Forecasting your freelance earnings is an essential practice for financial planning and maintaining business stability. By leveraging **Python**, you can automate this process and quickly identify potential shortfalls before they become issues. In this chapter, you will build a script that takes hardcoded data for your past monthly earnings and upcoming project payments, calculates projected totals for each month in the next quarter, and displays a summary table. You will also highlight any months where projected earnings fall below a threshold you set, helping you proactively address lean periods.


import unittest
import user_code
import ast
import re   
import importlib
import csv
import unittest
import importlib
import pandas as pd

class TestTask(unittest.TestCase):
    def setUp(self):
        import sys
        if "user_code" in sys.modules:
            del sys.modules["user_code"]

    def test_months_column(self):
        import user_code
        importlib.reload(user_code)
        df = getattr(user_code, 'df', None)
        expected_months = ["April", "May", "June"]
        _dynamic_test(
            self,
            isinstance(df, pd.DataFrame) and list(df["Month"]) == expected_months,
            "Month column contains correct month names.",
            f"Expected months: {expected_months}, got: {list(df['Month']) if isinstance(df, pd.DataFrame) else df}"
        )

    def test_projected_earnings(self):
        import user_code
        importlib.reload(user_code)
        df = getattr(user_code, 'df', None)
        expected_earnings = [2000, 3000, 1000]
        _dynamic_test(
            self,
            isinstance(df, pd.DataFrame) and list(df["Projected Earnings"]) == expected_earnings,
            "Projected Earnings column contains correct sums.",
            f"Expected earnings: {expected_earnings}, got: {list(df['Projected Earnings']) if isinstance(df, pd.DataFrame) else df}"
        )

    def test_below_threshold(self):
        import user_code
        importlib.reload(user_code)
        df = getattr(user_code, 'df', None)
        expected_flags = [True, False, True]
        _dynamic_test(
            self,
            isinstance(df, pd.DataFrame) and list(df["Below Threshold"]) == expected_flags,
            "Below Threshold column correctly flags months.",
            f"Expected flags: {expected_flags}, got: {list(df['Below Threshold']) if isinstance(df, pd.DataFrame) else df}"
        )

    def test_dataframe_shape(self):
        import user_code
        importlib.reload(user_code)
        df = getattr(user_code, 'df', None)
        _dynamic_test(
            self,
            isinstance(df, pd.DataFrame) and df.shape == (3, 3),
            "DataFrame has correct shape (3, 3).",
            f"Expected shape (3, 3), got: {df.shape if isinstance(df, pd.DataFrame) else df}"
        )

def _dynamic_test(test_case, condition, success_message, failure_message):
    if condition:
        test_case._testMethodName = success_message
        test_case.assertTrue(True, success_message)
    else:
        test_case._testMethodName = failure_message
        test_case.fail(failure_message)

def normalize_text(text):
    text = text.lower()
    text = re.sub(r"\\s{2,}", " ", text)
    text = re.sub(r"\\s*([,:?])\\s*", r"\\1 ", text)
    return text.strip()

def change_var(code: str, var_name: str, value: str) -> str:
    tree = ast.parse(code)
    lines = code.splitlines()
    changed = False
    # Collect all assignment nodes to modify
    assign_nodes = [
        (i, node)
        for i, node in enumerate(tree.body)
        if isinstance(node, ast.Assign)
        and any(isinstance(target, ast.Name) and target.id == var_name for target in node.targets)
    ]

    # If nothing to change, return unmodified code
    if not assign_nodes:
        return code

    # Perform replacements for all matching assignments (from last to first to not break line offsets)
    for i, node in reversed(assign_nodes):
        start_line = node.lineno - 1
        line = lines[start_line]
        indent = ' ' * (len(line) - len(line.lstrip()))
        lines[start_line] = f"{indent}{var_name} = {value}"
        next_line = len(lines)
        for next_node in tree.body[i+1:]:
            if hasattr(next_node, 'lineno'):
                next_line = next_node.lineno - 1
                break
        if next_line > start_line + 1:
            lines[start_line+1:next_line] = []
        changed = True

    return '\\n'.join(lines) if changed else code

if __name__ == "__main__":
    unittest.main()


test_main.py

Unlock the power of Python to streamline, automate, and enhance your freelance workflow. This course is designed for freelancers who already know basic Python syntax and want to apply their skills to real-world freelance scenarios, from automating repetitive tasks to analyzing client data and visualizing project progress.

Learn how to use Python to automate repetitive and time-consuming freelance tasks, such as time tracking, invoice generation, and email reminders.

Master the use of Python for analyzing and visualizing your freelance business data, helping you make informed decisions and present results to clients.

Explore advanced Python techniques to optimize your freelance business, including pricing analysis, workload balancing, and client communication automation.

Challenge: Earnings Forecaster

Ratkaisu

Challenge: Earnings Forecaster

Ratkaisu