隐藏Python模块中的中间计算

import numpy as np
import pandas as pd

sha256_perf = (
    pd.read_csv('data/hashbench-output.txt',sep='t',na_filter=False)
        .query('Algorithm == "SHA256"')
)

mean_throughput = sha256_perf['Throughput (MiB/s)'].mean()
variance = sha256_perf['Error (± MiB/s)'] ** 2
total_variance = variance.sum()
row_count = sha256_perf.shape[0]
variance_of_mean = total_variance / (row_count ** 2)
error_of_mean = variance_of_mean ** 0.5

sha256_summary = pd.DataFrame(data=[[mean_throughput,error_of_mean]])
sha256_summary.columns = ['Mean Throughput (MiB/s)','Error (± MiB/s)']

其中,我唯一关心的变量是输出表 – sha256_perf和sha256_summary.但是,Python无法知道,所以如果我dir()模块,我得到了一切：

>>> import src.data as data
>>> dir(data)
['__builtins__','__cached__','__doc__','__file__','__loader__','__name__','__package__','__spec__','assumptions','error_of_mean','mean_throughput','np','pd','prd_scratch_2018','row_count','sha256_perf','sha256_summary','total_variance','util','variance','variance_of_mean']

如果这是Ruby或Scala,我可以在块中初始化sha256_summary,例如：

sha256_summary = begin
  mean_throughput = sha256_perf['Throughput (MiB/s)'].mean()
  # ... etc. ...
  df.columns = ['Mean Throughput (MiB/s)','Error (± MiB/s)']
  df
end

即使在Java(8)中,我也可以与供应商和lambda一起破解.

但据我所知,Python没有匿名块或多行lambda.所以到目前为止,我能够提出的最好的方法就是将所有内容都放在一个函数中：

def create_summary():
    mean_throughput = sha256_perf['Throughput (MiB/s)'].mean()
    # ... etc. ...
    sha256_summary.columns = ['Mean Throughput (MiB/s)','Error (± MiB/s)']
    return sha256_summary

sha256_summary = create_summary()

但这仍然导出create_summary符号,我宁愿避免：

>>> dir(data)
['__builtins__','create_summary','util']

什么是Pythonic避免污染全局命名空间的方法？