EDS 217 Cheatsheet

Control Flows in Python

Conditional Statements

if / elif / else

A conditional runs one block of code and skips the others, depending on which test is true. Python checks the tests in order and stops at the first one that passes.

Code 
temperature = 10

if temperature > 25:
    print("hot")
elif temperature > 5:
    print("mild")
else:
    print("cold")
mild

The elif and else branches are both optional. You can write an if on its own.

Comparison operators

Operator Meaning
== equal to
!= not equal to
> < greater than, less than
>= <= greater than or equal to, less than or equal to
in is a member of

Combining tests

Code 
temperature = 10
raining = True

if temperature > 5 and not raining:
    print("go outside")
else:
    print("stay in")
stay in

In plain Python you combine tests with and, or and not. Inside a pandas filter you use &, | and ~ instead, and every condition needs its own parentheses. See the data selection cheatsheet.

Categorizing a value with a function

Conditionals become most useful when you wrap them in a function and hand that function to .apply(), which is the Day 4 pattern.

Code 
def classify(temperature):
    """Return a text label for a temperature in degrees C."""
    if temperature > 25:
        return "hot"
    elif temperature > 5:
        return "mild"
    else:
        return "cold"

print(classify(30), classify(10), classify(-2))
hot mild cold

Loops

Looping over a list

Code 
sites = ["Toolik", "Goleta", "CNSI"]

for site in sites:
    print(site)
Toolik
Goleta
CNSI

The loop variable, site here, takes each value in turn. The name is yours to choose.

Looping over groups in a DataFrame

This is the form live-coding session 5d uses, for the cases where one grouped sentence is not enough. Each turn of the loop gives you the group’s name and the rows belonging to it.

Code 
import pandas as pd

df = pd.DataFrame({
    "site": ["a", "a", "b", "b", "c"],
    "temperature_c": [16.1, 17.0, 18.2, 18.0, 21.9],
})

for name, group in df.groupby("site"):
    print(f"site {name}: {len(group)} rows, mean {group['temperature_c'].mean():.2f} C")
site a: 2 rows, mean 16.55 C
site b: 2 rows, mean 18.10 C
site c: 1 rows, mean 21.90 C

Reach for a loop when you need to do something to each group that .agg() cannot express, or when you want to build up a list of results and combine them afterwards with pd.concat().

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