python - 从文本中提取年龄值以在 Pandas 中创建新列

Question

我有如下数据集:

df=pd.DataFrame([["Sam is 5", 2000],["John is 3 years and 6 months",1200],["Jack is 4.5 years",7000],["Shane is 25 years old",2000]], columns = ['texts','amount'])

print(df)

    texts                          amount
0   Sam is 5                        2000
1   John is 3 years and 6 months    1200
2   Jack is 4.5 years               7000
3   Shane is 25 years old           2000

我想从 df['texts'] 中提取年龄值并用它来计算新列 df['value']。

df['value'] = df['amount'] / val 

其中 val 是来自 df['texts'] 的数值

这是我的代码

val = df['texts'].str.extract('(\d+\.?\d*)', expand=False).astype(float)
df['value'] = df['amount']/val
print(df)

输出:

    texts                          amount     value
0   Sam is 5                       2000     400.000000
1   John is 3 years and 6 months   1200     400.000000
2   Jack is 4.5 years              7000     1555.555556
3   Shane is 25 years old          2000     80.000000

预期输出:

    texts                          amount     value
0   Sam is 5                       2000     400.000000
1   John is 3 years and 6 months   1200     342.85
2   Jack is 4.5 years              7000     1555.555556
3   Shane is 25 years old          2000     80.000000

上面代码中的问题是我无法弄清楚如何将 3 年 6 个月转换为 3.5 年。

附加信息:文本列仅包含按年和月顺序排列的年龄值。

欢迎提出任何建议。谢谢

Answer 1

我相信你需要:

注意:如果没有年份和月份文本，则解决方案以年份计算

#extract all first numbers
a = df['texts'].str.extract('(\d+\.?\d*)', expand=False).astype(float)
#extract years only
b = df['texts'].str.extract('(\d+\.?\d*)\s+years', expand=False).astype(float)
#replace NaNs by a
y = b.combine_first(a)
print(y)
0     5.0
1     3.0
2     4.5
3    25.0
Name: texts, dtype: float64

#extract months only
m = df['texts'].str.extract('(\d+\.?\d*)\s+months', expand=False).astype(float) / 12
print (m)
0    NaN
1    0.5
2    NaN
3    NaN
Name: texts, dtype: float64

#add together
val = y.add(m, fill_value=0)
print (val)
0     5.0
1     3.5
2     4.5
3    25.0
Name: texts, dtype: float64

---

df['value'] = df['amount']/val
print (df)
                          texts  amount        value
0                      Sam is 5    2000   400.000000
1  John is 3 years and 6 months    1200   342.857143
2             Jack is 4.5 years    7000  1555.555556
3         Shane is 25 years old    2000    80.000000