字典键内数据帧上的外部合并

Question

我是python的新手，一直在网络上搜索这个问题的解决方案，但没有找到。我有一本熊猫数据帧字典，其中键是"年"，值是该年的熊猫数据帧。下面是示例数据：

import pandas as pd
import numpy as np
from collections import defaultdict
##Creating Dataframes
data1_2018 =[[1,2018,80], [2,2018,70]]
data2_2018 = [[1,2018,77], [3,2018,62]]
data3_2018 = [[1,2018,82], [2,2018,88], [4,2018,66]]
data1_2017 = [[1,2017,80], [5,2017,70]]
data2_2017 = [[1,2017,77], [3,2017,62]]
data3_2017 = [[1,2017,50], [2,2017,52], [4,2017,51]]
df1_2018 = pd.DataFrame(data1_2018, columns = ['ID', 'Year', 'Score_1'])
df2_2018 = pd.DataFrame(data2_2018, columns = ['ID', 'Year', 'Score_2'])
df3_2018 = pd.DataFrame(data3_2018, columns = ['ID', 'Year', 'Score_3'])

df1_2017 = pd.DataFrame(data1_2017, columns = ['ID', 'Year', 'Score_1'])
df2_2017 = pd.DataFrame(data2_2017, columns = ['ID', 'Year', 'Score_2'])
df3_2017 = pd.DataFrame(data3_2017, columns = ['ID', 'Year', 'Score_3'])
###Creating list of all dataframes
all_df_list = [df1_2018,df2_2018,df3_2018,df1_2017,df2_2017,df3_2017]

我选择从包含所有数据帧的列表开始，因为这就是在我的实际问题中导入数据的方式。获得数据帧列表后，我创建了这些数据帧的字典。

yearly_dfs = defaultdict(list)
####Loop for creating dict with keys being years and values being dfs for that year
for df in all_df_list:
    for yr, yr_df in df.groupby('Year'):
        yearly_dfs[yr].append(yr_df)

现在，我的问题是..您能否遍历每个组的数据帧，并通过"ID"将它们合并在一起。所需的输出是每年只有一个数据帧的列表或字典。以下是每年的预期结果：

desired_output_2018 = df1_2018.merge(df2_2018, how = 'outer', on = ['ID', 'Year']).merge(df3_2018, how = 'outer', on = ['ID', 'Year']) 
desired_output_2017 = df1_2017.merge(df2_2017, how = 'outer', on = ['ID', 'Year']).merge(df3_2017, how = 'outer', on = ['ID', 'Year'])
print(desired_output_2018)
   ID  Year  Score_1  Score_2  Score_3
0   1  2018     80.0     77.0     82.0
1   2  2018     70.0      NaN     88.0
2   3  2018      NaN     62.0      NaN
3   4  2018      NaN      NaN     66.0
print(desired_output_2017)
   ID  Year  Score_1  Score_2  Score_3
0   1  2017     80.0     77.0     50.0
1   5  2017     70.0      NaN      NaN
2   3  2017      NaN     62.0      NaN
3   2  2017      NaN      NaN     52.0
4   4  2017      NaN      NaN     51.0

任何帮助将不胜感激！

谢谢！

Answer 1

使用 pandas.concat 和 DataFrame.groupby 'Year'

和 'ID'，带有 agg 函数 first ，然后在字典理解中使用grouby 'Year'：

df_all = (pd.concat(all_df_list, sort=False)
          .groupby(['ID', 'Year']).first().reset_index())
df_years = {yr: df for yr, df in df_all.groupby('Year')}

访问如下：

df_years[2017]
   ID  Year  Score_1  Score_2  Score_3
0   1  2017     80.0     77.0     50.0
2   2  2017      NaN      NaN     52.0
4   3  2017      NaN     62.0      NaN
6   4  2017      NaN      NaN     51.0
8   5  2017     70.0      NaN      NaN
df_years[2018]
  ID  Year  Score_1  Score_2  Score_3
1   1  2018     80.0     77.0     82.0
3   2  2018     70.0      NaN     88.0
5   3  2018      NaN     62.0      NaN
7   4  2018      NaN      NaN     66.0