Lists: Your First Data Structure

Welcome to Module 2. So far each variable has held a single value - one price, one symbol. But markets come in series: a week of closing prices, a watchlist of stocks, the trades you did today. For that you need a container that holds many values at once, and the workhorse container in Python is the list. If you learn one data structure deeply, make it this one - lists are everywhere, and pandas (the heart of Module 4) is built right on top of the idea.

Making a list

A list is values in order, written inside square brackets and separated by commas. Because it's ordered, you reach into it with exactly the same indexing and slicing you learned for strings:

# A list holds many values in order, written inside square brackets.
closes = [101.2, 103.5, 102.8, 104.1, 105.6, 104.9, 106.3]   # a week of closes

print("All closes   :", closes)
print("How many     :", len(closes))
print("First (Mon)  :", closes[0])     # same indexing rules as strings
print("Last (today) :", closes[-1])
print("First three  :", closes[:3])    # a slice - Mon, Tue, Wed
print("Last two     :", closes[-2:])

All closes   : [101.2, 103.5, 102.8, 104.1, 105.6, 104.9, 106.3]
How many     : 7
First (Mon)  : 101.2
Last (today) : 106.3
First three  : [101.2, 103.5, 102.8]
Last two     : [104.9, 106.3]

Seven prices, one name. closes[0] is Monday's close, closes[-1] is today's, and closes[:3] slices out the first three days. Everything you learned about string positions - zero-based, negative-from-the-end, "stops just before" - works identically here. That's not a coincidence; Python keeps its rules consistent so what you learn once pays off everywhere.

Growing and shrinking

Unlike a string, a list can be changed - you add and remove items freely. This is what makes it perfect for a watchlist that grows and shrinks through the day:

# Lists can grow and shrink - perfect for a changing watchlist.
watchlist = ["RELIANCE", "TCS", "INFY"]

watchlist.append("HDFCBANK")     # add to the end
watchlist.insert(0, "NIFTY")     # add at a specific position (the front)
print("After adding :", watchlist)

watchlist.remove("TCS")          # remove the first matching value
last = watchlist.pop()           # remove AND return the last item
print("Popped off   :", last)
print("Final list   :", watchlist)

After adding : ['NIFTY', 'RELIANCE', 'TCS', 'INFY', 'HDFCBANK']
Popped off   : HDFCBANK
Final list   : ['NIFTY', 'RELIANCE', 'INFY']

The four you'll use most: append adds to the end, insert drops an item at a chosen position, remove deletes by value, and pop removes the last item and hands it back to you. A list is a living thing - it remembers every change.

Instant insight: max, min, sum and sorting

Hand a list of numbers to Python's built-in helpers and you get analysis for free:

# Built-in helpers turn a list of numbers into instant insight.
closes = [101.2, 103.5, 102.8, 104.1, 105.6, 104.9, 106.3]

print("Highest close:", max(closes))
print("Lowest close :", min(closes))
print("Average close:", round(sum(closes) / len(closes), 2))

# sorted() returns a NEW sorted list and leaves the original untouched.
print("Sorted (asc) :", sorted(closes))
print("Original kept:", closes)

# .sort() rearranges the list IN PLACE - it changes the original.
closes.sort(reverse=True)
print("Sorted (desc):", closes)

Highest close: 106.3
Lowest close : 101.2
Average close: 104.06
Sorted (asc) : [101.2, 102.8, 103.5, 104.1, 104.9, 105.6, 106.3]
Original kept: [101.2, 103.5, 102.8, 104.1, 105.6, 104.9, 106.3]
Sorted (desc): [106.3, 105.6, 104.9, 104.1, 103.5, 102.8, 101.2]

max, min, sum and len answer the obvious questions in one word each - there's your week's high, low and average. Sorting, though, hides a trap worth flagging:

A list can hold anything

One last superpower: a Python list isn't fussy about what goes inside. It can mix types - numbers, text, even other lists:

bar = ["RELIANCE", 1300.0, 1313.6, 50]      # symbol, open, close, qty
table = [["RELIANCE", 1313.6], ["TCS", 2109.0]]   # a list of rows

That second example - a list of lists - is secretly a little table, with each inner list a row. Hold that thought: it's exactly how spreadsheets and DataFrames are organised, and we'll meet it again in Module 4.

Try it yourself

• From the closes list, slice out just the middle three days and find their average.
• Start an empty watchlist with wl = [], then append three symbols of your choice and print the result.
• Run sorted(closes) and then print closes right after. Confirm for yourself that the original is unchanged - then do the same with .sort() and watch the difference.

Recap

• A list [...] stores ordered values; index and slice it exactly like a string.
• Lists are mutable: append (add to end), insert (add at position), remove (by value), pop (take the last).
• Built-ins max / min / sum / len summarise a list instantly.
• sorted() returns a new sorted list; .sort() reorders in place - know which you want.
• A list can hold mixed types, and a list of lists is a table in disguise.

Lists are flexible because they can change. But sometimes you want the opposite - a small group of values that's locked and can't be altered by accident, like the four numbers of an OHLC bar. For that, Python gives us the tuple - the subject of the next chapter.