php小编柚子在本文中将介绍如何使用Python将点分隔值（如"key1.subkey1.subkey2"）转换为Go语言中的结构体。这个转换过程对于从配置文件或API响应中提取和处理数据非常有用。我们将使用Python的递归函数和Go语言的结构体来实现这个转换，并给出详细的代码示例和解释。通过本文的学习，读者将能够轻松处理和转换点分隔值，提高数据处理的效率和灵活性。
这是对可以更改配置的应用程序的特定要求（特别是 wso2 identity server，因为我正在使用 go 为其编写 kubernetes 运算符）。但这里确实不相关。我想创建一个解决方案，允许轻松管理大量配置映射以生成 go 结构。这些配置映射在 .csv 中
链接到 .csv - my_configs.csv
我想要， 编写一个自动生成 go 结构的 python 脚本，这样对应用程序配置的任何更改都可以通过简单地执行 python 脚本创建相应的 go 结构来更新。我指的是应用程序本身的配置。例如，可以更改 csv 中的 toml 键名称/可以添加新值。
到目前为止，我已经成功创建了一个 python 脚本，几乎实现了我的目标。脚本是，
import pandas as pd
def convert_to_dict(data):
result = {}
for row in data:
current_dict = result
for item in row[:-1]:
if item is not none:
if item not in current_dict:
current_dict[item] = {}
current_dict = current_dict[item]
return result
def extract_json_key(yaml_key):
if isinstance(yaml_key, str) and '.' in yaml_key:
return yaml_key.split('.')[-1]
else:
return yaml_key
def add_fields_to_struct(struct_string,go_var,go_type,json_key,toml_key):
struct_string += str(go_var) + " " + str(go_type) + ' `json:"' + str(json_key) + ',omitempty" toml:"' +str(toml_key) + '"` ' + "\n"
return struct_string
def generate_go_struct(struct_name, struct_data):
struct_name="configurations" if struct_name == "" else struct_name
struct_string = "type " + struct_name + " struct {\n"
yaml_key=df['yaml_key'].str.split('.').str[-1]
# base case: generate fields for the current struct level
for key, value in struct_data.items():
selected_rows = df[yaml_key == key]
if len(selected_rows) > 1:
go_var = selected_rows['go_var'].values[1]
toml_key = selected_rows['toml_key'].values[1]
go_type=selected_rows['go_type'].values[1]
json_key=selected_rows['json_key'].values[1]
else:
go_var = selected_rows['go_var'].values[0]
toml_key = selected_rows['toml_key'].values[0]
go_type=selected_rows['go_type'].values[0]
json_key=selected_rows['json_key'].values[0]
# add fields to the body of the struct
struct_string=add_fields_to_struct(struct_string,go_var,go_type,json_key,toml_key)
struct_string += "}\n\n"
# recursive case: generate struct definitions for nested structs
for key, value in struct_data.items():
selected_rows = df[yaml_key == key]
if len(selected_rows) > 1:
go_var = selected_rows['go_var'].values[1]
else:
go_var = selected_rows['go_var'].values[0]
if isinstance(value, dict) and any(isinstance(v, dict) for v in value.values()):
nested_struct_name = go_var
nested_struct_data = value
struct_string += generate_go_struct(nested_struct_name, nested_struct_data)
return struct_string
# read excel
csv_file = "~/downloads/my_configs.csv"
df = pd.read_csv(csv_file)
# remove rows where all columns are nan
df = df.dropna(how='all')
# create the 'json_key' column using the custom function
df['json_key'] = df['yaml_key'].apply(extract_json_key)
data=df['yaml_key'].values.tolist() # read the 'yaml_key' column
data = pd.dataframe({'column':data}) # convert to dataframe
data=data['column'].str.split('.', expand=true) # split by '.'
