reduce + combine_first

关键是将'ID'设置为索引，这样我们就可以在两个轴上获得正确的对齐。我假设所有的DataFrame都在内存中，但是如果没有，您可以将它们读入一个列表，或者在reduce步骤中进行阅读。

from functools import reduce

my_dfs = [df.set_index('ID') for df in [df1, df2, df3, df4]]
#my_dfs = [pd.read_csv(file).set_index('ID') for file in your_list_of_files]

reduce(lambda l,r: l.combine_first(r), my_dfs)

Color   Fruit  Price    Size
ID                                 
1001   Green   Apple    NaN   Large
1002     NaN  Banana   20.0     NaN
1003     NaN    Kiwi    NaN     NaN
1005     Red     NaN    NaN     NaN
1006  Orange     NaN    NaN     NaN
1007  Yellow     NaN    NaN     NaN
1008     NaN     NaN    NaN   Small
1009     NaN     NaN   40.0  Medium
1010     NaN     NaN   30.0   Large
1011     NaN     NaN   50.0     NaN

类似这样的东西应该可以工作：

import pandas as pd

list_of_csv_filenames = ['csv1.csv', 'csv2.csv', 'csv3.csv', 'csv4.csv']
all_dfs = []
for i in range(1, 5):
    temp = pd.read_csv(list_of_csv_filesnames[i-1])
    temp['Number'] = i
    all_dfs.append(temp)
full_df = pd.concat(all_dfs)
full_df.to_csv('output_filename.csv', index=False)

您可以使用merge：

import pandas as pd

df1 = pd.read_csv('1.csv')
df2 = pd.read_csv('2.csv')
df3 = pd.read_csv('3.csv')
df4 = pd.read_csv('4.csv')
df = df1.merge(df2).merge(df3).merge(df4)
df.to_csv('result.csv')

Concat can be used to append data to dataframe.   
            
                file_path1 = r"path1.csv"
                df1 = pd.read_csv(file_path1)
                file_path2 = r"path2.csv"
                df2 = pd.read_csv(file_path2)
                file_path3 = r"path3.csv"
                df3 = pd.read_csv(file_path3)
                frames = [df1,df2,df3]
                df = pd.concat(frames)
reset index after concat
               df.reset_index(inplace=True)