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- ✕この概要は、複数のオンライン ソースに基づいて AI を使用して生成されました。元のソース情報を表示するには、[詳細情報] リンクを使用します。
Cleaning data is a critical step in preparing datasets for machine learning (ML). It ensures the data is accurate, consistent, and ready for analysis. Below are the essential steps to clean data systematically:
1. Understand the Dataset
Inspect the Data: Use .info() and .describe() to understand data types, missing values, and distributions.
Check for Duplicates: Identify duplicate rows using df.duplicated() and remove them with df.drop_duplicates().
import pandas as pddf = pd.read_csv('data.csv')print(df.info())df = df.drop_duplicates()コピーしました。✕コピー2. Handle Missing Values
Identify Missing Data: Use df.isnull().sum() to find columns with missing values.
Impute or Remove: Numerical: Replace with mean/median using SimpleImputer. Categorical: Fill with mode or "Unknown". Drop rows/columns if missing values are excessive.
from sklearn.impute import SimpleImputerimputer = SimpleImputer(strategy='mean')df['Age'] = imputer.fit_transform(df[['Age']])コピーしました。✕コピー3. Fix Structural Errors
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