Explaining Data Wrangling in Python

Python
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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 the 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 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 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 astype() method to convert data types of columns to their appropriate data types. Here is an example:

Exploring and pivoting the data:

  • Explore the data using various pandas methods such as describe() to get summary statistics, groupby() to group rows based on a column, and pivot_table() or pivot() to reshape the data.
  • Here is an example code that explores and pivots the data:

Sorting and renaming columns:

Sorting the data by Marks and Age and renaming the columns to meaningful names.

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