The Excel file contains multiple sheets, each representing different counties or specific crime categories such as 'Family Violence', 'Hate Crime', and 'Sexual Assault'. For the counties of interest—Jefferson, Orange, and Harris—we need to load their respective sheets to compare crime types.

Let's proceed by loading the data for these three counties and examining their contents to perform the comparison.

The data for Jefferson, Orange, and Harris counties has been loaded. Each dataset contains columns for various crime types, such as Murder, Rape, Robbery, Aggravated Assault, Burglary, Larceny, Auto Theft, Arson, and Human Trafficking.

To compare the crime types across these counties, I'll calculate the total counts for each crime type and rank Jefferson County among the three.

Let's proceed with this analysis.

It appears that the column names are not clearly defined in the datasets, and they are labeled as Unnamed. This suggests that the actual column headers might be located in a different row.

To address this, I will inspect the first few rows of each dataset to identify the correct row that contains the column headers. This will allow us to properly align the data for analysis.

The correct headers for the datasets appear to be on the third row (index 2) of each sheet. This row contains the relevant column names such as Population, Murder, Rape, Robbery, Aggravated Assault, Burglary, Larceny, Auto Theft, Arson, and Human Trafficking.

Let's reload the data using the correct header row and then proceed with the analysis to compare the crime types across Jefferson, Orange, and Harris counties.

The data for Jefferson, Orange, and Harris counties has been successfully loaded with the correct headers. Each dataset contains columns for various crime types, such as Murder, Rape, Robbery, Aggravated Assault, Burglary, Larceny, Auto Theft, Arson, and Human Trafficking.

To compare the crime types across these counties, I'll calculate the total counts for each crime type and rank Jefferson County among the three.

Let's proceed with this analysis.

The column names are still labeled as Unnamed, indicating that the actual column headers might not have been correctly set during the data loading process.

To address this, I'll manually set the column names based on the information observed in the preview, which includes crime types like Murder, Rape, Robbery, Aggravated Assault, Burglary, Larceny, Auto Theft, Arson, and Human Trafficking. Then, I'll proceed with the analysis to compare the crime types across Jefferson, Orange, and Harris counties.

The data for Jefferson, Orange, and Harris counties has been successfully reloaded with the correct headers. Each dataset now contains columns for various crime types, such as Murder, Rape, Robbery, Aggravated Assault, Burglary, Larceny, Auto Theft, Arson, and Human Trafficking.

Now, I will proceed to calculate the total counts for each crime type across these counties and verify the calculations to ensure they align with the expected results.

The bar chart above illustrates the comparison of crime rates across Jefferson, Orange, and Harris counties for various crime types. Each bar represents the total number of offenses for a specific crime type in each county.

This visualization helps in understanding the distribution and intensity of different crime types across these counties.

If you have any more questions or need further analysis, feel free to ask!

The dataset contains a column named MonthName, which lists the months for which the data is available. This indicates that the data might be aggregated monthly.

To analyze the trend of a specific crime type, such as Aggravated Assault, over time, we can extract the monthly data and plot it to visualize the trend.

Let's proceed with this analysis for Jefferson County.

The error occurred because the code attempted to assign a DataFrame with multiple columns to a single column named 'Safest County Rank'. This happens when trying to rank each crime type across counties and store the results in a single column.

To resolve this, we need to ensure that the ranking results are stored in a DataFrame with the appropriate structure. We should create a separate DataFrame to store the ranks for each crime type and then combine it with the original data for clarity.

Let's adjust the approach to correctly rank the counties for each crime type and display the results.

The table above ranks the counties from safest (1) to least safe (3) for each crime type:

This ranking provides a clear comparison of safety across the counties for each crime type. If you have any more questions or need further analysis, feel free to ask!

The total crime counts for each county are as follows:

These figures provide an overview of the crime volume in each county. If you have any further questions or need additional analysis, feel free to ask!

The data for Port Arthur within Jefferson County has been extracted. However, it seems that the dataset might not contain detailed crime counts for each year, as the values are NaN (Not a Number) for the crime categories.

To analyze the crime trend over the past few years, we would ideally need a dataset that includes crime counts by year. If you have such a dataset or additional data that includes yearly crime statistics for Port Arthur, please provide it so we can proceed with the trend analysis.

The calculated crime rate for Port Arthur is 0.0 per 100,000 population, which seems incorrect due to missing data in the dataset.

To accurately compare Port Arthur's crime rate to the state average, we need complete data for both Port Arthur and the state. If you have additional data or corrections, please provide them so we can proceed with a more accurate analysis.