Image
EAN-130000000000031   EAN-13 barcode 0000000000031
UPC-A000000000031   UPC-A barcode 000000000031
UPC-E00000301   UPC-E barcode 00000301
Product Name48" x 10" Floating Wall Shelf - Walnut (Walnut) (2"H x 48"W x 10"D)
LanguageEnglish
CategoryHome
Amazon.comA Buy on Amazon ~ B0050OI84G
Price New54.99 US Dollars    (curriencies)
Width50 centimeters    (convert)
Height1 millimeters    (convert)
Length70 centimeters    (convert)
Weight80 grams    (convert)
BindingKitchen
Features
  • Color: Walnut
  • Size: 2"H x 48"W x 10"D
  • This wood shelf has a glossy melamine laminated finish
  • Made from high quality MDF
  • Securely slides and locks into the mounting bracket
Long DescriptionThe sturdy yet lightweight design of the walnut 48" x 10" Floating Wall Shelf makes it an ideal display space. This floating shelf has sharp, clean lines that will fit in well with both contemporary and traditional decor. This wood shelf has a glossy melamine laminated finish. Made from high quality MDF. Securely slides and locks into the mounting bracket. No visible screws, connectors or tracks. Mounting hardware is included; all you need for installation is a Phillips screwdriver (power drill recommended). The shelf has a maximum load of 30 pounds. Assembly level/degree of difficulty: Moderate.
Created03-12-2013 7:44:06am
Modified05-01-2018 10:45:16pm
MD5a5531a5ea73a01cbe5c43a440a95d1a8
SHA2567cc7902491c22408a1986de1825102c29d59afbae31a8b69f3c6d1f465b12956
Search Googleby EAN or by Title
Search Amazonby EAN or by Title
Query Time0.0017300

Here we will demonstrate the most basic example of importing the CSV data files that we produce on this site into your MySQL database.

For information about various databases you can use and how to import CSV files into them, please view the overview article "Importing CSV data into your database".

For this example, we are going to import the product data CSV file out of the sample_ean_data.zip but this same process will work on the full data download file. We will also be executing the commands in the MySQL Workbench but you can also use the command line tool with the same commands if you like.

First, start by creating a blank table. Use the table layout described in the read_me file for the most up-to-date table layout. It is suggested that you not use any indexing at this point. You can add indexes later. It is most likely that you will have your own tables where you want to store your data so importing the CSV files can be done into temporary tables and then later copied over to your tables. Leaving off the indexes and constraints on these import tables reduces the risk of import errors. Here is an example:

create table ean_product
(
    EAN13             varchar(13),
    UPCA              varchar(12),
    UPCE              varchar(8),
    SKU               varchar(200),
    PriceNew          numeric(15,2),
    PriceUsed         numeric(15,2),
    PriceDate         date,
    company           varchar(13),
    product           varchar(100),
    description       varchar(100),
    category          int,
    url               varchar(500),
    created           datetime,
    modified          datetime
);

Next we perform the import using the LOAD DATA INFILE command. The path to the file depends on where you saved the data and which operating system you are on. For Windows users you might find your file on the C: drive and Linux users may find your date in your home (~) folder. This example shows a Linux import. Only the path would be different between the operating systems.

LOAD DATA LOCAL
    INFILE '~/sample_ean_data/sample_ean_product.csv' 
    INTO TABLE ean_product
    FIELDS TERMINATED BY ',' ENCLOSED BY '"' ESCAPED BY '\\'
    LINES TERMINATED BY '\r\n'
    IGNORE 1 LINES;

Finally, lets look at the data that we just imported.

SELECT * FROM EAN_PRODUCT;

You may have seen some warnings after the import command. If you are concerned about these warnings, examine the data. It could be that some data has grown beyond the size specified in the read_me file. If you are worried, make the fields larger and try the process again after deleting all of the data out of the table.

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