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Notice: Trying to get property of non-object problem

docid; echo

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TypeError: cannot unpack non-iterable NoneType object

交流、咨询,有疑问欢迎添加QQ 2125364717,一起交流、一起发现问题、一起进步啊,哈哈哈哈哈 python报错如下:TypeError: cannot unpack non-iterable NoneType object解决方法:报错的原因是函数返回值得数量不一致,查看函数返回值数量和调用函数时接收返回值的数量是不是一致,修改一致即可解决方法:报错的原因是函数返回值得数量不一致,查看函数返回值数量和调用函数时接收返回值的数量是不是一致

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    NSUserDefaults数据保存报错:Attempt to set a non-property-list object.

    1.这种错误的原因是插入了不识别的PaymentModel数据类型,NSUserDefaults支持的数据类型有NSString、 NSNumber、NSDat...

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    数据清洗之 字符串数据处理

    object Condition_Desc 1656 non-null object Price 7493 non-null object Location 7491 non-null object 7489 non-null object Warranty 5109 non-null object Model 7370 non-null object Sub_Model 2426 non-null object Type 6011 non-null object Vehicle_Title 268 non-null object OBO 7427 non-null object Feedback_Perc 6611 non-null object Watch_Count 3517 non-null object N_Reviews 7487 non-null object Seller_Status 6868 non-null object Vehicle_Tile 7439 non-null object Auction 7476 non-null object Buy_Now 7256 non-null

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    数据清洗之 重复值处理

    object Condition_Desc 1656 non-null object Price 7493 non-null float64 Location 7491 non-null object 7489 non-null object Warranty 5108 non-null object Model 7370 non-null object Sub_Model 2426 non-null object Type 6011 non-null object Vehicle_Title 268 non-null object OBO 7427 non-null object Feedback_Perc 6611 non-null object Watch_Count 3517 non-null object N_Reviews 7487 non-null object Seller_Status 6868 non-null object Vehicle_Tile 7439 non-null object Auction 7476 non-null object Buy_Now 7256 non-null

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    使用机器学习预测天气(第一部分)

    object maxtempm 1000 non-null object mintempm 1000 non-null object meantempm_1 999 non-null object meantempm _2 998 non-null object meantempm_3 997 non-null object meandewptm_1 999 non-null object meandewptm_2 non-null object maxtempm_3 997 non-null object mintempm_1 999 non-null object mintempm_2 998 non-null object mintempm_3 997 non-null object maxdewptm_1 999 non-null object maxdewptm_2 998 non-null object _3 997 non-null object precipm_1 999 non-null object precipm_2 998 non-null object precipm_3 997 non-null

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    用机器学习来预测天气Part 1

    object maxtempm 1000 non-null object mintempm 1000 non-null object meantempm_1 999 non-null object meantempm _2 998 non-null object meantempm_3 997 non-null object meandewptm_1 999 non-null object meandewptm_2 non-null object maxtempm_3 997 non-null object mintempm_1 999 non-null object mintempm_2 998 non-null object mintempm_3 997 non-null object maxdewptm_1 999 non-null object maxdewptm_2 998 non-null object _3 997 non-null object precipm_1 999 non-null object precipm_2 998 non-null object precipm_3 997 non-null

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    基于sklearn的特征筛选理论代码实现

    int64pclass 1313 non-null objectsurvived 1313 non-null int64name 1313 non-null objectage 633 non-null objectboat 347 non-null objectsex 1313 non-null objectdtypes: float64(1), int64(2), object(8)memory objectticket 69 non-null objectboat 347 non-null objectsex 1313 non-null objectdtypes: float64(1), object (1), object(7)memory usage: 82.1+ KB数据分割from sklearn.model_selection import train_test_splitx_train,x_test objectticket 984 non-null objectboat 984 non-null objectsex 984 non-null objectdtypes: float64(1), object

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    《Pandas 1.x Cookbook · 第二版》第01章 Pandas基础

