You have a dataset with inconsistent date formats (e.g., 'DD/MM/YYYY', 'MM-DD-YYYY', 'YYYY-MM-DD'). What is the first step you should take to standardize the date column in your dataset?

The first and most crucial step in handling inconsistent date formats is to convert all date entries to a single consistent format . This ensures that all data can be accurately analyzed and interpreted. Date formats need to be standardized so that further analysis, such as time series analysis or comparison across dates, can proceed without issues. You can use functions in Python (such as pd.to_datetime in pandas) or Excel to convert dates into a common format. Once standardized, the data is ready for analysis and visualization. Why Other Options Are Wrong : A) Incorrect : Applying a date format in the visualization tool only affects how the dates are displayed, not how they are stored or used for analysis. Standardizing the date format in the data itself is the proper first step. B) Incorrect : Using a regular expression might help identify the different formats, but it doesn’t solve the problem of standardizing them. You still need to convert them into a consistent format. D) Incorrect : Dropping rows with inconsistent date formats would result in data loss. Instead, standardizing the format preserves the data for analysis. E) Incorrect : Analyzing the distribution of dates doesn’t help solve the issue of inconsistent formats. You need to convert the dates first to ensure accurate analysis.