给网友朋友们带来一篇相关的编程文章，网友詹琬莹根据主题投稿了本篇教程内容，涉及到Pandas筛选某列过滤、Pandas列过滤、Pandas筛选某列过滤相关内容，已被672网友关注，下面的电子资料对本篇知识点有更加详尽的解释。
Pandas筛选某列过滤
通过dataframe的第二个条件，进行筛选
#make字段异常值清洗 new = data[['make', 'model', 'instance_id']] new['make_model'] = new['make']+':::'+new['model'] new.head(3)
# new.make_model.value_counts() # 统计make_model列属性值出现的次数 new.make_model.value_counts()[new.make_model.value_counts() <= 200] """ OPPO:::OPPO+A59st 200 OPPO:::3007 200 Xiaomi:::Redmi%20Note%203 200 Meizu:::MEIZU-M6 199 samsung:::SM-N9006 199 ... OPPO,OPPO A53,A53:::OPPO A53 1 boway U15:::boway U15 1 BaiMao:::BM I8 1 vivo:::vivoy75a 1 SUPERJO:::SUPERJO 1 Name: make_model, Length: 15597, dtype: int64 """
找出符合第二列筛选条件的index（这里index不是0-n，而是刚才value_counts()的index）
(new.make_model.value_counts()[new.make_model.value_counts() <= 200]).index
"""
Index(['OPPO:::OPPO+A59st', 'OPPO:::3007', 'Xiaomi:::Redmi%20Note%203',
'Meizu:::MEIZU-M6', 'samsung:::SM-N9006', 'Coolpad:::MTS-T0',
'OPPO R11st:::OPPO R11st', 'Blephone:::lephone T7A', 'GIONEE:::GN9011',
'Meizu:::PRO 7-S',
...
'HUAWEI:::HUAWEI%25252BG7-UL20', 'VOLTE:::L3', 'GIONEE:::GN868',
'alps:::SOP-i9', 'GT-I9300I:::GT-I9300I',
'OPPO,OPPO A53,A53:::OPPO A53', 'boway U15:::boway U15',
'BaiMao:::BM I8', 'vivo:::vivoy75a', 'SUPERJO:::SUPERJO'],
dtype='object', length=15597)
"""
new.make_model
"""
0 HUAWEI:::HUAWEI-CAZ-AL10
1 Xiaomi:::Redmi Note 4
2 OPPO:::OPPO+R11s
3 NaN
4 Apple:::iPhone 7
...
1041669 OPPO:::OPPO-R9s
1041670 Xiaomi:::MI-5X
1041671 vivo:::vivo Y37
1041672 vivo:::vivo%20Y75A
1041673 OPPO:::A31
Name: make_model, Length: 1041674, dtype: object
"""dataframe.loc(行索引, 列名)
# 在make_model列， # 定位符合 new.make_model.isin((new.make_model.value_counts()[new.make_model.value_counts() <= 200]).index) 的行 # new.loc[new.make_model.isin((new.make_model.value_counts()[new.make_model.value_counts() <= 200]).index), 'make_model'] = 'other' #去除低频词
再感受下第二个case
data['day'] = data['time'].apply(lambda x : int(time.strftime("%d", time.localtime(x))))
data['period'] = data['day']
data[['period']].head(3)
data['period'].unique() # array([29, 30, 31, 27, 1, 2, 28, 3])
直接用列筛选
[data['period']<27]
"""
[0 False
1 False
2 False
3 False
4 False
...
1041669 True
1041670 True
1041671 True
1041672 True
1041673 True
Name: period, Length: 1041674, dtype: bool]
"""
data['period']<27
"""
0 False
1 False
2 False
3 False
4 False
...
1041669 True
1041670 True
1041671 True
1041672 True
1041673 True
Name: period, Length: 1041674, dtype: bool
"""挑选period列，值<27的行（已成功挑选）
data['period'][data['period']<27]
"""
950 1
951 1
952 1
953 1
954 1
..
1041669 3
1041670 3
1041671 3
1041672 3
1041673 3
Name: period, Length: 348536, dtype: int64
"""
data['period'][data['period']<27] = data['period'][data['period']<27] + 31这样可以使用head展示
data[['period']][data['period']<27].head(3)
还有种单列就能筛选的方法
t2['receive_number'] = t2.date_received.apply(lambda s:len(s.split(':')))
t2 = t2[t2.receive_number>1]
t2.head(3)
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