df_dummies.head()什么意思

"df_dummies.head()" 是一个 Pandas DataFrame 对象的方法，用于显示 DataFrame 的前几行。其中，“df_dummies” 是 DataFrame 的名称，".head()" 是方法名。默认情况下，该方法返回前五行，但也可以指定要返回的行数，例如 "df_dummies.head(10)" 返回前十行。

相关问题

import streamlit as st import numpy as np import pandas as pd import pickle import matplotlib.pyplot as plt from sklearn import datasets from sklearn.model_selection import train_test_split from sklearn.decomposition import PCA from sklearn.svm import SVC from sklearn.neighbors import KNeighborsClassifier from sklearn.ensemble import RandomForestClassifier import streamlit_echarts as st_echarts from sklearn.metrics import accuracy_score,confusion_matrix,f1_score def pivot_bar(data): option = { "xAxis":{ "type":"category", "data":data.index.tolist() }, "legend":{}, "yAxis":{ "type":"value" }, "series":[ ] }; for i in data.columns: option["series"].append({"data":data[i].tolist(),"name":i,"type":"bar"}) return option st.markdown("mode pracitce") st.sidebar.markdown("mode pracitce") df=pd.read_csv(r"D:\课程数据\old.csv") st.table(df.head()) with st.form("form"): index_val = st.multiselect("choose index",df.columns,["Response"]) agg_fuc = st.selectbox("choose a way",[np.mean,len,np.sum]) submitted1 = st.form_submit_button("Submit") if submitted1: z=df.pivot_table(index=index_val,aggfunc = agg_fuc) st.table(z) st_echarts(pivot_bar(z)) df_copy = df.copy() df_copy.drop(axis=1,columns="Name",inplace=True) df_copy["Response"]=df_copy["Response"].map({"no":0,"yes":1}) df_copy=pd.get_dummies(df_copy,columns=["Gender","Area","Email","Mobile"]) st.table(df_copy.head()) y=df_copy["Response"].values x=df_copy.drop(axis=1,columns="Response").values X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2) with st.form("my_form"): estimators0 = st.slider("estimators",0,100,10) max_depth0 = st.slider("max_depth",1,10,2) submitted = st.form_submit_button("Submit") if "model" not in st.session_state: st.session_state.model = RandomForestClassifier(n_estimators=estimators0,max_depth=max_depth0, random_state=1234) st.session_state.model.fit(X_train, y_train) y_pred = st.session_state.model.predict(X_test) st.table(confusion_matrix(y_test, y_pred)) st.write(f1_score(y_test, y_pred)) if st.button("save model"): pkl_filename = "D:\\pickle_model.pkl" with open(pkl_filename, 'wb') as file: pickle.dump(st.session_state.model, file) 会出什么错误

我没有发现代码中的错误。但是，你需要明确一些内容：

1. 你需要保证数据集的路径正确，并且数据集的格式正确。

2. 如果使用了 `streamlit_echarts` 库，在运行代码前需要先安装该库，可以通过 `!pip install streamlit_echarts` 命令进行安装。

3. 确保所有的依赖项都已经被正确地导入。

4. 确认你的代码没有语法错误和逻辑错误。

如果你仍然遇到问题，请提供更详细的错误信息或者异常信息。

import seaborn as sns import pandas as pd import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LogisticRegression df = pd.read_csv('heart.csv') corrmat = df.corr() top_corr_features = corrmat.index plt.figure(figsize=(16,16)) sns.heatmap(df[top_corr_features].corr(),annot=True,cmap="RdYlGn") plt.show() sns.set_style('whitegrid') sns.countplot(x='target',data=df,palette='RdBu_r') plt.show() dataset = pd.get_dummies(df, columns=['sex', 'cp', 'fbs', 'restecg', 'exang', 'slope', 'ca', 'thal']) columns_to_scale = ['age', 'trestbps', 'chol', 'thalach', 'oldpeak'] scaler = StandardScaler() dataset[columns_to_scale] = scaler.fit_transform(dataset[columns_to_scale]) dataset.head() y = dataset['target'] X = dataset.drop(['target'], axis=1) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0) logreg = LogisticRegression() logreg.fit(X_train, y_train) print("Training accuracy: {:.3f}".format(logreg.score(X_train, y_train))) print("Test accuracy: {:.3f}".format(logreg.score(X_test, y_test)))改写为ROC代码

以下是将 Logistic Regression 模型的评估指标改为 ROC 曲线的代码：

import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_curve, auc

df = pd.read_csv('heart.csv')

# 查看特征相关性
corrmat = df.corr()
top_corr_features = corrmat.index
plt.figure(figsize=(16,16))
sns.heatmap(df[top_corr_features].corr(),annot=True,cmap="RdYlGn")
plt.show()

# 查看数据分布
sns.set_style('whitegrid')
sns.countplot(x='target',data=df,palette='RdBu_r')
plt.show()

# 对数据进行 One-hot 编码和标准化
dataset = pd.get_dummies(df, columns=['sex', 'cp', 'fbs', 'restecg', 'exang', 'slope', 'ca', 'thal'])
columns_to_scale = ['age', 'trestbps', 'chol', 'thalach', 'oldpeak']
scaler = StandardScaler()
dataset[columns_to_scale] = scaler.fit_transform(dataset[columns_to_scale])
dataset.head()

# 划分数据集
y = dataset['target']
X = dataset.drop(['target'], axis=1)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)

# 训练模型
logreg = LogisticRegression()
logreg.fit(X_train, y_train)

# 评估模型
y_train_pred = logreg.predict_proba(X_train)[:, 1]
y_test_pred = logreg.predict_proba(X_test)[:, 1]
fpr_train, tpr_train, thresholds_train = roc_curve(y_train, y_train_pred)
fpr_test, tpr_test, thresholds_test = roc_curve(y_test, y_test_pred)
roc_auc_train = auc(fpr_train, tpr_train)
roc_auc_test = auc(fpr_test, tpr_test)

# 绘制 ROC 曲线
plt.figure()
plt.plot(fpr_train, tpr_train, color='darkorange', lw=2, label='Train ROC curve (area = %0.2f)' % roc_auc_train)
plt.plot(fpr_test, tpr_test, color='navy', lw=2, label='Test ROC curve (area = %0.2f)' % roc_auc_test)
plt.plot([0, 1], [0, 1], color='black', lw=2, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic')
plt.legend(loc="lower right")
plt.show()

在这个代码中，我们使用 `roc_curve` 函数计算训练集和测试集的 FPR 和 TPR，然后使用 `auc` 函数计算 ROC 曲线下的面积。最后，我们使用 `matplotlib` 库绘制 ROC 曲线。