Pythons and Pandas: Animals That Make a Large Zoo Easier to Navigate
Navigating a ZOO!!!
There are tons of different types of animals at the zoo. Elephants, flamingoes, giraffes, leopards, lions, and octopi are a few on the list. How does a worker at a zoo learn about and understand such groupings of beings when many varieties of animals exist at a single zoo? How might a worker quickly and effectively decipher animal information when the numbers of animals at zoos are on extreme scales? The answers live with the pandas and pythons.
Background on Pandas and Pythons
Pandas and pythons are not only animal names, but also names associated with computer language and code. Python is a computer language that uses computer code to create and evaluate information provided in many different forms. Pandas is a library for data analysis within Python that takes data and transforms the data into more readable and more easily manipulatable forms. Pandas is a great computer tool to use when dealing with large sets of data. Pandas can make big data usable, understandable, and accessible on a simple level involving dataframes (two-dimensional tables similar to Excel spreadsheets) and specified functions with Python code.
Pythons and Pandas With DataFrames
How can a worker at the zoo with many animals to care for manage, simplify, and interpret data about animals? Pandas dataframes and Python code assists.
Here is an example zoo created by Richard Forsyth and posted on Kaggle to help with the solution. The zoo must be downloaded into Pandas libraries to get the zoo information. The Python code for this is as follows:
Df stands for DataFrame. The zoo information is downloaded and read in pandas with the ‘zoo.csv’ file name.
Pandas Functions and Python Code:
Suppose a zoo worker wants to obtain a number of how many kinds of animals are in the zoo and specific traits about them. What functions could easily assess the animal data and mitigate the big animal data?
The first function to run in Pandas to help access this zoo data and learn about the zoo more specifically is given below:
The df.shape function in Pandas libraries tells the number of rows and columns in a Pandas Dataframe. There are 18 columns identifying 18 categories for the animals (animal_name, hair, feathers, etc.) and 101 rows identifying 101 different animals in the zoo. That is a large grouping of animals and many categories to evaluate! The df.shape Pandas function took the data and gave an overall picture of the zoo on a smaller scale.
Pandas Function and Python Code Continued…
The number of animal types and animal categories is now determined. Other questions may be asked and other functions used for more detailed information on the zoo:
What if a worker wants to just know the first five animal names alphabetically and the last five animal names alphabetically in the zoo without going through every animal name? Two functions are used for this and written as follows:
The Pandas Dataframe df.head() function provides the names of the first five animals alphabetically in the zoo: aardvark, antelope, bass, bear, and boar. The Pandas Dataframe df.tail() function provides the names of the last five animals alphabetically in the zoo: wallaby, wasp, wolf, worm, and wren. These Pandas functions accessed these specific animals’ data without sorting through every single animal in the 101 animals listed.
What if the zoo worker has a list of animals and wants to find the animals that belong to the class type 6 without looking at all the animals in the zoo? The df.loc (locate) function can help with this:
The df.loc function in Pandas located the animals in class type 6 without displaying every animal name. This data pretty clearly demonstrates that class type 6 is insects without providing general information about all the animals in the zoo.
What if a worker wants to find the minimum and maximum amounts of legs on an animal in the zoo? The following Pandas functions can be used as follows:
The df[‘legs’].max() function expresses that the most amount of legs on any animal in the zoo is 8. The df[‘legs’].min() shows that the least amount of legs on any animal in the zoo is 0. With these functions the minimum and maximum amount of legs on animals in this zoo of 101 animals was determined with some minimal code.
What if a worker wants to find all the unique numbers of legs that animals can have in a zoo and the total number of unique animal leg numbers in the zoo?
The df[‘legs’].unique() function demonstrates that all animals in the zoo have either 4, 0, 2, 6, 8, or 5 legs. Also, the len(df[‘legs’].unique()) function (with ‘len’ standing for ‘length’) expresses that there are only 6 different options for the amount of legs an animal can have in the zoo. This was determined in array and integer format with computer Python code and Pandas functions. Every animal in the zoo did not have to be manually organized in order to achieve this data. Pandas and Pythons attained the data easily and understandably.
Python and Pandas: A Zoo Navigated
There are all types of functions and dataframe analyses using Pandas and Python computer programs that can help make a large zoo and big animal data more understandable. Without these computer programs, zoo data navigation is much more difficult. Imagine taking each individual animal in a large zoo and trying to make animal comparisons without these programs. Every animal might have to be sorted using a more strenuous process and locating a specific animal based on a trait or class could be extremely difficult. The dataframe and functions using Pandas and Python gave the answers with a data table and some simple code. Pandas and pythons are the necessary animals to make an enormous zoo more easily navigable.
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