Create Basic Lists and Dictionaries for Data Analysis in Python

Table of Contents

1. Challenge

   Step 1: Retrieve products from the inventory list

   Every data analysis starts with getting the data into a shape you can work with, and Python's built-in collections are the quickest way to do that. In this lab, you'll organize a neighborhood store's inventory in a Jupyter notebook. You'll keep its products in a list you can look up and change, compare its product categories with a second store's using sets, track how many of each product are on hand in a dictionary, and finish with a short inventory report built from Python's built-in functions.

   To begin, you'll work with the store's product list. A list is an ordered collection, so every product has a position, called an index. Indexes count up from 0 at the front of the list and down from -1 at the end. By the end of this step, you'll be able to pull out any single product by its position and any run of products with a slice. The notebook store_inventory.ipynb is already in the workspace with most of its code written. Open store_inventory.ipynb by double-clicking on the file name.

   A notebook is made of cells, and the kernel is the Python process that runs them. Run all of the code cells in this step by selecting each one in turn and pressing Shift+Enter.

   info> Feeling stuck? Open store_inventory_solution.ipynb from the File > Open menu to see every cell completed. Give each blank a try on your own first. The solution notebook is your safety net, not your starting point.

   Replace every ____ in a cell before you run it. If you run a cell with a placeholder still in it, Python stops with NameError: name '____' is not defined, which means the blank still needs your code. The first cell creates the products list with eight product names, in the order they appear on the store's shelf list. There's nothing to type in this cell. Run it as is.

   products = ["Notebook", "Pencil", "Stapler", "Desk Lamp", "Backpack", "Water Bottle", "Calculator", "Highlighter"]
   products
   

   Expected output

   You'll see all eight products, from 'Notebook' to 'Highlighter'.

   Retrieve a product by its position

   To get one item from a list, put its index in square brackets after the list's name. Index 0 is the first product.

   Replace ____ with the index 0, typed as a number without quotes.

   products[____]
   

   Expected output

   You'll see 'Notebook'.

   Index 3 is the fourth product, because indexes count from zero.

   Replace ____ with the index 3.

   products[____]
   

   Expected output

   You'll see 'Desk Lamp'.

   Retrieve a product from the end of the list

   A negative index counts backward from the end of the list. Index -1 is always the last item, no matter how long the list is. Replace ____ with the index -1.

   products[____]
   

   Expected output

   You'll see 'Highlighter'.

   Index -2 is the second-to-last item. Replace ____ with the index -2.

   products[____]
   

   Expected output

   You'll see 'Calculator'.

   Read a position that doesn't exist

   The list has eight items, so its last valid index is 7. The next cell asks for index 8, which doesn't exist. This error is expected: Python raises an IndexError whenever you ask a list for a position it doesn't have. There's nothing to type in this cell. Run it as is.

   products[8]
   

   Expected output

   You'll see IndexError: list index out of range.

   The error stops only this cell, so continue with the next one.

   Retrieve a range of products with a slice

   To get a range of items, use a slice. A slice has the form start:end inside the square brackets, and it returns a new list that starts at the start index and stops just before the end index.

   Replace ____ with the slice 1:4.

   products[____]
   

   Expected output

   You'll see ['Pencil', 'Stapler', 'Desk Lamp'], the products at indexes 1, 2, and 3.

   Leave out the start index and the slice begins at the front of the list.

   Replace ____ with the slice :3.

   products[____]
   

   Expected output

   You'll see ['Notebook', 'Pencil', 'Stapler'].

   Leave out the end index and the slice runs to the end of the list. Combined with a negative start index, this gets the last few items.

   Replace ____ with the slice -3:.

   products[____]
   

   Expected output

   You'll see ['Water Bottle', 'Calculator', 'Highlighter'].

   You can now retrieve one product by index and a group with a slice. Next, you'll change the list by adding and removing products.

2. Challenge

   Step 2: Update the inventory list

   With indexing and slicing, you can look up any product in the list. The store's inventory changes every week, though, so here you'll change the list itself as products arrive and are discontinued. Each cell ends with prewritten code that displays the list after your change so you can watch it update. By the end of this step, you'll know how to add a product to the end of a list, insert one at a specific position, and remove one by name or by position. Run all of the code cells in this step by selecting each one in turn and pressing Shift+Enter, after you replace the ____ the cell contains.

   warning> These cells change the list in place, which means they edit the list itself instead of making a copy. If you run one of them a second time after filling in its blank, the list changes again and the results no longer match these instructions. If that happens, select Kernel > Restart Kernel, then run the cells again from the top of the notebook down to where you left off.

