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在Python中，我們可以使用多種方法來填充缺失值，以下是一些常用的方法：

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1、刪除含有缺失值的行或列

2、使用常數(shù)填充缺失值

3、使用平均值填充缺失值

4、使用中位數(shù)填充缺失值

5、使用眾數(shù)填充缺失值

6、使用插值法填充缺失值

7、使用前向填充和后向填充

8、使用K近鄰算法填充缺失值

9、使用多重插補方法填充缺失值

下面是這些方法的具體實現(xiàn)：

1、刪除含有缺失值的行或列

import pandas as pd
讀取數(shù)據(jù)
data = pd.read_csv('data.csv')
刪除含有缺失值的行
data.dropna(axis=0, inplace=True)
刪除含有缺失值的列
data.dropna(axis=1, inplace=True)

2、使用常數(shù)填充缺失值

import pandas as pd
讀取數(shù)據(jù)
data = pd.read_csv('data.csv')
使用常數(shù)0填充缺失值
data.fillna(0, inplace=True)

3、使用平均值填充缺失值

import pandas as pd
讀取數(shù)據(jù)
data = pd.read_csv('data.csv')
使用列的平均值填充該列的缺失值
data.fillna(data.mean(), inplace=True)

4、使用中位數(shù)填充缺失值

import pandas as pd
讀取數(shù)據(jù)
data = pd.read_csv('data.csv')
使用列的中位數(shù)填充該列的缺失值
data.fillna(data.median(), inplace=True)

5、使用眾數(shù)填充缺失值

import pandas as pd
from scipy import stats
讀取數(shù)據(jù)
data = pd.read_csv('data.csv')
計算每列的眾數(shù)并填充缺失值
for column in data:
    mode = stats.mode(data[column])[0][0]
    data[column].fillna(mode, inplace=True)

6、使用插值法填充缺失值（線性插值）

import pandas as pd
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler, OneHotEncoder, MinMaxScaler, RobustScaler, MaxAbsScaler, PowerTransformer, FunctionTransformer, PolynomialFeatures, SelectKBest, chi2, SelectFromModel, KFold, cross_val_score, StratifiedKFold, GroupKFold, TimeSeriesSplit, LeaveOneOut, GroupShuffleSplit, ShuffleSplit, GridSearchCV, train_test_split, cross_validate, pipeline, ColumnTransformer, OneVsRestClassifier, OrdinalEncoder, StandardScaler, Binarizer, MultiLabelBinarizer, get_feature_names, SMOTE, MinMaxScaler, LogisticRegression, LogisticRegressionCV, RidgeCV, AdaBoostClassifier, GradientBoostingClassifier, ExtraTreesClassifier, VotingClassifier, BaggingClassifier, StackingClassifier, ClassifierChain, IsolationForest, LocalOutlierFactor, DBSCAN, GaussianNB, QuadraticDiscriminantAnalysis, NearestCentroid, OneClassSVM, BernoulliNB, MultinomialNB, ComplementNB, BaseNBC, ARDRegression, PassiveAggressiveRegressor, HuberRegressor, ElasticNetCV, LassoCV, RidgeCV, LassoLarsCV, RidgeLarsCV, LassoLarsICCV, RidgeLarsICCV, MultiTaskLassoCV, MultiTaskRidgeCV, MultiTaskElasticNetCV, MultiTaskHuberRegressorCV, MultiTaskPassiveAggressiveRegressorCV, isotonic_regression, NumericalFeaturesExtractor, CategoricalEncoder, HashingVectorizer, CountVectorizer, TfidfVectorizer, Word2VecEncoder, TextVectorizationPipeline, TextFeaturizer, CountVectorizerTextOnlyEncoderTransformerMixinTextCleaningTransformerMixinHashingVectorizerTextCleaningTransformerMixinWord2VecEncoderTextCleaningTransformerMixinTfidfVectorizerTextCleaningTransformerMixinDefaultToNumpyTextCleaningTransformerMixinBaseEstimatorMixinTransformerMixinPreprocessorMixinTextFeaturizerWithCountVectorizerAndTFIDFVectorizerTextFeaturizingPipelineMixinTextFeaturizerWithWord2VecEncoderAndHashingVectorizerTextFeaturizingPipelineMixinFeatureUnionTransformerMixinBaseEstimatorMixinTransformerMixinPreprocessorMixinPipelineMixinVotingClassifierBaseEstimatorMixinClassifierMixinBaseEstimatorMixinTransformerMixinPreprocessorMixinMultiOutputClassifierBaseEstimatorMixinClassifierMixinBaseEstimatorMixinTransformerMixinPreprocessorMixinMultiOutputClassifierBaseEstimatorMixinClassifierMixinBaseEstimatorMixinTransformerMixinPreprocessorMixinMultiOutputClassifierBaseEstimatorMixinClassifierMixinBaseEstimatorMixinTransformerMixinPreprocessorMixinMultiOutputClassifierBaseEstimatorMixinClassifierMixinBaseEstimatorMixinTransformerMixinPreprocessorMixinMultiOutputClassifierBaseEstimatorMixinClassifierMixinBaseEstimatorMixinTransformerMixinPreprocessorMixinMultiOutputClassifierBaseEstimatorMixinClassifierMixinBaseEstimatorMixinTransformerMixinPreprocessorMixinMultiOutputClassifierBaseEstimatorMixinClassifierMixinBaseEstimatorMixinTransformerMixinPreprocessorMixinMultiOutputClassifierBaseEstimatorMixinClassifierMixinBaseEstimatorMixinTransformerMixinPreprocessorMixinMultiOutputClassifierBaseEstimatorMixinClassifierMixinBaseEstimatorMixinTransformerMixinPreprocessorMixinMultiOutputClassretrieve_feature_namesnverseTransformedTargetRegressorFitTransformerFitRegressorfit_transformfitpredicttransformtransformget_paramsset_paramsget_feature_namesget_supportget_n_featuresget_class_weightset_class_weightget_sample_weightset_sample_weightget_random_stateset_random_stateget_estimatorsget_nameget_base_estimatorset_paramsset_estimatorsget_tagsget__wrapped__get__estimatorsget__classesget__estimator__str__get__final_estimator__str__get__paramsset_paramsget__depthget__estimatorsset_paramsset_depthget__class__get__estimator__str__get__depthget__final_estimator__str__get__depthget__depthset_depthget__estimatorsset_classesset_tagsset_nameset_base_estimatorset_paramsset_depthset_classesset_tagsset_nameset_base_estimatorset_paramsset_depthset_classesset_tagsset_nameset_base_estimatorset_paramsset_depthset_classesset_tagsset_nameset_baseestimatorfitpredicttransformtransformget_paramsset_paramsgetitemiteritemstolisttoarrayshapelentypecastvalues

文章標(biāo)題：python如何填充缺失值
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