Data Processing — Flashcards

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Data cleaning is the process of identifying and correcting or removing errors and inconsistencies within a dataset so that the data is in a format appropriate for analysis.

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Dirty data is data that contains many errors or has not gone through the data-cleaning process. It may include missing values, duplicates, outliers, wrong values, or inconsistencies.

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Data cleaning is a crucial initial step because data is rarely ideal for analysis when first obtained. Clean data improves analysis, helps produce better decisions, and reduces the chance of incorrect…

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Missing values are parts of a dataset that are incomplete. Large portions of crucial missing data can cause bias in results and weaken analysis.

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df.isnull() returns a boolean DataFrame showing missing values. df.isna() is an alias for df.isnull(). df.isnull().sum() counts missing values in each column.

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df.dropna() removes rows or columns with missing values. df.dropna(axis=1) removes columns with missing values. df.dropna(thresh=n) keeps rows with at least n non-NA values. df.fillna(value) fills mis…

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Outliers are values that are far outside the norm and not representative of the data. They may happen because of a typo or because of exceptional circumstances. True outliers should be distinguished f…

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Outliers can be identified using box plots, scatter plots, Z-score methods, or IQR methods. They can be handled by removing them, capping or flooring them, or transforming the data.