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Are you looking for a better solution than the one you’ve built in a spreadsheet? If so, then Python for Spreadsheet Users is a great introduction to the Python language, and will put you on the right path towards automating repetitive work, diving deeper into your data, and widening the scope of what you are capable of accomplishing. Throughout the course, we’ll draw parallels to common spreadsheet functions and techniques, so you’ll always have a familiar reference point as you dive head first into Python.
Let's get right into it! In this chapter, you'll become acclimated with Python syntax, loading your data into a Python session, and how to explore and edit this data to answer business questions.Welcome to Python!50 xpImporting packages100 xpLoading data100 xpLooking at data with print()100 xpDataFrames and their methods50 xpThe .head() method100 xp.info() and .describe() methods50 xpThe .sort_values() method100 xpFiltering rows and creating columns50 xpFiltering100 xpCreating columns100 xpPutting it all together100 xp
Pivoting in Python
The pivot table is a core tool in the savvy spreadsheet user's arsenal. In this chapter, we'll focus on simply recreating this functionality in Python using some handy DataFrame methods.Grouping and summing: the beginner's pivot table50 xpPure summary with .sum()50 xp.groupby() and .sum() together100 xpSummarizing and sorting values100 xpGrouping by multiple columns50 xpWhich is a list?50 xpMovie genre performance by location100 xpWhat do seniors like?100 xpMore useful summary tools50 xpBest-selling movie by location100 xpWhich movie averages the most sales100 xp
Working with Multiple Sheets
This chapter will focus on how to import and manage multiple sheets from a workbook, as well as how to join these sheets together using the Python equivalent of a VLOOKUP: the left join.Working with multiple sheets50 xppd.ExcelFile() function100 xp.sheet_names attribute100 xp.parse() method100 xpPreparing to put tables together50 xp.str.lower() and .str.title()100 xp.str.strip()100 xpSelecting and .drop()100 xpMerging: The VLOOKUP of Python50 xpMerging for ticket prices100 xpMerging for theater locations100 xpPutting it all together again100 xp
Now that you're able to import and manipulate your data in Python, let's shift our focus to visualizing this data so that our insights are easily communicable to others.How visualization works in Python50 xpHow to build a graph in Python50 xpTop grossing films100 xpBest genres100 xpBuilding up the barplot50 xpPut a title on it100 xpLabel your axes!100 xpMake it pretty with sns100 xpThe power of hue50 xpInterpreting hue50 xpGenre by market100 xpGenre by ticket type100 xpWrapping up50 xp
DatasetsMovie Theater Sales dataset
PrerequisitesPivot Tables in Spreadsheets
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