熊猫vs Numpy Speed

Question

对于Pandas在引擎盖下使用Numpy，我很好奇为什么在下面的例子中，直接的numpy代码(509ms)比用数据帧(6.38s)做同样的操作快12倍？

# function with numpy arrays
def f_np(freq, asd):
    for f in np.arange(21.,2000.,1.):
        fi = freq/f
        gi =  (1+fi**2) / ((1-fi**2)**2 + fi**2) * asd
        df['fi'] = fi
        df['gi'] = gi
        # process each df ...

# function with dataframe
def f_df(df):
    for f in np.arange(21.,2000.,1.):
        df['fi'] = df.Freq/f
        df['gi'] = (1+df.fi**2) / ((1-df.fi**2)**2 + df.fi**2) * df.ASD
        # process each df ...


freq =  np.arange(20., 2000., .1)
asd = np.ones(len(freq))
df = pd.DataFrame({'Freq':freq, 'ASD':asd})    

%timeit f_np(freq, asd)
%timeit f_df(df)

509 ms ± 723 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)
6.38 s ± 20.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

Answer 1

你确定速度的差异是因为在这个特定的情况下“一些数据帧的操作”吗？我认为速度上的差异是由于您在第一个示例中创建了fi和gi变量，并在列上分配了变量，但在第二个示例中没有这样做。当我在两者中分配一个变量时，结果是相似的。

import pandas as pd,numpy as np
# function with numpy arrays
def f_np(freq, asd):
    for f in np.arange(21.,2000.,1.):
        fi = freq/f
        gi =  (1+fi**2) / ((1-fi**2)**2 + fi**2) * asd
        df['fi'] = fi
        df['gi'] = gi
        # process each df ...

# function with dataframe
def f_df(df):
    for f in np.arange(21.,2000.,1.):
        fi = freq/f
        gi =  (1+fi**2) / ((1-fi**2)**2 + fi**2) * asd
        df['fi'] = fi
        df['gi'] = gi
        # process each df ...


freq =  np.arange(20., 2000., .1)
asd = np.ones(len(freq))
df = pd.DataFrame({'Freq':freq, 'ASD':asd})    

%timeit f_np(freq, asd)
%timeit f_df(df)
#562 ms ± 9.23 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
#569 ms ± 17.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)