   Add a product to the end of the list

   A new product, sticky notes, has arrived. The append() method adds one item to the end of a list. You call a method by typing the list's name, a dot, and the method name followed by parentheses that hold the value, such as products.append(item).

   Replace ____ with a call to append() on products that passes the string "Sticky Notes".

   ____
   products
   

   Expected output

   You'll see nine products, with 'Sticky Notes' at the end.

   Insert a product at a specific position

   Printer paper is the store's best seller, so it belongs at the front of the list. The insert() method takes an index and an item, places the item at that index, and shifts every later item one position to the right.

   Replace ____ with a call to insert() on products that passes the index 0 and the string "Printer Paper".

   ____
   products
   

   Expected output

   You'll see 'Printer Paper' first, followed by 'Notebook' and the rest of the products.

   Remove a product by name

   The store has discontinued its stapler. The remove() method deletes the first item that matches the value you pass it.

   Replace ____ with a call to remove() on products that passes the string "Stapler".

   ____
   products
   

   Expected output

   You'll see the list without 'Stapler'.

   Remove a product by position

   The order for sticky notes, the last product in the list, was canceled. The pop() method removes an item by position and returns it, so you can store the removed item in a variable. When you call pop() with no index, it removes the last item.

   Replace ____ with a call to pop() on products with nothing between the parentheses.

   canceled = ____
   print(canceled)
   print(products)
   

   Expected output

   You'll see Sticky Notes on the first line, followed by the eight remaining products from 'Printer Paper' to 'Highlighter'.

   info> If the first line shows Printer Paper instead, you passed an index to pop(). Select Kernel > Restart Kernel, remove the index so that nothing is between the parentheses of pop(), and run the cells again from the top.

   You can now add, insert, and remove list items. Next, you'll turn the product categories into sets and compare them across two stores.

3. Challenge

   Step 3: Compare categories across stores

   The product list is now current, and each product belongs to a category. Here, you'll turn category data into sets and compare the first store's categories with those of a second store run by the same owner. A set is an unordered collection that holds each value only once, which makes it the right tool for questions such as "which categories do both stores carry?" By the end of this step, you'll be able to build a set from a list and combine two sets to find what they share, what is unique to each, and everything they cover together. In store_inventory.ipynb, scroll down to the Step 3: Compare categories across stores heading. Run all of the code cells in this step by selecting each one in turn and pressing Shift+Enter, after you replace any ____ the cell contains.

   The first cell creates product_categories, a list with one category for each product in products, in the same order. Several products share a category. There's nothing to type in this cell. Run it as is.

   product_categories = ["Office Supplies", "Stationery", "Stationery", "Electronics", "Bags", "Kitchen", "Electronics", "Stationery"]
   product_categories
   

   Expected output

   You'll see eight categories, with 'Stationery' appearing three times and 'Electronics' appearing twice.

   Remove duplicates with a set

   Passing a list to set() builds a set from it, which keeps each value exactly once.

   Replace ____ with a call to set() that passes product_categories.

   store_a_categories = ____
   store_a_categories
   

   Expected output

   You'll see five categories:

   'Bags', 'Electronics', 'Kitchen', 'Office Supplies', and 'Stationery'.

   info> Sets have no order of their own. Jupyter displays a set's values sorted to make them easier to read, but you can't retrieve a set's values by index the way you can with a list.

   Create the second store's categories

   You can also write a set directly by putting its values in curly braces. The second store carries some of the same categories as the first store and some different ones. There's nothing to type in this cell. Run it as is.

   store_b_categories = {"Electronics", "Bags", "Toys", "Stationery", "Garden"}
   store_b_categories
   

   Expected output

   You'll see {'Bags', 'Electronics', 'Garden', 'Stationery', 'Toys'}.

   Find the categories both stores carry

   The intersection() method returns a new set of the values that appear in both sets. Here, those are the categories both stores carry.

   Replace ____ with a call to intersection() on store_a_categories that passes store_b_categories.

   shared_categories = ____
   shared_categories
   

   Expected output

   You'll see {'Bags', 'Electronics', 'Stationery'}.

   Find the categories only one store carries

   The difference() method returns the values in the first set that are not in the set you pass it. Starting from the first store shows the categories only it carries.

