如何在 Pandas 数据框中用 NaN 替换一系列值？
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How to replace a range of values with NaN in Pandas data-frame?
提问by Mat_python
I have a huge data-frame. How should I replace a range of values (-200, -100) with NaN?
我有一个巨大的数据框。我应该如何用 NaN 替换一系列值 (-200, -100)？
回答by jpp
dataframe
数据框
You can use pd.DataFrame.mask:
您可以使用pd.DataFrame.mask：
df.mask((df >= -200) & (df <= -100), inplace=True)
This method replaces elements identified by Truevalues in a Boolean array with a specified value, defaulting to NaNif a value is not specified.
此方法将True布尔数组中由值标识的元素替换为指定值，NaN如果未指定值，则默认为。
Equivalently, use pd.DataFrame.wherewith the reverse condition:
等效地，pd.DataFrame.where与相反的条件一起使用：
df.where((df < -200) | (df > -100), inplace=True)
series
系列
As with many methods, Pandas helpfully includes versions which work with series rather than an entire dataframe. So, for a column df['A'], you can use pd.Series.maskwith pd.Series.between:
与许多方法一样，Pandas 有助于包含适用于系列而不是整个数据框的版本。因此，对于 column df['A']，您可以使用pd.Series.maskwith pd.Series.between：
df['A'].mask(df['A'].between(-200, -100), inplace=True)
For chaining, note inplace=Falseby default, so you can also use:
对于链接，请注意inplace=False默认情况下，因此您也可以使用：
df['A'] = df['A'].mask(df['A'].between(-200, -100))
回答by MaxU
You can do it this way:
你可以这样做：
In [145]: df = pd.DataFrame(np.random.randint(-250, 50, (10, 3)), columns=list('abc'))
In [146]: df
Out[146]:
a b c
0 -188 -63 -228
1 -59 -70 -66
2 -110 39 -146
3 -67 -228 -232
4 -22 -180 -140
5 -191 -136 -188
6 -59 -30 -128
7 -201 -244 -195
8 -248 -30 -25
9 11 1 20
In [148]: df.loc[:, (df>=-200) & (df<=-100)] = np.nan
In [149]: df
Out[149]:
a b c
0 NaN -63.0 -228.0
1 -59.0 -70.0 -66.0
2 NaN 39.0 NaN
3 -67.0 -228.0 -232.0
4 -22.0 NaN NaN
5 NaN NaN NaN
6 -59.0 -30.0 NaN
7 -201.0 -244.0 NaN
8 -248.0 -30.0 -25.0
9 11.0 1.0 20.0
