Python 3.x 如何添加具有相同id的数据帧？

我是数据科学学习的初学者。浏览了熊猫主题，我在这里发现了一个任务，我无法理解哪里出了问题。让我解释一下这个问题

我有三个数据帧：

gold = pd.DataFrame({'Country': ['USA', 'France', 'Russia'],
                         'Medals': [15, 13, 9]}
                    )
silver = pd.DataFrame({'Country': ['USA', 'Germany', 'Russia'],
                        'Medals': [29, 20, 16]}
                    )
bronze = pd.DataFrame({'Country': ['France', 'USA', 'UK'],
                        'Medals': [40, 28, 27]}
                    )

在这里，我需要把所有的奖牌加在一列，国家在另一列。当我添加时，它正在显示NAN。所以，我用零值填充NAN，但仍然无法得到应有的输出

代码：

实际产量：

                Medals
    Country        
     France      NaN
     Germany     NaN
     Russia      NaN
     UK          NaN
     USA        72.0

预期：

               Medals
     Country        
     USA        72.0
     France     53.0
     UK         27.0
     Russia     25.0
     Germany    20.0

---

告诉我有什么问题。

只需使用

groupby

sum

pd.concat([gold,silver,bronze]).groupby('Country').sum()
Out[1306]: 
         Medals
Country        
France       53
Germany      20
Russia       25
UK           27
USA          72

修复代码

silver.add(gold,fill_value = 0).add(bronze,fill_value=0)

如果我们期望浮点数：

pd.concat([gold,silver,bronze]).groupby('Country').sum().astype(float)

pd.concat([gold,silver,bronze]).groupby('Country').sum().astype(float)

# For a video solution of the code, copy-paste the following link on your browser:
# https://youtu.be/p0cnApQDotA

import numpy as np 
import pandas as pd

# Defining the three dataframes indicating the gold, silver, and bronze medal counts
# of different countries
gold = pd.DataFrame({'Country': ['USA', 'France', 'Russia'],
                         'Medals': [15, 13, 9]}
                    )
silver = pd.DataFrame({'Country': ['USA', 'Germany', 'Russia'],
                        'Medals': [29, 20, 16]}
                    )
bronze = pd.DataFrame({'Country': ['France', 'USA', 'UK'],
                        'Medals': [40, 28, 27]}
                    )

# Set the index of the dataframes to 'Country' so that you can get the countrywise
# medal count
gold.set_index('Country', inplace = True)
silver.set_index('Country', inplace = True) 
bronze.set_index('Country', inplace = True) 

# Add the three dataframes and set the fill_value argument to zero to avoid getting
# NaN values
total = gold.add(silver, fill_value = 0).add(bronze, fill_value = 0)

# Sort the resultant dataframe in a descending order
total = total.sort_values(by = 'Medals', ascending = False)

# Print the sorted dataframe
print(total)