Carter's Digital Garden

DSC 80 Lecture 3

2 min read

Aggregating

Adding and Modifying Columns

Adding a new column to a dataframe using assign

dogs = pd.read_csv(Path('data') / 'dogs43.csv', index_col='breed')
dogs.assign(cost_per_year=dogs['lifetime_cost'] / dogs['longevity'])

Chain methods together instead of writing long, hard-to-read lines

  • Need to wrap expression in parentheses to add newlines before every method call
(dogs
 .assign(cost_per_year=dogs['lifetime_cost'] / dogs['longevity'])
 .sort_values('cost_per_year')
 .iloc[:5]
)

Assign with special column names (spaces, special characters)

dogs.assign(**{'cost per year 💵': dogs['lifetime_cost'] / dogs['longevity']})

df.copy() copies a dataframe in place

df[] = assigns column in place

df.assign() assigns a column to a new dataframe

Avoid inplace=True - plans to remove in future releases of pandas, not good practice

df[column].to_numpy() returns the numpy array of a column

dogs.max(axis=1) won’t work because you are trying to take the max of a mix of datatypes

Data Granularity and the groupby method

Fine granularity: small details
Coarse: bigger picture

You should opt for finer granularity for more detail if you have the resources to do so

How to go from fine to coarse granularity: Aggregating

Aggregation

Aggregating is the act of combining many values into a single value

penguins.groupby('species')['body_mass_g'].mean()

“Split-apply-combine” Paradigm

image_0.png

(penguins
 .assign(is_dream = penguins['island'] == 'Dream')
 .groupby('species')
 ['is_dream']
 .mean()
)
%%pt
penguins_small.groupby('species')

Allows us to visualize groupby_objects

Aggregation Methods

  • count()
  • sum()
  • mean()
  • max()
  • last()
  • first()
(penguins
 .sort_values('body_mass_g')
 .groupby('sex')
 .last()
)

Generally, you should select column(s) directly after groupby

Beyond default aggregation methods

(penguins
 .groupby('species')
 ['body_mass_g']
 .aggregate(['count', 'mean'])
)
(penguins
 .groupby('species')
 .aggregate({'bill_length_mm': 'max', 'island': 'unique'})
)
def iqr(s):
    return np.percntile(s, 75) - np.percentile(s, 25)
 
(penguins
 .groupby('species')
 ['body_mass_g']
 .agg(iqr) # agg is short for .aggregate
)

Graph View

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