from dsc80_utils import *

import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)

Lecture 3 – Aggregating

DSC 80, Winter 2025

Announcements 📣

• Lab 1 is due tomorrow at 11:59pm.
  • The Welcome Survey is part of your Lab 1; we won't count your Lab 1 as submitted unless your Welcome Survey is also submitted.
• Project 1 is released.
  • The checkpoint (Questions 1-7) is due on Friday, Jan 17th.
  • The full project is due on Friday, Jan 24th.
• Lab 2 will be released tomorrow.

Agenda

• Data granularity and the groupby method.
• DataFrameGroupBy objects and aggregation.
• Other DataFrameGroupBy methods.
• Pivot tables using the pivot_table method.

You will need to code a lot today – make sure to pull the course repository

Question 🤔 (Answer at dsc80.com/q)

Code: lec03-hello

A question for you now: Have you set up your development environment? Have you started on Lab 1?

• A. Yes, I have set up my development environment. I've started Lab 1.
• B. Yes, I have set up my development environment. I haven't started Lab 1.
• C. No, I am running into difficulties setting up my environment.
• D. No, I should really start getting set up!

Data granularity and the groupby method

Example: Palmer Penguins

Artwork by @allison_horst

The dataset we'll work with for the rest of the lecture involves various measurements taken of three species of penguins in Antarctica.

import seaborn as sns
penguins = sns.load_dataset('penguins').dropna()
penguins

Here, each row corresponds to a single penguin, and each column corresponds to a different attribute (or feature) we have for each penguin. Data formatted in this way is called tidy data.

Granularity

• Granularity refers to what each observation in a dataset represents.
  • Fine: small details.
  • Coarse: bigger picture.

• If you can control how your dataset is created, you should opt for finer granularity, i.e. for more detail.
  • You can always remove details, but it's difficult to add detail that isn't already there.
  • But obtaining fine-grained data can take more time/money.

• Today, we'll focus on how to remove details from fine-grained data, in order to help us understand bigger-picture trends in our data.

Aggregating

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

• What is the mean 'body_mass_g' for all penguins?

penguins['body_mass_g'].mean()

• What is the mean 'body_mass_g' for each species?

# ???

Naive approach: looping through unique values

species_map = pd.Series([], dtype=float)

for species in penguins['species'].unique():
    species_only = penguins.loc[penguins['species'] == species]
    species_map.loc[species] = species_only['body_mass_g'].mean()

species_map

• For each unique 'species', we make a pass through the entire dataset.
  • The asymptotic runtime of this procedure is $\Theta(ns)$, where $n$ is the number of rows and $s$ is the number of unique species.

• While there are other loop-based solutions that only involve a single pass over the DataFrame, we'd like to avoid Python loops entirely, as they're slow.

Grouping

A better solution, as we know from DSC 10, is to use the groupby method.

# Before:
penguins['body_mass_g'].mean()

# After:
penguins.groupby('species')['body_mass_g'].mean()

Somehow, the groupby method computes what we're looking for in just one line. How?

"Split-apply-combine" paradigm

The groupby method involves three steps: split, apply, and combine. This is the same terminology that the pandas documentation uses.

• Split breaks up and "groups" the rows of a DataFrame according to the specified key. There is one "group" for every unique value of the key.

• Apply uses a function (e.g. aggregation, transformation, filtration) within the individual groups.

• Combine stitches the results of these operations into an output DataFrame.

• The split-apply-combine pattern can be parallelized to work on multiple computers or threads, by sending computations for each group to different processors.

More examples

Before we dive into the internals, let's look at a few more examples.

Question 🤔 (Answer at dsc80.com/q)

Code: lec03-dream

What proportion of penguins of each 'species' live on 'Dream' island?

Your output should look like:

species
Adelie       0.38
Chinstrap    1.00
Gentoo       0.00

# Fill this in, then respond on dsc80.com/q

DataFrameGroupBy objects and aggregation

DataFrameGroupBy objects

We've just evaluated a few expressions of the following form.

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

There are two method calls in the expression above: .groupby('species') and .mean(). What happens in the .groupby() call?

penguins.groupby('species')

Peeking under the hood

If df is a DataFrame, then df.groupby(key) returns a DataFrameGroupBy object.

This object represents the "split" in "split-apply-combine".

