Python 比较DataFrames/csv并仅返回包含键值在内的差异列

我有两个CSV文件，我比较并只返回具有不同值的并排列

df1
Country 1980    1981    1982    1983    1984
Bermuda 0.00793 0.00687 0.00727 0.00971 0.00752
Canada  9.6947  9.58952 9.20637 9.18989 9.78546
Greenland   0.00791 0.00746 0.00722 0.00505 0.00799
Mexico  3.72819 4.11969 4.33477 4.06414 4.18464

df2
Country 1980    1981    1982    1983    1984
Bermuda 0.77777 0.00687 0.00727 0.00971 0.00752
Canada  9.6947  9.58952 9.20637 9.18989 9.78546
Greenland   0.00791 0.00746 0.00722 0.00505 0.00799
Mexico  3.72819 4.11969 4.33477 4.06414 4.18464

import pandas as pd
import numpy as np


df1=pd.read_csv('csv1.csv')
df2=pd.read_csv('csv2.csv')



def diff_pd(df1, df2):
    """Identify differences between two pandas DataFrames"""
    assert (df1.columns == df2.columns).all(), \
        "DataFrame column names are different"
    if any(df1.dtypes != df2.dtypes):
        "Data Types are different, trying to convert"
        df2 = df2.astype(df1.dtypes)
    if df1.equals(df2):
        print("Dataframes are the same")
        return None
    else:
        # need to account for np.nan != np.nan returning True
        diff_mask = (df1 != df2) & ~(df1.isnull() & df2.isnull())
        ne_stacked = diff_mask.stack()
        changed = ne_stacked[ne_stacked]
        changed.index.names = ['Country', 'Column']
        difference_locations = np.where(diff_mask)
        changed_from = df1.values[difference_locations][0]
        changed_to = df2.values[difference_locations]
        y=pd.DataFrame({'From': changed_from, 'To': changed_to},
                            index=changed.index)
        print(y)
        return pd.DataFrame({'From': changed_from, 'To': changed_to},
                            index=changed.index)



diff_pd(df1,df2)

我当前的输出是：

                   From       To
Country Column                  
0       1980    0.00793  0.77777

因此，我希望获得具有不匹配值的行的国家名称，而不是索引0。下面是一个例子

我希望我的输出是：

                   From       To
Country Column                  
Bermuda  1980    0.00793  0.77777

---

感谢所有能够提供解决方案的人。

一个略短的方法，在此过程中重命名：

def process_df(df):
    res = df.set_index('Country').stack()
    res.index.rename('Column', level=1, inplace=True)
    return res

df1 = process_df(df1)
df2 = process_df(df2)
mask = (df1 != df2) & ~(df1.isnull() & df2.isnull())
df3 = pd.concat([df1[mask], df2[mask]], axis=1).rename({0:'From', 1:'To'}, axis=1)
df3
                   From       To
Country Column                  
Bermuda 1980    0.00793  0.77777