Moreover, you can leverage advanced techniques like imputation to fill in missing values intelligently or regular expressions to identify and correct inconsistent formatting. For instance, you can create reusable functions or pipelines to handle missing values, drop duplicates, and replace values across multiple datasets. In addition to the panda's functions mentioned earlier, automation techniques can be applied to streamline data-cleaning workflows. There are many other functions you can use depending on your specific data-cleaning needs, such as fillna() to fill missing values with a specific value or method, astype() to convert data types of columns, clip() to trim outliers and more.ĭata cleaning plays a crucial role in preparing data for analysis, and automating the process can save time and ensure data quality. These are just a few examples of pandas functions you can use to clean data. Data = data.replace('old_value', 'new_value')
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