empty_columns = data.columns[data.isnull().all()] empty_columns

function median_target(var) { temp = data[data[var].notnull()]; temp = temp[[var, 'Outcome']].groupby(['Outcome'])[[var]].median().reset_index(); return temp; } data.loc[(data['Outcome'] == 0) & (data['Insulin'].isnull()), 'Insulin'] = 102.5; data.loc[(data['Outcome'] == 1) & (data['Insulin'].isnull()), 'Insulin'] = 169.5; data.loc[(data['Outcome'] == 0) & (data['Glucose'].isnull()), 'Glucose'] = 107; data.loc[(data['Outcome'] == 1) & (data['Glucose'].isnull()), 'Glucose'] = 1; data.loc[(data['Outcome'] == 0) & (data['SkinThickness'].isnull()), 'SkinThickness'] = 27; data.loc[(data['Outcome'] == 1) & (data['SkinThickness'].isnull()), 'SkinThickness'] = 32; data.loc[(data['Outcome'] == 0) & (data['BloodPressure'].isnull()), 'BloodPressure'] = 70; data.loc[(data['Outcome'] == 1) & (data['BloodPressure'].isnull()), 'BloodPressure'] = 74.5; data.loc[(data['Outcome'] == 0) & (data['BMI'].isnull()), 'BMI'] = 30.1; data.loc[(data['Outcome'] == 1) & (data['BMI'].isnull()), 'BMI'] = 34.3; target_col = ["Outcome"]; cat_cols = data.nunique()[data.nunique() < 12].keys().tolist(); cat_cols = [x for x in cat_cols]; num_cols = [x for x in data.columns if x not in cat_cols + target_col]; bin_cols = data.nunique()[data.nunique() == 2].keys().tolist(); multi_cols = [i for i in cat_cols if i in bin_cols]; le = LabelEncoder(); for i in bin_cols: data[i] = le.fit_transform(data[i]); data = pd.get_dummies(data=data, columns=multi_cols); std = StandardScaler(); scaled = std.fit_transform(data[num_cols]); scaled = pd.DataFrame(scaled, columns=num_cols); df_data_og = data.copy(); data = data.drop(columns=num_cols, axis=1); data = data.merge(scaled, left_index=True, right_index=True, how='left'); X = data.drop('Outcome', axis=1); y = data['Outcome']; X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8, shuffle=True, random_state=1); y_train = to_categorical(y_train); y_test = to_categorical(y_test);将这段代码添加注释

data.loc[(data['Outcome'] == 0) & (data['Insulin'].isnull()), 'Insulin'] = 102.5 data.loc[(data['Outcome'] == 1) & (data['Insulin'].isnull()), 'Insulin'] = 169.5 data.loc[(data['Outcome'] == 0) & ...

import tkinter as tk from tkinter import filedialog import pandas as pd import numpy as np from sklearn.preprocessing import StandardScaler class DataImporter: def init(self, master): self.file_path = self.master = master self.master.title("数据导入") # 创建用于显示文件路径的标签 self.path_label = tk.Label(self.master, text="请先导入数据集！") self.path_label.pack(pady=10) # 创建“导入数据集”按钮 self.load_button = tk.Button(self.master, text="导入数据集", command=self.load_data) self.load_button.pack(pady=10) # 创建“显示数据集”按钮 self.show_button = tk.Button(self.master, text="显示数据集", command=self.show_data) self.show_button.pack(pady=10) # 创建“退出程序”按钮 self.quit_button = tk.Button(self.master, text="退出程序", command=self.master.quit) self.quit_button.pack(pady=10) # 创建一个空的 DataFrame 用于存放数据集 self.data = pd.DataFrame() def load_data(self): # 弹出文件选择对话框 file_path = filedialog.askopenfilename() # 如果用户选择了文件，则导入数据集 if file_path: self.data = pd.read_csv(file_path) self.path_label.config(text=f"已导入数据集：{file_path}") else: self.path_label.config(text="未选择任何文件，请选择正确的文件") def show_data(self): if not self.data.empty: # 创建一个新窗口来显示数据集 top = tk.Toplevel(self.master) top.title("数据集") # 创建用于显示数据集的表格 table = tk.Text(top) table.pack() # 将数据集转换为字符串并显示在表格中 table.insert(tk.END, str(self.data)) table.config(state=tk.DISABLED) # 创建“数据预处理”按钮 process_button = tk.Button(top, text="数据预处理", command=self.process_data) process_button.pack(pady=10) else: self.path_label.config(text="请先导入数据集") def process_data(self): try: self.data = pd.read_csv(self.file_path) missing_values = self.data.isnull().sum() for col in self.data.columns: mean = np.mean(self.data[col]) std = np.std(self.data[col]) outliers = [x for x in self.data[col] if (x > mean + 2 * std)] if len(outliers) > 0: print('Column {} has outliers: {}'.format(col, outliers)) scaler = StandardScaler() data_scaled = scaler.fit_transform(self.data) print('Data preprocessing completed.') except Exception as e: print('Error: ' + str(e)) if name == "main": root = tk.Tk() app = DataImporter(root) root.geometry("400x300+100+100") root.mainloop()上面的这段代码中，file_path么有定义属性，帮我按照代码的环境，补全属性

