Answer:
# the dog dataframe has been loaded as mpr
# select the dogs where Age is greater than 2
greater_than_2 = mpr [mpr. age > 2]
print(greater_than_2)
# select the dogs whose status is equal to 'still missing'
still_missing = mpr[mpr. status == 'Still Missing']
print(still_missing)
# select all dogs whose dog breed is not equal to Poodle
not_poodle = mpr [mpr.breed != 'Poodle']
print(not_poodle)
Explanation:
The pandas dataframe is a tabular data structure that holds data in rows and columns like a spreadsheet. It is used for statistical data analysis and visualization.
The three program statements above use python conditional statements and operators to retrieve rows matching a given value or condition.
Answer:
a. Install more RAM
Explanation:
According to my research on information technology, I can say that based on the information provided within the question this server will not work for you purpose unless you install more RAM. This is because Hyper-V server's have a minimum requirement of 4gb, therefore if you want to run 2 servers you can divide all the resources you have since they are enough but not the RAM since you only have the bare minimum for one server. You need to add atleast 4 gb more of RAM.
I hope this answered your question. If you have any more questions feel free to ask away at Brainly.
The last option:
Avoid forwarding e-mail messages unless you have permission to do so.
Answer:
It we were asked to develop a new data compression tool, it is recommended to use Huffman coding since it is easy to implement and it is widely used.
Explanation:
The pros and the cons of Huffman coding
Huffman coding is one of the most simple compressing encoding schemes and can be implemented easily and efficiently. It also has the advantage of not being patented like other methods (e.g. arithmetic codingfor example) which however are superior to Huffman coding in terms of resulting code length.
One thing not mentioned so far shall not be kept secret however: to decode our 96 bit of “brief wit” the potential receiver of the bit sequence does need the codes for all letters! In fact he doesn’t even know which letters are encoded at all! Adding this information, which is also called the “Huffman table” might use up more space than the original uncompressed sentence!
However: for longer texts the savings outweigh the added Huffman table length. One can also agree on a Huffman table to use that isn’t optimized for the exact text to be transmitted but is good in general. In the English language for example the letters “e” and “t” occur most often while “q” and “z” make up the least part of an average text and one can agree on one Huffman table to use that on average produces a good (=short) result. Once agreed upon it doesn’t have to be transmitted with every encoded text again.
One last thing to remember is that Huffman coding is not restricted to letters and text: it can be used for just any symbols, numbers or “abstract things” that can be assigned a bit sequence to. As such Huffman coding plays an important role in other compression algorithms like JPG compression for photos and MP3 for audio files.
The pros and the cons of Lempel-Ziv-Welch
The size of files usually increases to a great extent when it includes lots of repetitive data or monochrome images. LZW compression is the best technique for reducing the size of files containing more repetitive data. LZW compression is fast and simple to apply. Since this is a lossless compression technique, none of the contents in the file are lost during or after compression. The decompression algorithm always follows the compression algorithm. LZW algorithm is efficient because it does not need to pass the string table to the decompression code. The table can be recreated as it was during compression, using the input stream as data. This avoids insertion of large string translation table with the compression data.
