Real tools: cheat sheet

Machine learning with Gradio · In any playground, open Reference to copy these.

square (loop)

Draw a square by repeating forward and right four times.

for i in range(4):
    forward(100)
    right(90)

every other

i % 2 == 0 is true on every second pass.

for i in range(8):
    if i % 2 == 0:
        pass
    else:
        pass

Example

for i in range(8):
    if i % 2 == 0:
        pencolor("red")
    else:
        pencolor("blue")
    pensize(6)
    forward(30)
    right(45)

Red and blue sides, taking turns

an app with Gradio

Gradio calls your function with what was chosen, and shows what it returns.

import gradio as gr

def answer(choice):
    return "You chose " + choice

gr.Interface(fn=answer, inputs=gr.Dropdown(["pop", "rock"]), outputs="text").launch()

predict with a model

The new example needs the same columns, in the same order, as the training data.

mystery = pd.DataFrame([[47.0, 215.0]], columns=X.columns)
print(model.predict(mystery)[0])

for loop

Repeat the indented lines this many times.

for i in range(4):
    pass

Example

for i in range(5):
    forward(15)
    penup()
    forward(10)
    pendown()

counter step

Use the counter i to make each pass different.

for i in range(20):
    forward(i * 5)
    right(90)

Example

for i in range(30):
    forward(i * 5)
    right(90)

read a CSV with pandas

Reads the whole file into a table (a DataFrame). Numbers stay numbers.

import pandas as pd

songs = pd.read_csv("music.csv")

train and score a model

fit learns from the training examples; score tests on the held-back ones (1.0 is 100%).

from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=1)
model = KNeighborsClassifier(n_neighbors=1)
model.fit(X_train, y_train)
print(model.score(X_test, y_test))