CSV files

Tables as text

A CSV (comma-separated values) file stores a table as text: one row per line, cells separated by commas, usually with a header row naming the columns. Price histories, trade blotters and exports from almost any tool come this way. It looks easy to parse with line.split(","), but a cell may itself contain a comma, in which case it is wrapped in quotes: AAPL,"Q3, adjusted". Splitting by hand breaks on that, so use the standard csv module, and open the file with newline="" so it handles line breaks inside cells correctly.

import csv

# Write a small table with csv.writer
with open("prices.csv", "w", newline="", encoding="utf-8") as file:
    writer = csv.writer(file)
    writer.writerow(["date", "symbol", "close", "note"])
    writer.writerow(["2025-01-02", "AAPL", 184.25, "Q3, adjusted"])
    writer.writerow(["2025-01-03", "AAPL", 185.5, ""])

# The comma inside the note is quoted for us
with open("prices.csv", encoding="utf-8") as file:
    print(file.read().splitlines()[1])  # 2025-01-02,AAPL,184.25,"Q3, adjusted"

Reading rows

csv.reader(file) yields each row as a list of strings. csv.DictReader(file) reads the header first and then yields each row as a dictionary keyed by column name, which is easier to read and survives columns being reordered. Either way every cell is text: convert with float(...) or int(...) before doing arithmetic. Pass delimiter=";" (or "\t") for files that use another separator.

import csv

# DictReader gives each row as a dict; cells are strings
closes = []
with open("prices.csv", newline="", encoding="utf-8") as file:
    for row in csv.DictReader(file):
        closes.append(float(row["close"]))
print(closes)  # [184.25, 185.5]

# csv.reader gives plain lists, header included
with open("prices.csv", newline="", encoding="utf-8") as file:
    rows = list(csv.reader(file))
print(rows[0])  # ['date', 'symbol', 'close', 'note']
print(rows[1])  # ['2025-01-02', 'AAPL', '184.25', 'Q3, adjusted']

Writing rows

csv.writer(file).writerow(values) writes one row and quotes cells when needed; numbers are converted to text for you. csv.DictWriter(file, fieldnames=[...]) writes dictionaries, and writeheader() writes the column names first. Open the file with "w" and newline=""; without newline="" Windows can end up with blank lines between rows.

import csv

# DictWriter: header first, then one dict per row
trades = [{"symbol": "AAPL", "quantity": 200}, {"symbol": "MSFT", "quantity": 50}]
with open("trades.csv", "w", newline="", encoding="utf-8") as file:
    writer = csv.DictWriter(file, fieldnames=["symbol", "quantity"])
    writer.writeheader()
    writer.writerows(trades)

with open("trades.csv", encoding="utf-8") as file:
    print(file.read().splitlines())  # ['symbol,quantity', 'AAPL,200', 'MSFT,50']

import csv

# Cells are text until you convert them
with open("prices.csv", newline="", encoding="utf-8") as file:
    row = next(csv.DictReader(file))
print(repr(row["close"]), row["close"] * 2)   # '184.25' 184.25184.25
print(float(row["close"]) * 2)                # 368.5

# Another separator: semicolons
with open("euro.csv", "w", newline="", encoding="utf-8") as file:
    csv.writer(file, delimiter=";").writerow(["AAPL", "184,25"])
with open("euro.csv", newline="", encoding="utf-8") as file:
    print(next(csv.reader(file, delimiter=";")))  # ['AAPL', '184,25']

• Open with newline=""; do not split or join CSV lines by hand.
• csv.DictReader reads rows as dicts; every cell is a string.
• csv.writer / csv.DictWriter write rows and quote cells for you.

Next up: JSON data, for nested records that do not fit a flat table.

Lab · what's graded

• Group closing prices by symbol with read_closes25 pts
• Save trades as a CSV file with write_trades25 pts
• Total the traded value per symbol with total_by_symbol25 pts
• Copy one symbol's rows to a new file with filter_rows25 pts