import matplotlib.pyplot as plt from sklearn.decomposition import PCA import pandas as pd df = pd.read_csv("iris.csv") pca = PCA(n_components=2) X = df.iloc[:, :-1].values Y = df.iloc[:, -1].values X_reduced = pca.fit_transform(X) plt.scatter(X_reduced[:, 0], X_reduced[:, 1], c=Y) plt.show() df_reduced = pd.DataFrame(data=X_reduced, columns=["PC1", "PC2"]) df_reduced["label"] = Y df_reduced.to_csv("iris_reduced.csv", index=False)这段代码运行报错，请为我修正

好的，这段代码存在两个问题：

1. 第一行代码中的 import 语句和第二行代码中的 from 语句应该分开写，中间需要加上换行符。
2. 代码中缺少对 pandas 和 matplotlib 库的导入语句。

您可以尝试使用以下修正后的代码来运行：

import pandas as pd
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA

df = pd.read_csv("iris.csv")

pca = PCA(n_components=2)
X = df.iloc[:, :-1].values
Y = df.iloc[:, -1].values
X_reduced = pca.fit_transform(X)

plt.scatter(X_reduced[:, 0], X_reduced[:, 1], c=Y)
plt.show()

df_reduced = pd.DataFrame(data=X_reduced, columns=["PC1", "PC2"])
df_reduced["label"] = Y
df_reduced.to_csv("iris_reduced.csv", index=False)

如果您还有问题，请随时提出。

相关问题

在Python中想运行from sklearn.model_selection import train_test_split from sklearn.decomposition import PCA import pandas as pd from sklearn import svm import numpy as np import math import matplotlib.pyplot as plt import matplotlib as mpl from matplotlib import colors from sklearn.model_selection import train_test_split from sklearn import datasets from matplotlib.colors import ListedColormap from sklearn.svm import SVC from sklearn.preprocessing import StandardScaler from sklearn.model_selection import StratifiedShuffleSplit,StratifiedKFold from sklearn.model_selection import GridSearchCV from sklearn.model_selection import GridSearchCV, LeaveOneOut, cross_val_predict from sklearn.model_selection import KFold from sklearn.linear_model import LogisticRegression from sklearn.naive_bayes import GaussianNB from sklearn.neighbors import KNeighborsClassifier from sklearn import svm from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import roc_auc_score from sklearn.metrics import roc_auc_score import math import datetime import multiprocessing as mp from sklearn.ensemble import StackingClassifier from sklearn.pipeline import make_pipeline from sklearn.svm import LinearSVC import random怎么操作

要在Python中运行这些代码，你需要确保已经安装了相应的包。大多数包都可以使用`pip`进行安装。

首先，在命令行中运行以下命令来安装所需的包：

pip install scikit-learn pandas matplotlib numpy

然后，在你的Python脚本中导入所需的库：

from sklearn.model_selection import train_test_split
from sklearn.decomposition import PCA
import pandas as pd
from sklearn import svm
import numpy as np
import math
import matplotlib.pyplot as plt
import matplotlib as mpl
from matplotlib import colors
from sklearn.model_selection import train_test_split
from sklearn import datasets
from matplotlib.colors import ListedColormap
from sklearn.svm import SVC
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import StratifiedShuffleSplit, StratifiedKFold
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import GridSearchCV, LeaveOneOut, cross_val_predict
from sklearn.model_selection import KFold
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import roc_auc_score
import datetime
import multiprocessing as mp
from sklearn.ensemble import StackingClassifier
from sklearn.pipeline import make_pipeline
from sklearn.svm import LinearSVC

import random

请确保在运行这些代码之前，已经安装了所需的Python库。如果遇到任何问题，请确保已正确安装这些库，并且版本与代码兼容。

import pandas as pd import numpy as np import scipy.stats as stats import seaborn as sns from sklearn.metrics import RocCurveDisplay from sklearn.model_selection import train_test_split from sklearn.metrics import confusion_matrix,accuracy_score from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LogisticRegression from sklearn import tree from sklearn.decomposition import PCA import matplotlib.pyplot as plt from sklearn.tree import DecisionTreeClassifier请在此基础上续写代码块，要求是（1） 读入数据后，选取自变量"sysBP", "diaBP","age","totChol","BMI", "heartRate", "glucose"记为X，因变量"TenYearCHD"记为y，组成新的数据集。¶

import pandas as pd
import numpy as np
import scipy.stats as stats
import seaborn as sns
from sklearn.metrics import RocCurveDisplay
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix,accuracy_score
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn import tree
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
from sklearn.tree import DecisionTreeClassifier

# 读入数据
data = pd.read_csv("heart.csv")

# 选取自变量
X = data[["sysBP", "diaBP", "age", "totChol", "BMI", "heartRate", "glucose"]]
# 因变量
y = data["TenYearCHD"]

# 组成新的数据集
new_data = pd.concat([X, y], axis=1)

# 打印新的数据集
print(new_data.head())