在Panda中使用Add()函数合并多个数据帧

Question

给定三个数据帧，其中包含一些国家获得的奥运金牌、银牌和铜牌的数量，确定每个国家获得的奖牌总数。注意:这三个数据框架的国家并不完全相同。此外，根据总奖牌数降序排序最终数据框。

下面是我的代码-但是我没有得到想要的输出。谁能告诉我出了什么问题?

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]}
)
#gold.set_index('Country',inplace = True)
#silver.set_index('Country',inplace = True)
#bronze.set_index('Country',inplace = True)
Total = gold.add(silver,fill_value = 0).add(bronze,fill_value = 0)
Total.sort_values('Medals',ascending = True)

Answer 1

您可以尝试:

pd.concat([gold, silver, bronze]).groupby('Country').sum().
sort_values('Medals', ascending=False).reset_index()

如果你喜欢，你有三个数据帧在一个。它是按国家分组的，你会得到每个国家的奖牌数。最后按降序排序并重置索引。

输出:

Country  Medals
0       USA      72
1    France      53
2        UK      27
3    Russia      25
4   Germany      20

Answer 2

你也可以这样做:

gold.set_index('Country', inplace=True)
silver.set_index('Country', inplace=True)
bronze.set_index('Country', inplace=True)
#print(gold)
#print(silver)
#print(bronze)
Total= gold.add(silver,    fill_value=0).add(bronze,fill_value=0).sort_values('Medals', ascending=False)

输出:

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

Answer 3

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).astype("int64")
# Print the sorted dataframe
print(total)
int64 is used to convert the float value into integer and 64 indicates 64bit memory location

Answer 4

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]}
)
gold.set_index('Country' , inplace = True)
silver.set_index('Country' , inplace = True) 
bronze.set_index('Country' , inplace = True ) 
total = gold.add(silver , fill_value = 0).add(bronze , fill_value = 0)
total = total.sort_values(by = 'Medals', ascending = False)
total = total.astype(int)
print(total)