In this analysis, we focused on the armed status of individuals who were shot, utilizing a dataset represented by the variable ‘data.’ To ensure the accuracy of our analysis, we removed any rows where information on the armed status was not available (NaN values).
Subsequently, we tallied the occurrences of each armed status category using the ‘value_counts()’ function, producing a count for each distinct armed status in the dataset.
The results were visualized through a bar chart, generated using the ‘matplotlib’ library, with the x-axis representing different armed statuses and the y-axis indicating the frequency of occurrences. The chart was customized with a title, labels for both axes, and proper rotation for better readability of the x-axis labels.
The chosen color scheme for the bars was derived from the ‘tab10’ colormap. The final plot provides a clear overview of the distribution of armed statuses among individuals who were shot, offering insights into the prevalence of each category.
In this analysis individuals were fleeing from the police using a dataset labeled as ‘data.’ To ensure the accuracy of our examination, we first removed any rows where information about the fleeing status was not available (NaN values).
Then, we proceeded to count the occurrences of each fleeing status category through the ‘value_counts()’ function, generating a count for each unique fleeing status in the dataset.
The findings were visually presented using a bar chart, crafted with the ‘matplotlib’ library. The x-axis of the chart illustrates different fleeing statuses, while the y-axis indicates the frequency of occurrences. To enhance readability, the chart includes a title, labels for both axes, and proper rotation of the x-axis labels.
The color palette chosen for the bars was derived from the ‘tab10’ colormap. The resulting plot offers a clear representation of the distribution of fleeing statuses among individuals involved in police shootings, shedding light on the prevalence of each category.
Certainly! In this analysis, we delved into whether individuals exhibited signs of mental illness using a dataset denoted as ‘data.’ To ensure the reliability of our examination, we initially removed any rows where information regarding signs of mental illness was not available (NaN values).
Subsequently, we tabulated the occurrences of each mental illness status category through the ‘value_counts()’ function, producing a count for each distinct status in the dataset.
The results were visually communicated via a bar chart, crafted with the ‘matplotlib’ library. The x-axis of the chart illustrates different mental illness statuses, while the y-axis denotes the frequency of occurrences. For clarity, the chart features a title, labels for both axes, and appropriate rotation of the x-axis labels.
The color scheme chosen for the bars was derived from the ‘tab10’ colormap. The resultant plot provides a visual insight into the distribution of mental illness statuses among individuals involved in the context under consideration, offering a glimpse into the prevalence of each category.
The bar plot indicates that the signs of mental illness in the individual shot may also contribute to the occurrence of fatal police shootings in some cases. However, individuals exhibiting no signs of mental illness were more likely to be shot by police compared to the one with the illness.
Fatal police shootings predominantly involved men aged between 25 and 35 who were armed with either a gun or a knife and did not exhibit signs of mental illness. The majority of these individuals were not fleeing from the police, and the prevalent racial demographic was white.
Geographically, the incidents were concentrated primarily in California, with Los Angeles emerging as the top city in this context. Other states, such as Texas and Florida, also experienced these incidents, with a relatively consistent but varying frequency. Notably, California exhibited the highest occurrence, while Texas and Florida, along with several other states, followed the trend, showing an average fluctuation of approximately -180, reaching a minimum at 250.
On a city level, Phoenix secured the second position, while at the state level, Arizona, where Phoenix is situated, held the second position. In the third position at the city level was Houston, while at the state level, Texas, where Houston is located, secured the second position. This analysis sheds light on the patterns and distribution of fatal police shootings, emphasizing demographic and geographic factors.