhd8ed1ab_0 conda-forge
tenacity 8.2.2 pyhd8ed1ab_0 conda-forge
terminado 0.17.1 pyh41d4057_0 conda-forge
tinycss2 1.2.1 pyhd8ed1ab_0 conda-forge
tk 8.6.12 h27826a3_0 conda-forge
tomli 2.0.1 pyhd8ed1ab_0 conda-forge
tornado 6.3.2 py311h459d7ec_0 conda-forge
traitlets 5.9.0 pyhd8ed1ab_0 conda-forge
typing-extensions 4.7.1 hd8ed1ab_0 conda-forge
typing_extensions 4.7.1 pyha770c72_0 conda-forge
typing_utils 0.1.0 pyhd8ed1ab_0 conda-forge
tzdata 2023c h71feb2d_0 conda-forge
uri-template 1.3.0 pyhd8ed1ab_0 conda-forge
urllib3 2.0.4 pyhd8ed1ab_0 conda-forge
wcwidth 0.2.6 pyhd8ed1ab_0 conda-forge
webcolors 1.13 pyhd8ed1ab_0 conda-forge
webencodings 0.5.1 py_1 conda-forge
websocket-client 1.6.1 pyhd8ed1ab_0 conda-forge
wheel 0.41.0 pyhd8ed1ab_0 conda-forge
xz 5.2.6 h166bdaf_0 conda-forge
yaml 0.2.5 h7f98852_2 conda-forge
zeromq 4.3.4 h9c3ff4c_1 conda-forge
zipp 3.16.2 pyhd8ed1ab_0 conda-forge
MWE:
import datetime
import pandas as pd
import plotly.express as px
import plotly.io as pio
pio.renderers.default = "svg"
# build example data
df = pd.read_json(
"""
{
"start":{
"0":"2023-07-27T10:54:28.000Z",
"1":"2023-07-27T11:40:15.000Z",
"2":"2023-07-27T15:00:58.000Z",
"3":"2023-07-27T11:52:28.000Z",
"4":"2023-07-27T12:52:57.000Z",
"5":"2023-07-27T13:20:45.000Z",
"6":"2023-07-27T13:44:24.000Z"
},
"end":{
"0":"2023-07-27T10:54:28.000Z",
"1":"2023-07-27T11:53:44.000Z",
"2":"2023-07-27T15:06:50.000Z",
"3":"2023-07-27T11:55:19.000Z",
"4":"2023-07-27T13:01:35.000Z",
"5":"2023-07-27T13:23:10.000Z",
"6":"2023-07-27T13:47:03.000Z"
},
"device_number":{
"0":"168012",
"1":"168012",
"2":"168012",
"3":"202052",
"4":"202052",
"5":"202052",
"6":"202052"
}
}
""",
dtype=dict(device_number=str)
)
# code for a workaround using value/dvalue rangebreaks for every date in a (known) xrange
df[['start', 'end']] = df[['start', 'end']].apply(lambda x: pd.to_datetime(x).astype('datetime64[ns, UTC]'))
dt_interval = (datetime.datetime(2023, 7, 27, 0, 0, 0, 0), datetime.datetime(2023, 7, 27, 23, 59, 59, 99999))
restrict_timeinterval = (datetime.time(6, 24, 0, 0), datetime.time(17, 24, 0, 0))
no_days_in_range_x = (dt_interval[1].date() - dt_interval[0].date()).days + 1
date_list = [(dt_interval[0].date() + datetime.timedelta(days=k))
for k in range(no_days_in_range_x)] if no_days_in_range_x > 1 else [dt_interval[0].date()]
dvalue0 = datetime.datetime.combine(datetime.date.today(), restrict_timeinterval[0]) \
- datetime.datetime.combine(datetime.date.today(), datetime.time.min)
dvalue1 = datetime.datetime.combine(datetime.date.today(), datetime.time.max) \
- datetime.datetime.combine(datetime.date.today(), restrict_timeinterval[1])
values0 = [datetime.datetime.combine(dt, tm)
for dt, tm in zip(date_list, (datetime.time.min,) * no_days_in_range_x)]
values1 = [datetime.datetime.combine(dt, tm)
for dt, tm in zip(date_list, (restrict_timeinterval[1],) * no_days_in_range_x)]
print(dvalue0, dvalue1)
print(values0, values1)
print(df.values)
# define different rangebreaks to test
l_rangebreaks_default = []
l_rangebreaks_pattern_0 = [
#dict(values=values0, dvalue=dvalue0.total_seconds() * 1e3),
