General Analysis of Philadelphia Bail Fund Data

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  Amount of Arrests per Zip Code

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  Amount of Arrests per Zip Code (TOP 50)

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  Number of Bails Paid Based on Cost

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  Bail Amount vs. Bail Paid

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  Frequency of Bail Amounts

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  Status of Arrest Cases

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  Bail Amount Over Time

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  Offense Date vs. Amount of Offenses (MAY-JUNE 2020)

Inspiration

Considering the recent events regarding Black Lives Matter protests and the wrongful arrests of many peaceful protesters, we were inspired by the Philadelphia Bail Fund's efforts and were excited to work with them and their cause. We know that many people need to pay bail but cannot afford it, so we hope that our data can help the Philadelphia Bail Fund's project.

What it does

Our code sorts and formats many of the different data sets and organizes them into helpful graphs. Some of these include Number of Arrests compared to Zip Code and a comparison of the Case Statuses.

How I built it

Using Python 3.9, we imported four different graph building libraries, including "datascience", "numpy", "seaborn", and "matplotlib." Then, using the given data set from the Philadelphia Bail Fund, and reorganized and reformatted the data to be usable in Python. Then, we performed a preliminary analysis of the data using the functions "dtypes" and "describes." Then, we began to make graphs – including bar graphs, scatter plots, histograms, and pie charts – to further analyze the data. All of these were created using the "plot" function and it's various parameters. Then, we focused in on the data from May-June 2020 to analyze the data with respect to the death of George Floyd.

Challenges I ran into

One of the biggest challenges was importing the data into Python. Another big issue we ran into was reformatting the dates into a workable format for Python and the "plot" function to use. We also had less experience with Python, especially when using such large data sets, yet it provided for a nice challenge.

Accomplishments that I'm proud of

We are proud that we were able to succeed in our hands on use of large data sets, and our proper graph creation and analysis of said data sets. We were able to achieve what we set out to do in the first place.

What I learned

We learned a lot from this experience, especially the use of the "pandas" library. "Pandas" was a brand new experience, even for our team members that had experienced Python data analysis before, so it was interesting to see how it behaves.

What's next for General Analysis of Philadelphia Bail Fund Data

While we could only able to do so much with our allotted time, there is far more that we would be able to do with the data set we were given. This data set has a lot of interesting options for the future, but specifically we had thought about how we could compare the 2020 arrest data to 2019 arrest data so that we could try to compare the data with respect to the COVID-19 outbreak.

Built With

• python