关于statement和resultset
1。例如Statement st = conn.createStatement();
ResultSet rs = st.executeQuery(sql);
这时,如果st先关闭了,rs还能用吗?
2。 String name = "myname";
PreparedStatement ps = conn.createPreparedStatement("insert mytable values(?)");
ps.setString(1,name);
和
PreparedStatement ps = conn.createPreparedStatement("insert mytable values('"+name+"')");
有什么区别??
相关推荐
AI实战-世界卫生组织预期寿命数据集分析预测实例(含20个源代码+325.63 KB完整的数据集).zip
AI实战-世界卫生组织预期寿命数据集分析预测实例(含20个源代码+325.63 KB完整的数据集) 代码手工整理,无语法错误,可运行。 包括:20个代码,共145.15 KB;数据大小:1个文件共325.63 KB。 使用到的模块: matplotlib.pyplot numpy os pandas seaborn warnings sklearn.impute.KNNImputer sklearn.model_selection.train_test_split sklearn.linear_model.LinearRegression sklearn.linear_model.Ridge sklearn.linear_model.Lasso sklearn.metrics.mean_squared_error sklearn.metrics.r2_score sklearn.preprocessing.PowerTransformer sklearn.preprocessing.StandardScaler sklearn.preprocessing.OneHotEncoder plotly.express scipy.stats sklearn.preprocessing.LabelEncoder sklearn.preprocessing.MinMaxScaler tensorflow.keras.layers.Dense tensorflow.keras.models.Sequential tensorflow.keras.losses.Huber tensorflow.keras.layers.Dropout tensorflow.keras.layers.BatchNormalization tensorflow.keras.optimizers.Adam tensorflow.keras.callbacks.EarlyStopping tensorflow.keras.callbacks.ReduceLROnPlateau tensorflow.keras.regularizers.l2 kerastuner.HyperModel kerastuner.RandomSearch sklearn.ensemble.RandomForestRegressor sklearn.metrics sklearn.pipeline.Pipeline sklearn.compose.ColumnTransformer sklearn.metrics.mean_absolute_error math.sqrt sklearn.linear_model.SGDRegressor sklearn.linear_model.ElasticNet sklearn.svm.SVR sklearn.tree.DecisionTreeRegressor sklearn.model_selection.cross_val_predict time sklearn.model_selection.cross_val_score sklearn.model_selection.KFold tensorflow.keras tensorflow.keras.layers sklearn.ensemble.GradientBoostingRegressor sklearn.model_selection.GridSearchCV matplotlib scipy.stats.skew sklearn.preprocessing.KBinsDiscretizer statsmodels.stats.outliers_influence.variance_inflation_factor statsmodels.tools.tools.add_constant statsmodels.api statsmodels.formula.api statsmodels.stats.api statsmodels.api.qqplot pingouin joblib sklearn.impute.SimpleImputer sklearn.preprocessing.FunctionTransformer sklearn.preprocessing.QuantileTransformer sklearn.preprocessing.OrdinalEncoder sklearn.ensemble.VotingRegressor sklearn.ensemble.StackingRegressor sklearn.ensemble.ExtraTreesRegress
AI实战-健身房成员锻炼数据集分析预测实例(含19个源代码+63.61 KB完整的数据集).zip
AI实战-健身房成员锻炼数据集分析预测实例(含19个源代码+63.61 KB完整的数据集) 代码手工整理,无语法错误,可运行。 包括:19个代码,共148.00 KB;数据大小:1个文件共63.61 KB。 使用到的模块: pandas matplotlib.pyplot seaborn plotly.express sklearn.model_selection.train_test_split sklearn.linear_model.LogisticRegression sklearn.svm.SVC sklearn.neighbors.KNeighborsClassifier sklearn.tree.DecisionTreeClassifier sklearn.metrics.accuracy_score sklearn.metrics.classification_report warnings.filterwarnings sklearn.preprocessing.LabelEncoder sklearn.model_selection.GridSearchCV os warnings plotly.graph_objects plotly.subplots.make_subplots sklearn.ensemble.RandomForestClassifier sklearn.ensemble.GradientBoostingClassifier sklearn.naive_bayes.GaussianNB sklearn.preprocessing.StandardScaler sklearn.ensemble.VotingClassifier sklearn.model_selection.cross_val_score sklearn.decomposition.PCA