PERF: Period plotting performance #24308
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Setup
```python
In [3]: N = 2000
...: M = 5
...: idx = date_range('1/1/1975', periods=N)
...: df = DataFrame(np.random.randn(N, M), index=idx)
```
Master:
```python
In [3]: %time df.plot()
CPU times: user 274 ms, sys: 66.7 ms, total: 340 ms
Wall time: 377 ms
Out[3]: <matplotlib.axes._subplots.AxesSubplot at 0x11eda6128>
```
HEAD:
```python
In [4]: %time df.plot()
CPU times: user 141 ms, sys: 32.6 ms, total: 173 ms
Wall time: 194 ms
Out[4]: <matplotlib.axes._subplots.AxesSubplot at 0x11550a5f8>
```
|
Hello @TomAugspurger! Thanks for submitting the PR.
|
TomAugspurger
added
Visualization
|
The slowdown was operating element wise on an ndarray of Period objects. The array was coming from matplotlib. I'm not sure if we always got the ndarray of Period objects and previously handled them differently, or if something changed on MPL's end to convert what we're giving them (PeriodIndex) to an ndarray. |
Codecov Report
@@ Coverage Diff @@
## master #24308 +/- ##
==========================================
+ Coverage 92.28% 92.28% +<.01%
==========================================
Files 162 162
Lines 51827 51829 +2
==========================================
+ Hits 47827 47829 +2
Misses 4000 4000
Continue to review full report at Codecov.
|
Codecov Report
@@ Coverage Diff @@
## master #24308 +/- ##
==========================================
+ Coverage 92.28% 92.28% +<.01%
==========================================
Files 162 162
Lines 51827 51831 +4
==========================================
+ Hits 47827 47833 +6
+ Misses 4000 3998 -2
Continue to review full report at Codecov.
|
jreback
reviewed
Dec 16, 2018
jreback
reviewed
Dec 17, 2018
|
lgtm. |
Pingviinituutti
pushed a commit
to Pingviinituutti/pandas
that referenced
this pull request
Feb 28, 2019
* PERF: Period plotting performance
Setup
```python
In [3]: N = 2000
...: M = 5
...: idx = date_range('1/1/1975', periods=N)
...: df = DataFrame(np.random.randn(N, M), index=idx)
```
Master:
```python
In [3]: %time df.plot()
CPU times: user 274 ms, sys: 66.7 ms, total: 340 ms
Wall time: 377 ms
Out[3]: <matplotlib.axes._subplots.AxesSubplot at 0x11eda6128>
```
HEAD:
```python
In [4]: %time df.plot()
CPU times: user 141 ms, sys: 32.6 ms, total: 173 ms
Wall time: 194 ms
Out[4]: <matplotlib.axes._subplots.AxesSubplot at 0x11550a5f8>
```
Pingviinituutti
pushed a commit
to Pingviinituutti/pandas
that referenced
this pull request
Feb 28, 2019
* PERF: Period plotting performance
Setup
```python
In [3]: N = 2000
...: M = 5
...: idx = date_range('1/1/1975', periods=N)
...: df = DataFrame(np.random.randn(N, M), index=idx)
```
Master:
```python
In [3]: %time df.plot()
CPU times: user 274 ms, sys: 66.7 ms, total: 340 ms
Wall time: 377 ms
Out[3]: <matplotlib.axes._subplots.AxesSubplot at 0x11eda6128>
```
HEAD:
```python
In [4]: %time df.plot()
CPU times: user 141 ms, sys: 32.6 ms, total: 173 ms
Wall time: 194 ms
Out[4]: <matplotlib.axes._subplots.AxesSubplot at 0x11550a5f8>
```
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Setup
0.23.4:
HEAD:
Closes #24304