EDS 217 Cheatsheet

dict Dictionaries

1. Creating Dictionaries

A dictionary stores pairs of keys and values. You look a value up by its key.

1.1 Empty Dictionary

my_dict = {}

1.2 Dictionary with Initial Values

my_dict = {"name": "Alice", "age": 30}

1.3 Dictionary with Mixed Data Types

mixed_dict = {"number": 42, "text": "hello", "flag": True}

2. Accessing Elements

2.1 Access by Key

print(my_dict["name"])  # Output: Alice

2.2 Safely Access Using .get()

print(my_dict.get("name"))  # Output: Alice
print(my_dict.get("profession", "Unknown"))  # Output: Unknown if not present

.get() returns a default instead of raising an error, which is useful when you are not sure the key is there.

3. Modifying Dictionaries

3.1 Add or Update an Element

my_dict["profession"] = "Engineer"  # Adds a new key, or updates it if it exists

3.2 Remove Elements

del my_dict["age"]                  # Removes the key 'age'
my_dict.pop("profession", None)     # Removes and returns, with a default if missing
my_dict.clear()                     # Clears all elements

4. Dictionary Operations

4.1 Check if a Key Exists

"name" in my_dict  # Returns True if 'name' is a key

4.2 Get Dictionary Length

len(my_dict)  # Returns the number of key-value pairs

4.3 Copy a Dictionary

new_dict = my_dict.copy()  # Creates a shallow copy of the dictionary

4.4 Get All Keys or Values

all_keys = list(my_dict.keys())
all_values = list(my_dict.values())

4.5 Update a Dictionary from Another

my_dict.update({"age": 26, "city": "Boston"})  # Updates and adds multiple keys at once

5. Where You Use Dictionaries in This Course

Two places, and in both of them the dictionary is an argument to a pandas method rather than something you loop over.

5.1 Renaming columns (Day 2)

The keys are the old column names and the values are the new ones.

df = df.rename(columns={"Daily_AirTemp_Mean_C": "temp_c",
                        "Daily_Precip_Total_mm": "precip_mm"})

5.2 Aggregating different columns differently (Day 5)

The keys are column names and the values name the summary you want from each.

summary = df.groupby("site").agg({"temperature_c": "mean",
                                  "dissolved_oxygen": "max"})

Compare that with .agg(["mean", "max"]), which applies the same two summaries to every column. The dictionary form is what lets you ask a different question of each column.