I make Data wrangling using  python dealing with the below functionalities:

1. Data exploration: In this process, the data is studied, analyzed and understood by visualizing representations of data.
2. Dealing with missing values: Most of the datasets having a vast amount of data contain missing values of NaN, they are needed to be taken care of by replacing them with mean, mode, the most frequent value of the column or simply by dropping the row having a NaN value.
3. Reshaping data: In this process, data is manipulated according to the requirements, where new data can be added or pre-existing data can be modified.
4. Filtering data: Some times datasets are comprised of unwanted rows or columns which are required to be removed or filtered
5. Other: After dealing with the raw dataset with the above functionalities we get an efficient dataset as per our requirements and then it can be used for a required purpose like data analyzing, machine learning, data visualization, model training etc.