self.load_button = tk.Button(self.master, text="导入数据集", command=self.load_data) self.load_button.pack(pady=10) self.show_button = tk.Button(self.master, text="显示数据集", command=self.show_...

将下列代码变为伪代码def median_target（var）： temp = data[data[var].notnull（）] temp = temp[[var， 'Outcome']].groupby（['Outcome']）[[var]].median（）.reset_index（） return temp data.loc[（data['Outcome'] == 0 ） & （data['Insulin'].isnull（））， 'Insulin'] = 102.5 data.loc[（data['Result'] == 1 ） & （data['Insulin'].isnull（））， 'Insulin'] = 169.5 data.loc[（data['Result'] == 0 ） & （data['Glucose'].isnull（））， 'Glucose'] = 107 data.loc[（data['Result'] == 1 ） & （data['Glucose'].isnull（））， 'Glucose'] = 1 data.loc[（data['Result'] == 0 ） & （data['SkinThickness'].isnull（））， 'SkinThickness'] = 27 data.loc[（data['Result'] == 1 ） & （data['SkinThickness'].isnull（））， 'SkinThickness'] = 32 data.loc[（data['Result'] == 0 ） & （data['BloodPressure'].isnull（））， 'BloodPressure'] = 70 data.loc[（data['Result'] == 1 ） & （data['BloodPressure'].isnull（））， 'BloodPressure'] = 74.5 data.loc[（data['Result'] == 0 ） & （data['BMI'].isnull（））， 'BMI'] = 30.1 data.loc[（data['Result'] == 1 ） & （data['BMI'].isnull（））， 'BMI'] = 34.3 target_col = [“Outcome”] cat_cols = data.nunique（）[data.nunique（） < 12].keys（）.tolist（） cat_cols = [x for x in cat_cols ] #numerical列 num_cols = [x for x in data.columns if x 不在 cat_cols + target_col] #Binary列有 2 个值 bin_cols = data.nunique（）[data.nunique（） == 2].keys（）.tolist（） #Columns 2 个以上的值 multi_cols = [i 表示 i in cat_cols if i in bin_cols] #Label编码二进制列 le = LabelEncoder（） for i in bin_cols ： data[i] = le.fit_transform（data[i]） #Duplicating列用于多值列 data = pd.get_dummies（data = data，columns = multi_cols ） #Scaling 数字列 std = StandardScaler（）缩放 = std.fit_transform（数据[num_cols]）缩放 = pd。数据帧（缩放，列=num_cols） #dropping原始值合并数字列的缩放值 df_data_og = 数据.copy（）数据 = 数据.drop（列 = num_cols，轴 = 1）数据 = 数据.合并（缩放，left_index=真，right_index=真，如何 = “左”） # 定义 X 和 Y X = 数据.drop（'结果'，轴=1） y = 数据['结果'] X_train， X_test， y_train， y_test = train_test_split（X， y， train_size=0.8， shuffle=True， random_state=1） y_train = to_categorical（y_train） y_test = to_categorical（y_test）

data.loc[(data['Outcome'] == 0) & (data['Insulin'].isnull()), 'Insulin'] = 102.5; data.loc[(data['Outcome'] == 1) & (data['Insulin'].isnull()), 'Insulin'] = 169.5; data.loc[(data['Outcome'] == 0) & ...

