File I/O

reading

# Read entire file
with open("file.txt", "r") as f:
    content = f.read()

# Read lines into list
with open("file.txt", "r") as f:
    lines = f.readlines()

# Read line by line (memory efficient)
with open("file.txt", "r") as f:
    for line in f:
        print(line.strip())

writing

# Write (overwrites existing)
with open("output.txt", "w") as f:
    f.write("Hello, World!\n")

# Append to existing
with open("output.txt", "a") as f:
    f.write("Another line\n")

# Write multiple lines
lines = ["Line 1", "Line 2", "Line 3"]
with open("output.txt", "w") as f:
    f.writelines(line + "\n" for line in lines)

the with statement

Always use with to ensure files are properly closed:

# Good - file automatically closes
with open("file.txt") as f:
    data = f.read()

# Bad - you might forget to close
f = open("file.txt")
data = f.read()
f.close()  # Easy to forget!

file modes

the csv module

import csv

# Read CSV
with open("data.csv") as f:
    reader = csv.reader(f)
    for row in reader:
        print(row)  # row is a list

# Read as dictionaries
with open("data.csv") as f:
    reader = csv.DictReader(f)
    for row in reader:
        print(row["column_name"])

# Write CSV
with open("output.csv", "w", newline="") as f:
    writer = csv.writer(f)
    writer.writerow(["Name", "Age"])
    writer.writerow(["Alice", 25])

loading real-size data

The browser can't read /Users/you/data.csv. But it can pyfetch a URL and feed the bytes to pd.read_csv or csv.DictReader through io.StringIO. That's how the examples below switch from 8-row toy CSVs to real datasets in the hundreds-to-thousands of rows.

import io
import pandas as pd
from pyodide.http import pyfetch

URL = "https://raw.githubusercontent.com/plotly/datasets/master/diabetes.csv"
resp = await pyfetch(URL)
df = pd.read_csv(io.StringIO(await resp.string()))
print(df.shape)  # (768, 9)

Any raw.githubusercontent.com URL is CORS-friendly. Plotly and Vega both maintain large dataset repos that work without auth.