nested_list = data.values.tolist() # convert to nested list
data=nested_list
result_json = convert_to_dict(data) # convert to dict (json)
# the generated co code
go_struct = generate_go_struct("", result_json)
# write to file
file_path = "output.go"
with open(file_path, "w") as file:
file.write(go_struct)问题是（查看 csv 的下面部分），
authentication.authenticator.basic authentication.authenticator.basic.parameters authentication.authenticator.basic.parameters.showAuthFailureReason authentication.authenticator.basic.parameters.showAuthFailureReasonOnLoginPage authentication.authenticator.totp authentication.authenticator.totp.parameters authentication.authenticator.totp.parameters.showAuthFailureReason authentication.authenticator.totp.parameters.showAuthFailureReasonOnLoginPage authentication.authenticator.totp.parameters.encodingMethod authentication.authenticator.totp.parameters.timeStepSize
这里，由于 basic 和 totp 字段 parameters 重复，因此脚本会混淆自身并生成两个 totpparameters 结构。预期结果是具有 basicparameters 和 totpparameters 结构。 csv 的 yaml_key 列中存在许多类似的重复单词。
我知道这与 go_var = selected_rows['go_var'].values[1] 中索引被硬编码为 1 有关，但很难修复此问题。
有人可以指点我一个答案吗？我认为，
谢谢！
我也尝试过使用 chatgpt，但是由于这与嵌套和递归有关，因此 chatgpt 提供的答案不是很有效。
更新
我发现包含 properties、pooloptions、endpoint 和 parameters 字段的行存在问题。这是因为它们在 yaml_key 列中重复。
我能够解决这个问题。但是，我必须完全使用一种新方法来解决问题，即使用树数据结构，然后遍历它。这是其背后的主要逻辑 - https://www.geeksforgeeks.org/level-顺序树遍历/
这是工作的python代码。
import pandas as pd
from collections import deque
structs=[]
class TreeNode:
def __init__(self, name):
self.name = name
self.children = []
self.path=""
def add_child(self, child):
self.children.append(child)
def create_tree(data):
root = TreeNode('')
for item in data:
node = root
for name in item.split('.'):
existing_child = next((child for child in node.children if child.name == name), None)
if existing_child:
node = existing_child
else:
new_child = TreeNode(name)
node.add_child(new_child)
node = new_child
return root
def generate_go_struct(struct_data):
struct_name = struct_data['struct_name']
fields = struct_data['fields']
go_struct = f"type {struct_name} struct {{\n"
for field in fields:
field_name = field['name']
field_type = field['type']
field_default_val = str(field['default_val'])
json_key=field['json_key']
toml_key=field['toml_key']
tail_part=f"\t{field_name} {field_type} `json:\"{json_key},omitempty\" toml:\"{toml_key}\"`\n\n"
if pd.isna(field['default_val']):
go_struct += tail_part
else:
field_default_val = "\t// +kubebuilder:default:=" + field_default_val
go_struct += field_default_val + "\n" + tail_part
go_struct += "}\n\n"
return go_struct
def write_go_file(go_structs, file_path):
with open(file_path, 'w') as file:
for go_struct in go_structs:
file.write(go_struct)
def create_new_struct(struct_name):
struct_name = "Configurations" if struct_name == "" else struct_name
struct_dict = {
"struct_name": struct_name,
"fields": []
}
return struct_dict
def add_field(struct_dict, field_name, field_type,default_val,json_key, toml_key):
field_dict = {
"name": field_name,
"type": field_type,
"default_val": default_val,
"json_key":json_key,
"toml_key":toml_key
}
struct_dict["fields"].append(field_dict)
return struct_dict
def traverse_tree(root):
queue = deque([root])
while queue:
node = queue.popleft()
filtered_df = df[df['yaml_key'] == node.path]
go_var = filtered_df['go_var'].values[0] if not filtered_df.empty else None
go_type = filtered_df['go_type'].values[0] if not filtered_df.empty else None
if node.path=="":
go_type="Configurations"
# The structs themselves
current_struct = create_new_struct(go_type)
for child in node.children:
if (node.name!=""):
child.path=node.path+"."+child.name
else:
child.path=child.name
filtered_df = df[df['yaml_key'] == child.path]
go_var = filtered_df['go_var'].values[0] if not filtered_df.empty else None
go_type = filtered_df['go_type'].values[0] if not filtered_df.empty else None
default_val = filtered_df['default_val'].values[0] if not filtered_df.empty else None
# Struct fields
json_key = filtered_df['yaml_key'].values[0].split('.')[-1] if not filtered_df.empty else None
toml_key = filtered_df['toml_key'].values[0].split('.')[-1] if not filtered_df.empty else None
current_struct = add_field(current_struct, go_var, go_type,default_val,json_key, toml_key)
if (child.children):
# Add each child to the queue for processing
queue.append(child)
go_struct = generate_go_struct(current_struct)
# print(go_struct,"\n")
structs.append(go_struct)
write_go_file(structs, "output.go")
csv_file = "~/Downloads/my_configs.csv"
df = pd.read_csv(csv_file)
sample_data=df['yaml_key'].values.tolist()
# Create the tree
tree = create_tree(sample_data)
# Traverse the tree
traverse_tree(tree)
非常感谢您的帮助！
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