Reading CSV and JSON

Getting data in

pandas reads most formats in one call:

pd.read_csv("sales.csv")
pd.read_json("sales.json")
pd.read_csv(path, parse_dates=["date"])       # real dates, not strings
pd.read_csv(path, na_values=["", "n/a", "-"]) # what counts as missing

And writes them back the same way — df.to_csv(path, index=False), where index=False stops pandas adding a column of row numbers nobody asked for.

When the data is already in memory, StringIO lets you read a string as if it were a file, which is how you write a test for a parser:

from io import StringIO
pd.read_csv(StringIO(text))

Your turn: parse the CSV text into a DataFrame, treating n/a as missing, and report the row count and the total revenue.