   Replace ____ with a call to difference() on store_a_categories that passes store_b_categories.

   ____
   

   Expected output

   You'll see {'Kitchen', 'Office Supplies'}.

   Starting from the second store gives a different answer, because difference() depends on which set you call it on.

   Replace ____ with a call to difference() on store_b_categories that passes store_a_categories.

   ____
   

   Expected output

   You'll see {'Garden', 'Toys'}.

   Combine both stores' categories

   The union() method returns every value that appears in either set, each one exactly once.

   Replace ____ with a call to union() on store_a_categories that passes store_b_categories.

   all_categories = ____
   all_categories
   

   Expected output

   You'll see seven categories, from 'Bags' to 'Toys'.

   You can now remove duplicates with a set and compare two sets. Next, you'll track stock levels in a dictionary.

4. Challenge

   Step 4: Track stock levels with a dictionary

   The product list tells you what the store sells, but not how much of each product is on hand. Here, you'll complete a dictionary named stock in which each key is a product name and each value is the quantity on hand, so each product maps directly to its stock level. You'll then look up quantities and update the dictionary as products sell, arrive, and are discontinued. By the end of this step, you'll be able to create a dictionary, read a value by its key, change a value, add a new key, and remove a key.

   warning> Like the cells in Step 2, these cells change their data in place. If you run one of them twice after filling in its blank, select Kernel > Restart Kernel, then run the cells again from the top of the notebook. A dictionary is written in curly braces as key: value pairs separated by commas. The first cell creates stock with one entry for each of the eight products currently in products. Every entry is written except the value for "Pencil".

   Replace ____ with the number 120, typed without quotes so it's a number rather than text.

   stock = {
       "Printer Paper": 40,
       "Notebook": 25,
       "Pencil": ____,
       "Desk Lamp": 6,
       "Backpack": 12,
       "Water Bottle": 30,
       "Calculator": 9,
       "Highlighter": 55,
   }
   stock
   

   Expected output

   You'll see all eight products with their quantities, from 'Printer Paper': 40 to 'Highlighter': 55.

   info> If the output shows 'Pencil': '120' with quotes around the number, you typed 120 as text, which makes sum() and min() fail in Step 5. Remove the quotes and run the cell again.

   Look up a stock level

   To read a value, put its key in square brackets after the dictionary's name.

   Replace ____ with the key "Pencil", typed in quotes because the key is a string.

   stock[____]
   

   Expected output

   You'll see 120.

   info> If you see NameError: name 'Pencil' is not defined, you left out the quotes. Type "Pencil" with them and run the cell again.

   Look up a product that might be missing

   The stapler was removed from the product list earlier in the lab, so it has no entry in stock. Reading a missing key with square brackets raises an error, but the get() method returns a default value that you pass as its second argument instead.

   Replace ____ with a call to get() on stock that passes the key "Stapler" and the default 0.

   ____
   

   Expected output

   You'll see 0.

   Record a sale

   Five notebooks just sold. The -= operator subtracts from the current value and stores the result under the same key.

   Replace ____ with a statement that subtracts 5 from the "Notebook" entry using -=.

   ____
   stock["Notebook"]
   

   Expected output

   You'll see 20, down from 25.

   info> If you see 5, you typed = instead of -=. If you see 25, you left out the =. Either way, select Kernel > Restart Kernel, fix the statement, and run the cells again from the top.

   Record a delivery

   Twenty desk lamps just arrived. The += operator adds to the current value.

   Replace ____ with a statement that adds 20 to the "Desk Lamp" entry using +=.

   ____
   stock["Desk Lamp"]
   

   Expected output

   You'll see 26, up from 6.

   info> If you see 20, you typed = instead of +=. If you see 6, you left out the =. Either way, select Kernel > Restart Kernel, fix the statement, and run the cells again from the top.

   Add a new product

   The store has started carrying scissors. Assigning a value to a key that isn't in the dictionary yet creates a new entry. Dictionaries keep keys in the order you add them, so the new entry appears at the end.

   Replace ____ with a statement that assigns 15 to a new "Scissors" key.

   ____
   stock
   

   Expected output

   You'll see nine entries, ending with 'Scissors': 15.

   Remove a discontinued product

   The store has discontinued its calculator. The del statement removes a key and its value from the dictionary. You write del, a space, and then the entry the same way you read it, with the key in square brackets.

