Data wrangling, also known as data munging, is the process of cleaning, transforming, and organizing data in a way that makes it more suitable for analysis. It is a crucial step in the data science process as real-world data is often messy and inconsistent.

The general steps to do Data Wrnagling in python are as follows:

Steps to perform data wrangling on the Titanic dataset in Python using pandas library: The steps of data wrangling in Python typically include:

1. Importing necessary libraries such as Pandas, NumPy, and Matplotlib
2. Loading the data into a Pandas DataFrame
3. Assessing the data for missing values, outliers, and inconsistencies
4. Cleaning the data by filling in missing values, removing outliers, and correcting errors
5. Organizing the data by creating new columns, renaming columns, sorting, and filtering the data
6. Storing the cleaned data in a format that can be used for future analysis, such as a CSV or Excel file
7. Exploring the data by creating visualizations and using descriptive statistics
8. Creating a pivot table to summarize the data
9. Checking for and handling duplicate rows
10. Encoding categorical variables
11. Removing unnecessary columns or rows
12. Merging or joining multiple datasets
13. Handling missing or null values
14. Reshaping the data
15. Formatting the data
16. Normalizing or scaling the data
17. Creating new features from existing data
18. Validating data integrity
19. Saving the final data for future use
20. Documenting the data wrangling process for reproducibility

Please note that the steps may vary depending on the data, the requirements, and the goals of the analysis. It’s worth noting that these are general steps and the specific steps you take will depend on the dataset you are working with and the analysis you plan to perform.

All codes can be found here