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:
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() 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.