Image
EAN-139780452283879   EAN-13 barcode 9780452283879
Product NameThe Rough Guide To India
LanguageEnglish
CategoryBook / Magazine / Publication
Short DescriptionPaperback
Amazon.comA Buy on Amazon ~ 1848365632
SKUMON0000031005
Price New33.32 US Dollars    (curriencies)
Price Used1.56 US Dollars    (curriencies)
Width1.69 inches    (convert)
Height7.77 inches    (convert)
Length5.13 inches    (convert)
Weight26.24 ounces    (convert)
AuthorRough Guides
Page Count1200
BindingPaperback
Published03/07/2011
Long Description"The Rough Guide to India" is the essential travel guide to this fascinating country. It covers all the major areas, from Delhi's Paharganj to Havelock Island in the Andamans, with reviews of the best resorts, hotels, restaurants and nightlife for every taste and budget. The guide includes practical advice on exploring all the attractions, like the stunning temples, mosques and museums, and details all you need to know about the country's history, religions, wildlife and predominant language, Hindi. "The Rough Guide to India" has dozens of easy-to-use maps, covering all the states, major cities and other areas of interest to travellers. Plus, superb photography and three full colour sections show India's highlights and a basics section covers essentials such as social and etiquette tips. Make the most of your trip with "The Rough Guide to India".
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Modified05-08-2017 10:49:37am
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SHA2568a9fd585b92efeb93125e4a840d39f530728c25e4c38855c1de1514388e1677d
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Article of interest

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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