def deduplicate(self, df: DataFrame) -> DataFrame: key_columns = [F.col(column_name) for column_name in self.config.deduplication_key_columns] order_by_columns = [F.col(column_name).desc() for column_name in self.config.deduplication_order_columns] if self.config.deduplicate_order_by_type == "asc": order_by_columns = [F.col(column_name) for column_name in self.config.deduplication_order_columns] ranking_column = "duplicates_rank" is_deleted_column = "IsDeleted" if (self.config.filter_deleted_flag) & ( is_deleted_column in df.columns): # if True removes records that marked deleted filter = (F.col(ranking_column) == 1) & (F.col(is_deleted_column) == "False") else: self.logger.warning(f"Records marked as deleted will be loaded to {self.config.table_full_name} table!") filter = F.col(ranking_column) == 1 return df.withColumn(ranking_column, F.row_number().over(Window.partitionBy(key_columns).orderBy(order_by_columns))).filter( filter).drop(ranking_column)怎么用临时表和cte的形式写成sql

AND (<is_deleted_column> = 'False' OR <is_deleted_column> IS NULL) ) SELECT * FROM filtered_cte; 其中，tmp_table是源数据的临时表，<key_columns>是self.config.deduplication_key_columns中的列名，...

import pandas as pdfrom sklearn.ensemble import RandomForestRegressor# 读取数据集wine_data = pd.read_csv('wine_data.csv')# 将数据集按照价格是否缺失分为两个部分known_price = wine_data[wine_data.price.notnull()]unknown_price = wine_data[wine_data.price.isnull()]# 特征工程，得到新的数据集x = known_price.iloc[:, 1:]y = known_price.iloc[:, 0]rf = RandomForestRegressor(n_estimators=200, random_state=0)rf.fit(x, y)predicted_price = rf.predict(unknown_price.iloc[:, 1:])# 将预测得到的价格值填充到原来的数据集中wine_data.loc[wine_data.price.isnull(), 'price'] = predicted_price报 could not convert string to float: 'US'的错误怎么解决

unknown_price = wine_data[wine_data.price.isnull()] # 特征工程，得到新的数据集 known_price = pd.get_dummies(known_price, columns=['country']) # 将产地转换为虚拟变量 unknown_price = pd.get_dummies...

SELECT DISTINCT p.*, u1.id AS directorId, CASE WHEN up1.member_type = 348 THEN u1.nickname ELSE up1.member_name END AS directorName FROM project p LEFT JOIN userproject up1 ON p.p_id=up1.p_id AND up1.director=1 LEFT JOIN sys_user u1 ON up1.member_id = u1.id LEFT JOIN userproject up ON p.p_id=up.p_id LEFT JOIN sys_user u ON up.member_id = u.id LEFT JOIN sys_user_department d ON d.user_id = u.id WHERE (p.enable = ? AND p.audit_status = ? AND p.organization_id = ? AND p.project_name IS NOT NULL) GROUP BY p.project_name,p.project_code,p.project_nature ORDER BY p.p_id DESC报错Expression #1 of SELECT list is not in GROUP BY clause and contains nonaggregated column 'spms.p.p_id' which is not functionally dependent on columns in GROUP BY clause; this is incompatible with sql_mode=only_full_group_by怎么处理

这是因为 MySQL 的 sql_mode 设置了 only_full_group_by，导致在 GROUP BY 子句中未包含的... AND p.project_name IS NOT NULL) GROUP BY p.project_name,p.project_code,p.project_nature ORDER BY p.p_id DESC

UPDATE a SET a.refund_status = b.dealstat , a.refund_time = b.dealtime FROM dwi_store_setmt_ord_dtl_p a INNER JOIN sdi_dgmps_dgmpsdb_mstore.chainstorefeemangdtl_p b ON a.comp_code = trim(upper(b.companycode)) AND a.src_pid = trim(b.pid) WHERE a.refund_status = 0 AND ISNULL(a.refund_time,'') = '' AND b.dealstat = 1 AND ISNULL(a.dealtime,'') <> '' ;改为用merge into 更新

AND ISNULL(a.refund_time, '') = '' AND ISNULL(a.dealtime, '') <> '' WHEN MATCHED THEN UPDATE SET a.refund_status = b.dealstat, a.refund_time = b.dealtime; This MERGE statement combines the ...

