Вивчайте Introduction to Data Cleaning in Banking

Banking data is a critical asset for any financial institution, but it often arrives with a range of quality issues that can affect your analysis and reporting. The most common data quality problems you will encounter include missing values, incorrect data types, and duplicate records. Missing values may occur when transaction details are not recorded or are lost during data transfer, which can lead to incomplete analyses or inaccurate reporting. Incorrect data types, such as storing transaction amounts as strings instead of numbers, can cause calculation errors and hinder aggregation. Duplicate records, often resulting from system errors or repeated uploads, can inflate totals and distort metrics. These issues can undermine your ability to make sound financial decisions and comply with regulatory requirements, so it is essential to address them systematically.


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import pandas as pd

# Sample DataFrame with missing values
data = {
    "account_id": [101, 102, 103, 104],
    "transaction_amount": [250.0, None, 400.0, None],
    "transaction_type": ["deposit", "withdrawal", None, "deposit"]
}
df = pd.DataFrame(data)

# Detect missing values
print(df.isnull())

# Fill missing values with defaults
df["transaction_amount"].fillna(0, inplace=True)
df["transaction_type"].fillna("unknown", inplace=True)

print(df)

To ensure the integrity of your banking data, it is also important to remove duplicate transactions and maintain consistency across records. Duplicate transactions can occur when the same transaction is recorded more than once, leading to inaccurate account balances and misleading analyses. By identifying and dropping these duplicates, you help preserve the accuracy of your financial records. It is equally important to ensure that data types are consistent—transaction amounts, for instance, should always be stored as numeric values to allow for reliable calculations and aggregation. Using pandas, you can efficiently drop duplicate rows and convert columns to the correct data types as part of your data cleaning workflow.


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# Remove duplicate transactions
df = df.drop_duplicates()

# Convert transaction_amount to float type
df["transaction_amount"] = df["transaction_amount"].astype(float)

print(df)

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Suggested prompts:

Can you explain how to handle other data quality issues in banking data?

What are some best practices for maintaining data consistency in financial datasets?

Can you show how to identify and handle outliers in transaction data?

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              123456789101112131415161718
            
import pandas as pd

# Sample DataFrame with missing values
data = {
    "account_id": [101, 102, 103, 104],
    "transaction_amount": [250.0, None, 400.0, None],
    "transaction_type": ["deposit", "withdrawal", None, "deposit"]
}
df = pd.DataFrame(data)

# Detect missing values
print(df.isnull())

# Fill missing values with defaults
df["transaction_amount"].fillna(0, inplace=True)
df["transaction_type"].fillna("unknown", inplace=True)

print(df)


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# Remove duplicate transactions
df = df.drop_duplicates()

# Convert transaction_amount to float type
df["transaction_amount"] = df["transaction_amount"].astype(float)

print(df)

Все було зрозуміло?

Дякуємо за ваш відгук!

Секція 1. Розділ 6

Introduction to Data Cleaning in Banking

1. What pandas method is used to fill missing values in a DataFrame?

2. Why is it important to remove duplicate transactions in banking data?

3. How can you check for missing values in a pandas DataFrame?

Introduction to Data Cleaning in Banking

1. What pandas method is used to fill missing values in a DataFrame?

2. Why is it important to remove duplicate transactions in banking data?

3. How can you check for missing values in a pandas DataFrame?