Most important MySQL Keywords and their Description.

-
Most used Keywords in MySQL

S.No

Keywords

Description

1

ADD

Add’s columns to the existing table.

2

ALTER

Modifies columns in the table and changes the  data type of column

3

ANY

Return’s True (or)False, if any subquery meets the condition

4

AS

Used for aliasing

5

BETWEEN

Selects values on a  given range

6

COLUMN

Changes the  data type of column or delete column

7

CREATE DATABASE

Creates a database

8

CREATE INDEX

Creates index for all the rows including duplicate values 

9

CREATE UNIQUE INDEX

Creates index for all the rows excluding duplicate values 

10

DISTINCT

Selects different/unique values in the column

11

DROP COLUMN

Deletes the column

12

DROP INDEX

Deletes Index column

13

DROP TABLE

Deletes table from the database

14

FOREIGN KEY

Constraint used to link two tables

15

FROM 

Specifies/Locates the table 

16

FULL JOIN

Return all the records where there is a match in either the  left (or) right table

17

GROUP BY

Groups the results based on column (used with aggregate function)

18

HAVING

Used instead of WHERE clause, since WHERE clause cannot deal with aggregate functions

19

IN

Specifies multiple columns in WHERE clause

20

INNER JOIN

Joins two tables with matching values in both tables

21

INSERT INTO SELECT

Copies data from one table to another table 

22

IS NULL

Tests empty values

23

IS NOT NULL

Tests non-empty values

24

LIKE

Searches for a specific pattern in the column

25

LIMIT

Specifies the number of records to be shown in the result

26

NOT

Only includes rows where the  condition is not true 

27

NOT NULL

Enforces columns, not to accept null values 

28

ORDER BY

Sorts the result

29

OUTER JOIN

Returns all rows where there is a  match in either left (or) right tables 

30

PRIMARY KEY

Unique identifier

31

RIGHT JOIN

Returns all rows from the right table and matching rows from the left table

32

TRUNCATE TABLE

Deletes data from the table(table will remain, data will be deleted)

33

UNION

Combines the result set of two or more select statements(only unique values)

34

UNION ALL

Combines the result set of two or more select statements(allows duplicates values)

35

WHERE

Filters the result


Comments

Post a Comment

Popular posts from this blog

Object Detection Using OpenCV and Transfer Learning.

-
Image
 Object Detection Using OpenCV and Transfer Learning. Environment setup/Project Requirements : Python OpenCV, Matplotlib libraries need to be installed. Jupyter Notebook/Pycharm/ Atom  Note : You cannot implement this project in Google-Colaboratory or in any python online Interpreter Workflow: Importing Transfer Learning model  Import dnn_DetecctionModel Looping through the coco dataset Initializing Threshold  Testing the model on Image Looping through the Labels Implementing on Video Step 1: Importing the Transfer learning model Firstly, we initialize the transfer learning model weights and frozen graph t some variable names. config_file = "ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt" frozen_model = "frozen_inference_graph.pb" Step 2: Importing dnn_DetectionModel OpenCV contains dnn_DetectionModel, we can call it from cv2 by importing cv2. model = cv2.dnn_DetectionModel(frozen_model,config_file) Step 3: Looping through the coco dataset Since the coco data set that...
Read more

Path Detection based on Bird Eye view with OpenCV module

-
Image
Path Detection based on Birds Eye view with OpenCV module   Environment setup/ Project Requirements : 1)Python  2)OpenCV, Numpy, Pickle libraries need to be installed on your computer/laptop 3) Pycharm/Visual studio/Atom must be installed Note: You can't run this code in google-colab (or) in any online compiler/interpreter  Work Flow:   We make a copy of the original image and Undistort the copied image Apply threshold and canny filter to the copied image. Apply warp perspective to get a birds-eye view of the image. Place virtual windows at the starting of the image, which reflects the shape of the curve. Using the center of the windows we generate a second-order polynomial function. We apply the entire process to the original image. Step 1: Image Preprocessing Firstly we have to undistort the image because, when a large amount of area is getting into the smaller area there will be some distortions in the image. We use the undistort function to remove distortion...
Read more

Traffic signs recognition with convolutional Neural networks

-
Image
A traffic sign detection system with Convolutional neural networks and OpenCV. Previously we implemented a path detection code for an autonomous vehicle. In this, article let's see how to implement a traffic sign detection system for an autonomous vehicle using deep convolutional networks and OpenCV module. Environment setup/ Project Requirements : 1)Python. 2)OpenCV, Pickle, Numpy, Matplotlib, Keras, Sklearn libraries need to be installed on your computer/laptop. 3) I'm using the Jupyter Notebook environment, You can use any environment. I think you can use google-colab. Note: You can't run this code in any python online compiler/interpreter. Work Flow : Import the data-set and map them to corresponding labels. We divide the data into a Training set, Validation set, and Testing set. We convert RGB images to gray and resize them and standardize the images. Iterate and pre-process all the images. Data Argumentation to make data more generic. Neural Network Model. Setup camer...
Read more