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Day 29 - Pandas basics (creating, loading, accessing)

Skills: None​

Pre-reading: 10.1.1, 10.1.2, 10.1.3​

Reference: For all work with tables, refer to the Tables page in the menu at the top of the page! It covers Pyret tables, but is good reference regardless.​

Intro (20 mins)​

Today we learn Pandas, Python's library for working with tables (called DataFrames). Everything you learned about Pyret tables has a Pandas equivalent, but with different syntax.

Creating tables literally (like Pyret's table: ... row: ... end):

# Pyret way:
# orders = table: date, dish, quantity, order_type
#   row: "2023-07-01", "Pasta", 2, "dine-in"
#   row: "2023-07-01", "Salad", 1, "takeout"
# end

# Pandas way -- note that data is given column by column, rather than row by row.
import pandas as pd
data = {
    'date': ['2023-07-01', '2023-07-01', '2023-07-02'],
    'dish': ['Pasta', 'Salad', 'Burger'],
    'quantity': [2, 1, 3],
    'order_type': ['dine-in', 'takeout', 'dine-in']
}
orders = pd.DataFrame(data)

Loading from CSV (like Pyret's load-table: ... source: csv-table-url(...)):

# Pyret way:
# orders = load-table: date, dish, quantity, order_type
#   source: csv-table-url("https://pdi.run/f25-restaurant-orders.csv", default-options)
# end

# Pandas way -- note that it automatically infers columns names.
orders = pd.read_csv("https://pdi.run/f25-restaurant-orders.csv")

Accessing data (like Pyret's .row-n(N) and row["column"]):

# Pyret way:
# orders.row-n(1)["dish"]

# Pandas way:
orders.iloc[1]         # Get row by index (like .row-n)
orders.iloc[1]['dish'] # Get single value from row (like .row-n(1)["dish"])

Extracting columns as lists (like Pyret's .get-column()):

# Pyret way:
# quantities = orders.get-column("quantity")  # Extract column as list

# Pandas way:
quantities = orders['quantity'] # Extract column as Series -- not quite a list, but similar

Built-in statistics (like Pyret's mean(table, "column")):

# Pyret way:
# mean(orders, "quantity")    # Direct table operation
# sum(orders, "quantity")     # Direct table operation

# Pandas way:
orders['quantity'].mean()     # Series operation
orders['quantity'].sum()      # Series operation

Class Exercises (35 mins)​

Creating and loading tables:

  • Create a DataFrame manually with workout data: columns date, activity, duration. Make at least 5 rows.
  • Load the CSV from https://pdi.run/f25-2000-photos.csv into a DataFrame. Print the first 5 rows.

Accessing data:

  • Get the second row from your workout DataFrame (remember: Python uses 0-based indexing).
  • Extract the activity column and print all unique activity names.
  • Get the duration value from the third workout (combining row and column access).
  • What happens if you try to access a row that doesn't exist? Try it and note the error.
  • What happens if you try to access a column that doesn't exist? Try it and note the error.

Extracting columns and statistics:

  • Extract the duration column from your workout DataFrame and store it in a variable called durations.
  • Work with the durations Series to find: .mean(), .sum(), .max(), .min().
  • Calculate the range (difference between max and min) of workout durations.
  • For the photos dataset, extract a numeric column and calculate its median using .median().

Wrap-up (5 mins)​

  • Pandas DataFrames provide the same basic table operations you learned in Pyret Day 7: creating tables, loading from CSV, accessing rows/columns, and computing statistics.
  • The syntax is different, but the concepts are identical. Tomorrow we'll see filtering, sorting, and column operations.