Cleans large datasets, formats values, and exports matplotlib analytics reports.
Tick off requirements as you build to track real-time completion.
Follow this chronological guide to build the project from scratch.
Read dataset files, drop invalid null fields, and clean numbers.
import pandas as pd
import matplotlib.pyplot as plt
def clean_sales_csv(input_path, output_path):
df = pd.read_csv(input_path)
# Drop rows missing critical cells
df.dropna(subset=["Sales", "Date"], inplace=True)
# Clean currency values
df["Sales"] = df["Sales"].replace('[\\$,]', '', regex=True).astype(float)
# Save clean dataset
df.to_csv(output_path, index=False)
# Render chart
df.groupby("Category")["Sales"].sum().plot(kind="bar")
plt.savefig("category_report.png")Real issues students hit during development and how to troubleshoot them fast.
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