Explain the Data Wrangling in Python

Data Wrangling

Pandas is a Python library that is commonly used for data wrangling, which is the process of cleaning, organizing, and transforming data. Pandas is an open-source library specifically designed for data analysis and data science. Data wrangling in Python involves a variety of operations, such as sorting, filtering, grouping, and more, that are used to manipulate and prepare data for further analysis. These operations can be useful for organizing and standardizing data so that it can be more easily analyzed and understood.

Here are some steps you can follow to perform data wrangling on the provided dataset using pandas:

Loading dataset

Import the pandas library and the numpy library (if you want to use np.nan values in your dataset).Load the data into a DataFrame using the pd.DataFrame() function. Inspect the data using the DataFrame.info() method to check for data types, missing values, and other issues.

Handling missing values

To handle missing values in the Marks column by replacing them with the average of the column, you can use the pd.DataFrame.fillna() method along with the df['Marks'].mean() function to calculate the mean of the column. Here’s how you can do it:

Converting data types

You can also use the DataFrame.astype() method to convert data types.

Exploring and pivoting the data

Explore the data using various pandas methods such as DataFrame.describe() to get summary statistics, DataFrame.groupby() to group rows based on a column, and DataFrame.pivot_table() or DataFrame.pivot() to reshape the data.

Sorting and renaming columns

sorting the data by Marks and Age and rename the columns.

You now have a dataset that has been cleaned and can be further preprocessed. This is just a simple example, but in real-world datasets, there is usually more involved in the data wrangling process.