python 29 lines · 1 tab

pandas DataFrame essentials: loading, indexing, and selection

Dr. Elena Vasquez Apr 2026

1 tab

import pandas as pd

df = pd.read_csv(
    'orders.csv',
    parse_dates=['created_at'],
    dtype={
        'customer_id': 'int64',
        'country': 'string',
        'status': 'category',
    },
)

df.columns = (
    df.columns.str.strip()
    .str.lower()
    .str.replace(' ', '_', regex=False)
)

active_orders = df.loc[
    (df['status'] == 'paid') &
    (df['country'].isin(['US', 'CA'])),
    ['customer_id', 'created_at', 'total_amount']
].copy()

active_orders = active_orders.set_index('created_at').sort_index()
latest_orders = active_orders.last('30D')

print(latest_orders.head())
print(latest_orders.dtypes)

1 file · python Explain with highlit

I treat pandas as the default workbench for structured data. The goal is to make loading explicit, indexes predictable, and selection operations readable under maintenance pressure. I prefer stable column naming, typed parsing for dates, and avoiding chained indexing. Once a DataFrame is shaped well, downstream feature engineering and model training get dramatically simpler.

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