# Simplified DataFrame for demonstration:
penguins_small = penguins.iloc[[0, 150, 300, 1, 251, 151, 301], [0, 5, 6]]
penguins_small

# Creates one group for each unique value in the species column.
penguin_groups = penguins_small.groupby('species')
penguin_groups

DataFrameGroupBy objects have a groups attribute, which is a dictionary in which the keys are group names and the values are lists of row labels.

penguin_groups.groups

DataFrameGroupBy objects also have a get_group(key) method, which returns a DataFrame with only the values for the given key.

penguin_groups.get_group('Chinstrap')

# Same as the above!
penguins_small.query('species == "Chinstrap"')

We usually don't use these attributes and methods, but they're useful in understanding how groupby works under the hood.

Aggregation

• Once we create a DataFrameGroupBy object, we need to apply some function to each group, and combine the results.

• The most common operation we apply to each group is an aggregation.

  • Remember, aggregation is the act of combining many values into a single value.

• To perform an aggregation, use an aggregation method on the DataFrameGroupBy object, e.g. .mean(), .max(), or .median().

Let's look at some examples.

penguins_small

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

# Whoa, what happened in the sex column?
penguins_small.groupby('species').sum()

penguins_small.groupby('species').last()

penguins_small.groupby('species').max()

Column independence

Within each group, the aggregation method is applied to each column independently.

penguins_small.groupby('species').max()

It is not telling us that there is a 'Male' 'Adelie' penguin with a 'body_mass_g' of 3800.0!

# This penguin is Female!
penguins_small.loc[(penguins['species'] == 'Adelie') & (penguins['body_mass_g'] == 3800.0)]

Question 🤔 (Answer at dsc80.com/q)

Code: lec03-heavy

Find the species, island, and body_mass_g of the heaviest Male and Female penguins in penguins (not penguins_small).

# Your code goes here.

Column selection and performance implications

• By default, the aggregator will be applied to all columns that it can be applied to.
  • max, min, and sum are defined on strings, while median and mean are not.

• If we only care about one column, we can select that column before aggregating to save time.
  • DataFrameGroupBy objects support [] notation, just like DataFrames.

# Back to the big penguins dataset!
penguins

# Works, but involves wasted effort since the other columns had to be aggregated for no reason.
penguins.groupby('species').sum()['bill_length_mm']

# This is a SeriesGroupBy object!
penguins.groupby('species')['bill_length_mm']

# Saves time!
penguins.groupby('species')['bill_length_mm'].sum()

To demonstrate that the former is slower than the latter, we can use %%timeit. For reference, we'll also include our earlier for-loop-based solution.

%%timeit
penguins.groupby('species').sum()['bill_length_mm']

%%timeit
penguins.groupby('species')['bill_length_mm'].sum()

%%timeit
species_map = pd.Series([], dtype=float)

for species in penguins['species'].unique():
    species_only = penguins.loc[penguins['species'] == species]
    species_map.loc[species] = species_only['body_mass_g'].mean()

species_map

Takeaways

• It's important to understand what each piece of your code evaluates to – in the first two timed examples, the code is almost identical, but the performance is quite different.

  # Slower
  penguins.groupby('species').sum()['bill_length_mm']
  
  # Faster
  penguins.groupby('species')['bill_length_mm'].sum()
  

• The groupby method is much quicker than for-looping over the DataFrame in Python. It can often produce results using just a single, fast pass over the data, updating the sum, mean, count, min, or other aggregate for each group along the way.

• You should always select the columns you want after groupby, unless you really know what you're doing!

Beyond default aggregation methods

• There are many built-in aggregation methods.
• What if you want to apply different aggregation methods to different columns?
• What if the aggregation method you want to use doesn't already exist in pandas?

The aggregate method

• The DataFrameGroupBy object has a general aggregate method, which aggregates using one or more operations.
  • Remember, aggregation is the act of combining many values into a single value.
• There are many ways of using aggregate; refer to the documentation for a comprehensive list.
• Example arguments:
  • A single function.
  • A list of functions.
  • A dictionary mapping column names to functions.
• Per the documentation, agg is an alias for aggregate.

Example

How many penguins are there of each 'species', and what is the mean 'body_mass_g' of each 'species'?

(penguins
 .groupby('species')
 ['body_mass_g']
 .aggregate(['count', 'mean'])
)

Example

What is the maximum 'bill_length_mm' of each 'species', and which 'island's is each 'species' found on?

(penguins
 .groupby('species')
 .aggregate({'bill_length_mm': 'max', 'island': 'unique'})
)

Example

What is the interquartile range of the 'body_mass_g' of each 'species'?

# Here, the argument to agg is a function,
# which takes in a pd.Series and returns a scalar.

def iqr(s):
    return np.percentile(s, 75) - np.percentile(s, 25)

(penguins
 .groupby('species')
 ['body_mass_g']
 .agg(iqr)
)

Other DataFrameGroupBy methods

Split-apply-combine, revisited

When we introduced the split-apply-combine pattern, the "apply" step involved aggregation – our final DataFrame had one row for each group.

