pandas 如何在熊猫填充中使用“无”值
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How to proceed with `None` value in pandas fillna
提问by Andrii Furmanets
I have the following dictionary:
我有以下字典：
fillna(value={'first_name':'Andrii', 'last_name':'Furmanets', 'created_at':None})
fillna(value={'first_name':'Andrii', 'last_name':'Furmanets', 'created_at':None})
When I pass that dictionary to fillnaI see:
当我把那本字典传给fillna我时，我看到：
raise ValueError('must specify a fill method or value')\nValueError: must specify a fill method or value\n"
raise ValueError('必须指定一个填充方法或值')\nValueError: 必须指定一个填充方法或值\n"
It seems to me that it fails on Nonevalue.
在我看来，它在None价值上失败了。
I use pandas version 0.20.3.
我使用Pandas版本 0.20.3。
采纳答案by atwalsh
What type of data structure are you using? This works for a pandas Series:
你使用什么类型的数据结构？这适用于Pandas系列：
import pandas as pd
d = pd.Series({'first_name': 'Andrii', 'last_name':'Furmanets', 'created_at':None})
d = d.fillna('DATE')
回答by piRSquared
Setup
Consider the sample dataframe df
设置
考虑示例数据框df
df = pd.DataFrame(dict(A=[1, None], B=[None, 2], C=[None, 'D']))
df
A B C
0 1.0 NaN None
1 NaN 2.0 D
I can confirm the error
我可以确认错误
df.fillna(dict(A=1, B=None, C=4))
ValueError: must specify a fill method or value
ValueError: must specify a fill method or value
This happens because pandas is cycling through keys in the dictionary and executing a fillnafor each relevant column. If you look at the signature of the pd.Series.fillnamethod
发生这种情况是因为 Pandas 正在遍历字典中的键并fillna为每个相关列执行 a 。如果你看pd.Series.fillna方法的签名
Series.fillna(value=None, method=None, axis=None, inplace=False, limit=None, downcast=None, **kwargs)
Series.fillna(value=None, method=None, axis=None, inplace=False, limit=None, downcast=None, **kwargs)
You'll see the default value is None. So we can replicate this error with
您将看到默认值为None。所以我们可以复制这个错误
df.A.fillna(None)
Or equivalently
或等效地
df.A.fillna()
I'll add that I'm not terribly surprised considering that you are attempting to fill a null value with a null value.
我要补充一点，考虑到您试图用空值填充空值，我并不感到非常惊讶。
What you need is a work around
你需要的是一个解决方法
Solution
Use pd.DataFrame.fillnaover columns that you want to fill with non-null values. Then follow that up with a pd.DataFrame.replaceon the specific columns you want to swap one null value with another.
解决方案在您想要填充非空值的列上
使用pd.DataFrame.fillna。然后pd.DataFrame.replace在您希望将一个空值与另一个空值交换的特定列上使用 a 。
df.fillna(dict(A=1, C=2)).replace(dict(B={np.nan: None}))
A B C
0 1.0 None 2
1 1.0 2 D
回答by addicted
An alternative method to fillna with None. I am on pandas 0.24.0and I am doing this to insert NULL values to POSTGRES database.
用 .fillna 填充的另一种方法None。我在Pandas上0.24.0，我这样做是为了将 NULL 值插入到 POSTGRES 数据库中。
# Stealing @pIRSquared dataframe
df = pd.DataFrame(dict(A=[1, None], B=[None, 2], C=[None, 'D']))
df
A B C
0 1.0 NaN None
1 NaN 2.0 D
# fill NaN with None. Basically it says, fill with None whenever you see NULL value.
df['A'] = np.where(df['A'].isnull(), None, df['A'])
df['B'] = np.where(df['B'].isnull(), None, df['B'])
# Result
df
A B C
0 1.0 None None
1 None 2.0 D
回答by smci
It's a bad idea to try to fill a datetime with None, this is exactly what pandas NaT(NotATime), is for: for missing datetimes.
尝试用 填充日期时间是一个坏主意None，这正是pandasNaT(NotATime) 的用途：用于缺少日期时间。