dict(bounds=[17.4, 24], pattern='hour'),
dict(bounds=[0, 6.4], pattern='hour'),
]
l_rangebreaks_pattern_1 = [
#dict(values=values0, dvalue=dvalue0.total_seconds() * 1e3),
dict(bounds=[17.4, 24], pattern='hour'),
dict(bounds=[0.2, 6.4], pattern='hour'),
]
l_rangebreaks_values_0 = [
dict(values=values0, dvalue=dvalue0.total_seconds() * 1e3),
dict(values=values1, dvalue=dvalue1.total_seconds() * 1e3),
]
# build plotly timelines
for l_rangebreaks in [
l_rangebreaks_default,
l_rangebreaks_pattern_0,
l_rangebreaks_pattern_1,
l_rangebreaks_values_0,
]:
print(l_rangebreaks)
fig = px.timeline(
data_frame=df,
x_start='start',
x_end='end',
y='device_number',
range_x=dt_interval,
color_continuous_scale=px.colors.sequential.Rainbow,
color='device_number',
)
fig.update_xaxes(
rangebreaks=l_rangebreaks
)
fig.show(renderer='svg')
Output
6:24:00 6:35:59.999999
[datetime.datetime(2023, 7, 27, 0, 0)] [datetime.datetime(2023, 7, 27, 17, 24)]
[[Timestamp('2023-07-27 10:54:28+0000', tz='UTC')
Timestamp('2023-07-27 10:54:28+0000', tz='UTC') '168012']
[Timestamp('2023-07-27 11:40:15+0000', tz='UTC')
Timestamp('2023-07-27 11:53:44+0000', tz='UTC') '168012']
[Timestamp('2023-07-27 15:00:58+0000', tz='UTC')
Timestamp('2023-07-27 15:06:50+0000', tz='UTC') '168012']
[Timestamp('2023-07-27 11:52:28+0000', tz='UTC')
Timestamp('2023-07-27 11:55:19+0000', tz='UTC') '202052']
[Timestamp('2023-07-27 12:52:57+0000', tz='UTC')
Timestamp('2023-07-27 13:01:35+0000', tz='UTC') '202052']
[Timestamp('2023-07-27 13:20:45+0000', tz='UTC')
Timestamp('2023-07-27 13:23:10+0000', tz='UTC') '202052']
[Timestamp('2023-07-27 13:44:24+0000', tz='UTC')
Timestamp('2023-07-27 13:47:03+0000', tz='UTC') '202052']]
Plots
No rangebreaks
[]
Fails
[{'bounds': [17.4, 24], 'pattern': 'hour'}, {'bounds': [0, 6.4], 'pattern': 'hour'}]
[{'bounds': [17.4, 24], 'pattern': 'hour'}, {'bounds': [0.2, 6.4], 'pattern': 'hour'}]
Fails: note the vanishing timeslot on the upper device!
Workaround works as expected.
[{'values': [datetime.datetime(2023, 7, 27, 0, 0)], 'dvalue': 23040000.0}, {'values': [datetime.datetime(2023, 7, 27, 17, 24)], 'dvalue': 23759999.998999998}]
Other matters
I assume it has something to do how x_start and x_end are internally translated to base and x for px.bar/px.timeline...
Hướng dẫn đóng góp
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- Fork repository và làm thay đổi trên một nhánh.
- Mở pull request có tham chiếu số hiệu của issue.
Hướng nghiên cứu
Bắt đầu bằng cách chạy MWE Python được cung cấp với pandas và Plotly Express, so sánh các rangebreaks sử dụng pattern="hour" và bounds=[17.4, 6.4] với dạng values và dvalue. Theo dõi cách hình timeline áp dụng các rangebreaks đó và xác nhận rằng hành vi đã sửa vẫn giữ lại toàn bộ dữ liệu qua thời điểm nửa đêm và trong nhiều ngày.
Do mô hình lập chỉ mục viết ra từ nội dung của issue.
Đánh giá
- Công nghệ
- pandas, python
- Lĩnh vực
- data-visualization
- Loại issue
- Lỗi
- Độ khó
- 3/5
- Thời gian dự kiến
- 1-2 ngày
- Mức độ hoạt động
- Đình trệ
- Độ rõ ràng
- Khá rõ ràng
- Mức phù hợp với người mới
- 48/100