Question

我有一个包含某些列的数据框，其中之一是排名，另一列是服务年限。基于这些，我想创建一个新列“ Life Cover”。我已经为此创建了此功能。

def LifeCover(row):
if row['Years of Service']>5:
    val = 8
elif row['Years of Service']>2 and row['Position'] in ['Associate', 'Director', 'Director of Facilities Management', 'Director of Promise', 'Director, Head of Facilities Management']:
    val = 8
elif row['Years of Service']>2 and row['Position'] not in ['Associate', 'Director', 'Director of Facilities Management', 'Director of Promise', 'Director, Head of Facilities Management']:
    val = 7
else:
    val = 3
return val

df['Potential Life Cover Level'] = df.apply(LifeCover, axis=1)

这行得通，但是我不喜欢拥有如此庞大的职位清单，事实证明，这个清单可能也需要增加，因此也不切实际。

我需要包含/排除任何包含“协理”，“董事或合伙人”一词的职位。

我设法进行了这样的过滤：

target = ['Associate', 'Director', 'Partner']
dfhigh = df[df['Position'].apply(lambda sentence: any(word in sentence for word in target))]
dflow = df[~df['Position'].apply(lambda sentence: any(word in sentence for word in target))]

所以我得到一个数据框，位置较高，一个位置较低。

然后我尝试将其包括在我的函数中：

def LifeCover2(row):
if row['Years of Service']>5:
    val = 8
elif row['Years of Service']>2 and row['Position'] in dfhigh['Position']:
    val = 8
elif row['Years of Service']>2 and row['Position'] in dflow['Position']:
    val = 7
else:
    val = 3
return val

但是由于某些原因，它仅返回值8或3。

我也尝试过：

def LifeCover2(row):
if row['Years of Service']>5:
    val = 8
elif row['Years of Service']>2 and row['Position'].str.contains('Associate|Director|Partner'):
    val = 8
elif row['Years of Service']>2 and (~row['Position'].str.contains('Associate|Director|Partner')):
    val = 7
else:
    val = 3
return val

返回AttributeError ：（“ str对象没有属性'str'“，“发生在索引69”）

Answer 1

str.contains方法是向量化的字符串操作（see here）。这意味着它是用于熊猫系列而不是字符串类型的方法。当您使用df.apply时，pandas会尝试将str.contains用于您选择的列的每个元素，而不是在“系列”级别进行。

我建议采用以下方法：

df['LifeCover2'] = 3

df['LifeCover2'] = np.where(df['Years of Service']>5, 8, df['LifeCover2'])

df['LifeCover2'] = np.where((df['Years of Service']>2) & 
                        (df['Position'].str.contains('Associate|Director|Partner')), 8, df['LifeCover2'])

df['LifeCover2'] = np.where((df['Years of Service']>2) & 
                        (~df['Position'].str.contains('Associate|Director|Partner')), 7, df['LifeCover2'])

筛选数据框列中包含某些文本的行

1 个答案: