Pandas Read CSV

How it works

The fixture uses a string identifier so 001 remains distinct from the integer one. NA is an actual collection-point code here, not an unknown value, so default missing markers are disabled and an empty weight alone is configured as missing. A real import also needs encoding, delimiter, quoting, required-field and row-limit policies. This in-memory source needs no network or personal file access.

Run the code

Select Try in playground to load the complete editable source. Nothing executes until you choose Run. The browser uses Pyodide 0.27.7, Pandas 2.2.3 and NumPy 2.0.2. Each ordinary execution starts a fresh isolated runtime. The tables are fictional and do not use personal files.

For local work, use a separate virtual environment and run python read-csv.py. Download the complete source. The reviewed local environment uses Python 3.12.14, Pandas 3.0.6 and NumPy 2.5.4; the browser uses Pyodide 0.27.7 with Pandas 2.2.3 and NumPy 2.0.2. Compatible examples are checked in both. See the environment setup. No Apache or database server is required.

Example

import pandas as pd
import numpy as np

from io import StringIO
source = StringIO("drop_id,point,kg\n001,NA,12\n002,north,\n")
table = pd.read_csv(source, dtype={"drop_id":"string", "point":"string", "kg":"Float64"}, keep_default_na=False, na_values={"kg":[""]})
print(table["drop_id"].tolist())
print(table["point"].tolist())
print(table["kg"].isna().tolist())

Expected result

['001', '002']
['NA', 'north']
[False, True]

The textual code survives intact while the missing numerical value is identified by its field-specific contract. That distinction avoids silent loss of valid labels through a parser’s broad default marker list. Inspect the imported schema before calculations.