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'})
以上创建了4个数据框,我希望在以下代码中实现。
# 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')
我已经为不太大的四个数据框实现了这一点。
基本上,我希望将以上外部合并解决方案扩展到大小为62245 X 3的48个数据框:
所以我通过另一个使用lambda reduce的StackOverflow答案进行构建,得出了这个解决方案:
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')
这导致了一个MemoryError
。
我不知道该怎么做才能防止内核崩溃..我被卡在这里已经两天了..如果有一些确切的合并操作的代码,它不会引起MemoryError
,或者是一些可以给你相同结果的东西,那将非常感谢。
另外,主要数据框中的3列(示例中不可再现的48个数据框除外)的类型分别为int64
、int64
和float64
,因其表示整数和浮点数,我希望它们保持原样。
编辑:
与其反复尝试运行合并操作或使用reduce lambda函数,不如分成2组!此外,我已经更改了一些列的数据类型,有些列不需要是float64
。所以我把它降到了float16
。虽然它可以进行很长时间,但最终仍然会出现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
,我甚至使用了 AWS 的 192GB RAM(现在我欠他们7美元,我本可以给你们其中一个),这比我得到的更远,但在将28个数据框的列表减少到4个后,它仍然会抛出 MemoryError
。