import pandas as pd import matplotlib.pyplot as plot raw_data = pd.read_csv(r'/Users/wangruihan/Desktop/time_series_covid19_confirmed_global.csv',na_values='?') #设定'?'为缺失值 #查看有哪些列 print(raw_data.columns) #查看前几行数据 print(raw_data[:10]) #检查含有缺失值的列 print(raw_data.isnull().any() ) print(raw_data.shape) #缺失值处理 print(raw_data[raw_data.isnull().values==True]) fill_na = lambda col:col.fillna('not know')#定义fill_na函数，用'not know'字符串填充缺失值 fill_data = raw_data.apply(fill_na, axis=0) #将填充后的数据赋给fill_data print(fill_data.isnull().any()) #检查是否填充成功 isDuplicated=fill_data.duplicated() #判断重复数据记录 print(isDuplicated) #初步了解数据构成 print(fill_data[fill_data['Country/Region']=='China']) data = fill_data.drop(['Province/State','Lat','Long'],axis=1) #删除属性列 data = data.groupby('Country/Region').sum() # 按Country/Region并求和 print(data.index.values) #列出全部可索引的Country/Region名称 data_us = data[data.index.isin(['US','China','United Kingdom','Italy','Germany','France'])] print(data_us) data_us.T.plot(figsize=(15,7)) #数据趋势图最后数据趋势图不显示怎么办

如果你在Jupyter Notebook...data_us.T.plot(figsize=(15,7)) plt.show() 这个命令将显示图表并将其添加到Jupyter Notebook中。请注意，你需要首先导入matplotlib库，使用import matplotlib.pyplot as plt命令。

# 空值处理 train_shape = (train.count(), len(train.columns)) train_null_count = train.select([sum(col(c).isNull().cast("int")).alias(c) for c in train.columns]).collect()[0] train_null_count = {col: count for col, count in zip(train.columns, train_null_count)}报错TypeError: Column is not iterable怎么办

这个错误提示是因为你尝试将列对象作为可迭代对象进行循环，而列对象并不是可迭代对象，因此...train_null_count = train_pd.isnull().sum().to_dict() 这样可以得到一个字典，其中键为列名，值为该列的空值数量。

test_df = data[data["label"].isnull() == True].copy().reset_index(drop=True) train_df = data[~data["label"].isnull() == True].copy().reset_index(drop=True) feature_name = [f for f in data.columns if f not in drop_cols] X_train = train_df[feature_name].reset_index(drop=True) X_test = test_df[feature_name].reset_index(drop=True) y = train_df['label'].reset_index(drop=True)

data[data["label"].isnull() == True] 会根据这个布尔数组筛选出缺失 "label" 的数据点，也就是测试集。data[~data["label"].isnull() == True] 则是选择不缺失 "label" 的数据点，也就是训练集。 reset_...

train_df = pd.read_csv('./data/train.csv', index_col = 0) test_df = pd.read_csv('./data/test.csv', index_col = 0) print("Number of rows: ", train_df.shape[0]) print("Number of columns: ", train_df.shape[1]) display(train_df) train_df.info() print("Number of rows: ", test_df.shape[0]) print("Number of columns: ", test_df.shape[1]) display(test_df) test_df.info() train_df.describe() # Check for NaN values and the number of unique values print("Are there any NaN values: ", train_df.isnull().values.any()) print(train_df.nunique()) # Count NaN values in each column nan_counts = train_df.isna().sum() # Print the counts print(nan_counts)

这段代码是用来读取两个csv文件，并对它们进行一些数据分析的。具体来说，它首先读取了一个名为train.csv的文件和一个名为test.csv的文件，并打印了它们的行数和列数。然后，它显示了train_df和test_df的内容和信息...

data = dataset.copy() # 折扣率处理 is_manjian = data['Discount_rate'].str.contains(':').astype(int) discount_rate = data['Discount_rate'].apply(lambda x: float(x) if ':' not in str(x) else (float(str(x).split(':')[0]) - float(str(x).split(':')[1])) / float(str(x).split(':')[0])) min_cost_of_manjian = data['Discount_rate'].apply(lambda x: -1 if ':' not in str(x) else int(str(x).split(':')[0])) data['is_manjian'] = is_manjian data['discount_rate'] = discount_rate data['min_cost_of_manjian'] = min_cost_of_manjian # 距离处理 data['Distance'].fillna(-1, inplace=True) null_distance = (data['Distance'] == -1).astype(int) data['null_distance'] = null_distance # 时间处理 data['date_received'] = pd.to_datetime(data['Date_received'], format='%Y%m%d') if 'Date' in data.columns.tolist(): data['date'] = pd.to_datetime(data['Date'], format='%Y%m%d') return data出现cannot convert float NaN to integer的报错怎么解决

出现 cannot convert float NaN to integer 的报错是因为在 null_distance 的计算中，data['Distance'] 中存在缺失值 NaN，无法将其转换为整数类型。可以尝试将 null_distance 的计算改为： python ...

