Oppiskele Using Pandas for Compliance Data | Analyzing and Visualizing Compliance Data

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Compliance professionals often work with large volumes of transaction data, making it essential to use tools that allow for efficient analysis and manipulation. The pandas library is a powerful tool in Python designed for handling tabular data, much like a spreadsheet but with far greater flexibility. At the core of pandas is the DataFrame, a two-dimensional structure that holds data in rows and columns. This structure is especially useful for compliance data because it allows you to quickly filter, sort, and summarize information. For example, you can easily isolate transactions that exceed a certain threshold, identify unusual patterns, or aggregate data by account to spot potential compliance issues.


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import pandas as pd

# Create a list of transactions
transactions = [
    {"id": 1, "amount": 1200, "account_id": "A100"},
    {"id": 2, "amount": 500, "account_id": "A101"},
    {"id": 3, "amount": 2500, "account_id": "A100"},
    {"id": 4, "amount": 700, "account_id": "A102"},
    {"id": 5, "amount": 3000, "account_id": "A101"},
]

# Create a DataFrame from the list
df = pd.DataFrame(transactions)

print(df)

Once your data is loaded into a DataFrame, you can perform powerful operations with just a few lines of code. Suppose you want to focus on transactions above a certain amount, such as those over $1,000. This can be accomplished by filtering the DataFrame using a condition. You simply specify the column and the condition, and pandas returns only the rows that match. This makes it easy to narrow down your analysis to just the transactions that might require further scrutiny under compliance rules.


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# Filter transactions above $1,000
high_value = df[df["amount"] > 1000]

print(high_value)

Beyond filtering, you often need to summarize data by categories, such as grouping all transactions by account_id to calculate the total amount moved in each account. This is especially valuable in compliance, where understanding the flow of funds at the account level can help identify suspicious activity or ensure that accounts are operating within regulatory limits. Using the groupby method, you can quickly aggregate data and produce summaries that inform your compliance decisions.


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# Group transactions by account_id and calculate total amounts per account
totals_by_account = df.groupby("account_id")["amount"].sum()

print(totals_by_account)

1. What is a pandas DataFrame?

2. How can you filter rows in a DataFrame based on a condition?

3. Why is grouping data by account useful in compliance?

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