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loop, jreback, performance, sign, perf, dataframeround, unnecessarily, slow, copared, npround, timeit, std, dev, runs, gfyoung, milestone, lithomas, pandas, code, projects, issue, numpy, decimals, added, navigation, pull, requests, actions, security, closed, description, aberres, import, showed, major, rounding, data, frame, objects, rounded, fasterrounddf, memory, execution, speed, commented, column, block, difficulty, intermediate, effort,

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pull-requests personal information perf unnecessarily slow copared complete data frame underlying data frame quick test showed numpy performance type projects pd import numpy projects milestone import pandas milestone lithomas1 pandas objects lithomas1 mentioned major hotspot milestone relationships return pd copy-pastable series objects def faster_round %timeit faster_round good question get_values method returns ndarray df = pd %timeit np code %timeit df github perf rounded = np columns=df index=df implement round block dev showed numpy np np_df df columns index implement rounded sign dataframe round skip jump

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DiscussionForumPosting:
      context:https://schema.org
      headline:PERF: DataFrame.round() unnecessarily slow copared to np.round()
      articleBody:#### Code Sample, a copy-pastable example if possible ```python import pandas as pd import numpy as np np_df = np.random.randn(10000, 4000) df = pd.DataFrame(np_df) %timeit np.round(np_df, 2) # 416 ms ± 10.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) %timeit df.round(2) # 1.69 s ± 27.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) %timeit np.round(df, 2) # 1.74 s ± 112 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) ``` #### Problem description Completely unexpected DataFrage.round() showed up as a major hotspot during profiling. When looking at the code, we see that even when rounding the complete data frame to a given number of decimals it is split into series objects which are then rounded. I am wondering if there is a reason not to pass the underlying data frame to numpy and do the rounding there in this case. A quick test showed that something like this would give us the numpy performance: ```python def faster_round(df, decimals): rounded = np.round(df.values, decimals) return pd.DataFrame(rounded, columns=df.columns, index=df.index) %timeit faster_round(df, 2) # 417 ms ± 14.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
      author:
         url:https://github.com/aberres
         type:Person
         name:aberres
      datePublished:2017-08-15T06:54:35.000Z
      interactionStatistic:
         type:InteractionCounter
         interactionType:https://schema.org/CommentAction
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      url:https://github.com/17254/pandas/issues/17254
      context:https://schema.org
      headline:PERF: DataFrame.round() unnecessarily slow copared to np.round()
      articleBody:#### Code Sample, a copy-pastable example if possible ```python import pandas as pd import numpy as np np_df = np.random.randn(10000, 4000) df = pd.DataFrame(np_df) %timeit np.round(np_df, 2) # 416 ms ± 10.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) %timeit df.round(2) # 1.69 s ± 27.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) %timeit np.round(df, 2) # 1.74 s ± 112 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) ``` #### Problem description Completely unexpected DataFrage.round() showed up as a major hotspot during profiling. When looking at the code, we see that even when rounding the complete data frame to a given number of decimals it is split into series objects which are then rounded. I am wondering if there is a reason not to pass the underlying data frame to numpy and do the rounding there in this case. A quick test showed that something like this would give us the numpy performance: ```python def faster_round(df, decimals): rounded = np.round(df.values, decimals) return pd.DataFrame(rounded, columns=df.columns, index=df.index) %timeit faster_round(df, 2) # 417 ms ± 14.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
      author:
         url:https://github.com/aberres
         type:Person
         name:aberres
      datePublished:2017-08-15T06:54:35.000Z
      interactionStatistic:
         type:InteractionCounter
         interactionType:https://schema.org/CommentAction
         userInteractionCount:2
      url:https://github.com/17254/pandas/issues/17254
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      url:https://github.com/aberres
      name:aberres
      url:https://github.com/aberres
      name:aberres
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