na_index_1 = na_userid[na_userid['uniqueVisitorId'].isnull()].index.tolist() na_uniqueVisitorId = na_userid.iloc[na_index_1] nona_uniqueVisitorId = na_userid.drop(index=na_index_1) # 替换userid na_uniqueVisitorId.loc[:, 'userid'] = na_uniqueVisitorId['ip'] nona_uniqueVisitorId.loc[:, 'userid'] = na_uniqueVisitorId['uniqueVisitor'] # 将数据重新整合 con_data = pd.concat([nona_userid, na_uniqueVisitorId, nona_uniqueVisitorId], axis=0) con_data['userid'] = con_data['userid'].apply(lambda x: str(x)) con_data['reallID'] = con_data['userid'].rank() print(con_data) total_user = len(con_data['reallID'].drop_duplicates()) print('用户的总数:', total_user) # 数据清洗 # 寻找reallID.sessionid的全部组合 reallid_sessionid = con_data[['reallID', 'sessionid']].drop_duplicates() # 对reallID进行统计 reallid_count = pd.DataFrame(reallid_sessionid.groupby('reallID')['reallID'].count()) print(reallid_count) reallid_count.columns = ['count'] print(reallid_count) reallid_count['reallID'] = reallid_count.index.tolist() print(reallid_count)请解释代码

最后，将三个数据集按行合并成一个新的数据集 con_data，并为每个用户生成一个新的 reallID 列，表示其在数据集中的排名。接下来，代码统计了所有可能的 reallID 和 sessionid 的组合，并对每个 reallID...

def get_missing_ratio(data, ratio): missing_ratio = pd.DataFrame({'ratio': data.isnull().sum() / data.shape[0]}) missing_ratio['name'] = [data[c] if c != 'target' else 'target' for c in missing_ratio.index] return missing_ratio[missing_ratio['ratio'] >= ratio].reset_index().rename(columns={'index': 'col'})

missing_ratio = pd.DataFrame({'ratio': data.isnull().sum() / data.shape[0]}) missing_ratio['name'] = [data[c] if c != 'target' else 'target' for c in missing_ratio.index] return missing_ratio...

import numpy as np import pandas as pd import matplotlib.pyplot as plt from decision_tree_classifier import DecisionTreeClassifier from random_forest_classifier import RandomForestClassifier from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score #读取数据 df = pd.read_csv('adult.csv',encoding='gbk') df.head() col_names=['age','workclass','fnlwgt','education','educational-num','marital-status','occupation','relationship','race','gender','capital-gain','capital-loss','hours-per-week','native-country','income'] df.columns = col_names categorical = ['workclass','education','marital-status','occupation','relationship','race','gender','native-country','income'] # print(f'分类特征：\n{categorical}') # for var in categorical: # print(df[var].value_counts()) #缺失值处理 df['occupation'].replace('?', np.NaN, inplace=True) df['workclass'].replace('?', np.NaN, inplace=True) df['native-country'].replace('?', np.NaN, inplace=True) df.isnull().sum() df['income'].value_counts() plt.rcParams['font.sans-serif'] = ['Microsoft YaHei'] df.isnull().sum() df['workclass'].fillna(df['workclass'].mode()[0], inplace=True) df['occupation'].fillna(df['occupation'].mode()[0], inplace=True) df['native-country'].fillna(df['native-country'].mode()[0], inplace=True) df = pd.get_dummies(df,columns=categorical,drop_first=True) print(df.head()) y = df.loc[:,'income_>50K'] X = np.array(df.loc[:,['age', 'educational-num', 'hours-per-week']]) y = np.array(y) x = np.array(X) y = y.reshape(-1,1) X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=1234) from sklearn.ensemble import RandomForestClassifier rtree = RandomForestClassifier(n_estimators=100,max_depth=5,max_features=0.2,max_samples=50,random_state=1234) X_train = np.array(X_train) rtree.fit(X_train, y_train) X_test = np.array(X_test) y_pred = rtree.predict(X_test) accuracy = accuracy_score(y_test,y_pred) print("accuracy={}".format((accuracy)))我这个代码如何更换特征向量

如果想更换特征向量，只需要修改以下代码段： X = np.array(df.loc[:,['age', 'educational-num', 'hours-per-week']]) y = np.array(y) 将 ['age', 'educational-num', 'hours-per-week'] 替换为你想要...

empty_columns = data.columns[data.isnull().all()] empty_columns

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