python - 求和一列值，包括 Python 中的字母

Question

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

Answer 1

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