   Replace ____ with a del statement for the "Calculator" key.

   ____
   stock
   

   Expected output

   You'll see eight entries, without 'Calculator'.

   You now know how to read, change, add, and remove dictionary entries. Next, you'll use Python's built-in functions to summarize the inventory.

5. Challenge

   Step 5: Summarize the inventory

   The store's data now lives in three collections: the products list, the all_categories set, and the stock dictionary. Here, you'll pass those collections to Python's built-in functions to answer summary questions about the inventory, storing each answer in a variable, then combine the answers into a short report. By the end of this step, you'll be able to count, total, compare, and sort the items in any collection with a single function call.

   The store's data now lives in three collections: the products list, the all_categories set, and the stock dictionary. In this step, you'll use Python's built-in functions to answer summary questions about the inventory, store each answer in a variable, and combine the answers into a short report. Built-in functions are ready to use without importing anything. By the end of this step, you'll be able to count, total, compare, and sort inventory data using built-in functions. In store_inventory.ipynb, scroll down to the Step 5: Summarize the inventory heading.

   Run all of the code cells in this step by selecting each one in turn and pressing Shift+Enter, after you replace any ____ the cell contains.

   info> These cells use variables from Steps 2 to 4. If you restarted the kernel, run the cells from the top of the notebook first.

   Count items

   The len() function returns the number of items in any collection. Start with the product list. It's separate from stock, so the Step 4 changes to stock didn't change it.

   Replace ____ with a call to len() that passes products.

   product_count = ____
   product_count
   

   Expected output

   You'll see 8.

   The same function counts the distinct categories across both stores.

   Replace ____ with a call to len() that passes all_categories.

   category_count = ____
   category_count
   

   Expected output

   You'll see 7.

   Total the stock on hand

   The values() method gives you just a dictionary's values, here the quantities. The sum() function adds them up to give the total units in stock.

   Replace ____ with a call to sum() that passes stock.values().

   total_units = ____
   total_units
   

   Expected output

   You'll see 318.

   Find the lowest and highest stock levels

   The min() function returns the smallest value in a collection, which here is the lowest stock level.

   Replace ____ with a call to min() that passes stock.values().

   lowest_stock = ____
   lowest_stock
   

   Expected output

   You'll see 12.

   The max() function returns the largest value, which here is the highest stock level.

   Replace ____ with a call to max() that passes stock.values().

   highest_stock = ____
   highest_stock
   

   Expected output

   You'll see 120.

   info> If a cell in this section shows a TypeError or a product name instead of a number, you passed stock instead of stock.values(). Add .values() and run the cell again.

   Sort the data for review

   The sorted() function returns a new list with the items in order, alphabetically for strings. It leaves the original list unchanged, so products keeps its current order.

   Replace ____ with a call to sorted() that passes products.

   ____
   

   Expected output

   You'll see the eight products in alphabetical order, from 'Backpack' to 'Water Bottle'.

   When you pass a dictionary to a built-in function such as sorted(), the function works on the dictionary's keys.

   Replace ____ with a call to sorted() that passes stock.

   ____
   

   Expected output

   You'll see the eight product names in stock in alphabetical order, including 'Scissors'.

   Print the inventory report

   The last cell brings everything together. It prints a short inventory report from the variables you filled in during this step. The code uses f-strings, which insert a variable's value wherever its name appears in curly braces ({}). There's nothing to type in this cell. Run it as is.

   print("Inventory report")
   print(f"Products tracked: {product_count}")
   print(f"Categories across both stores: {category_count}")
   print(f"Total units in stock: {total_units}")
   print(f"Lowest stock level: {lowest_stock}")
   print(f"Highest stock level: {highest_stock}")
   

   Expected output

   You'll see the report:

   Inventory report
   Products tracked: 8
   Categories across both stores: 7
   Total units in stock: 318
   Lowest stock level: 12
   Highest stock level: 120
   

   Well done. You organized a store's inventory from start to finish in a Jupyter notebook and turned it into a printed inventory report.

   You looked up products by index and by slice, then kept the product list current by appending, inserting, and removing items. You compared two stores' categories with set intersection, difference, and union, and you tracked stock levels in a dictionary that you read, updated, extended, and pruned.

   The report you just printed came from len(), sum(), min(), and max(). The same collections and built-in functions are the starting point for most data analysis you'll do in Python.