In one of my recent projects, I utilized Python extensively for data cleaning and transformation. I primarily used the Pandas library due to its powerful capabilities in handling and manipulating tabular data. For data cleaning, I addressed missing values by either filling them with appropriate values or removing them if necessary. I also ensured data consistency by removing duplicates. Regarding data transformation, I performed data type conversions using functions like astype to ensure that data types were appropriate for analysis, especially for date and time data. Additionally, I used Pandas for merging and joining datasets to create comprehensive data frames that facilitated deeper analysis. This approach helped streamline the data preparation process and ensured the accuracy and reliability of the data for further analysis.