BLUEWATER COUNTRYADULTBusiness Directories,Company Directories

Company Name: Corporate Name:	BLUEWATER COUNTRYADULT
Company Title:
Company Description:
Keywords to Search:
Company Address:	5700 Blackwell Side Rd,SARNIA,ON,Canada
ZIP Code: Postal Code:	N7W1B7
Telephone Number:	5195427800
Fax Number:	5195424377
Website:
Email:
USA SIC Code(Standard Industrial Classification Code):	805904
USA SIC Description:	Retirement Communities & Homes
Number of Employees:	10 to 19
Sales Amount:	$500,000 to $1 million
Credit History: Credit Report:	Excellent
Contact Person:	Ross Mc Eachran
Remove my name

Company Directories & Business Directories

copy and paste this google map to your website or blog!

Press copy button and paste into your blog or website.
(Please switch to 'HTML' mode when posting into your blog. Examples:
WordPress Example, Blogger Example)

Input Form:Deal with this potential dealer,buyer,seller,supplier,manufacturer,exporter,importer

(Any information to deal,buy, sell, quote for products or service)

Previous company profile:
BREADMAN PIZZA PASTA & CAFE
BRAUNVALLEY ASSOC LTD
BLUEWATER CENTREOCCUPATIONAL

Next company profile:
BLUEWATER SCHOOLHAIR DESIGN
BIG SISTERS OF SARNIALAMBTON
BIG BROTHERS OF SARNIALAMBTON

Company News:

How do I select rows from a DataFrame based on column values?
Only, when the size of the dataframe approaches million rows, many of the methods tend to take ages when using df[df['col']==val] I wanted to have all possible values of "another_column" that correspond to specific values in "some_column" (in this case in a dictionary)
How can I iterate over rows in a Pandas DataFrame?
I have a pandas dataframe, df: c1 c2 0 10 100 1 11 110 2 12 120 How do I iterate over the rows of this dataframe? For every row, I want to access its elements (values in cells) by the n
How do I get the row count of a Pandas DataFrame?
could use df info () so you get row count (# entries), number of non-null entries in each column, dtypes and memory usage Good complete picture of the df If you're looking for a number you can use programatically then df shape [0]
In pandas, whats the difference between df[column] and df. column?
The book typically refers to columns of a dataframe as df['column'] however, sometimes without explanation the book uses df column I don't understand the difference between the two
disk usage - Differences between df, df -h, and df -l - Ask Ubuntu
Question What are the differences between the following commands? df df -h df -l Feedback Information is greatly appreciated Thank you
How to get set a pandas index column title or name?
To just get the index column names df index names will work for both a single Index or MultiIndex as of the most recent version of pandas As someone who found this while trying to find the best way to get a list of index names + column names, I would have found this answer useful:
python - Renaming column names in Pandas - Stack Overflow
To focus on the need to rename of replace column names with a pre-existing list, I'll create a new sample dataframe df with initial column names and unrelated new column names
python - pandas extract year from datetime: df [year] = df [date . . .
A subtle but important difference worth noting is that df index month gives a NumPy array, while df['Dates'] dt month gives a Pandas series Above, we use pd Series values to extract the NumPy array representation
Creating an empty Pandas DataFrame, and then filling it
df loc[len(df)] = [a, b, c] As before, you have not pre-allocated the amount of memory you need each time, so the memory is re-grown each time you create a new row It's just as bad as append, and even more ugly Empty DataFrame of NaNs And then, there's creating a DataFrame of NaNs, and all the caveats associated therewith