[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-1-en-105":3,"doc-seo-571757-105":53,"doc-detail-571757-en":127},{"code":4,"msg":5,"data":6},0,"success",[7,14,19,24,29,34,39,44,49],{"id":8,"doc_module":9,"doc_module_name":10,"category_name":11,"show_sort_weight":12,"slug":13},11,1,"Template","Presentations",90,"presentations",{"id":15,"doc_module":9,"doc_module_name":10,"category_name":16,"show_sort_weight":17,"slug":18},12,"Resumes",80,"resumes",{"id":20,"doc_module":9,"doc_module_name":10,"category_name":21,"show_sort_weight":22,"slug":23},14,"Invoices",70,"invoices",{"id":25,"doc_module":9,"doc_module_name":10,"category_name":26,"show_sort_weight":27,"slug":28},15,"Posters",60,"posters",{"id":30,"doc_module":9,"doc_module_name":10,"category_name":31,"show_sort_weight":32,"slug":33},16,"Social Media",50,"social-media",{"id":35,"doc_module":9,"doc_module_name":10,"category_name":36,"show_sort_weight":37,"slug":38},17,"Forms",40,"forms",{"id":40,"doc_module":9,"doc_module_name":10,"category_name":41,"show_sort_weight":42,"slug":43},18,"Letters",30,"letters",{"id":45,"doc_module":9,"doc_module_name":10,"category_name":46,"show_sort_weight":47,"slug":48},21,"Paper Templates",5,"papers-templates",{"id":50,"doc_module":9,"doc_module_name":10,"category_name":51,"show_sort_weight":4,"slug":52},158,"General","general-158",{"code":4,"msg":54,"data":55},"ok",{"site_id":56,"language":57,"slug":58,"title":59,"keywords":60,"description":61,"schema_data":62,"social_meta":120,"head_meta":122,"extra_data":124,"updated_unix":126},105,"en","intermediate-advanced-python-day-3","Intermediate-Advanced Python - Day 3","","This document outlines the curriculum for the third day of an Intermediate and Advanced Python programming course taught by Michael Weinstein. It focuses on practical data analysis techniques, specifically covering the use of NumPy, Pandas, and Matplotlib. The content emphasizes data structure manipulation, including extracting columns from DataFrames, transposing data, and counting base frequencies from SAM files. Detailed examples and logical workflows are provided to help learners transition from raw string data to structured, analytical formats compatible with R-style dataframes. By emphasizing iterative testing and refinement, the guide provides a robust framework for handling heterogeneous datasets effectively. Participants are introduced to real-world data science challenges, such as analyzing base frequency across biological sequence positions, while learning to optimize code performance through appropriate library selection and data transposition techniques.",{"@graph":63,"@context":119},[64,80,102],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,71,74,77],{"item":68,"name":69,"@type":70,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":72,"name":10,"@type":70,"position":73},"https://docshare.wps.com/template/",2,{"item":75,"name":51,"@type":70,"position":76},"https://docshare.wps.com/template/general/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/intermediate-advanced-python-day-3/571757/",4,{"url":78,"name":59,"@type":81,"image":82,"author":87,"headline":59,"publisher":90,"fileFormat":93,"inLanguage":57,"description":61,"dateModified":94,"datePublished":95,"encodingFormat":93,"isAccessibleForFree":96,"interactionStatistic":97},"DigitalDocument",{"url":83,"@type":84,"width":85,"height":86},"https://docshare.wps.com/thumbnails/intermediate-advanced-python-day-3/571757.png","ImageObject",442,249,{"name":88,"@type":89},"Mimi","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-10-24","2026-10-13",true,{"@type":98,"interactionType":99,"userInteractionCount":101},"InteractionCounter",{"@type":100},"ViewAction",6,{"@type":103,"mainEntity":104},"FAQPage",[105,111,115],{"name":106,"@type":107,"acceptedAnswer":108},"What are the primary Python libraries covered in the Day 3 session?","Question",{"text":109,"@type":110},"The session focuses on three core data science libraries: NumPy for efficient numerical arrays, Pandas for flexible data