Pandas 问题：标签编码器和拆分列以提供带有标签的数据集答案

【问题标题】：Pandas questions: Label Encoder and splitting columns to provide datasets with labelsPandas 问题：标签编码器和拆分列以提供带有标签的数据集
【发布时间】：2017-07-15 03:42:54
【问题描述】：

我刚刚开始尝试使用 pandas 和 scikit 进行数据分析。我的测试集是NHSTA's open crash dataset——我现在的目标是做一个简单的随机森林分类，根据其他参数预测司机的性别（我现在不关注准确性——我想让事情先运行）

我的代码：

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split


crashes = pd.read_csv("crashes.csv", nrows=100000)


crashes.drop("Case Individual ID", axis=1, inplace = True)
crashes.drop("Case Vehicle ID", axis=1, inplace = True)
crashes.drop("Transported By", axis=1, inplace = True)
crashes.drop("Injury Descriptor", axis=1, inplace = True)
crashes.drop("Injury Location", axis=1, inplace = True)

crashes = crashes [pd.notnull(crashes['Age'])]
crashes = crashes[crashes.Age >= 10 ]

le = LabelEncoder()
crashes = crashes[crashes.columns[:]].apply(le.fit_transform)
crashes = crashes._get_numeric_data()

crashes_train, crashes_test = train_test_split(crashes, test_size = 0.2)

print "After numeric mapping:",list(crashes_train)

X = crashes_train[:,[0,1,2,3,4,5]]
Y = crashes_train[:,[6]]
print "X=",list (X) #error
print "Y=",list (Y) #error

数据列：

After numeric mapping: ['Year', 'Victim Status', 'Role Type', 'Seating Position', 'Ejection', 'License State Code', 'Sex', 'Safety Equipment', 'Injury Severity', 'Age']

我的问题：

我正在尝试将第 0-5 列拆分为数据集，将第 6 列（性别）拆分为标签。为什么我在尝试打印 X 和 Y 时收到 TypeError: unhashable type？
即使在使用将文本值转换为数字映射的LabelEncoder 之后，当我打印“数字映射后”时，它如何打印实际标签？

谢谢

【问题讨论】：

标签： python pandas scikit-learn

【解决方案1】：

好的，我想多了 - 这很好 - 列名上的简单拆分将熊猫导入为 pd

import matplotlib.pyplot as plt
import numpy as np
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
#from sklearn.cross_validation import train_test_split

crashes = pd.read_csv("crashes.csv", nrows=100000)


crashes.drop("Case Individual ID", axis=1, inplace = True)
crashes.drop("Case Vehicle ID", axis=1, inplace = True)
crashes.drop("Transported By", axis=1, inplace = True)
crashes.drop("Injury Descriptor", axis=1, inplace = True)
crashes.drop("Injury Location", axis=1, inplace = True)

crashes = crashes [pd.notnull(crashes['Age'])]
crashes = crashes[crashes.Age >= 10 ]

le = LabelEncoder()
crashes = crashes[crashes.columns[:]].apply(le.fit_transform)
crashes = crashes._get_numeric_data()

crashes_train, crashes_test = train_test_split(crashes, test_size = 0.2)



print "After numeric mapping:",list(crashes_train)

#X = crashes_train.set_index['Year', 'Victim Status', 'Role Type', 'Seating Position', 'Ejection', 'License State Code']
#Y = crashes_train.set_index['Sex']

Y = crashes_train[['Age', 'Year']]
X  =  crashes_train[['Year', 'Victim Status', 'Role Type', 'Seating Position', 'Ejection', 'License State Code']]
names = crashes_train.columns.values

print "X=",list (X)
print "Y=",list (Y)

rfc = RandomForestClassifier()
rfc.fit(X, Y)
print("Features sorted by their score:")
print(sorted(zip(map(lambda x: round(x, 4), rfc.feature_importances_), names), reverse=True))

【讨论】：