In [1]:

    

import pandas as pd


    

In [2]:

    

df = pd.DataFrame(pd.np.arange(1000).reshape(100, 10))
print(df.shape)


    

    




(100, 10)

In [3]:

    

print(df.head())


    

    




    0   1   2   3   4   5   6   7   8   9
0   0   1   2   3   4   5   6   7   8   9
1  10  11  12  13  14  15  16  17  18  19
2  20  21  22  23  24  25  26  27  28  29
3  30  31  32  33  34  35  36  37  38  39
4  40  41  42  43  44  45  46  47  48  49

In [4]:

    

print(df.tail())


    

    




      0    1    2    3    4    5    6    7    8    9
95  950  951  952  953  954  955  956  957  958  959
96  960  961  962  963  964  965  966  967  968  969
97  970  971  972  973  974  975  976  977  978  979
98  980  981  982  983  984  985  986  987  988  989
99  990  991  992  993  994  995  996  997  998  999

In [5]:

    

%%timeit
for i, row in df.iterrows():
    pass


    

    




4.53 ms ± 325 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

In [6]:

    

%%timeit
for t in df.itertuples():
    pass


    

    




981 µs ± 43.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

In [7]:

    

%%timeit
for t in df.itertuples(name=None):
    pass


    

    




718 µs ± 10.9 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

In [8]:

    

%%timeit
for i in df[0]:
    pass


    

    




15.6 µs ± 446 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

In [9]:

    

%%timeit
for i, j, k in zip(df[0], df[4], df[9]):
    pass


    

    




46.1 µs ± 588 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)

In [10]:

    

%%timeit
for t in zip(df[0], df[1], df[2], df[3], df[4], df[5], df[6], df[7], df[8], df[9]):
    pass


    

    




147 µs ± 3.78 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)