{"id":1060736,"date":"2024-12-31T15:36:36","date_gmt":"2024-12-31T07:36:36","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1060736.html"},"modified":"2024-12-31T15:36:38","modified_gmt":"2024-12-31T07:36:38","slug":"python%e5%a6%82%e4%bd%95%e5%af%b9%e9%83%a8%e5%88%86%e5%b1%9e%e6%80%a7%e5%81%9a%e8%bd%ac%e6%8d%a2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1060736.html","title":{"rendered":"python\u5982\u4f55\u5bf9\u90e8\u5206\u5c5e\u6027\u505a\u8f6c\u6362"},"content":{"rendered":"

\"python\u5982\u4f55\u5bf9\u90e8\u5206\u5c5e\u6027\u505a\u8f6c\u6362\"<\/p>\n

Python\u5bf9\u90e8\u5206\u5c5e\u6027\u505a\u8f6c\u6362<\/strong>\uff0c\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528\u5185\u7f6e\u7684\u51fd\u6570\u548c\u65b9\u6cd5\uff0c\u5982map<\/code>\u3001filter<\/code>\u3001lambda<\/code>\u3001\u5217\u8868\u63a8\u5bfc\u5f0f\u7b49\uff0c\u6765\u5bf9\u5bf9\u8c61\u6216\u6570\u636e\u7ed3\u6784\u7684\u90e8\u5206\u5c5e\u6027\u8fdb\u884c\u8f6c\u6362\u3002\u901a\u8fc7\u81ea\u5b9a\u4e49\u51fd\u6570\u8fdb\u884c\u8f6c\u6362\u3001\u4f7f\u7528\u7c7b\u65b9\u6cd5\u3001\u4f7f\u7528\u7b2c\u4e09\u65b9\u5e93\u5982Pandas\u8fdb\u884c\u8f6c\u6362<\/strong>\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u8fd9\u4e9b\u65b9\u6cd5\u8fdb\u884c\u90e8\u5206\u5c5e\u6027\u8f6c\u6362\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u81ea\u5b9a\u4e49\u51fd\u6570\u8fdb\u884c\u5c5e\u6027\u8f6c\u6362<\/h3>\n<\/p>\n

\u5728Python\u4e2d\uff0c\u81ea\u5b9a\u4e49\u51fd\u6570\u53ef\u4ee5\u5e2e\u52a9\u4f60\u5bf9\u5bf9\u8c61\u7684\u5c5e\u6027\u8fdb\u884c\u5404\u79cd\u8f6c\u6362\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\uff0c\u5c55\u793a\u4e86\u5982\u4f55\u5bf9\u5b57\u5178\u4e2d\u7684\u67d0\u4e9b\u952e\u8fdb\u884c\u8f6c\u6362\uff1a<\/p>\n<\/p>\n

def convert_attributes(data):<\/p>\n
    def convert(value):<\/p>\n
        # \u8fd9\u91cc\u53ef\u4ee5\u8fdb\u884c\u4f60\u9700\u8981\u7684\u8f6c\u6362<\/p>\n
        return value.upper() if isinstance(value, str) else value<\/p>\n
    return {k: convert(v) for k, v in data.items()}<\/p>\n
data = {'name': 'Alice', 'age': 25, 'city': 'New York'}<\/p>\n
converted_data = convert_attributes(data)<\/p>\n
print(converted_data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0cconvert_attributes<\/code> \u51fd\u6570\u5bf9\u4f20\u5165\u5b57\u5178\u7684\u6bcf\u4e2a\u503c\u8fdb\u884c\u68c0\u67e5\u5e76\u8f6c\u6362\uff0c\u5c06\u5b57\u7b26\u4e32\u8f6c\u6362\u4e3a\u5927\u5199\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528\u7c7b\u65b9\u6cd5\u8fdb\u884c\u8f6c\u6362<\/h3>\n<\/p>\n
\u5982\u679c\u4f60\u5728\u5904\u7406\u5bf9\u8c61\u7684\u5c5e\u6027\uff0c\u53ef\u4ee5\u5728\u7c7b\u4e2d\u5b9a\u4e49\u65b9\u6cd5\u6765\u8fdb\u884c\u8f6c\u6362\uff1a<\/p>\n<\/p>\n
class Person:<\/p>\n
    def __init__(self, name, age, city):<\/p>\n
        self.name = name<\/p>\n
        self.age = age<\/p>\n
        self.city = city<\/p>\n
    def convert_attributes(self):<\/p>\n
        self.name = self.name.upper()<\/p>\n
        self.city = self.city.upper()<\/p>\n
person = Person('Alice', 25, 'New York')<\/p>\n
person.convert_attributes()<\/p>\n
