Meet your data before you model it.
Direct answer
Exploratory data analysis is looking at a dataset to understand its structure, quality and patterns. Check the shape and types, summarise numbers, find missing values and outliers, look at distributions and explore relationships between columns. It guides cleaning and modelling.
Steps with pandas
import pandas as pd
df = pd.read_csv("data.csv")
df.shape
df.head()
df.info()
df.describe()
df.isna().sum()
df["city"].value_counts()
df.corr(numeric_only=True)
What to look for
- Shape: how many rows and columns?
- Types: are numbers stored as text?
- Missing values: how many, and why?
- Distributions: skewed, odd peaks?
- Outliers: errors or real extremes?
- Relationships: which columns move together?
Plot it
df["price"].hist()
df.plot.scatter(x="size", y="price")
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
Time spent on EDA saves time and mistakes later.
