我已将连续特征转换为分类特征。我在 Pandas 中得到 NaN

Question

我已将连续数据集转换为分类数据。当转换后连续数据的值为 0.0 时，我会得到 nan 值。下面是我的代码

import pandas as pd
import matplotlib as plt
df = pd.read_csv('NSL-KDD/KDDTrain+.txt',header=None)
data = df[33]
bins = [0.000,0.05,0.10,0.15,0.20,0.25,0.30,0.35,0.40,0.45,0.50,0.55,0.60,0.65,0.70,0.75,0.80,0.85,0.90,0.95,1.00]
category = pd.cut(data,bins)
category = category.to_frame()
print (category)

如何转换这些值，以免得到 NaN 值。我附上了两个屏幕截图，以便更好地了解实际数据的外观以及转换数据的外观。 This is the main dataset。 This is the what it becomes after using bins and pandas.cut()。 “0.00”如何与数据集中的其他值保持一致。

Answer 1

使用pd.cut时，可以指定参数include_lowest = True。这将使第一个内部左包含（它将包含 0 值，因为您的第一个间隔以 0 开头）。

所以在你的情况下，你可以将你的代码调整为

import pandas as pd
import matplotlib as plt
df = pd.read_csv('NSL-KDD/KDDTrain+.txt',header=None)
data = df[33]
bins = [0.000,0.05,0.10,0.15,0.20,0.25,0.30,0.35,0.40,0.45,0.50,0.55,0.60,0.65,0.70,0.75,0.80,0.85,0.90,0.95,1.00]
category = pd.cut(data,bins,include_lowest=True)
category = category.to_frame()
print (category)

pd.cut 的文档参考