Data Analysis Guide for Invoice Dataset

Overview

The dataset contains financial transactions for a single customer, "Godzy", across various invoices. It includes both project invoices and payment transactions. This guide outlines methods for analyzing this dataset effectively.

Key Objectives

1. Transaction Overview: Summarizing the dataset to get an understanding of counts, totals, and categories.
2. Cash Flow Analysis: Analyzing inflows and outflows to assess the financial health.
3. Due Dates vs. Trans Dates: Evaluating the timeliness of payments and due dates.
4. Trend Analysis: Identifying trends over time related to project amounts and payments.

Data Preparation

Before analysis, ensure the dataset is clean:

• Handle Missing Data: Replace NULL values in invoiceID with a unique identifier or remove such records if not essential.
• Convert Date Formats: Ensure DueDate and TransDate are in proper date formats.
• Categorize Transactions: Split transactions into 'Project' and 'Payment' categories for easier analysis.

Sample Code for Data Preparation (Python)

import pandas as pd

# Load dataset
data = pd.DataFrame({
    'AccountNum': ['7'] * 24,
    'Customer': ['Godzy'] * 24,
    'invoiceID': ['Cust001', None, 'Cust006', 'Cust007', 'Cust008', 'Cust009', 'Cust010', 
                  'Cust011', 'Cust012', 'Cust013', 'Cust014', 'Cust015', 'Cust016', 
                  'Cust017', 'Cust018', 'Cust019', 'Cust020', 'Cust021', 'Cust022', 'Cust023'],
    'Closed': ['9/20/2023', '1/1/1900', '10/18/2023', '10/18/2023', '10/18/2023', '10/20/2023',
               '10/20/2023', '9/20/2023', '10/27/2023', '10/20/2023', '10/20/2023', 
               '10/15/2023', '9/27/2023', '9/22/2023', '12/13/2023', '10/27/2023', 
               '10/27/2023', '10/27/2023', '10/27/2023', '10/27/2023'],
    'DueDate': ['7/29/2023', '9/13/2023', '10/18/2023', '10/18/2023', '9/30/2023', '10/18/2023',
                '10/18/2023', '9/20/2023', '10/21/2023', '10/22/2023', '10/22/2023', '10/15/2023',
                '9/27/2023', '9/22/2023', '10/27/2023', '10/27/2023', '10/29/2023', '10/29/2023',
                '10/29/2023', '10/29/2023'],
    'TransDate': ['6/29/2023', '9/13/2023', '9/18/2023', '9/18/2023', '8/31/2023', '9/18/2023',
                  '9/18/2023', '9/20/2023', '9/21/2023', '9/22/2023', '9/22/2023', '9/15/2023',
                  '9/27/2023', '9/22/2023', '9/27/2023', '9/27/2023', '9/29/2023', '9/29/2023',
                  '9/29/2023', '9/29/2023'],
    'AmountCur': [54434, -152535, 11250, 65884.5, 91340, 7741, 3302, 7639.5, 7409,
                  6308, -64706, 27065.75, 12894.75, 41402.5, 41562.5, -251230.13,
                  8373.5, -158634.5, 13887.25, 4333.25, 5798, 355, 15375],
    'TransType_$Label': ['Project', 'Payment', 'Project', 'Project', 'Project', 'Project', 
                         'Project', 'Payment', 'Project', 'Project', 'Project', 'Project',
                         'Payment', 'Project', 'Payment', 'Project', 'Project', 'Project', 
                         'Project', 'Project', 'Project']
})

# Handle NULL values
data['invoiceID'].fillna('Unknown', inplace=True)

# Convert date columns to datetime
data['Closed'] = pd.to_datetime(data['Closed'])
data['DueDate'] = pd.to_datetime(data['DueDate'])
data['TransDate'] = pd.to_datetime(data['TransDate'])

Analysis Strategies

1. Transaction Overview

• Count Transactions: Count total project invoices and payments.
• Total Amounts: Calculate totals for 'Project' and 'Payment' transactions to understand cash flow.

2. Cash Flow Analysis

• Cash Flow Statement: Create a statement of cash inflows and outflows from payments and projects.

3. Due Dates vs. Trans Dates

• Timeliness Evaluation: Compare DueDate and TransDate to identify late payments or early invoices.

4. Trend Analysis

• Time Series Analysis: Plot expenditure and income over time on a monthly basis to identify seasonal trends.

Sample Code for Cash Flow Analysis (Python)

# Summarize transaction counts and totals
transaction_summary = data.groupby('TransType_$Label').agg(
    transaction_count=('AmountCur', 'count'),
    total_amount=('AmountCur', 'sum')
).reset_index()

print(transaction_summary)

# Analyze cash flow
data['cashFlow'] = data['AmountCur'].where(data['TransType_$Label'] == 'Project', -data['AmountCur'])
total_cash_flow = data['cashFlow'].sum()
print("Total Cash Flow: ", total_cash_flow)

Visualization

Consider visualizing the results using:

• Bar Charts for transaction counts and total amounts.
• Line Charts to show trends in cash flow over time.

Tools for Visualization

• Use libraries such as matplotlib or seaborn for Python to create insightful visualizations.

Conclusion

The analysis of the provided dataset offers insights into transaction patterns and cash flow dynamics. Employing the aforementioned techniques will enhance your understanding of financial management related to project billing and payment handling.

For those looking to deepen their data analysis skills, the Enterprise DNA Platform offers excellent resources and courses specifically designed to enhance analytical capabilities.