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Raahaao Code: Python per i dati

Pulisci, interroga e visualizza dati reali con pandas.

9+ ore di lezioni · 2 progetti · €35

Ti aiuta a prepararti per: PCED – Certified Entry-Level Data Analyst with Python (Python Institute)

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Cosa imparerai, piano per piano

  1. 1

    Data basics (Beginner)

    • What data analysis is
    • Reading CSV: DataFrames and Series
    • Selecting rows and columns
  2. 2

    Cleaning (Beginner)

    • Missing values and wrong types
    • Text and dates in data
    • Sorting, filtering, new columns
  3. 3

    Answering questions (Intermediate)

    • groupby and aggregation
    • Merging and joining tables
    • Pivot tables and reshaping
  4. 4

    Showing results (Intermediate)

    • Charts with matplotlib
    • Choosing the right chart
    • Basic statistics
  5. 5

    Pro (Job-ready)

    • Time series
    • NumPy underneath pandas
    • Reproducible analysis
    • Telling the story

I due progetti sul tetto

Sales analysis

Analyse a fictional coffee roaster’s sales file by cleaning inconsistent dates, text, and numeric fields before answering business questions. You will compare monthly revenue, product performance, weekdays, and returning customers.

  • load_clean(path) returns a cleaned DataFrame with parsed dates, standardised customer and product names, numeric quantity and price, and a revenue column; rows missing required fields or with non-positive quantity or price are removed
  • revenue_by_month(df) returns a pandas Series of revenue totals indexed by sorted YYYY-MM strings
  • top_products(df, n) returns a list of product names ordered by total revenue, highest first
  • best_weekday(df) returns the English weekday name with the highest total revenue
  • repeat_customers(df) returns the number of customers with at least two cleaned sales
  • make_charts(df) creates three charts titled Monthly coffee revenue, Revenue by coffee product, and Top customers by revenue

City bikes study

Analyse a fictional city bike-share dataset with pandas. You will rank stations, summarize daily demand, compare rainy and dry days, and identify the busiest start hour.

  • busiest_stations(trips, stations, n) returns a DataFrame with station names and trip counts, ordered highest first; a trip counts once at its start and once at its end.
  • trips_per_day(trips) returns a Series indexed by date in ascending order, with the number of trips on each date.
  • rain_effect(trips, weather) returns the percentage change in mean daily trips on rainy versus dry dates, rounded to 2 decimal places; rainy means rain_mm > 0, and only dates represented in both tables are used.
  • peak_hour(trips) returns the integer hour with the most trip starts; ties go to the earlier hour.
  • round_trips(trips) returns the number of rows whose start_station equals end_station.
  • summary(trips, stations, weather) returns exactly five lines: Trips: N; Stations: N; Busiest station: NAME (N visits); Rain effect: N.NN%; Peak hour: HH:00. Use the actual calculated values.

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