duplicates

Duplicate Symbol Error: SBJsonParser.o?

由 好久不见. 提交于 2019-12-04 06:59:18
I currently have ShareKit in my project that is compiled as a static library. It is properly implemented. I also have implemented Amazon's AWS SDK by just adding their framework into my project. It seems that the duplicate symbol is coming from Amazon's AWS SDK file, "AWSIOSSDK". This is what it looks like: And that file is colliding with ShareKit's file, libShareKit.a. This is what that file looks like: Anyway both of these files are ones that I haven't seen before. And it seems that some JSON files are colliding within them. I have looked at other SO questions and they say to do some things

Find near-duplicates of comma-separated lists using Levenshtein distance [duplicate]

由 自闭症网瘾萝莉.ら 提交于 2019-12-04 06:43:59
问题 This question already has an answer here : Potential Duplicates Detection, with 3 Severity Level (1 answer) Closed 5 years ago . This question based on the answer of my question yesterday. To solve my problem, Jean-François Corbett suggested a Levenshtein distance approach. Then I found this code somewhere to get Levenshtein distance percentage. Public Function GetLevenshteinPercentMatch( _ ByVal string1 As String, ByVal string2 As String, _ Optional Normalised As Boolean = False) As Single

Expand data.frame by creating duplicates based on group condition (2)

由 别说谁变了你拦得住时间么 提交于 2019-12-04 06:24:17
问题 Starting from @AndrewGustar answer/code: Expand data.frame by creating duplicates based on group condition 1) What about if I have the input data.frame with ID values not in sequence and that can also duplicate theirselves? Example data.frame: df = read.table(text = 'ID Day Count Count_group 18 1933 6 11 33 1933 6 11 37 1933 6 11 18 1933 6 11 16 1933 6 11 11 1933 6 11 111 1932 5 8 34 1932 5 8 60 1932 5 8 88 1932 5 8 18 1932 5 8 33 1931 3 4 13 1931 3 4 56 1931 3 4 23 1930 1 1 6 1800 6 10 37

move one row value to another sql without deleting the last row

由 蓝咒 提交于 2019-12-04 06:17:52
问题 I currently have a temporary table like so DBName API50 CounterValue NULL NULL 1 test1 34.5 NULL NULL NULL 2 test1 38.5 NULL I want a script which will make my temporary table as below DBName API50 CounterValue test1 34.5 1 test1 38.5 2 I got some help from stackexchange and managed to achieve the above result by using the following script. SELECT t1.DBName, t1.API50, t2.CounterValue FROM MyTable t1 INNER JOIN MyTable t2 ON t1.PrimaryKey -1 = t2.PrimaryKey WHERE t1.DBName IS NOT NULL However,

coloring cells in excel with pandas

由 岁酱吖の 提交于 2019-12-04 06:08:38
I need some help here. So i have something like this import pandas as pd path = '/Users/arronteb/Desktop/excel/ejemplo.xlsx' xlsx = pd.ExcelFile(path) df = pd.read_excel(xlsx,'Sheet1') df['is_duplicated'] = df.duplicated('#CSR') df_nodup = df.loc[df['is_duplicated'] == False] df_nodup.to_excel('ejemplo.xlsx', encoding='utf-8') So basically this program load the ejemplo.xlsx (ejemplo is example in Spanish, just the name of the file) into df (a DataFrame ), then checks for duplicate values in a specific column​​. It deletes the duplicates and saves the file again. That part works correctly. The

Why do I get an unhashable type 'list' error when converting a list to a set and back

由 天大地大妈咪最大 提交于 2019-12-04 06:05:03
问题 Like many other questions on here, I'm attempting to remove duplicates from a list. However, when I execute code that other answers claim work I get the following error: TypeError: unhashable type: 'list' on the following line of code: total_unique_words = list(set(total_words)) Does anyone know a possible solution to this problem? Is this because in most cases the original structure isn't a list? Thanks! 回答1: total_words must contain sublists for this error to occur. Consider: >>> total

“INSERT INTO .. ON DUPLICATE KEY UPDATE” Only inserts new entries rather than replace?

由 情到浓时终转凉″ 提交于 2019-12-04 05:31:19
问题 I have a table, say table1, which has 3 columns: id, number, and name. id is auto_incremented. I want to achieve an sql statement which inserts entries into a table, but if the row already exists, then ignore it. However, Every time I run: INSERT INTO table1( number, name) VALUES(num, name) ON DUPLICATE KEY UPDATE number = VALUES(number), name = VALUES(name) It seems to ignore rows with matching number and name values and appends entries to the end of the table no matter what. Is there

Hibernate Many-to-Many, duplicates same record

由 我与影子孤独终老i 提交于 2019-12-04 05:15:12
问题 I tried Hibernate Mapping Many-to-Many using Annotations with the example given in vaannila. http://www.vaannila.com/hibernate/hibernate-example/hibernate-mapping-many-to-many-using-annotations-1.html Set<Course> courses = new HashSet<Course>(); courses.add(new Course("Maths")); courses.add(new Course("Computer Science")); Student student1 = new Student("Eswar", courses); Student student2 = new Student("Joe", courses); session.save(student1); session.save(student2); This thing works fine. But

ON DUPLICATE KEY UPDATE with WHERE condition

由 有些话、适合烂在心里 提交于 2019-12-04 05:01:45
I update/insert values in a single table with the ON DUPLICATE KEY UPDATE function. So far everything is fine. INSERT INTO table1 SET field1=aa, field2=bb, field3=cc ON DUPLICATE KEY UPDATE SET field1=aa, field2=bb, field3=cc; But now I would like to achieve that the update only is done if a condition ( WHERE ) is true. Syntactically not correct: INSERT INTO table1 SET field1=aa, field2=bb, field3=cc ON DUPLICATE KEY UPDATE SET field1=aa, field2=bb, field3=cc WHERE field4=zz; Any ideas how the correct SQL statement is? Thanks a lot. Using IF() should work, though it's not nice: INSERT INTO

Duplicate values in a single row in dataframe

由 蓝咒 提交于 2019-12-04 04:30:34
问题 df <- data.frame(label = c("a","b","c"), val=c("x","b","c"), val1=c("z","b","d")) label val val1 1 a x z 2 b b b 3 c c d I want find out the duplicate values in each row. for 1st row, there is no duplicate for 2nd row , "b" is duplicate for 3rd row, "c" is duplicate. How to find this duplicate in R programming. Also I need to replace the duplicate elements with NA value. 回答1: Using duplicated with apply apply(df,1,duplicated) [,1] [,2] [,3] [1,] FALSE FALSE FALSE [2,] FALSE TRUE TRUE [3,]
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