print(person.name, person.city)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0cconvert_attributes<\/code> \u65b9\u6cd5\u5c06 name<\/code> \u548c city<\/code> \u5c5e\u6027\u8f6c\u6362\u4e3a\u5927\u5199\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528Pandas\u8fdb\u884c\u8f6c\u6362<\/h3>\n<\/p>\n
Pandas\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u5e93\uff0c\u5e38\u7528\u4e8e\u5bf9\u5927\u89c4\u6a21\u6570\u636e\u8fdb\u884c\u64cd\u4f5c\u3002\u4ee5\u4e0b\u662f\u5982\u4f55\u4f7f\u7528Pandas\u5bf9\u90e8\u5206\u5c5e\u6027\u8fdb\u884c\u8f6c\u6362\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
data = {'name': ['Alice', 'Bob', 'Charlie'], 'age': [25, 30, 35], 'city': ['New York', 'Los Angeles', 'Chicago']}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
\u8f6c\u6362\u90e8\u5206\u5217<\/strong><\/h2>\n
df['name'] = df['name'].str.upper()<\/p>\n
df['city'] = df['city'].str.upper()<\/p>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0cPandas DataFrame\u7684 str.upper<\/code> \u65b9\u6cd5\u5c06 name<\/code> \u548c city<\/code> \u5217\u8f6c\u6362\u4e3a\u5927\u5199\u3002<\/p>\n<\/p>\n
\u56db\u3001\u4f7f\u7528\u5217\u8868\u63a8\u5bfc\u5f0f\u548cmap\u51fd\u6570<\/h3>\n<\/p>\n
\u5217\u8868\u63a8\u5bfc\u5f0f\u548cmap<\/code>\u51fd\u6570\u4e5f\u53ef\u4ee5\u65b9\u4fbf\u5730\u5bf9\u90e8\u5206\u5c5e\u6027\u8fdb\u884c\u8f6c\u6362\uff1a<\/p>\n<\/p>\n
data = [{'name': 'Alice', 'age': 25, 'city': 'New York'},<\/p>\n
        {'name': 'Bob', 'age': 30, 'city': 'Los Angeles'},<\/p>\n
        {'name': 'Charlie', 'age': 35, 'city': 'Chicago'}]<\/p>\n
\u4f7f\u7528map\u51fd\u6570<\/strong><\/h2>\n
def convert_name(person):<\/p>\n
    person['name'] = person['name'].upper()<\/p>\n
    return person<\/p>\n
converted_data = list(map(convert_name, data))<\/p>\n
print(converted_data)<\/p>\n
\u4f7f\u7528\u5217\u8868\u63a8\u5bfc\u5f0f<\/strong><\/h2>\n
converted_data = [{'name': person['name'].upper(), 'age': person['age'], 'city': person['city'].upper()} for person in data]<\/p>\n
print(converted_data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u4f7f\u7528map<\/code>\u51fd\u6570\u548c\u5217\u8868\u63a8\u5bfc\u5f0f\u5bf9\u5217\u8868\u4e2d\u7684\u5b57\u5178\u8fdb\u884c\u8f6c\u6362\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u4f7f\u7528\u751f\u6210\u5668\u8868\u8fbe\u5f0f\u548cfilter\u51fd\u6570<\/h3>\n<\/p>\n
\u751f\u6210\u5668\u8868\u8fbe\u5f0f\u548cfilter<\/code>\u51fd\u6570\u4e5f\u53ef\u4ee5\u5e2e\u52a9\u4f60\u5bf9\u90e8\u5206\u5c5e\u6027\u8fdb\u884c\u8f6c\u6362\uff1a<\/p>\n<\/p>\n
data = [{'name': 'Alice', 'age': 25, 'city': 'New York'},<\/p>\n
        {'name': 'Bob', 'age': 30, 'city': 'Los Angeles'},<\/p>\n
        {'name': 'Charlie', 'age': 35, 'city': 'Chicago'}]<\/p>\n
\u4f7f\u7528\u751f\u6210\u5668\u8868\u8fbe\u5f0f<\/strong><\/h2>\n
def convert_person(person):<\/p>\n
    person['name'] = person['name'].upper()<\/p>\n
    return person<\/p>\n
converted_data = (convert_person(person) for person in data)<\/p>\n
print(list(converted_data))<\/p>\n
\u4f7f\u7528filter\u51fd\u6570<\/strong><\/h2>\n
def is_from_new_york(person):<\/p>\n
    return person['city'] == 'New York'<\/p>\n