Instead of aggregating during the apply step, we could instead perform a:

• Transformation, in which we perform operations to every value within each group.

• Filtration, in which we keep only the groups that satisfy some condition.

Transformations

Suppose we want to convert the 'body_mass_g' column to to z-scores (i.e. standard units):

$$z(x_i) = \frac{x_i - \text{mean of } x}{\text{SD of } x}$$

def z_score(x):
    return (x - x.mean()) / x.std(ddof=0)

z_score(penguins['body_mass_g'])

Transformations within groups

• Now, what if we wanted the z-score within each group?

• To do so, we can use the transform method on a DataFrameGroupBy object. The transform method takes in a function, which itself takes in a Series and returns a new Series.

• A transformation produces a DataFrame or Series of the same size – it is not an aggregation!

z_mass = (penguins
          .groupby('species')
          ['body_mass_g']
          .transform(z_score))
z_mass

penguins.assign(z_mass=z_mass)

display_df(penguins.assign(z_mass=z_mass), rows=8)

Note that above, penguin 340 has a larger 'body_mass_g' than penguin 0, but a lower 'z_mass'.

• Penguin 0 has an above average 'body_mass_g' among 'Adelie' penguins.
• Penguin 340 has a below average 'body_mass_g' among 'Gentoo' penguins. Remember from earlier that the average 'body_mass_g' of 'Gentoo' penguins is much higher than for other species.

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

Filtering groups

• To keep only the groups that satisfy a particular condition, use the filter method on a DataFrameGroupBy object.

• The filter method takes in a function, which itself takes in a DataFrame/Series and return a single Boolean. The result is a new DataFrame/Series with only the groups for which the filter function returned True.

For example, suppose we want only the 'species' whose average 'bill_length_mm' is above 39.

(penguins
 .groupby('species')
 .filter(lambda df: df['bill_length_mm'].mean() > 39)
)

No more 'Adelie's!

Or, as another example, suppose we only want 'species' with at least 100 penguins:

(penguins
 .groupby('species')
 .filter(lambda df: df.shape[0] > 100)
)

No more 'Chinstrap's!

Question 🤔 (Answer at dsc80.com/q)

Code: lec03-aggs

Answer the following questions about grouping:

• In .agg(fn), what is the input to fn? What is the output of fn?
• In .transform(fn), what is the input to fn? What is the output of fn?
• In .filter(fn), what is the input to fn? What is the output of fn?

Grouping with multiple columns

When we group with multiple columns, one group is created for every unique combination of elements in the specified columns.

penguins

species_and_island = (
    penguins
    .groupby(['species', 'island'])
    [['bill_length_mm', 'body_mass_g']]
    .mean()
)
species_and_island

Grouping and indexes

• The groupby method creates an index based on the specified columns.
• When grouping by multiple columns, the resulting DataFrame has a MultiIndex.
• Advice: When working with a MultiIndex, use reset_index or set as_index=False in groupby.

species_and_island

species_and_island['body_mass_g']

species_and_island.loc['Adelie']

species_and_island.loc[('Adelie', 'Torgersen')]

species_and_island.reset_index()

(penguins
 .groupby(['species', 'island'], as_index=False)
 [['bill_length_mm', 'body_mass_g']]
 .mean()
)

Question 🤔 (Answer at dsc80.com/q)

Code: lec03-names

Find the most popular Male and Female baby Name for each Year in baby. Exclude Years where there were fewer than 1 million births recorded.

baby_path = Path('data') / 'baby.csv'
baby = pd.read_csv(baby_path)
baby

# Your code goes here.

Pivot tables using the pivot_table method

Pivot tables: an extension of grouping

Pivot tables are a compact way to display tables for humans to read:

Sex	F	M
Year
2018	1698373	1813377
2019	1675139	1790682
2020	1612393	1721588
2021	1635800	1743913
2022	1628730	1733166

• Notice that each value in the table is a sum over the counts, split by year and sex.
• You can think of pivot tables as grouping using two columns, then "pivoting" one of the group labels into columns.

pivot_table

The pivot_table DataFrame method aggregates a DataFrame using two columns. To use it:

df.pivot_table(index=index_col,
               columns=columns_col,
               values=values_col,
               aggfunc=func)

The resulting DataFrame will have:

• One row for every unique value in index_col.
• One column for every unique value in columns_col.
• Values determined by applying func on values in values_col.

