SQL Joins

-
Image
Joins are used to combine rows from two or more tables based on related columns between them. Joins allow you to retrieve data from multiple tables simultaneously, enabling you to create complex queries that fetch data from different sources. There are different types of joins in SQL, including: INNER JOIN Returns only the rows that have matching values in both tables based on the specified join condition. It discards non-matching rows from both tables. Example:           create table t1(x int); insert into t1 values(1); insert into t1 values(1); insert into t1 values(0); create table t2(y int); insert into t2 values(0); insert into t2 values(1); insert into t2 values(1);           select * from t1 inner join t2 on t1.x = t2.y Output: 2. LEFT JOIN (or) LEFT OUTER JOIN Returns all the rows from the left (or first) table and the matching rows from the right (or second) table. If there is no match, NULL values are
Post a Comment

ag-grid load data

-


Add New Data to ag-grid

 agGrid.initialiseAgGridWithAngular1(angular);
var app = angular.module('App', ['agGrid']);

 $scope.gridOptions.api.setColumnDefs(columnDefs);
$scope.gridOptions.api.setRowData(rowData);
$scope.gridOptions.columnApi.autoSizeColumns(columnDefs);

<div ag-grid="gridOptions" class="ag-fresh" style="height: 100px;"></div>

Comments

  1. AnonymousApril 20, 2022 at 12:41 AM

    Ag-Grid Load Data >>>>> Download Now

    >>>>> Download Full

    Ag-Grid Load Data >>>>> Download LINK

    >>>>> Download Now

    Ag-Grid Load Data >>>>> Download Full

    >>>>> Download LINK rg

    ReplyDelete

Post a Comment

Popular posts from this blog

Machine Learning Foundations - Deep Learning Summary - Quiz

-
1) Which of the following statements are true? (Check all that apply) a) Linear classifiers are never useful, because they cannot represent XOR. b) Linear classifiers are useful, because, with enough data, they can represent anything. c) Having good non-linear features can allow us to learn very accurate linear classifiers. d) none of the above 2) A simple linear classifier can represent which of the following functions? (Check all that apply) Hint: If you are stuck, see https://www.coursera.org/learn/ml-foundations/module/nqC1t/discussions/AAIUurrtEeWGphLhfbPAyQ a) x1 OR x2 OR NOT x3 b) x1 AND x2 AND NOT x3 c) x1 OR (x2 AND NOT x3) d) none of the above 3) Which of the the following neural networks can represent the following function? Select all that apply. (x1 AND x2) OR (NOT x1 AND NOT x2) Hint: If you are stuck, see https://www.coursera.org/learn/ml-foundations/module/nqC1t/discussions/AAIUurrtEeWGphLhfbPAyQ d 4) Which of the following statements is true? (Che
Read more

Machine Learning Foundation - Deep Learning - Programming Assignment

-
1) What’s the least common category in the training data? a) bird b) dog c) cat d) automobile 2) Of the images below, which is the nearest ‘cat’ labeled image in the training data to the the first image in the test data (image_test[0:1])? f 3) Of the images below, which is the nearest ‘dog’ labeled image in the training data to the the first image in the test data (image_test[0:1])? d 4) For the first image in the test data, in what range is the mean distance between this image and its 5 nearest neighbors that were labeled ‘cat’ in the training data? a) 33 to 35 b) 35 to 37 c) 37 to 39 d) 39 to 41 e) Above 41 5) For the first image in the test data, in what range is the mean distance between this image and its 5 nearest neighbors that were labeled ‘dog’ in the training data? a) 33 to 35 b) 35 to 37 c) 37 to 39 d) 39 to 41 e) Above 41 6) On average, is the first image in the test data closer to its 5 nearest neighbors in the ‘cat’ data or in the ‘dog’ dat
Read more

Machine Learning Foundations - Recommender System - Quiz

-
1) Recommending items based on global popularity can (check all that apply): a) provide personalization b) capture context (e.g., time of day) c) none of the above 2) Recommending items using a classification approach can (check all that apply): a) provide personalization b) capture context (e.g., time of day) c) none of the above 3) Recommending items using a simple count based co-occurrence matrix can (check all that apply): a) provide personalization b) capture context (e.g., time of day) c) none of the above 4) Recommending items using featurized matrix factorization can (check all that apply): a) provide personalization b) capture context (e.g., time of day) c) none of the above 5) Normalizing co-occurrence matrices is used primarily to account for: a) people who purchased many items b) items purchased by many people c) eliminating rare products d) none of the above 6) A store has 3 customers and 3 products. Below are the learned feature vectors for
Read more