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TL;DR:
TL;DR: Pandas is a powerful library for data science, but it is important to use it responsibly. In this tutorial, we use the Stanford Open Policing Project dataset to analyze factors such as driver gender, age, and time of day on police stops. We also explore how to handle missing data, use descriptive statistics and data visualizations. By the end, you will know how to use Pandas in a responsible and effective manner.
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Summary:
Data analysis is an essential tool for any data scientist or business analyst. The Pandas library offers a wide range of capabilities for data science, from cleaning and visualization to exploration and analysis. However, mastering the library and effectively using it to its fullest potential can be tricky. This tutorial will help guide you through common data science tasks, showing you both how to use Pandas and how to avoid common mistakes. In this project, we are examining the data obtained from the number of traffic stops made by US police in Stanford region of the United States. We wish to understand the association between driver age and gender, as well as the time of day on police stops. After reading the data, we need to check our data. Pandas have a lot of functions that allows us to discover our data effectively and quickly. We need to know the data types, missing values, descriptive statistics and the shape of the data. We then used .describe() to generate descriptive statistics and .shape to see the DataFrame shape. We need to identify and remove the column that only contains missing values, which in this case is the county_name column. After that, we need to check the portion of men and women stopped for a speed violation. We (32%)Concluding this data analysis, it can be seen that men are more likely to speed than women, with men being more likely to receive a speed violation than women. Men also accounted for a higher percentage of all traffic stops than women, with the majority of these stops being for speeding. This data analysis project provides an insight into the interactions between law enforcement and the public in the United States. It is important to note, however, that this analysis is only based on the data provided by Stanford Open Policing Project and may only be applicable to this region. In conclusion, this data analysis project provides an insight into the interactions between law enforcement and the public in the United States. The data analysis shows that men are more likely to speed than women, with men being more likely to receive a speed violation than women. Men also accounted for a higher percentage of all traffic stops than women, with the majority of these stops being for speeding. This data analysis project provides a look at the data from the Stanford Open Policing Project and can be used to understand the interactions between law enforcement and the public in this region.