last_5_years = baby.query('Year >= 2018')
last_5_years

last_5_years.pivot_table(
    index='Year',
    columns='Sex',
    values='Count',
    aggfunc='sum',
)

# Look at the similarity to the snippet above!
(last_5_years
 .groupby(['Year', 'Sex'])
 [['Count']]
 .sum()
)

Example

Find the number of penguins per 'island' and 'species'.

penguins

penguins.pivot_table(
    index='species',
    columns='island',
    values='bill_length_mm', # Choice of column here doesn't actually matter!
    aggfunc='count',
)

Note that there is a NaN at the intersection of 'Biscoe' and 'Chinstrap', because there were no Chinstrap penguins on Biscoe Island.

We can either use the fillna method afterwards or the fill_value argument to fill in NaNs.

penguins.pivot_table(
    index='species',
    columns='island',
    values='bill_length_mm',
    aggfunc='count',
    fill_value=0,
)

Reshaping

• pivot_table reshapes DataFrames from "long" to "wide".
• Other DataFrame reshaping methods:
  • melt: Un-pivots a DataFrame. Very useful in data cleaning.
  • pivot: Like pivot_table, but doesn't do aggregation.
  • stack: Pivots multi-level columns to multi-indices.
  • unstack: Pivots multi-indices to columns.
  • Google and the documentation are your friends!

We will most likely end lecture here.

Distributions

Joint distribution

When using aggfunc='count', a pivot table describes the joint distribution of two categorical variables. This is also called a contingency table.

counts = penguins.pivot_table(
    index='species',
    columns='sex',
    values='body_mass_g',
    aggfunc='count',
    fill_value=0
)
counts

We can normalize the DataFrame by dividing by the total number of penguins. The resulting numbers can be interpreted as probabilities that a randomly selected penguin from the dataset belongs to a given combination of species and sex.

joint = counts / counts.sum().sum()
joint

Marginal probabilities

If we sum over one of the axes, we can compute marginal probabilities, i.e. unconditional probabilities.

joint

# Recall, joint.sum(axis=0) sums across the rows,
# which computes the sum of the **columns**.
joint.sum(axis=0)

joint.sum(axis=1)

For instance, the second Series tells us that a randomly selected penguin has a 0.36 chance of being of species 'Gentoo'.

Conditional probabilities

Using counts, how might we compute conditional probabilities like $$P(\text{species } = \text{"Adelie"} \mid \text{sex } = \text{"Female"})?$$

counts

$$\begin{align*} P(\text{species} = c \mid \text{sex} = x) &= \frac{\# \: (\text{species} = c \text{ and } \text{sex} = x)}{\# \: (\text{sex} = x)} \end{align*}$$

➡️ Click here to see more of a derivation.

$$\begin{align*} P(\text{species} = c \mid \text{sex} = x) &= \frac{P(\text{species} = c \text{ and } \text{sex} = x)}{P(\text{sex = }x)} \\ &= \frac{\frac{\# \: (\text{species } = \: c \text{ and } \text{sex } = \: x)}{N}}{\frac{\# \: (\text{sex } = \: x)}{N}} \\ &= \frac{\# \: (\text{species} = c \text{ and } \text{sex} = x)}{\# \: (\text{sex} = x)} \end{align*}$$

Answer: To find conditional probabilities of 'species' given 'sex', divide by column sums. To find conditional probabilities of 'sex' given 'species', divide by row sums.

Conditional probabilities

To find conditional probabilities of 'species' given 'sex', divide by column sums. To find conditional probabilities of 'sex' given 'species', divide by row sums.

counts

counts.sum(axis=0)

The conditional distribution of 'species' given 'sex' is below. Note that in this new DataFrame, the 'Female' and 'Male' columns each sum to 1.

counts / counts.sum(axis=0)

For instance, the above DataFrame tells us that the probability that a randomly selected penguin is of 'species' 'Adelie' given that they are of 'sex' 'Female' is 0.442424.

Question 🤔 (Answer at dsc80.com/q)

Code: lec03-dist

Find the conditional distribution of 'sex' given 'species'.

Hint: Use .T.

# Your code goes here.

Summary, next time

Summary

• Grouping allows us to change the level of granularity in a DataFrame.
• Grouping involves three steps – split, apply, and combine.
  • Usually, what is applied is an aggregation, but it could be a transformation or filtration.
• pivot_table aggregates data based on two categorical columns, and reshapes the result to be "wide" instead of "long".

Next time

• Simpson's paradox.
• Merging.
  • Review this diagram from DSC 10!
• The pitfalls of the apply method.