pandas 熊猫数据框 fillna() 不起作用？
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pandas dataframe fillna() not working?
提问by user21478
I have a data set in which I am performing a principal components analysis (PCA). I get a ValueErrormessage when I try to transform the data. Below is some of the code:
我有一个数据集，我正在其中执行主成分分析 (PCA)。ValueError当我尝试转换数据时收到一条消息。下面是部分代码：
import pandas as pd
import numpy as np
import matplotlib as mpl
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA as sklearnPCA
data = pd.read_csv('test.csv',header=0)
X = data.ix[:,0:1000].values # values of 1000 predictor variables
Y = data.ix[:,1000].values # values of binary outcome variable
sklearn_pca = sklearnPCA(n_components=2)
X_std = StandardScaler().fit_transform(X)
It is here that I get the following error message:
在这里，我收到以下错误消息：
ValueError: Input contains NaN, infinity or a value too large for dtype('float64').
So I then checked whether the original data set had any NaN values:
所以我然后检查原始数据集是否有任何 NaN 值：
print(data.isnull().values.any()) # prints True
data.fillna(0) # replace NaN values with 0
print(data.isnull().values.any()) # prints True
I don't understand why data.isnull().values.any()is still printing Trueeven after I replaced the NaN values with 0.
我不明白为什么即使在我用 0 替换 NaN 值之后data.isnull().values.any()仍然打印True。
回答by Jesse
There are two way to achieve, try replace in place:
有两种实现方式，尝试就地替换：
import pandas as pd
data = pd.DataFrame(data=[0,float('nan'),2,3])
print('BEFORE:', data.isnull().values.any()) # prints True
# fillna function
data.fillna(0, inplace=True)
print('AFTER:',data.isnull().values.any()) # prints False now :)
Or, use returned object:
或者，使用返回的对象：
data = data.fillna(0)
Both case have same result as following:
两种情况的结果相同，如下所示：
BEFORE: True
AFTER: False
回答by Jean-Fran?ois Fabre
You have to replace data by the returned object from fillna
你必须用返回的对象替换数据 fillna
Small reproducer:
小型复制器：
import pandas as pd
data = pd.DataFrame(data=[0,float('nan'),2,3])
print(data.isnull().values.any()) # prints True
data = data.fillna(0) # replace NaN values with 0
print(data.isnull().values.any()) # prints False now :)
