python之求和一列值，包括 Python 中的字母

我有一个 CSV 输入文件，需要在其中一列中添加所有值，但这些值不是普通整数，我不确定如何处理。

总输出应该在15k左右，也就是整列的总和。我正在使用 Pandas 数据框来存储 .csv 文件。

这是我输入的 .csv 文件中的其中一列:

DAMAGE_PROPERTY 
0K 
0K 
2.5K 
2.5K 
.25K 
.25K 
2.5K 
25K 
2.5K 
.25K 
25K 
25K 
250K 
2.5K 
25K 
2.5K 
2.5K 
2.5K 
0K 
2.5K 
.25K 
2.5K 
25K 

请您参考如下方法:

我认为您需要先通过 str.replace 删除 K ，然后通过 astype 转换为 float最后sum :

print (df.DAMAGE_PROPERTY.str.replace('K','').astype(float).sum()) 
401.0 

然后可以乘以1000:

print (df.DAMAGE_PROPERTY.str.replace('K','').astype(float).sum() * 1000) 
401000.0 

如果需要添加K:

print (str(df.DAMAGE_PROPERTY.str.replace('K','').astype(float).sum()) + 'K') 
401.0K 

---

通过评论编辑:

如果需要在K中输出:

print (df) 
  DAMAGE_PROPERTY 
0            2.5K 
1            2.5K 
2             25M 
 
#create mask where values `M` 
mask = df.DAMAGE_PROPERTY.str.contains('M') 
print (mask) 
0    False 
1    False 
2     True 
Name: DAMAGE_PROPERTY, dtype: bool 
 
#multiple by 1000 where is mask 
df['DAMAGE_PROPERTY'] = df.DAMAGE_PROPERTY.str.replace(r'[KM]','').astype(float) 
df['DAMAGE_PROPERTY'] = df.DAMAGE_PROPERTY.mask(mask, df.DAMAGE_PROPERTY*1000) 
print (df) 
   DAMAGE_PROPERTY 
0              2.5 
1              2.5 
2          25000.0 
 
print (df['DAMAGE_PROPERTY'].sum()) 
25005.0 
 
print (str(df['DAMAGE_PROPERTY'].sum()) + 'K' ) 
25005.0K 

如果需要输出为数字:

df['DAMAGE_PROPERTY'] = df.DAMAGE_PROPERTY.str.replace(r'[KM]','').astype(float) 
df['DAMAGE_PROPERTY'] = df.DAMAGE_PROPERTY.mask(mask, df.DAMAGE_PROPERTY*1000) * 1000 
print (df) 
   DAMAGE_PROPERTY 
0           2500.0 
1           2500.0 
2       25000000.0 
 
print (df['DAMAGE_PROPERTY'].sum()) 
25005000.0 

编辑1:

如果有B的值:

print (df) 
  DAMAGE_PROPERTY 
0            2.5K 
1            2.5B 
2             25M 
 
maskM = df.DAMAGE_PROPERTY.str.contains('M') 
print (maskM) 
0    False 
1    False 
2     True 
Name: DAMAGE_PROPERTY, dtype: bool 
 
maskB = df.DAMAGE_PROPERTY.str.contains('B') 
print (maskB) 
0    False 
1     True 
2    False 
Name: DAMAGE_PROPERTY, dtype: bool 
 
df['DAMAGE_PROPERTY'] = df.DAMAGE_PROPERTY.str.replace(r'[KMB]','').astype(float) 
df['DAMAGE_PROPERTY'] = df.DAMAGE_PROPERTY.mask(maskM, df.DAMAGE_PROPERTY*1000) 
df['DAMAGE_PROPERTY'] = df.DAMAGE_PROPERTY.mask(maskB, df.DAMAGE_PROPERTY*1000000) 
print (df) 
   DAMAGE_PROPERTY 
0              2.5 
1        2500000.0 
2          25000.0 
 
print (df['DAMAGE_PROPERTY']) 
0          2.5 
1    2500000.0 
2      25000.0 
Name: DAMAGE_PROPERTY, dtype: float64