データクリーニング、データクレンジング
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Data CleaningPython df.columns = ['a','b','c'] # Renames columns pd.isnull() # Checks for null Values, Returns Boolean Array pd.notnull() # Opposite of s.isnull() df.dropna() # Drops all rows that contain null values df.dropna(axis=1) # Drops all columns that contain null values df.dropna(axis=1,thresh=n) # Drops all rows have have less than n non null values df.fillna(x) # Replaces all null values with x s.fillna(s.mean()) # Replaces all null values with the mean (mean can be replaced with almost any function from the statistics section) s.astype(float) # Converts the datatype of the series to float s.replace(1,'one') # Replaces all values equal to 1 with 'one' s.replace([1,3],['one','three']) # Replaces all 1 with 'one' and 3 with 'three' df.rename(columns=lambda x: x + 1) # Mass renaming of columns df.rename(columns={'old_name': 'new_ name'}) # Selective renaming df.set_index('column_one') # Changes the index df.rename(index=lambda x: x + 1) # Mass renaming of index |
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