获取数据帧列表并按变量分组，然后将该变量用作字典的键 [英] Taking a list of data frames and grouping by a variable and using that variable as the key to a dictionary

问题描述

我是python编程的新手.我有一个熊猫数据框的列表，所有的数据框都具有年份"列.我正在尝试按该列分组并转换为字典，其中字典键是变量"Year"，而值是该年的数据帧列表.在python中有可能吗?

I am relatively new to python programming. I have a list of pandas dataframes that all have the column 'Year'. I am trying to group by that column and convert to a dictionary where the dictionary key is the variable 'Year' and values is a list of dataframes of that year. Is this possible in python?

我尝试过:

grouped_dict = list_of_csv_files.groupby(by = 'Year').to_dict()

我相信我将不得不遍历每个数据帧吗?我没有提供任何数据，因为我希望它是一个简单的解决方案.

I believe I will have to loop through each dataframe? I did not provide any data because I am hoping it is a somewhat simple solution.

我也尝试过:

grouped_dict = list_of_csv_files.groupby(by = 'Year').apply(lambda dfg: dfg.to_dict(orient='list')).to_dict()

任何指导将不胜感激！

推荐答案

到目前为止，其他答案都未达到要求，所以我给您一个替代方法.假设您有CSV文件(因为您的变量是以这种方式命名的):

Other answers have missed the mark so far, so I'll give you an alternative. Assuming you have CSV files (since your variable is named that way):

from collections import defaultdict

yearly_dfs = defaultdict(list)
for csv in list_of_csv_files:
    df = pd.read_csv(csv)
    for yr, yr_df in df.groupby("Year"):
        yearly_dfs[yr].append(yr_df)

假设您已经有DataFrames:

Assuming you have DataFrames already:

from collections import defaultdict

yearly_dfs = defaultdict(list)
for df in list_of_csv_files:
    for yr, yr_df in df.groupby("Year"):
        yearly_dfs[yr].append(yr_df)

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