filtered_data = filter(is_from_new_york, data)<\/p>\n
print(list(filtered_data))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u751f\u6210\u5668\u8868\u8fbe\u5f0f\u548cfilter<\/code>\u51fd\u6570\u5206\u522b\u5bf9\u90e8\u5206\u5c5e\u6027\u8fdb\u884c\u8f6c\u6362\u548c\u8fc7\u6ee4\u3002<\/p>\n<\/p>\n
\u516d\u3001\u7ed3\u5408\u591a\u4e2a\u65b9\u6cd5\u8fdb\u884c\u590d\u6742\u8f6c\u6362<\/h3>\n<\/p>\n
\u6709\u65f6\u4f60\u53ef\u80fd\u9700\u8981\u7ed3\u5408\u591a\u79cd\u65b9\u6cd5\u8fdb\u884c\u66f4\u590d\u6742\u7684\u8f6c\u6362\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\uff0c\u5c55\u793a\u5982\u4f55\u7ed3\u5408\u81ea\u5b9a\u4e49\u51fd\u6570\u548cPandas\u8fdb\u884c\u590d\u6742\u6570\u636e\u8f6c\u6362\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
def custom_conversion(value):<\/p>\n
    if isinstance(value, str):<\/p>\n
        return value.lower()<\/p>\n
    elif isinstance(value, int) and value > 30:<\/p>\n
        return 'senior'<\/p>\n
    return value<\/p>\n
data = {'name': ['Alice', 'Bob', 'Charlie'], 'age': [25, 30, 35], 'city': ['New York', 'Los Angeles', 'Chicago']}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
\u4f7f\u7528applymap\u8fdb\u884c\u590d\u6742\u8f6c\u6362<\/strong><\/h2>\n
df = df.applymap(custom_conversion)<\/p>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0ccustom_conversion<\/code> \u51fd\u6570\u5bf9\u5b57\u7b26\u4e32\u8fdb\u884c\u5c0f\u5199\u8f6c\u6362\uff0c\u5e76\u5c06\u5e74\u9f84\u5927\u4e8e30\u7684\u503c\u8f6c\u6362\u4e3a'senior'\u3002<\/p>\n<\/p>\n
\u4e03\u3001\u5728\u6570\u636e\u6d41\u4e2d\u8fdb\u884c\u8f6c\u6362<\/h3>\n<\/p>\n
\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u6570\u636e\u8f6c\u6362\u901a\u5e38\u662f\u6570\u636e\u5904\u7406\u6d41\u6c34\u7ebf\u7684\u4e00\u90e8\u5206\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\uff0c\u5c55\u793a\u5982\u4f55\u5728\u6570\u636e\u6d41\u4e2d\u4f7f\u7528\u8f6c\u6362\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
def preprocess_data(df):<\/p>\n
    df['name'] = df['name'].str.upper()<\/p>\n
    df['age'] = df['age'].apply(lambda x: 'senior' if x > 30 else 'junior')<\/p>\n
    df['city'] = df['city'].str.lower()<\/p>\n
    return df<\/p>\n
data = {'name': ['Alice', 'Bob', 'Charlie'], 'age': [25, 30, 35], 'city': ['New York', 'Los Angeles', 'Chicago']}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
processed_df = preprocess_data(df)<\/p>\n
print(processed_df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0cpreprocess_data<\/code> \u51fd\u6570\u5bf9DataFrame\u7684\u591a\u4e2a\u5217\u8fdb\u884c\u8f6c\u6362\uff0c\u6f14\u793a\u4e86\u5982\u4f55\u5728\u6570\u636e\u5904\u7406\u6d41\u6c34\u7ebf\u4e2d\u8fdb\u884c\u8f6c\u6362\u3002<\/p>\n<\/p>\n
\u516b\u3001\u4f7f\u7528\u7b2c\u4e09\u65b9\u5e93\u8fdb\u884c\u9ad8\u7ea7\u8f6c\u6362<\/h3>\n<\/p>\n
\u9664\u4e86Pandas\uff0cPython\u7684\u751f\u6001\u7cfb\u7edf\u4e2d\u8fd8\u6709\u8bb8\u591a\u5176\u4ed6\u5e93\u53ef\u4ee5\u5e2e\u52a9\u4f60\u8fdb\u884c\u9ad8\u7ea7\u6570\u636e\u8f6c\u6362\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\uff0c\u5c55\u793a\u5982\u4f55\u4f7f\u7528\u7b2c\u4e09\u65b9\u5e93marshmallow<\/code>\u8fdb\u884c\u6570\u636e\u8f6c\u6362\uff1a<\/p>\n<\/p>\n
