[已解决]pandas列有空格时，求平均值出现问题，求大师帮忙，谢谢

futui

data = {"A":['5','95','0.43','86.33','84.17','95','72.81','69.93','95','83.86','64.11','','89.16','','','0.11','','89.92','96','34.82','79.88','79.54','86.35','16.65','87.72'],
"B":['0','0','0','-8.67','-3.61','0','-6.49','0','0','0','0','','-1.35','','','0','','-8.34','0','0','0','-9.72','0','0','-7.28']}

df = pd.DataFrame(data)

求A列的平均数
求B列  大于0的个数，等于0的个数，小于0的个数，为空的个数

由于A列、B列都有空格，下面求值似乎用不了，求大师帮忙再看看，谢谢

# 计算 A 列的平均值
A_average = df['A'].astype(float).mean()
print(A_average)

# 计算 B 列的平均值
greater_than_zero = (df['B'].astype(float) > 0).sum()
equals_zero = (df['B'].astype(float) == 0).sum()
less_than_zero = (df['B'].astype(float) < 0).sum()
null_values = df['B'].isnull().sum()

print(greater_than_zero)
print(equals_zero)
print(less_than_zero)
print(null_values)

最佳答案

月排行榜 / 总排行榜

isdkz

2023-4-8 22:55:58

您好！在这种情况下，您可以先将空字符串替换为numpy.nan，这样才能进行相应的计算。请尝试以下代码：

1. import pandas as pd
   
2. import numpy as np
   
3. 
   
4. data = {"A":['5','95','0.43','86.33','84.17','95','72.81','69.93','95','83.86','64.11','','89.16','','','0.11','','89.92','96','34.82','79.88','79.54','86.35','16.65','87.72'],
   
5. "B":['0','0','0','-8.67','-3.61','0','-6.49','0','0','0','0','','-1.35','','','0','','-8.34','0','0','0','-9.72','0','0','-7.28']}
   
6. 
   
7. df = pd.DataFrame(data)
   
8. 
   
9. # 将空字符串替换为 np.nan
   
10. df.replace("", np.nan, inplace=True)
    
11. 
    
12. # 计算 A 列的平均值
    
13. A_average = df['A'].astype(float).mean()
    
14. print(A_average)
    
15. 
    
16. # 计算 B 列的各项数值
    
17. greater_than_zero = (df['B'].astype(float) > 0).sum()
    
18. equals_zero = (df['B'].astype(float) == 0).sum()
    
19. less_than_zero = (df['B'].astype(float) < 0).sum()
    
20. null_values = df['B'].isnull().sum()
    
21. 
    
22. print(greater_than_zero)
    
23. print(equals_zero)
    
24. print(less_than_zero)
    
25. print(null_values)

复制代码

这段代码应该能满足您的需求。如果您还有其他问题，请随时提问！

跳转到最佳答案楼层

isdkz

您好！在这种情况下，您可以先将空字符串替换为numpy.nan，这样才能进行相应的计算。请尝试以下代码：

1. import pandas as pd
   
2. import numpy as np
   
3. 
   
4. data = {"A":['5','95','0.43','86.33','84.17','95','72.81','69.93','95','83.86','64.11','','89.16','','','0.11','','89.92','96','34.82','79.88','79.54','86.35','16.65','87.72'],
   
5. "B":['0','0','0','-8.67','-3.61','0','-6.49','0','0','0','0','','-1.35','','','0','','-8.34','0','0','0','-9.72','0','0','-7.28']}
   
6. 
   
7. df = pd.DataFrame(data)
   
8. 
   
9. # 将空字符串替换为 np.nan
   
10. df.replace("", np.nan, inplace=True)
    
11. 
    
12. # 计算 A 列的平均值
    
13. A_average = df['A'].astype(float).mean()
    
14. print(A_average)
    
15. 
    
16. # 计算 B 列的各项数值
    
17. greater_than_zero = (df['B'].astype(float) > 0).sum()
    
18. equals_zero = (df['B'].astype(float) == 0).sum()
    
19. less_than_zero = (df['B'].astype(float) < 0).sum()
    
20. null_values = df['B'].isnull().sum()
    
21. 
    
22. print(greater_than_zero)
    
23. print(equals_zero)
    
24. print(less_than_zero)
    
25. print(null_values)

复制代码

这段代码应该能满足您的需求。如果您还有其他问题，请随时提问！