Lære Basic Data Aggregation and Summarization | Financial Data Analysis for Bankers

Understanding how to aggregate and summarize financial data is essential for bankers, as it enables you to extract meaningful insights from large sets of transactions. Summarizing data can help you answer important questions such as: What are the total deposits and withdrawals for a given period? What is the average account balance? By quickly calculating monthly totals or average balances, you can monitor trends, spot anomalies, and report key metrics to clients or regulators. These skills are crucial when preparing financial statements, analyzing customer behavior, or making informed lending decisions.


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import pandas as pd

# Sample transaction data
data = {
    "date": ["2024-06-01", "2024-06-02", "2024-06-03", "2024-06-04"],
    "type": ["deposit", "withdrawal", "deposit", "withdrawal"],
    "amount": [1000, 200, 500, 300]
}
df = pd.DataFrame(data)

# Calculate total deposits
total_deposits = df[df["type"] == "deposit"]["amount"].sum()
print("Total deposits:", total_deposits)

# Calculate total withdrawals
total_withdrawals = df[df["type"] == "withdrawal"]["amount"].sum()
print("Total withdrawals:", total_withdrawals)

To efficiently summarize financial data, you often need to analyze patterns across different categories or time periods. The groupby operation in pandas is a powerful tool that lets you split your data into groups based on the values in one or more columns, and then apply aggregation functions like sum or mean. For instance, you can group transactions by month to see how activity changes over time, or by transaction type to compare deposits and withdrawals. This approach makes it easy to generate reports and spot trends that are important for decision-making in banking.


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# Group transactions by type and calculate the sum for each type
grouped = df.groupby("type")["amount"].sum()
print(grouped)

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Seksjon 1. Kapittel 2

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Suggested prompts:

Can you explain how the groupby operation works in more detail?

How can I group transactions by month instead of by type?

What other aggregation functions can I use with groupby in pandas?

Sveip for å vise menyen


              1234567891011121314151617
            
import pandas as pd

# Sample transaction data
data = {
    "date": ["2024-06-01", "2024-06-02", "2024-06-03", "2024-06-04"],
    "type": ["deposit", "withdrawal", "deposit", "withdrawal"],
    "amount": [1000, 200, 500, 300]
}
df = pd.DataFrame(data)

# Calculate total deposits
total_deposits = df[df["type"] == "deposit"]["amount"].sum()
print("Total deposits:", total_deposits)

# Calculate total withdrawals
total_withdrawals = df[df["type"] == "withdrawal"]["amount"].sum()
print("Total withdrawals:", total_withdrawals)


              123
            
# Group transactions by type and calculate the sum for each type
grouped = df.groupby("type")["amount"].sum()
print(grouped)

Alt var klart?

Takk for tilbakemeldingene dine!

Seksjon 1. Kapittel 2

Basic Data Aggregation and Summarization

1. What pandas method is commonly used to group data by a specific column?

2. How can you calculate the average withdrawal amount from a DataFrame?

3. Why might a banker want to group transactions by month?

Basic Data Aggregation and Summarization

1. What pandas method is commonly used to group data by a specific column?

2. How can you calculate the average withdrawal amount from a DataFrame?

3. Why might a banker want to group transactions by month?