The dataset contains information about the crusher detail analysis from May 2024 to July 2024 at the Doratiyawa Plant. The columns include production and working hours for each month.

To identify trends, we need to clean and organize the data, then visualize it. Let's proceed with cleaning the data and plotting the trends for production and working hours.

It seems that the data cleaning step resulted in an empty DataFrame. This might be due to the presence of NaN values in the rows we need.

Let's adjust our approach to clean the data more carefully by retaining the necessary rows and columns.

The dataset contains multiple categories such as Production, Working hrs, Cube per hr, Productivity, and Fuel consumption (l/Hrs) for the months of May, June, July, and August.

To identify trends, let's focus on the 'Production' and 'Working hrs' categories and visualize their trends over the months.

The error occurred because the DataFrame has 6 columns, but we attempted to rename them with only 5 new column names. This mismatch caused the ValueError.

To resolve this, we need to correctly identify and rename all columns in the DataFrame. Let's inspect the DataFrame again to understand its structure and then rename the columns appropriately.

The DataFrame has 6 columns, but only 5 of them are relevant for our analysis. The columns are:

To proceed, we will rename the relevant columns and drop the unnecessary ones. Let's rename the columns to Category, May, June, July, and August.