Input/Output & File Formats

Most Common File Formats

CSV (Comma-Separated Values)

The most universal format — works everywhere.

# Read CSV
df = pd.read_csv("data.csv")
df = pd.read_csv("data.csv", sep=";", encoding="utf-8")

# Write CSV
df.to_csv("output.csv", index=False)

Excel

Perfect for business users and data sharing.

# Read Excel
df = pd.read_excel("data.xlsx", sheet_name="Sheet1")

# Write Excel
df.to_excel("output.xlsx", index=False)

JSON

Common in APIs and web data.

# Read JSON
df = pd.read_json("data.json")

# Write JSON
df.to_json("output.json", orient="records")  # or "split", "index", etc.

Parquet (Modern, Efficient)

Used by data engineers for big data — compressed and fast.

# Read/Write Parquet
df = pd.read_parquet("data.parquet")
df.to_parquet("output.parquet")

Tip: Always use index=False when writing unless you need to preserve row indices.