pandas turns messy files into answers.
Direct answer
pandas is Python's main library for tabular data. Load a file with pd.read_csv, inspect it with head, info and describe, clean column names, fix types, remove duplicates, handle missing values and then group and summarise.
A first workflow
import pandas as pd
df = pd.read_csv("sales.csv")
df.head()
df.info()
df.columns = df.columns.str.strip().str.lower().str.replace(" ", "_")
df = df.drop_duplicates()
df["date"] = pd.to_datetime(df["date"])
df["revenue"] = df["revenue"].fillna(0)
summary = df.groupby("region")["revenue"].sum().sort_values(ascending=False)
print(summary)
Habits
- Keep the raw file untouched.
- Write cleaning steps as code.
- Check results after each step.
- Save the cleaned data separately.
Where Teyro fits
Teyro's Data Science for Beginners course turns this into short lessons you can play in a few minutes a day. It has 50 lessons, and you can start free.
The bottom line
Load, inspect, clean, summarise. Repeat on every dataset.
