Final answer:
In Python, data wrangling can be accomplished using functions such as read_csv() for data import, drop() for removal of rows or columns, groupby() for data aggregation, fillna() to handle missing values, apply() for applying functions, and merge() to join datasets.
Step-by-step explanation:
When it comes to data wrangling with Python, several functions and methods come into play. Here are some commonly used ones: read_csv() - Used for importing data from a CSV file into a DataFrame. It allows for preliminary data inspection and manipulation. drop() - Utilized for removing unwanted rows or columns from a DataFrame. groupby() - Helps aggregate data based on some criteria, useful for summarizing data.
fillna() - Fills in missing values with a specified input. apply() - Allows you to apply a function to an axis of the DataFrame, making it highly versatile for data transformation. merge() - This method joins two dataframes similar to SQL joins and is vital for combining datasets. The utilization of these functions/methods simplifies data organization. Frequency, relative frequency, and cumulative relative frequency are statistical measures that can be calculated using these functions to understand how often values appear within a dataset. This becomes especially useful when making decisions, such as determining the median house price in a real estate market analysis.