Tokenize Pandas Column

Nov 03, 2020 nltk tokenize pandas column 2) Stemming: reducing related words to a common stem. The nltk.stem package will allow for stemming and lemmatization (normalization techniques). Here is an example of Combining text columns for tokenization: In order to get a bag-of-words representation for all of the text data in our DataFrame, you must first convert the text data in each row of the DataFrame into a single string.

Nltk Tokenize Pandas Column
Tokenize Pandas Column Pictures
Tokenize Column Pandas
Tokenize Pandas Column Definition
Tokenize Pandas Column Meaning

By: Aubrey Love | Updated: 2020-02-19 | Comments (6) | Related: More >T-SQL

Problem

Typically, in SQL Server data tables and views, values such as a person's name or theiraddress is stored in either a concatenated string or as individual columns for eachpart of the whole value. For example: John Smith 123 Happy St Labor Town, CA. Thisdata could be stored in a single column with all the data, in two columns separatingthe person's name from their address or in multiple columns with a column for eachpiece of the total value of the data.

With this, DBA's are inevitably always having to concatenate or parsethe values to suit our customer's needs.

To build on our sample from above, I have an address column that has the fulladdress (street number and name, city and state) separated by commas in a concatenatedcolumn. I want to break that data into three columns (street, city and state) respectively.

Autocad 2008 torrent grade la sopa en. AutoCAD 2008 is an old product with obsolete interface - you should be learning the new Ribbon interface pioneered by Microsoft.;) Oh yes, the new Ribbon interface that will delay our upgrade purchases for a good year or three. I'm glad I like ACA 2008, because it will be here for a while. Autocad software 2008 free download. Photo & Graphics tools downloads - AutoCAD Architecture by Autodesk and many more programs are available for instant and free download.

Solution

For this tip, let's create a test database and test table for the parsing example. In this scenario, we have a street name and number, followed by the city,then the state.

The following code creates a table in the new (test) database with only two columns- an id column and an address column.

Populate the table with some generic data. Notice that the address is one stringvalue in one column.

Confirm the table creation and data entry with the following SQL SELECT statement.

Your results should return, as expected, two columns - the id column and theaddress column. Notice the address column is returned as a single string value inimage 1.

Breaking down the data into individual columns. The next step will be to parse outthe three individual parts of the address that are separated by a comma.

Since we didn't call the id column in the SELECT statement above, the resultsonly returned the address string divided into three columns, each column returninga portion of the address for the respective column as shown in image 2.

Here in the real world, DBA's are often faced with more complex tables or viewsand not just a simple two column table as in the above sample. Although the sampleabove is a great primer for dissecting how to parse a string value, thefollowing section demonstrates a more complex situation.

Again, create a sample table in our test database to work with. Here, we createa slightly more complex table with some additional columns.

Insert some generic data into the test table.

Now, let's create a mixed SELECT statement to pull all the columns that breaksdown the 'empName', 'empAddress' and 'empPhone' columns into multiple columns.

In the following block of code, notice that I restarted my 'place / position'count on each column that I want to parse. Each time you start parsing a new column, you must start your count over. You should also note that the 'empName'and 'empAddress' string values are separated with a comma while the 'empPhone' stringvalue is separated by a hyphen and thus should be reflected in your 'parse' functionbetween the first set of single quotes.

The results should return thirteen columns from the six columns queried againstas shown in image 3.

Nltk Tokenize Pandas Column

In summary, the PARSENAME function is a handy addition to your T-SQL toolkit forwriting queries involving delimited data. It allows for parsing out and returningindividual segments of a string value into separate columns. Since the PARSENAMEfunction breaks down the string, you are not obligated to return all the delimitedvalues. As in our sample above, you could have returned only the area code fromthe 'empPhone' column to filter certain area codes in your search.