V.A11

Created a dataframe named ign_scores and set the Platform column to the labels for the columns. Found the highest score value for the Wii column and the lowest score value for PlayStation Vita. Created a bar graph to show which platform had the best racing games and found that XBox One had the best racing games. Then created a heatmap to find the highest and lowest scoring Platform to Genre relation. Finally created dataframe of pokemon characters where the names became the index values.

V.A10

Created a dataframe and manipulated the Fields to become Column labels, found the average of the publications number, found all the bonds that had a value over 15000 and listed those acc.ver values, made a plot that reflected the relationship between identity and positives, and merged the protein sources from another dataframe to the main dataframe.

V.A6

Plot 1: Created a seaborn relplot to visualize pokemon data based on the relationship between defense and attack. Using the hues, we defined the variations between HP.

Plot 2: Created a seaborn relplot jointplot with a bargraph and hexbin plot to display the relationship between total and attack. Hues were also used to determine the density of plot populations.

Plot 3: Created two categorical for two separate pokemon types and determined what were the most common and the most rare from each type.

V.A5

Took a dataset and performed functions to create a visualization of specific data that I, the user, wanted to see.

V.A4

With the use of pandas data structures and algorithms, I analyzed an eukaryote genome dataset through a hands-on exercise that is included in this release. Answering important biological problems including recognizing mammals with at least 20,000 genes, counting the number of distinct species and records for every animal class, and extracting particular scientific names are all part of the research. I constructed a new dataframe, changed the names of the species to just include the scientific portions, and used a pie chart to display the results. Users can clean, alter, and analyze genomic data with this dataset, which is offered as a tab-delimited file. This provides insightful information on gene information and animal classification.