duplicates

问题 This question already has answers here : Duplicated rows: select rows based on criteria and store duplicated values (2 answers) Closed 3 months ago . I have a large data set with text comments and their ratings on different variables, like so: df <- data.frame( comment = c("commentA","commentB","commentB","commentA","commentA","commentC" sentiment=c(1,2,1,4,1,2), tone=c(1,5,3,2,6,1) ) Every comment is present between one and 3 times, since multiple people are asked to rate the same comment

问题 This question already has answers here : Duplicated rows: select rows based on criteria and store duplicated values (2 answers) Closed 3 months ago . I have a large data set with text comments and their ratings on different variables, like so: df <- data.frame( comment = c("commentA","commentB","commentB","commentA","commentA","commentC" sentiment=c(1,2,1,4,1,2), tone=c(1,5,3,2,6,1) ) Every comment is present between one and 3 times, since multiple people are asked to rate the same comment

问题 I am selecting multiple rows and inserting them into another table. I want to make sure that it doesn't already exists in the table I am inserting multiple rows into. DISTINCT works when there are duplicate rows in the select, but not when comparing it to the data already in the table your inserting into. If I Selected one row at a time I could do a IF EXIST but since its multiple rows (sometimes 10+) it doesn't seem like I can do that. 回答1: INSERT INTO target_table (col1, col2, col3) SELECT

问题 I've done some search and tried new codes since last night but haven't yet found the answer I was looking for. I'm working with multiple arrays but am only looking for duplicates in one array at a time. Having duplicates across different arrays doesn't matter; only duplicates within a single array matters. Each array has between 5 and 7 elements. Each element is an integer between 1 and 10. Some sample arrays can be Array1 = (5, 6, 10, 4, 2) Array2 = (1, 1, 9, 2, 5) Array3 = (6, 3, 3, 3, 6)

问题 Bit of a strange one, so I am looking to get all duplicates in an array, and save each of them in a separate array. It's a bit difficult to explain so I will try with an example. $array = array('apple', 'apple', 'apple', 'orange', 'orange', 'banana'); I am looking to find all duplicates (in this instance, apples and oranges) and save each in their own separate array, which will then be counted afterwards to find out how many of each duplicate exists in each of the arrays. Once I have counted

问题 I have been looking at all questions/answers about to how drop consecutive duplicates selectively in a pandas dataframe, still cannot figure out the following scenario: import pandas as pd import numpy as np def random_dates(start, end, n, freq, seed=None): if seed is not None: np.random.seed(seed) dr = pd.date_range(start, end, freq=freq) return pd.to_datetime(np.sort(np.random.choice(dr, n, replace=False))) date = random_dates('2018-01-01', '2018-01-12', 20, 'H', seed=[3, 1415]) data = {

问题 I have been looking at all questions/answers about to how drop consecutive duplicates selectively in a pandas dataframe, still cannot figure out the following scenario: import pandas as pd import numpy as np def random_dates(start, end, n, freq, seed=None): if seed is not None: np.random.seed(seed) dr = pd.date_range(start, end, freq=freq) return pd.to_datetime(np.sort(np.random.choice(dr, n, replace=False))) date = random_dates('2018-01-01', '2018-01-12', 20, 'H', seed=[3, 1415]) data = {

问题 I know that this topic came up many times before here but none of the suggested solutions worked for my dataset because my laptop stopped calculating due to memory issues or full storage. My table looks like the following and has 108 Mio rows: Col1 |Col2 | Col3 |Col4 |SICComb | NameComb Case New |3523 | Alexander |6799 |67993523| AlexanderCase New Case New |3523 | Undisclosed |6799 |67993523| Case NewUndisclosed Undisclosed|6799 | Case New |3523 |67993523| Case NewUndisclosed Case New |3523 |

问题 SQL query: Dumping data for table new_recipe INSERT INTO `new_recipe` (`id`, `post_title`, `post_image`, `post_author`, `post_date`, `post_desc`) VALUES (4, 'Daal Chawal', 'DDAa.jpg', 'Asad Khan', '2016-05-29', '\r\n Gujranwala agr pyara na hota\r\n\r\nGulshan Iqbal Park ka nizara na hota\r\n\r\nBypass pr ishara na hota\r\n\r\nSialkoti drwazy ka shara na hota\r\n\r\nPace pr janay ka mode dobara na hota\r\n\r\nBashir k dal chawal ka swad krara na hota\r\n\r\nsb Sattelite Town Girls Collage ka

问题 Suppose that there are three variables in my data frame (mydata): 1) id, 2) case, and 3) value. mydata <- data.frame(id=c(1,1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4), case=c("a","b","c","c","b","a","b","c","c","a","b","c","c","a","b","c","a"), value=c(1,34,56,23,34,546,34,67,23,65,23,65,23,87,34,321,87)) mydata id case value 1 1 a 1 2 1 b 34 3 1 c 56 4 1 c 23 5 1 b 34 6 2 a 546 7 2 b 34 8 2 c 67 9 2 c 23 10 3 a 65 11 3 b 23 12 3 c 65 13 3 c 23 14 4 a 87 15 4 b 34 16 4 c 321 17 4 a 87 For each id, we