Working with Strings

Look around a trading screen and you'll see text everywhere: symbols like RELIANCE, exchange codes like NSE, dates like 2026-06-25, order types, news headlines. To a computer all of this is one type - the string - and learning to slice, clean and reshape strings is a genuinely useful everyday skill. Data almost never arrives in the exact shape you want; strings are how you fix that. This chapter rounds off the basics, and it's a practical one.

A string is a sequence of characters

A string is just characters in a row, and each character has a position - an index - starting at 0. Square brackets reach in by position, and a colon inside them takes a slice (a range):

# A string is a sequence of characters - reach in by position with [ ].
symbol = "RELIANCE"

print("First letter:", symbol[0])     # R  - counting starts at 0
print("Last letter :", symbol[-1])    # E  - negative counts from the end
print("First three :", symbol[:3])    # REL - a "slice" from start up to 3
print("Length      :", len(symbol))   # 8  - how many characters

# Slicing is a quick way to pull pieces out of a date string.
date = "2026-06-25"
print("Year :", date[:4])             # 2026
print("Month:", date[5:7])            # 06
print("Day  :", date[8:])             # 25

First letter: R
Last letter : E
First three : REL
Length      : 8
Year : 2026
Month: 06
Day  : 25

The two ideas that trip people up are zero-based counting and negative indexing. Picture the word laid out with its index numbers:

So symbol[0] is R, symbol[-1] is the last E, and the slice symbol[:3] grabs REL - it stops just before index 3. That "up to, but not including" rule is everywhere in Python, so it's worth fixing in your mind now.

Cleaning and checking text

Real data is messy - stray spaces, mixed case, codes glued together. String methods, attached with a dot, tidy it up. Here are the ones you'll use weekly:

# Messy text in, tidy text out. Methods are attached with a dot.
raw = "  reliance  "
clean = raw.strip().upper()           # strip removes outer spaces, upper capitalises
print("Cleaned:", repr(clean))        # repr shows the quotes, proving spaces are gone

# Checking what a string contains.
symbol = "NSE:RELIANCE"
print("Starts with NSE? ", symbol.startswith("NSE"))   # True
print("Contains a colon?", ":" in symbol)              # True

# Replacing part of a string (it returns a NEW string).
print("Swap exchange   :", symbol.replace("NSE", "BSE"))  # BSE:RELIANCE

Cleaned: 'RELIANCE'
Starts with NSE?  True
Contains a colon? True
Swap exchange   : BSE:RELIANCE

strip() shaves off surrounding spaces, upper()/lower() fix the case, startswith() and the in keyword check contents, and replace() swaps text out. Notice we reassigned the result - because of an important quirk:

Splitting and joining

Two methods are a matched pair and worth memorising together. split() breaks a string into a list of pieces; join() glues a list of strings back into one. They're how you read and write the comma-separated data you'll meet constantly:

# split() breaks text into a list; join() glues a list back into text.
line = "RELIANCE,TCS,INFY,HDFCBANK"
symbols = line.split(",")             # split on each comma

print("Symbols list:", symbols)
print("Count       :", len(symbols))
print("Third symbol:", symbols[2])    # INFY

# join() is split's mirror image - here with " | " between items.
print("Joined      :", " | ".join(symbols))

# Building a Yahoo Finance ticker by adding the .NS suffix (a taste of Chapter 34).
nse_symbol = "RELIANCE"
print("Yahoo ticker:", nse_symbol + ".NS")

Symbols list: ['RELIANCE', 'TCS', 'INFY', 'HDFCBANK']
Count       : 4
Third symbol: INFY
Joined      : RELIANCE | TCS | INFY | HDFCBANK
Yahoo ticker: RELIANCE.NS

We split "RELIANCE,TCS,INFY,HDFCBANK" on the comma to get four separate symbols, then joined them back with " | ". (Don't worry about lists yet - that's literally the next chapter.) And building a Yahoo Finance ticker is just sticking .NS on the end, a small taste of the symbol systems we'll untangle in Chapter 34.

Try it yourself

• From the string "2026-06-25", use slicing to print it in DD-MM-YYYY order: 25-06-2026. (Combine three slices with +.)
• Take " nse:tcs " and turn it into the clean, uppercase "NSE:TCS" in a single chained line of methods.
• Split the sentence "buy 50 RELIANCE" on spaces. What's at index 1, and what type is it - could you multiply it yet?

Recap

• A string is a sequence of characters, indexed from 0; negative indices count from the end.
• A slice text[start:stop] includes start and stops just before stop.
• Methods clean and inspect text - strip upper lower replace, startswith, and in - but strings are immutable, so they return new strings.
• split() turns text into a list and join() turns a list back into text - the everyday tools for comma-separated data.

That wraps up Module 1 - you can install Python, run code, store and convert values, calculate, format output, and handle text. You officially know the basics. In Module 2 we level up to the structures that hold many values at once, starting with the most important one of all: the list, perfect for a whole series of prices.