Programming For Data Engineering course final project

https://pypi.org/project/cleaner-panda/

pip install cleaner-panda

• strategy enum {MEAN, MEDIAN, CONSTANT, REMOVE_ROW, REMOVE_COLUMN, FORWARD_BACKWARD}
• cont_int = 0, const_str =”none”, const_date=01.01.2024…
• replace_missing_values(dataFrame, strategy=”strategy.MEAN”, column=0)
• replace_mean(dataframe, column)
• replace_median(dataframe, column)
• replace_constant(dataframe, column, constant)
• replace_remove_row(dataframe, column)
• replace_remove_column(dataframe, column)
• replace_forward_backward(dataframe, column)

• identify_outliers_iqr(data, threshold=1.5)
• handle_outliers_iqr(data, threshold=1.5, replacement=None) //replacement: Value to replace outliers with (e.g., median, mean) or None to remove outliers

• standardize_data(dataframe)
• normalize_data(dataframe)
• robust_scale_data(dataframe)
• normalize_vectors(dataframe)
• log_transform_data(dataframe)

• remove_common_words(dataframe, column)
• convert_to_lowercase(dataframe, column) // Stopwords are words like "the", "is", "and", "in", etc., that occur frequently in a language
• remove_punctuation(dataframe, column)
• lemmatization(dataframe, column)
• expand_contractions(dataframe, column) // (e.g., "can't" to "cannot", "won't" to "will not")
• remove_special_characters(dataframe, column, remove=[‘.’])
• remove_numerical(dataframe, column)
• filter_words(dataframe, column, remove=[“fuck”])

• label_encoding(dataframe, column)
• one_hot_encoding(dataframe, column)
• ordinal_encoding(dataframe, column)

• convert_date_to_strings(dataframe column)
• extract_components(dataframe, column)
• reformat_date(dataframe, column)
• calculate_datetime_differences()
• convert_datetime_to_different_timezones
• shift_time()
• handle_irregular_time_intervals()