Question

我有一个名为＆＃34; df_business＆＃34;的pandas数据框。我从下面的数据框中得到了样本。我想过滤包含＆＃34;餐厅＆＃34;的记录的数据框。在类别列中。任何人都可以建议如何做到这一点？

Code:

print(df_business[1:3])


Sample Data:

               address                                         attributes  \
1       2824 Milton Rd  {u'GoodForMeal': {u'dessert': False, u'latenig...   
2  337 Danforth Avenue  {u'BusinessParking': {u'garage': False, u'stre...   

              business_id                                         categories  \
1  mLwM-h2YhXl2NCgdS84_Bw  [Food, Soul Food, Convenience Stores, Restaura...   
2  v2WhjAB3PIBA8J8VxG3wEg                               [Food, Coffee & Tea]   

        city                                              hours  is_open  \
1  Charlotte  {u'Monday': u'10:00-22:00', u'Tuesday': u'10:0...        0   
2    Toronto  {u'Monday': u'10:00-19:00', u'Tuesday': u'10:0...        0   

    latitude  longitude                                name neighborhood  \
1  35.236870 -80.741976  South Florida Style Chicken & Ribs     Eastland   
2  43.677126 -79.353285                    The Tea Emporium    Riverdale   

  postal_code  review_count  stars state  
1       28215             4    4.5    NC  
2     M4K 1N7             7    4.5    ON

Answer 1

将您的categories列转换为字符串并使用str.contains：

m = df_business['categories'].astype(str).str.contains('Restaurant')
df_business = df_business.loc[m]

如果您担心部分匹配，可能会对您的正则表达式添加单词边界检查有所帮助：

r'\bRestaurant\b'

这应该对误报有一点宽容。

借鉴jez' data（谢谢！）：

In [1864]: df_business

           categories  review_count
0  [Restaurant, Food]             4
1              [Food]             7


m = df_business['categories'].astype(str).str.contains(r'\bRestaurant\b')
m

0     True
1    False
Name: categories, dtype: bool

df_business = df_business.loc[m]
df_business

           categories  review_count
0  [Restaurant, Food]             4

Answer 2

选项1
将所有列表元素组合在一起并查找'Restaurant'

df_business[
    df_business.categories.str.join('').str.contains('Restaurant')]

           categories  review_count
0  [Restaurant, Food]             4

选项2
查找列表中'Restaurant'所在的索引值

mask = np.concatenate(df_business.categories) == 'Restaurant'
idx = df_business.index.repeat(df_business.categories.str.len())
df_business.loc[np.unique(idx[mask])]

           categories  review_count
0  [Restaurant, Food]             4

<强>设置
借用@jezrael

df_business = pd.DataFrame({'categories':[['Restaurant','Food'],['Food']],
                            'review_count':[4,7]})

Answer 3

如果in中的值是列表，则需要categories参数：

df_business = df_business[df_business['categories'].apply(lambda x: 'Restaurant' in x)]

或者：

df_business = df_business[df_business['categories'].astype(str).str.contains('Restaurant')]

样品：

df_business = pd.DataFrame({'categories':[['Restaurant','Food'],['Food']],
                            'review_count':[4,7]})

print (df_business)
           categories  review_count
0  [Restaurant, Food]             4
1              [Food]             7

df_business = df_business[df_business['categories'].apply(lambda x: 'Restaurant' in x)]
print (df_business)
           categories  review_count
0  [Restaurant, Food]             4

过滤列中包含的值的数据帧

3 个答案: