Python Data Analysis
Bring NumPy, Pandas and visualization together to turn raw data into useful insights.
A practical analysis workflow
A typical Python analysis moves through loading data, inspecting it, cleaning important issues, summarizing patterns and communicating findings with visualizations.
Load a dataset
Pandas can read common tabular formats such as CSV files into a DataFrame.
analysis.py
import pandas as pd
df = pd.read_csv("sales.csv")
print(df.head())
print(df.info())Find useful patterns
Use descriptive statistics, grouping and filtering to understand distributions, totals and relationships in the dataset.
summary.py
summary = df.groupby("category")["sales"].sum()
print(summary.sort_values(ascending=False))Communicate the result
Charts can make trends and comparisons easier to see. Choose a visualization that matches the question you are trying to answer.
Key takeaway
Good analysis is a repeatable process: understand the question, prepare the data, analyze it carefully and communicate the result clearly.
Ready to continue?