sklearn.neighbors.KNeighborsRegressor sklearn.metrics.mean_absolute_error sklearn.metrics.r2_score sklearn.preprocessing.MinMaxScaler sklearn.metrics.mean_squared_error xgboost.XGBRegressor sklearn.discriminant_analysis.LinearDiscriminantAnalysis numpy random missingno pandas.api.types.CategoricalDtype sklearn.preprocessing.OneHotEncoder sklearn.compose.ColumnTransformer sklearn.pipeline.Pipeline sklearn.impute.SimpleImputer sklearn.linear_model.LinearRegression sklearn.preprocessing.PolynomialFeatures sklearn.tree.DecisionTreeRegressor sklearn.ensemble.RandomForestRegressor sklearn.ensemble.GradientBoostingRegressor kagglehub math sklearn.preprocessing.OrdinalEncoder sklearn.ensemble.IsolationForest tensorflow.keras.models.Sequential tensorflow.keras.layers.Dense tensorflow.keras.callbacks.EarlyStopping scipy.stats.zscore sklearn.model_selection.learning_curve lightgbm.LGBMRegressor sklearn.linear_model.Ridge sklearn.linear_model.Lasso sklearn.linear_model.ElasticNet sklearn.svm.SVR xgboost sklearn.pipeline.make_pipeline sklearn.metrics.confusion_matrix sklearn.metrics.ConfusionMatrixDisplay deap.base deap.creator deap.tools deap.algorithms
AI实战-Pendigits数据集分析预测实例(含2个源代码+730.06 KB完整的数据集).zip
AI实战-Pendigits数据集分析预测实例(含2个源代码+730.06 KB完整的数据集) 代码手工整理,无语法错误,可运行。 包括:2个代码,共26.30 KB;数据大小:1个文件共730.06 KB。 使用到的模块: pandas numpy matplotlib.pyplot seaborn sklearn.metrics.accuracy_score sklearn.metrics.f1_score sklearn.metrics.classification_report sklearn.metrics.confusion_matrix sklearn.model_selection.train_test_split sklearn.model_selection.GridSearchCV sklearn.svm.SVC sklearn.ensemble.RandomForestClassifier sklearn.tree.DecisionTreeClassifier yellowbrick.classifier.ClassPredictionError yellowbrick.classifier.ROCAUC sklearn.pipeline.Pipeline sklearn.preprocessing.StandardScaler warnings sklearn.metrics.log_loss sklearn.tree sklearn.model_selection.cross_validate scipy.stats.friedmanchisquare itertools keras.models.Sequential keras.layers.Dense keras.layers.Dropout keras.layers.Flatten keras.layers.Conv2D keras.layers.MaxPool2D keras.optimizers.RMSprop keras.callbacks.ReduceLROnPlateau tensorflow.keras.preprocessing.image.ImageDataGenerator tensorflow
AI实战-水卫生数据集分析预测实例(含8个源代码+128.43 KB完整的数据集).zip
AI实战-水卫生数据集分析预测实例(含8个源代码+128.43 KB完整的数据集) 代码手工整理,无语法错误,可运行。 包括:8个代码,共19.97 KB;数据大小:1个文件共128.43 KB。 使用到的模块: pandas numpy matplotlib.pyplot seaborn warnings os sklearn.impute.SimpleImputer sklearn.preprocessing.LabelEncoder sklearn.preprocessing.OneHotEncoder sklearn.model_selection.train_test_split sklearn.preprocessing.StandardScaler geopandas squarify sklearn.linear_model.LinearRegression sklearn.metrics.mean_squared_error sklearn.metrics.r2_score sklearn.ensemble.RandomForestRegressor
AI实战-全球COVID-19统计数据分析预测实例(含1个源代码+546.59 KB完整的数据集).zip
AI实战-全球COVID-19统计数据分析预测实例(含1个源代码+546.59 KB完整的数据集) 代码手工整理,无语法错误,可运行。 包括:1个代码,共12.63 KB;数据大小:1个文件共546.59 KB。 使用到的模块: pandas matplotlib.pyplot numpy lifelines.KaplanMeierFitter lifelines.CoxPHFitter lifelines.statistics.multivariate_logrank_test sksurv.nonparametric.kaplan_meier_estimator IPython.display.display seaborn sksurv.preprocessing.OneHotEncoder datetime.datetime lifelines.plotting.plot_lifetimes lifelines.utils.median_survival_times lifelines.utils.to_long_format lifelines.CoxTimeVaryingFitter


Java SE
