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Sanskar
Keen Learner and Exp... • 14h
Day 13 of learning data science as a beginner. Topic: data cleaning and preprocessing In most of the real world applications we rarely get almost perfect data most of the time we get a raw data dump which needs to be cleaned and preprocessed before being made use of (funfact: data scientist put 80% of their time in cleaning and preprocessing the data) Pandas not only allows us to analyse the data but also helps us to clean and process the data some of the most commonly used pandas data preprocessing functions are .isnull: checks whether there are any missing values in the data set or not .dropna: deletes all the rows containing any missing value .fillna: fills the missing value using Nan .ffill: fills the last know value from top in place of missing value .bfill: fills the last know value from bottom in place of missing value .drop_duplicates: drop the rows with duplicate values Then there are some functions for cleaning the data (particularly strings) .str.lower: converts all the character into lowercase .str.contains: checks wheter the string contains something specific .str.split: split the string based on either a white space or a special character .astype: changes the data type .apply: applies a function or method directly to a row or column .map: applies a transformation to each value .replace: replaces something with another And also here is my code and its result
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