from marshmallow import Schema, fields, post_load<\/p>\n
class PersonSchema(Schema):<\/p>\n
    name = fields.Str()<\/p>\n
    age = fields.Int()<\/p>\n
    city = fields.Str()<\/p>\n
    @post_load<\/p>\n
    def convert_name(self, data, kwargs):<\/p>\n
        data['name'] = data['name'].upper()<\/p>\n
        return data<\/p>\n
data = {'name': 'Alice', 'age': 25, 'city': 'New York'}<\/p>\n
schema = PersonSchema()<\/p>\n
result = schema.load(data)<\/p>\n
print(result)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0cmarshmallow<\/code>\u5e93\u7684post_load<\/code>\u65b9\u6cd5\u5728\u6570\u636e\u52a0\u8f7d\u540e\u5bf9name<\/code>\u5b57\u6bb5\u8fdb\u884c\u8f6c\u6362\u3002<\/p>\n<\/p>\n
\u4e5d\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
Python\u63d0\u4f9b\u4e86\u591a\u79cd\u65b9\u6cd5\u548c\u5de5\u5177\u6765\u5bf9\u5bf9\u8c61\u6216\u6570\u636e\u7ed3\u6784\u7684\u90e8\u5206\u5c5e\u6027\u8fdb\u884c\u8f6c\u6362\u3002\u901a\u8fc7\u7ed3\u5408\u4f7f\u7528\u5185\u7f6e\u51fd\u6570\u3001\u81ea\u5b9a\u4e49\u51fd\u6570\u3001\u7c7b\u65b9\u6cd5\u3001Pandas\u3001\u5217\u8868\u63a8\u5bfc\u5f0f\u3001\u751f\u6210\u5668\u8868\u8fbe\u5f0f\u3001\u7b2c\u4e09\u65b9\u5e93\u7b49\uff0c\u53ef\u4ee5\u5b9e\u73b0\u7075\u6d3b\u548c\u9ad8\u6548\u7684\u6570\u636e\u8f6c\u6362\u3002\u5e0c\u671b\u4ee5\u4e0a\u5185\u5bb9\u80fd\u591f\u5e2e\u52a9\u4f60\u66f4\u597d\u5730\u7406\u89e3\u548c\u5e94\u7528\u8fd9\u4e9b\u65b9\u6cd5\u3002<\/p>\n<\/p>\n
\u5341\u3001\u5b9e\u73b0\u4ee3\u7801\u590d\u7528<\/h3>\n<\/p>\n
\u5728\u5b9e\u9645\u9879\u76ee\u4e2d\uff0c\u4ee3\u7801\u590d\u7528\u662f\u63d0\u9ad8\u5f00\u53d1\u6548\u7387\u548c\u4ee3\u7801\u8d28\u91cf\u7684\u91cd\u8981\u624b\u6bb5\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\uff0c\u5c55\u793a\u5982\u4f55\u5c06\u8f6c\u6362\u903b\u8f91\u5c01\u88c5\u4e3a\u53ef\u590d\u7528\u7684\u6a21\u5757\uff1a<\/p>\n<\/p>\n
# converter.py<\/p>\n
def convert_to_upper(value):<\/p>\n
    return value.upper() if isinstance(value, str) else value<\/p>\n
def convert_age(value):<\/p>\n
    return 'senior' if value > 30 else 'junior'<\/p>\n
mAI<\/a>n.py<\/strong><\/h2>\n
import pandas as pd<\/p>\n
from converter import convert_to_upper, convert_age<\/p>\n
data = {'name': ['Alice', 'Bob', 'Charlie'], 'age': [25, 30, 35], 'city': ['New York', 'Los Angeles', 'Chicago']}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
df['name'] = df['name'].apply(convert_to_upper)<\/p>\n
df['age'] = df['age'].apply(convert_age)<\/p>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u8f6c\u6362\u903b\u8f91\u88ab\u5c01\u88c5\u5728converter.py<\/code>\u6a21\u5757\u4e2d\uff0c\u4f7f\u5f97\u4ee3\u7801\u66f4\u52a0\u6a21\u5757\u5316\u548c\u53ef\u590d\u7528\u3002<\/p>\n<\/p>\n
