python – 合并多个大型DataFrame的有效方法

假设我有4个小型DataFrame

df1,df2,df3和df4

import pandas as pd
from functools import reduce
import numpy as np

df1 = pd.DataFrame([['a', 1, 10], ['a', 2, 20], ['b', 1, 4], ['c', 1, 2], ['e', 2, 10]])
df2 = pd.DataFrame([['a', 1, 15], ['a', 2, 20], ['c', 1, 2]])
df3 = pd.DataFrame([['d', 1, 10], ['e', 2, 20], ['f', 1, 1]])  
df4 = pd.DataFrame([['d', 1, 10], ['e', 2, 20], ['f', 1, 15]])   


df1.columns = ['name', 'id', 'price']
df2.columns = ['name', 'id', 'price']
df3.columns = ['name', 'id', 'price']    
df4.columns = ['name', 'id', 'price']   

df1 = df1.rename(columns={'price':'pricepart1'})
df2 = df2.rename(columns={'price':'pricepart2'})
df3 = df3.rename(columns={'price':'pricepart3'})
df4 = df4.rename(columns={'price':'pricepart4'})

上面创建的是4个DataFrame,我想在下面的代码中.

# Merge dataframes
df = pd.merge(df1, df2, left_on=['name', 'id'], right_on=['name', 'id'], how='outer')
df = pd.merge(df , df3, left_on=['name', 'id'], right_on=['name', 'id'], how='outer')
df = pd.merge(df , df4, left_on=['name', 'id'], right_on=['name', 'id'], how='outer')

# Fill na values with 'missing'
df = df.fillna('missing')

所以我已经为4个没有很多行和列的DataFrame实现了这个功能.

基本上,我想将上面的外部合并解决方案扩展到大小为62245 X 3的MULTIPLE(48)DataFrame：

所以我通过构建另一个使用lambda reduce的StackOverflow答案来提出这个解决方案：

from functools import reduce
import pandas as pd
import numpy as np
dfList = []

#To create the 48 DataFrames of size 62245 X 3
for i in range(0, 49):

    dfList.append(pd.DataFrame(np.random.randint(0,100,size=(62245, 3)), columns=['name',  'id',  'pricepart' + str(i + 1)]))


#The solution I came up with to extend the solution to more than 3 DataFrames
df_merged = reduce(lambda  left, right: pd.merge(left, right, left_on=['name', 'id'], right_on=['name', 'id'], how='outer'), dfList).fillna('missing')

这导致了MemoryError.

我不知道如何阻止内核死亡..我已经坚持了两天..我执行的EXACT合并操作的一些代码不会导致MemoryError或者某些东西给你同样的结果,真的很感激.

此外,主DataFrame中的3列(不是示例中可重现的48个DataFrame)的类型是int64,int64和float64,我更喜欢它们保持这种方式,因为它表示的是整数和浮点数.

编辑：

我没有迭代地尝试运行合并操作或使用reduce lambda函数,而是以2个为一组进行操作！此外,我已经更改了某些列的数据类型,有些不需要是float64.所以我把它带到了float16.它变得非常远但仍然最终抛出MemoryError.

intermediatedfList = dfList    

tempdfList = []    

#Until I merge all the 48 frames two at a time, till it becomes size 2
while(len(intermediatedfList) != 2):

    #If there are even number of DataFrames
    if len(intermediatedfList)%2 == 0:

        #Go in steps of two
        for i in range(0, len(intermediatedfList), 2):

            #Merge DataFrame in index i, i + 1
            df1 = pd.merge(intermediatedfList[i], intermediatedfList[i + 1], left_on=['name',  'id'], right_on=['name',  'id'], how='outer')
            print(df1.info(memory_usage='deep'))

            #Append it to this list
            tempdfList.append(df1)

        #After DataFrames in intermediatedfList merging it two at a time using an auxillary list tempdfList, 
        #Set intermediatedfList to be equal to tempdfList, so it can continue the while loop. 
        intermediatedfList = tempdfList 

    else:

        #If there are odd number of DataFrames, keep the first DataFrame out

        tempdfList = [intermediatedfList[0]]

        #Go in steps of two starting from 1 instead of 0
        for i in range(1, len(intermediatedfList), 2):

            #Merge DataFrame in index i, i + 1
            df1 = pd.merge(intermediatedfList[i], intermediatedfList[i + 1], left_on=['name',  'id'], right_on=['name',  'id'], how='outer')
            print(df1.info(memory_usage='deep'))
            tempdfList.append(df1)

        #After DataFrames in intermediatedfList merging it two at a time using an auxillary list tempdfList, 
        #Set intermediatedfList to be equal to tempdfList, so it can continue the while loop. 
        intermediatedfList = tempdfList

有没有什么方法可以优化我的代码以避免MemoryError,我甚至使用AWS 192GB RAM(我现在欠他们7美元,我可以给它一个yall),这比我得到的更远,并且在将28个DataFrames列表减少到4之后,它仍会抛出MemoryError.

解决方法:

使用pd.concat执行索引对齐串联可能会带来一些好处.这应该比外部合并更快,更高效.

df_list = [df1, df2, ...]
for df in df_list:
    df.set_index(['name', 'id'], inplace=True)

df = pd.concat(df_list, axis=1) # join='inner'
df.reset_index(inplace=True)

或者,您可以通过迭代连接替换concat(第二步)：

from functools import reduce
df = reduce(lambda x, y: x.join(y), df_list)

这可能会或可能不会比合并更好.

python – 合并多个大型DataFrame的有效方法

相关推荐