    _2_name 4903 non-null objectactor_1_facebook_likes 4909 non-null float64gross 4054 non-null float64genres 4916 non-null objectactor_1_name 4909 non-null objectmovie_title 4916 non-null objectnum_voted_users 4895 non-null float64language 4904 non-null objectcountry 4911 non-null objectcontent_rating 4616 non-null objectbudget 4432 non-null float64title_year 4810 non-null float64actor_2_facebook_likes 4903 non-null object类型中可能包含任意Python的数据类型,也可能包含缺失值。

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    对不起!《唐人街探案3》和《你好,李焕英》相比,我更推荐《你好,李焕英》!

    Count Dtype --- ------ -------------- ----- 0 用户名 498 non-null object 1 有用 498 non-null int64 2 评分 498 non-null object 3 日期 498 non-null object 4 评论 498 non-null objectdtypes: int64(1), object(4)memory usage --- ------ -------------- ----- 0 用户名 494 non-null object 1 有用 494 non-null int64 2 评分 493 non-null object 3 日期 488 non-null object 4 评论 494 non-null objectdtypes: int64(1), object(4)memory usage: 23.2 non-null object 3 日期 487 non-null object 4 评论 487 non-null objectdtypes: int64(1), object(4)memory usage

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    数据清洗之 日期格式数据处理

    object auction_id 29971 non-null int64 cat_id 29971 non-null int64 cat1 29971 non-null int64 property 29827 non-null object buy_mount 29971 non-null int64 day 29971 non-null int64 dtypes: int64(5), object object auction_id 29971 non-null int64 cat_id 29971 non-null int64 cat1 29971 non-null int64 property 29827 non-null object buy_mount 29971 non-null int64 day 29971 non-null int64 buy_date 29971 non-null datetime64 dtypes: datetime64(1), int64(5), object(2) memory usage: 1.8+ MB df.head(5) .dataframe tbody

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    数据科学家们更换工作都有哪些特征(上)?

    Dtype --- ------ -------------- ----- 0 enrollee_id 2129 non-null int64 1 city 2129 non-null object 2 city_development_index 2129 non-null float64 3 gender 1621 non-null object 4 relevent_experience 2129 non-null object 5 enrolled_university 2098 non-null object 6 education_level 2077 non-null object 7 major_discipline 1817 non-null object 8 experience 2124 non-null object 9 company_size 1507 non-null object 10 company_type 1495 non-null object 11 last_new_job 2089 non-null object 12 training_hours 2129

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    快速目标检测--YOLO-LITE: A Real-Time Object Detection Algorithm Optimized for Non-GPU Computers

    YOLO-LITE: A Real-Time Object Detection Algorithm Optimized for Non-GPU Computers https:github.comreu2018DLYOLO-LITE https:github.comStinky-TofuStronger-yoloYOLO-LITE runs at about 21 FPS on a non-GPU computer 本文觉得对于小的网络

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    关于巧克力数据集的数据分析数据读取数据预处理问题分析探索分析

    objectSpecific Bean Origin or Bar Name 1795 non-null objectREF 1795 non-null int64Review Date 1795 non-null Type 1794 non-null objectBroad Bean Origin 1794 non-null objectdtypes: float64(1), int64(2), object( non-null objectBroad Bean Origin 1793 non-null objectdtypes: float64(1), int64(1), object(6)memory usage non-null objectBroad Bean Origin 1793 non-null objectdtypes: float64(2), int64(1), object(5)memory usage objectRating 1793 non-null float64dtypes: float64(1), object(1)memory usage: 42.0+ KBbest_country_data

