Python 合并熊猫数据框列表
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Merge a list of pandas dataframes
提问by Jake
There has been many similar questions but none specifically to this.
有很多类似的问题，但没有一个专门针对这一点。
I have a list of data frames and I need to merge them together using a unique column (date). Field names are different so concat is out.
我有一个数据框列表，我需要使用唯一的 column 将它们合并在一起(date)。字段名称不同，因此 concat 已出局。
I can manually use df[0].merge(df[1],on='Date').merge(df[3],on='Date)etc. to merge each df one by one, but the issue is that the number of data frames in the list differs with user input.
我可以手动使用df[0].merge(df[1],on='Date').merge(df[3],on='Date)etc. 将每个 df 一一合并，但问题是列表中的数据框数量因用户输入而异。
Is there any way to merge that just combines all data frames in a list at one go? Or perhaps some for in loop at does that?
有没有什么方法可以合并一次将所有数据框合并到一个列表中？或者也许有一些 for in 循环呢？
I am using Python 2.7.
我正在使用 Python 2.7。
回答by Psidom
You can use reducefunction where dfListis your list of data frames:
您可以使用reduce函数 wheredfList是您的数据框列表：
import pandas as pd
from functools import reduce
reduce(lambda x, y: pd.merge(x, y, on = 'Date'), dfList)
As a demo:
作为演示：
df = pd.DataFrame({'Date': [1,2,3,4], 'Value': [2,3,3,4]})
dfList = [df, df, df]
dfList
# [ Date Value
# 0 1 2
# 1 2 3
# 2 3 3
# 3 4 4, Date Value
# 0 1 2
# 1 2 3
# 2 3 3
# 3 4 4, Date Value
# 0 1 2
# 1 2 3
# 2 3 3
# 3 4 4]
reduce(lambda x, y: pd.merge(x, y, on = 'Date'), dfList)
# Date Value_x Value_y Value
# 0 1 2 2 2
# 1 2 3 3 3
# 2 3 3 3 3
# 3 4 4 4 4