\u901a\u8fc7\u4ee5\u4e0a\u65b9\u6cd5\u548c\u6280\u5de7\uff0c\u4f60\u53ef\u4ee5\u5728Python\u4e2d\u7075\u6d3b\u5730\u5bf9\u90e8\u5206\u5c5e\u6027\u8fdb\u884c\u8f6c\u6362\uff0c\u6ee1\u8db3\u5404\u79cd\u6570\u636e\u5904\u7406\u9700\u6c42\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u9009\u62e9\u6027\u5730\u8f6c\u6362\u5bf9\u8c61\u7684\u5c5e\u6027\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7\u5b9a\u4e49\u81ea\u5b9a\u4e49\u65b9\u6cd5\u6216\u4f7f\u7528\u7c7b\u7684__dict__<\/code>\u5c5e\u6027\u6765\u9009\u62e9\u6027\u5730\u8f6c\u6362\u5bf9\u8c61\u7684\u67d0\u4e9b\u5c5e\u6027\u3002\u53ef\u4ee5\u904d\u5386\u5bf9\u8c61\u7684\u5c5e\u6027\u5b57\u5178\uff0c\u68c0\u67e5\u6bcf\u4e2a\u5c5e\u6027\u7684\u540d\u79f0\u548c\u7c7b\u578b\uff0c\u7136\u540e\u6839\u636e\u9700\u8981\u8fdb\u884c\u8f6c\u6362\u3002\u4f8b\u5982\uff0c\u5982\u679c\u5e0c\u671b\u5c06\u67d0\u4e2a\u5b57\u7b26\u4e32\u5c5e\u6027\u8f6c\u6362\u4e3a\u6574\u6570\uff0c\u53ef\u4ee5\u4f7f\u7528int()<\/code>\u51fd\u6570\u8fdb\u884c\u8f6c\u6362\u3002<\/p>\n
Python\u4e2d\u6709\u54ea\u4e9b\u5e38\u7528\u7684\u5c5e\u6027\u8f6c\u6362\u65b9\u6cd5\uff1f<\/strong>
\u5e38\u7528\u7684\u5c5e\u6027\u8f6c\u6362\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528\u5185\u7f6e\u51fd\u6570\uff08\u5982int()<\/code>\u3001float()<\/code>\u3001str()<\/code>\u7b49\uff09\u8fdb\u884c\u57fa\u672c\u7c7b\u578b\u8f6c\u6362\uff0c\u6216\u8005\u4f7f\u7528\u5e93\u51fd\u6570\uff08\u5982datetime.strptime()<\/code>\u8fdb\u884c\u65e5\u671f\u8f6c\u6362\uff09\u3002\u8fd8\u53ef\u4ee5\u901a\u8fc7\u81ea\u5b9a\u4e49\u8f6c\u6362\u51fd\u6570\u6765\u5904\u7406\u590d\u6742\u7684\u8f6c\u6362\u903b\u8f91\uff0c\u4f8b\u5982\u5c06JSON\u5b57\u7b26\u4e32\u8f6c\u6362\u4e3a\u5b57\u5178\u5bf9\u8c61\u3002<\/p>\n
\u5982\u4f55\u5728\u6570\u636e\u6846\u4e2d\u5bf9\u7279\u5b9a\u5217\u8fdb\u884c\u5c5e\u6027\u8f6c\u6362\uff1f<\/strong>
\u5982\u679c\u4f7f\u7528Pandas\u5e93\u5904\u7406\u6570\u636e\u6846\uff0c\u53ef\u4ee5\u901a\u8fc7\u9009\u62e9\u7279\u5b9a\u5217\u5e76\u5e94\u7528\u8f6c\u6362\u51fd\u6570\u6765\u8fdb\u884c\u5c5e\u6027\u8f6c\u6362\u3002\u4f8b\u5982\uff0c\u4f7f\u7528dataframe['column_name'] = dataframe['column_name'].apply(some_conversion_function)<\/code>\u7684\u65b9\u5f0f\uff0c\u5bf9\u7279\u5b9a\u5217\u8fdb\u884c\u8f6c\u6362\u3002\u6b64\u5916\uff0c\u8fd8\u53ef\u4ee5\u4f7f\u7528astype()<\/code>\u65b9\u6cd5\u5feb\u901f\u8f6c\u6362\u6570\u636e\u7c7b\u578b\uff0c\u5982dataframe['column_name'] = dataframe['column_name'].astype(int)<\/code>\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u5bf9\u90e8\u5206\u5c5e\u6027\u505a\u8f6c\u6362\uff0c\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528\u5185\u7f6e\u7684\u51fd\u6570\u548c\u65b9\u6cd5\uff0c\u5982map\u3001filter\u3001lambda\u3001\u5217\u8868\u63a8\u5bfc\u5f0f\u7b49 […]","protected":false},"author":3,"featured_media":1060742,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1060736"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=1060736"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1060736\/revisions"}],"predecessor-version":[{"id":1060743,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1060736\/revisions\/1060743"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1060742"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1060736"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1060736"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1060736"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}