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    《Pandas Cookbook》第03章 数据分析入门1. 规划数据分析路线2. 改变数据类型,降低内存消耗3. 从最大中选择最小4. 通过排序选取每组的最大值5. 用sort_values复现nl

    objectCITY 7535 non-null objectSTABBR 7535 non-null objectHBCU 7164 non-null float64MENONLY 7164 non-null 1196 non-null float64DISTANCEONLY 7164 non-null float64UGDS 6874 non-null float64UGDS_WHITE 6874 non-null non-null objectGRAD_DEBT_MDN_SUPP 7503 non-null objectdtypes: float64(20), int64(2), object(5)memory # 查看数据类型 In: col2.dtypesOut: RELAFFIL int64 SATMTMID float64 CURROPER int64 INSTNM object STABBR object STABBR object dtype: object# 检查两个对象列的独立值的个数 In: col2.select_dtypes(include=).nunique()Out: INSTNM 7535

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    聊聊GarbageCollectionNotificationInfo

    get(String key) ;​ public Object keys) ;​ public boolean containsKey(String key) ;​ public boolean containsValue (Object value) ;​ public Collection values() ;​ public boolean equals(Object obj) ;​ public int hashCode final long endTime; private final Map usageBeforeGc; private final Map usageAfterGc; private final Object value) { return cdata.containsValue(value); }​ public boolean equals(Object obj) { return cdata.equals (obj); }​ public Object get(String key) { return cdata.get(key); }​ public Object keys) { return cdata.getAll

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    关于空难数据集的探索分析导入数据集伤亡分析机型处理时间分析

    float64Summary 4878 non-null objectdtypes: float64(3), object(10)memory usage: 535.1+ KBcrash = crash.drop : float64(3), object(5)memory usage: 329.3+ KBprint(crash) Date Time Operator Type 2200 03061968 8:00 objectFatalities 5256 non-null float64Aboard 5246 non-null float64dtypes: float64(2), object(1)memory objectFatalities 5256 non-null float64Aboard 5246 non-null float64dtypes: float64(2), object(1)memory 3036 non-null float64dtypes: float64(4), object(9)memory usage: 333.5+ KB c:usersqiankappdatalocalprogramspythonpython35libsite-packagesipykernel_launcher.py

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    COVID-19数据分析实战:数据清洗篇

    date 1084 non-null object 3 Unnamed: 3 0 non-null float64 4 summary 1080 non-null object 5 location 1085 non-null object 6 country 1085 non-null object 7 gender 902 non-null object 8 age 843 non-null float64 507 non-null object 12 exposure_start 128 non-null object 13 exposure_end 341 non-null object 14 visiting 1085 non-null object 18 symptom 270 non-null object 19 source 1085 non-null object 20 link 1085 non-null object 21 Unnamed: 21 0 non-null float64 22 Unnamed: 22 0 non-null float64 23 Unnamed: 23 0 non-null

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    Kaggle Titanic 生存预测比赛超完整笔记(上)

    int64 Survived 891 non-null int64 Pclass 891 non-null int64 Name 891 non-null object Sex 891 non-null object Fare 891 non-null float64 Cabin 204 non-null object Embarked 889 non-null object dtypes: float64 int64 Name 418 non-null object Sex 418 non-null object Age 332 non-null float64 SibSp 418 non-null int64 Parch 418 non-null int64 Ticket 418 non-null object Fare 417 non-null float64 Cabin 91 non-null object object Fare 891 non-null float64 Cabin 891 non-null object Embarked 891 non-null object dtypes: float64

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    Java | synchronized 不同情况下的对象头测试

    lock = new Object(); syncLock(lock); Assertions.assertTrue(true); } void syncLock(Object lock) { log.info 情况一:同线程直接调用 void testSynchronizedLock() throws InterruptedException { Object lock = new Object(); syncLock )加锁后 0x0000000000000001 (non-biasable; age: 0) 加锁前 0x0000000000000001 (non-biasable; age: 0)加锁中 0x00007000028aaf10 (thin lock: 0x00007000028aaf10)加锁后 0x0000000000000001 (non-biasable; age: 0) 两次获取锁都使用的轻量级锁情况三:延迟 10s )加锁后 0x0000000000000001 (non-biasable; age: 0) 加锁前 0x0000000000000001 (non-biasable; age: 0)加锁中 0x000000ca803ff308

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