如何解决当我尝试处理 Pandas 中的缺失值时，某些方法不起作用
我正在尝试处理数据集中的一些缺失值。这是我用来学习的教程的 link。下面是我用来读取数据的代码。
import pandas as pd
import numpy as np
questions = pd.read_csv("./archive/questions.csv")
print(questions.head())
这就是我的数据的样子
这些是我用来处理缺失值的方法。他们都没有工作。
questions.replace(to_replace = np.nan,value = -99)
questions = questions.fillna(method ='pad')
questions.interpolate(method ='linear',limit_direction = 'forward')
然后我尝试删除包含缺失值的行。他们都没有工作。所有这些都返回空数据帧。
questions.dropna()
questions.dropna(how = "all")
questions.dropna(axis = 1)
我做错了什么？
编辑：
来自 questions.head() 的值
[[1 '2008-07-31T21:26:37Z' nan '2011-03-28T00:53:47Z' 1 nan 0.0]
[4 '2008-07-31T21:42:52Z' nan nan 458 8.0 13.0]
[6 '2008-07-31T22:08:08Z' nan nan 207 9.0 5.0]
[8 '2008-07-31T23:33:19Z' '2013-06-03T04:00:25Z' '2015-02-11T08:26:40Z'
42 nan 8.0]
[9 '2008-07-31T23:40:59Z' nan nan 1410 1.0 58.0]]
来自 questions.head() 的字典形式的值。
{'Id': {0: 1,1: 4,2: 6,3: 8,4: 9},'CreationDate': {0: '2008-07-31T21:26:37Z',1: '2008-07-31T21:42:52Z',2: '2008-07-31T22:08:08Z',3: '2008-07-31T23:33:19Z',4: '2008-07-31T23:40:59Z'},'ClosedDate': {0: nan,1: nan,2: nan,3: '2013-06-03T04:00:25Z',4: nan},'DeletionDate': {0: '2011-03-28T00:53:47Z',3: '2015-02-11T08:26:40Z','score': {0: 1,1: 458,2: 207,3: 42,4: 1410},'OwnerUserId': {0: nan,1: 8.0,2: 9.0,3: nan,4: 1.0},'AnswerCount': {0: 0.0,1: 13.0,2: 5.0,3: 8.0,4: 58.0}}
关于数据集的信息
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 17203824 entries,0 to 17203823
Data columns (total 7 columns):
# Column Dtype
--- ------ -----
0 Id int64
1 CreationDate object
2 ClosedDate object
3 DeletionDate object
4 score int64
5 OwnerUserId float64
6 AnswerCount float64
dtypes: float64(2),int64(2),object(3)
memory usage: 918.8+ MB
解决方法
您能否尝试明确指定 axis 并查看它是否有效？另一个 fillna() 应该仍然可以在没有轴的情况下工作，但是对于 pad，您需要它以便它知道如何填充缺失值。
>>> questions.fillna(method='pad',axis=1)
Id CreationDate ClosedDate DeletionDate Score OwnerUserId AnswerCount
0 1 2008-07-31T21:26:37Z 2008-07-31T21:26:37Z 2011-03-28T00:53:47Z 1 1 0
1 4 2008-07-31T21:42:52Z 2008-07-31T21:42:52Z 2008-07-31T21:42:52Z 458 8 13
2 6 2008-07-31T22:08:08Z 2008-07-31T22:08:08Z 2008-07-31T22:08:08Z 207 9 5
3 8 2008-07-31T23:33:19Z 2013-06-03T04:00:25Z 2015-02-11T08:26:40Z 42 42 8
4 9 2008-07-31T23:40:59Z 2008-07-31T23:40:59Z 2008-07-31T23:40:59Z 1410 1 58
只需将 fillna() 应用于整个 DataFrame 即可正常工作。
>>> questions.fillna('-')
Id CreationDate ClosedDate DeletionDate Score OwnerUserId AnswerCount
0 1 2008-07-31T21:26:37Z - 2011-03-28T00:53:47Z 1 - 0.0
1 4 2008-07-31T21:42:52Z - - 458 8 13.0
2 6 2008-07-31T22:08:08Z - - 207 9 5.0
3 8 2008-07-31T23:33:19Z 2013-06-03T04:00:25Z 2015-02-11T08:26:40Z 42 - 8.0
4 9 2008-07-31T23:40:59Z - - 1410 1 58.0
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