6 façons d'accélérer considérablement les pandas avec quelques lignes de code. Partie 1

Pandas API, , . , , , .

, Spark DataFlow. :

1:

• Numba
• Multiprocessing
• Pandarallel

2:

• Swifter
• Modin
• Dask

Numba

Python. Numba — JIT , , Numpy, Pandas. , .

— , - apply.

import numpy as np
import numba

#    100 000   4 ,     0  100
df = pd.DataFrame(np.random.randint(0,100,size=(100000, 4)),columns=['a', 'b', 'c', 'd'])

#     
def multiply(x):
    return x * 5
    
#    numba 
@numba.vectorize
def multiply_numba(x):
    return x * 5

, . . .

#  
In [1]: %timeit df['new_col'] = df['a'].apply(multiply)
23.9 ms ± 1.93 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

#   Pandas
In [2]: %timeit df['new_col'] = df['a'] * 5
545 µs ± 21.4 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

#    numba
#     ,  numba    
In [3]: %timeit df['new_col'] = multiply_numba(df['a'].to_numpy())
329 µs ± 2.37 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

~70 ! , , Pandas , . :

#          
def square_mean(row):
    row = np.power(row, 2)
    return np.mean(row)
# :
# df['new_col'] = df.apply(square_mean, axis=1)

# numba      pandas (Dataframe, Series  .)
#       numpy
@numba.njit
def square_mean_numba(arr):
    res = np.empty(arr.shape[0])
    arr = np.power(arr, 2)
    for i in range(arr.shape[0]):
        res[i] = np.mean(arr[i])
    return res
# :
# df['new_col'] = square_mean_numba(df.to_numpy())

:

• , Numba
• ,
• , Numba python numpy

Multiprocessing

, , , . - , python.

. . , apply:

df = pd.read_csv('abcnews-date-text.csv', header=0)
#    10 ,    
df = pd.concat([df] * 10)
df.head()

#      
def mean_word_len(line):
    #     
    for i in range(6):
        words = [len(i) for i in line.split()]
        res = sum(words) / len(words)
    return res

def compute_avg_word(df):
    return df['headline_text'].apply(mean_word_len)

:

from multiprocessing import Pool

#   4  
n_cores = 4
pool = Pool(n_cores)

def apply_parallel(df, func):
    #    
    df_split = np.array_split(df, n_cores)
    #       
    df = pd.concat(pool.map(func, df_split))
    return df
# df['new_col'] = apply_parallel(df, compute_avg_word)

:

• x2-3
• — , .

Pandarallel

Pandarallel — pandas, . , , + progress bar ;) , pandarallel.

. , . pandarallel — :

from pandarallel import pandarallel
# pandarallel      ,     
pandarallel.initialize()

, — apply parallel_aply:

df['headline_text'].parallel_apply(mean_word_len)

:

• overhead 0.5 . parallel_apply , . 1 , , .
• , , , 2-3 .
• Pandarallel parallel_apply (groupby), .

, , . / , API progress bar.

To be continued

> Partie 2

PS: Faites confiance, mais vérifiez - tout le code utilisé dans l'article (benchmarks et graphisme), j'ai posté sur github