To analyze the distribution of CA scores, we'll first need to clean the data by removing the introductory text and handling any non-score values. After cleaning, we can calculate statistical measures like mean, median, and create visualizations such as histograms to understand the distribution.

Let's proceed with cleaning the data.

It looks like the actual data starts further down. We'll need to adjust our approach to locate the start of the scores correctly and then proceed with analyzing the distribution of CA scores. Let's refine our method to identify the starting point of the data accurately.

It seems there was an issue identifying the starting row for CA scores using the previous method. We'll need to manually inspect the data or use a different approach to find the correct starting row. Let's try a different method to accurately locate the start of the CA scores.

The attempt to programmatically identify the starting row for CA scores encountered difficulties. To overcome this, a visual inspection method was proposed using a plot, but it faced execution issues due to mismatched data lengths. Adjustments were made to align the data, and a visualization was successfully created to assist in identifying where the actual CA scores might begin. This approach aims to visually pinpoint the start of the CA scores for further analysis.

These statistics provide insights into the central tendency of the CA scores, indicating that on average, students scored around 26 to 27 out of a possible score range. This suggests a relatively high performance level among the students.