并行编程方法解决熊猫问题

Question

我有一个以下格式的数据框。 df

A   B  Target
5   4   3
1   3   4

我使用pd.DataFrame(df.corr().iloc[:-1,-1])找到每个列（Target除外）与Target列的相关性。 但问题是 - 我的实际数据帧的大小是(216, 72391)，至少需要30分钟来处理我的系统。有没有办法使用gpu并行它？我需要多次找到类似的值，所以不能等待每次30分钟的正常处理时间。

Answer 1

在这里，我尝试使用numba实现您的操作

import numpy as np
import pandas as pd
from numba import jit, int64, float64

# 
#------------You can ignore the code starting from here---------
#
# Create a random DF with cols_size = 72391 and row_size =300
df_dict = {}
for i in range(0, 72391):
  df_dict[i] = np.random.randint(100, size=300)
target_array = np.random.randint(100, size=300)

df = pd.DataFrame(df_dict)
# ----------Ignore code till here. This is just to generate dummy data-------

# Assume df is your original DataFrame
target_array = df['target'].values

# You can choose to restore this column later
# But for now we will remove it, since we will 
# call the df.values and find correlation of each 
# column with target
df.drop(['target'], inplace=True, axis=1)

# This function takes in a numpy 2D array and a target array as input
# The numpy 2D array has the data of all the columns
# We find correlation of each column with target array
# numba's Jit required that both should have same columns
# Hence the first 2d array is transposed, i.e. it's shape is (72391,300)
# while target array's shape is (300,) 
def do_stuff(df_values, target_arr):
  # Just create a random array to store result
  # df_values.shape[0] = 72391, equal to no. of columns in df
  result = np.random.random(df_values.shape[0])

  # Iterator over each column
  for i in range(0, df_values.shape[0]):

    # Find correlation of a column with target column
    # In order to find correlation we must transpose array to make them compatible
    result[i] = np.corrcoef(np.transpose(df_values[i]), target_arr.reshape(300,))[0][1]
  return result

# Decorate the function do_stuff
do_stuff_numba = jit(nopython=True, parallel=True)(do_stuff)

# This contains all the correlation
result_array = do_stuff_numba(np.transpose(df.T.values), target_array)

链接到colab notebook。

Answer 2

你应该看看dask。它应该能够做你想要的和更多。它并行化了大多数DataFrame函数。