假设我有4个小数据帧
df1
,请
df2
,请
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个数据帧,我想要的是下面的代码。
# 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数据帧实现了这一点。
基本上,我想将上述外部合并解决方案扩展到多(48)个62245 x 3大小的数据帧:
因此,我通过从另一个StackOverflow答案构建这个解决方案,该答案使用lambda reduce:
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
.
我不知道该怎么做才能阻止内核死亡。我在这个问题上坚持了两天。我执行的合并操作的某些代码不会导致
存储器错误
或者给你同样的结果的东西,会得到真正的赞赏。
此外,主数据帧中的3列(示例中不是可复制的48个数据帧)属于类型
int64
,请
国际贸易64
和
float64
我希望它们保持这种状态,因为它代表的是整数和浮点。
编辑:
我不是迭代地尝试运行合并操作,也不是使用reduce lambda函数,而是在2组中完成的!另外,我更改了一些列的数据类型,有些列不需要
浮动64
.所以我把它降到
float16
.它走得很远,但最后还是扔了一个
存储器错误
.
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
我有什么方法可以优化我的代码以避免
存储器错误
,我甚至使用了AWS 192GB RAM(我现在欠他们7美元,我可以给其中一个yall),这比我得到的要远,它仍然抛出
存储器错误
将28个数据帧的列表减少到4个之后。
