Run pandas Online

Read CSV Data with pandas

pandas can load comma-separated data from a file or any file-like object such as StringIO.

When is pandas read_csv used?

CSV commonly moves data between spreadsheets, business systems, analytics tools, APIs, and databases. pandas.read_csv() converts it into a DataFrame for cleaning, filtering, analysis, and visualization.

Common use cases

Analyze exports from Excel, Google Sheets, or a CRM.
Load datasets for machine learning and data science.
Clean missing, incorrectly typed, or duplicated data before import.

Tip: For production data, consider dtype, parse_dates, usecols, and na_values so parsing is explicit.

Basic example

Run code →

main.py

import pandas as pd
from io import StringIO

csv_text = "name,score\nAda,95\nLin,88\nSam,91"
students = pd.read_csv(StringIO(csv_text))

print(students)

Expected output

name  score
0  Ada     95
1  Lin     88
2  Sam     91

Advanced example

Real CSV files are often large and imperfect. This example selects required columns, parses dates, controls data types, and recognizes several missing-value formats.

advanced_csv.py

import pandas as pd

sales = pd.read_csv(
    "sales.csv",
    usecols=["order_date", "product", "quantity", "revenue"],
    parse_dates=["order_date"],
    dtype={"product": "string", "quantity": "Int64"},
    na_values=["", "N/A", "unknown"],
)

sales = sales.dropna(subset=["order_date", "product"])
monthly_revenue = sales.groupby(
    sales["order_date"].dt.to_period("M")
)["revenue"].sum()

print(monthly_revenue)

How it works

StringIO makes the text behave like an open file, allowing read_csv to parse it without creating a temporary file.

Change the values and run the program in the CodeUtility online Python compiler without installing Python locally.

Practice exercises

Modify the runnable example with the exercises below to build understanding beyond copying the result.

Add rows, columns, and missing values.
Combine filtering, sorting, and groupby into a small report.
Load a real file, inspect dtypes, and handle invalid data.

Run in Python IDE →