问题描述
def create_ratio(data,num,den):
if data[num].isnull():
ratio = -9997
if data[den].isnull():
ratio = -9998
if data[num].isnull() & data[den].isnull():
ratio = -9999
else:
ratio = data[num]/data[den]
return ratio
我有 Pandas 数据框 (df_credit)，其中包括信用卡余额 (cc_bal) 和限制 (cc_limit)，我想计算信用卡利用率，即余额超过限制
df_credit['cc_util'] = create_ratio(df_credit,'cc_bal','cc_limit')
我收到以下错误：
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-66-d53809a7690d> in <module>
----> 1 data['ratio_cc_util'] = create_ratio(data,'open_credit_card_credit_limit_nomiss','open_credit_card_credit_limit_nomiss')
2 data['ratio_cc_util'].hist()
<ipython-input-65-99bc55b184ed> in create_ratio(data,den)
1 def create_ratio(data,den):
----> 2 if data[num].isnull():
3 ratio = -9997
4 if data[den].isnull():
5 ratio = -9998
/opt/conda/lib/python3.7/site-packages/pandas/core/generic.py in __nonzero__(self)
1441 def __nonzero__(self):
1442 raise ValueError(
-> 1443 f"The truth value of a {type(self).__name__} is ambiguous. "
1444 "Use a.empty,a.bool(),a.item(),a.any() or a.all()."
1445 )
ValueError: The truth value of a Series is ambiguous. Use a.empty,a.any() or a.all().
解决方法
- 您在标量和系列之间混合使用，您的函数需要根据其调用上下文返回系列或数组
- 实现这种条件逻辑的最简单方法是
np.select() - 拥有模拟数据，包括满足您的用例的缺失值
df = pd.DataFrame({
"cc_bal": np.random.uniform(200,1000,200),"cc_limit": np.random.uniform(800,1200,})
df.loc[np.unique(np.random.choice(range(len(df)),30)),"cc_bal"] = None
df.loc[np.unique(np.random.choice(range(len(df)),"cc_limit"] = None
def create_ratio(df,num,den):
return np.select(
[
df[num].isnull() & df[den].isnull(),df[num].isnull(),df[den].isnull(),],[-9999,-9997,-9998],df[num] / df[den],)
df["ratio"] = create_ratio(df,"cc_bal","cc_limit")
df
示例输出
| cc_bal | cc_limit | 比例 | |
|---|---|---|---|
| 0 | 372.633 | 981.996 | 0.379465 |
| 1 | 845.541 | 1133.69 | 0.745831 |
| 2 | 449.406 | 975.903 | 0.460503 |
| 3 | 209.827 | 922.829 | 0.227374 |
| 4 | 237.347 | 936.654 | 0.253398 |
| 5 | 351.154 | nan | -9998 |
| 6 | nan | 873.671 | -9997 |
| 7 | 803.396 | 861.791 | 0.93224 |
| 8 | 591.136 | 807.176 | 0.732352 |
| 9 | 675.397 | 847.059 | 0.797344 |