The Problem With Most Pandas Tutorials
You install pandas, load a CSV, and immediately hit SettingWithCopyWarning. Or your datetime column parses as strings. Or groupby() returns something that isn't a DataFrame and you don't know why.
Most cheat sheets show you the syntax but skip the part where real data is messy and pandas has opinions about how you should use it. This guide covers 20 operations I actually use when exploring a new dataset — the ones that matter between "CSV loaded" and "I understand what's in here."
I'm using a realistic scenario: analyzing user activity logs from a fictional web app. The data has missing timestamps, mixed data types, and some rows that shouldn't exist. Exactly the kind of mess you'll see in practice.
Load and Inspect: The First 60 Seconds
Start by actually looking at what you loaded. Don't assume the CSV is clean.
python
import pandas as pd
import numpy as np
# Explicit dtype parsing saves headaches later
df = pd.read_csv(
'user_activity.csv',
---
*Continue reading the full article on [TildAlice](https://tildalice.io/pandas-cheat-sheet-20-operations-csv-to-insights/)*
