new dataframe containing only that weight class.) Use examples to justify that your dataframes have been created correctly. In [ ]: # Put your pseudocode and code here # List to help extract information from the base dataframe weight_class_list = ['47', '52', '57', '63', '72', '84', '84+', '59', '66', '74', '83', '93', '105', '120', frame_names_list = ['class_47kg','class_52kg','class_57kg','class_63kg','class_72kg','class_84kg','class_84 'class_59kg','class_66kg','class_74kg','class_83kg','class_93kg','class_105kg','class_1 Step 2: Loop through all the weight classes and store the dataframes for each weight classs in a list (Lists can store all types of variables Pandas dataframes!) In [ ]: # Put your pseudocode and code here
Types of Linked List
A sequence of data elements connected through links is called a linked list (LL). The elements of a linked list are nodes containing data and a reference to the next node in the list. In a linked list, the elements are stored in a non-contiguous manner and the linear order in maintained by means of a pointer associated with each node in the list which is used to point to the subsequent node in the list.
Linked List
When a set of items is organized sequentially, it is termed as list. Linked list is a list whose order is given by links from one item to the next. It contains a link to the structure containing the next item so we can say that it is a completely different way to represent a list. In linked list, each structure of the list is known as node and it consists of two fields (one for containing the item and other one is for containing the next item address).
![Step 1: Write a function to create a new dataframe(s) for a given weight class. (Inputs should be base dataframe and weight class; Output should be a
new dataframe containing only that weight class.)
Use examples to justify that your dataframes have been created correctly.
In [ ]: # Put your pseudocode and code here
# List to help extract information from the base dataframe
weight_class_list =
frame_names_list
['47', '52', '57', '63', '72', '84', '84+', '59', '66', '74', '83', '93', '105', '120', '120+']
['class_47kg','class_52kg','class_57kg','class_63kg','class_72kg','class_84kg','class_84pluskg',\
'class_59kg','class_66kg','class_74kg','class_83kg','class_93kg','class_105kg','class_120kg','cl
Step 2: Loop through all the weight classes and store the dataframes for each weight classs in a list (Lists can store all types of variables including
Pandas dataframes!)
In [ ]: # Put your pseudocode and code here](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F67da618c-d681-4779-adbe-24e8bae00938%2F230bf45a-02c5-4811-ac1f-33adc7ab44d5%2F4tqmuk_processed.png&w=3840&q=75)
![Write a function to calculate the mean, median, and standard deviation for a single weight class. Then, write a loop through all of the weight
classes and save the results of your calculations for plotting.
]: # Put your pseudocode and code here](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F67da618c-d681-4779-adbe-24e8bae00938%2F230bf45a-02c5-4811-ac1f-33adc7ab44d5%2Fad09srs_processed.png&w=3840&q=75)
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