python打印是这样的，我希望resource_id能够对齐输出：

3种格式化方法

%格式化

>>> pi=3.141592653589793
>>> e=2.718281828459045
>>> print("pi=%.2f e=%.3f" % (pi, e) )
pi=3.14 e=2.718

“”.format()格式化

>>> pi=3.141592653589793
>>> e=2.718281828459045
>>> print("pi={:.2f} e={:.3f}".format(pi, e))
pi=3.14 e=2.718

f-string格式化

>>> pi=3.141592653589793
>>> e=2.718281828459045
>>> print(f'pi={pi:.2f} e={e:.3f}')
pi=3.14 e=2.718

占位符的使用和对齐

在占位符中指定对齐方式、宽度和精度等格式。

左对齐、右对齐和居中对齐

• 左对齐：:
• 右对齐：:>width
• 居中对齐：:^width

name = "Alice"
formatted_string = "{:<10} is left-aligned".format(name)
print(formatted_string)

formatted_string = "{:>10} is right-aligned".format(name)
print(formatted_string)

formatted_string = "{:^10} is centered".format(name)
print(formatted_string)

指定宽度和精度

number = 123.456789
formatted_string = "{:.2f}".format(number)  # 保留两位小数
print(formatted_string)

formatted_string = "{:10.2f}".format(number)  # 总宽度为10，保留两位小数
print(formatted_string)

Python 的字符串格式化方法

要使 resource_id 对齐，可以使用 Python 的字符串格式化方法。以下是一个示例代码，展示如何使用字符串格式化来对齐输出：

data = [
    ("FieldA_2_0", 0.0, "5237aa80185eb4bc84af9d5b57c64562", "2024-06"),
    ("FieldA_3_0", 6913682.0, "c8f7cac5c6e85efd3690650dfcf598e6", "2024-06"),
    ("FieldA_4_0", 0.0, "cb5c0e991ba1bdc8ce5111a3cd50a878", "2024-06"),
    ("FieldA_5_0", 0.0, "gap-resource", "2024-06"),
    ("FieldB_0_1", 0.0, "4354", "2024-07"),
    ("FieldB_1_1", 27900000.0, "4769", "2024-07"),
    ("FieldA_0_1", 0.0, "0d9bbcf7c0f1e598d31031dca796337b", "2024-07"),
    ("FieldA_1_1", 0.0, "1b5f8e7771d12e3424542624805d8816", "2024-07"),
    ("FieldA_2_1", 0.0, "5237aa80185eb4bc84af9d5b57c64562", "2024-07"),
    ("FieldA_3_1", 4100000.0, "c8f7cac5c6e85efd3690650dfcf598e6", "2024-07"),
    ("FieldA_4_1", 0.0, "cb5c0e991ba1bdc8ce5111a3cd50a878", "2024-07"),
]

# Define the format string with fixed widths for alignment
format_str = "{:<30} = {:<10} resource_id={:<36}, month={}"

# Print each line with the specified format
for item in data:
    print(format_str.format(item[0], item[1], item[2], item[3]))

使用 pandas 库来处理和格式化数据

将需要格式化的数据存储到一个列表list中（若涉及多个字段，将多个字段打包为元组再append到list中即可），然后将list转为pandas的DataFrame即可。

solver_params = pywraplp.MPSolverParameters()
solver_params.SetDoubleParam(pywraplp.MPSolverParameters.RELATIVE_MIP_GAP, 1e-8)
solver_params.SetDoubleParam(pywraplp.MPSolverParameters.PRIMAL_TOLERANCE, 1e-8)
status = solver.Solve(solver_params)
result = []
if status == pywraplp.Solver.OPTIMAL:
    print("Solution:")
    print("Objective value =", solver.Objective().Value())
    for m in range(data["M"]):
        for c in range(data["ContractPngCount"]):
            result.append(
                (
                    "FieldB_{c}_{m}",
                    PurchasedPng[c, m].solution_value(),
                    data["ContractPngId"][c],
                    data["MonthList"][m],
                )
            )
        for l in range(data["ContractLngCount"]):
            result.append(
                (
                    "FieldC_{l}_{m}",
                    PurchasedLng[l, m].solution_value(),
                    data["ContractLngId"][c],
                    data["MonthList"][m],
                )
            )
        for i in range(data["IncrementalPngCount"]):
            result.append(
                (
                    "FieldA_{i}_{m}",
                    IncrementalPngPurchased[i, m].solution_value(),
                    data["IncrementalPngId"][i],
                    data["MonthList"][m],
                )
            )
        for j in range(data["IncrementalLngCount"]):
            result.append(
                (
                    "FieldD_{j}_{m}",
                    IncrementalLngPurchased[j, m].solution_value(),
                    data["IncrementalLngId"][i],
                    data["MonthList"][m],
                )
            )
    print(
        pd.DataFrame(result, columns=["Name", "Value", "Resource ID", "Month"]).to_string(
            index=False
        )
    )
else:
    print("The problem does not have an optimal solution.")