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54 lines
1.6 KiB
54 lines
1.6 KiB
import pandas as pd
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from sklearn.linear_model import LinearRegression
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from sklearn.impute import SimpleImputer
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# 加载数据集
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file_path = 'row_data.csv'
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data = pd.read_csv(file_path)
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# 将日期列转换为时间戳
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data['date'] = pd.to_datetime(data['date']).astype('int64') / 10 ** 9
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# 定义一个函数来进行回归填补
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def fill_missing_values_with_regression(df, target_column):
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# 拆分数据为训练集和测试集
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train_data = df[df[target_column].notna()]
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test_data = df[df[target_column].isna()]
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# 特征和目标
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X_train = train_data.drop(columns=[target_column])
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y_train = train_data[target_column]
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X_test = test_data.drop(columns=[target_column])
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# 简单填补其他特征的缺失值
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imputer = SimpleImputer(strategy='mean')
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X_train = imputer.fit_transform(X_train)
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X_test = imputer.transform(X_test)
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# 训练回归模型
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model = LinearRegression()
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model.fit(X_train, y_train)
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# 预测缺失值
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predicted_values = model.predict(X_test)
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# 填补缺失值
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df.loc[df[target_column].isna(), target_column] = predicted_values
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return df
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# 只对数值列进行回归填补
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numeric_columns = data.select_dtypes(include=['float64', 'int64']).columns
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for column in numeric_columns:
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if data[column].isna().sum() > 0:
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data = fill_missing_values_with_regression(data, column)
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data['date'] = pd.to_datetime(data['date'], unit='s')
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# 显示填补后的数据集前几行
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print("填补后的数据：")
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print(data.head())
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# 保存填补后的数据集
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data.to_csv('filled_row_data.csv', index=False) |