manipulation, and Matplotlib for data visualization.","Answer",{"name":112,"@type":107,"acceptedAnswer":113},"What is the specific goal of the base frequency analysis task described?",{"text":114,"@type":110},"The goal is to calculate the frequency of DNA bases at each position across the first 50 bases of each read from a SAM file, requiring iteration, list conversion, and data structure organization.",{"name":116,"@type":107,"acceptedAnswer":117},"Why does the course recommend using Pandas DataFrames for heterogeneous data?",{"text":118,"@type":110},"Pandas DataFrames are recommended because they provide a highly flexible structure for handling non-uniform data types, offering a balance between speed and ease of use, similar to dataframes found in the R programming language.","https://schema.org",{"og:url":78,"og:type":121,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":123,"canonical":78},"index,follow",{"doc_id":125,"site_id":56},571757,1792113655,{"code":4,"msg":5,"data":128},{"doc_id":125,"user_id":129,"nickname":88,"user_avatar":130,"doc_module":9,"category_id":50,"category_name":51,"doc_title":59,"doc_description":61,"doc_content":131,"file_id":132,"file_url":133,"file_type":134,"file_size":135,"view_count":101,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":45,"language":136,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":137,"faqs":138,"seo_title":139,"seo_description":61,"update_tm":140,"read_time":141},2336477974920,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Intermediate/Advanced Python  \nMichael Weinstein (Day 3)  \nTopics  \n› Review of basic data structures › Accessing and working with objects in python › Numpy  \n› Pandas  \n– What are dataframes?  \n– Sample column counting operation  \n– Test, refine, test, refine, test, refine to get the data structure we want  \n– Extracting a column › Matplotlib  \n– Making a simple line plot › Scipy  \nNext goal: Analyze base frequency at each position in the first 50 bases  \n› Need to iterate over sam file again › Need to grab the first 50 bases of each read › Need to turn the string into a list  \n› Need to move the data to a pandas dataframe  \n– This is a more flexible structure for holding heterogenous data  \n– Not as efficient as a numpy array, but still very fast  \n– Very similar to dataframes in R language  \n› Need to count base occurrences in each column › New file: [baseFrequencyCounter.py](baseFrequencyCounter.py)  \nHow to think of a dataframe?  \nHow to count letters in a column?  \nSome test code  \nData looks almost right, except we need it transposed (we want position tobe the columns) .  \nHow to transpose  \nTesting our data structure","cbCaitsfqCNZW1xg","https://ap.wps.com/l/cbCaitsfqCNZW1xg","pdf",2835129,"English","# Review of Core Data Structures\n## Object Access and Manipulation\n# Advanced Libraries\n## Numpy Arrays\n## Pandas Dataframes\n## Matplotlib Plotting\n# Practical Data Analysis Workflow\n## Base Frequency Analysis\n## SAM File Iteration\n## Data Structure Refinement and Transposition","[{\"question\":\"What are the primary Python libraries covered in the Day 3 session?\",\"answer\":\"The session focuses on three core data science libraries: NumPy for efficient numerical arrays, Pandas for flexible data manipulation, and Matplotlib for data visualization.\"},{\"question\":\"What is the specific goal of the base frequency analysis task described?\",\"answer\":\"The goal is to calculate the frequency of DNA bases at each position across the first 50 bases of each read from a SAM file, requiring iteration, list conversion, and data structure organization.\"},{\"question\":\"Why does the course recommend using Pandas DataFrames for heterogeneous data?\",\"answer\":\"Pandas DataFrames are recommended because they provide a highly flexible structure for handling non-uniform data types, offering a balance between speed and ease of use, similar to dataframes found in the R programming language.\"}]","Intermediate-Advanced Python - Day 3 | PDF",1791923597,7]