• pandas.core.groupby.DataFrameGroupBy.__iter__
• pandas.core.groupby.SeriesGroupBy.__iter__
• pandas.core.groupby.DataFrameGroupBy.groups
• pandas.core.groupby.SeriesGroupBy.groups
• pandas.core.groupby.DataFrameGroupBy.indices
• pandas.core.groupby.SeriesGroupBy.indices
• pandas.core.groupby.DataFrameGroupBy.get_group
• pandas.core.groupby.SeriesGroupBy.get_group
• pandas.Grouper
• pandas.NamedAgg
• pandas.core.groupby.SeriesGroupBy.apply
• pandas.core.groupby.DataFrameGroupBy.apply
• pandas.core.groupby.SeriesGroupBy.agg
• pandas.core.groupby.DataFrameGroupBy.agg
• pandas.core.groupby.SeriesGroupBy.aggregate
• pandas.core.groupby.DataFrameGroupBy.aggregate
• pandas.core.groupby.SeriesGroupBy.transform
• pandas.core.groupby.DataFrameGroupBy.transform
• pandas.core.groupby.SeriesGroupBy.pipe
• pandas.core.groupby.DataFrameGroupBy.pipe
• pandas.core.groupby.DataFrameGroupBy.filter
• pandas.core.groupby.SeriesGroupBy.filter
• pandas.core.groupby.DataFrameGroupBy.all
• pandas.core.groupby.DataFrameGroupBy.any
• pandas.core.groupby.DataFrameGroupBy.bfill
• pandas.core.groupby.DataFrameGroupBy.corr
• pandas.core.groupby.DataFrameGroupBy.corrwith
• pandas.core.groupby.DataFrameGroupBy.count
• pandas.core.groupby.DataFrameGroupBy.cov
• pandas.core.groupby.DataFrameGroupBy.cumcount
• pandas.core.groupby.DataFrameGroupBy.cummax
• pandas.core.groupby.DataFrameGroupBy.cummin
• pandas.core.groupby.DataFrameGroupBy.cumprod
• pandas.core.groupby.DataFrameGroupBy.cumsum
• pandas.core.groupby.DataFrameGroupBy.describe
• pandas.core.groupby.DataFrameGroupBy.diff
• pandas.core.groupby.DataFrameGroupBy.ffill
• pandas.core.groupby.DataFrameGroupBy.fillna
• pandas.core.groupby.DataFrameGroupBy.first
• pandas.core.groupby.DataFrameGroupBy.head
• pandas.core.groupby.DataFrameGroupBy.idxmax
• pandas.core.groupby.DataFrameGroupBy.idxmin
• pandas.core.groupby.DataFrameGroupBy.last
• pandas.core.groupby.DataFrameGroupBy.max
• pandas.core.groupby.DataFrameGroupBy.mean
• pandas.core.groupby.DataFrameGroupBy.median
• pandas.core.groupby.DataFrameGroupBy.min
• pandas.core.groupby.DataFrameGroupBy.ngroup
• pandas.core.groupby.DataFrameGroupBy.nth
• pandas.core.groupby.DataFrameGroupBy.nunique
• pandas.core.groupby.DataFrameGroupBy.ohlc
• pandas.core.groupby.DataFrameGroupBy.pct_change
• pandas.core.groupby.DataFrameGroupBy.prod
• pandas.core.groupby.DataFrameGroupBy.quantile
• pandas.core.groupby.DataFrameGroupBy.rank
• pandas.core.groupby.DataFrameGroupBy.resample
• pandas.core.groupby.DataFrameGroupBy.rolling
• pandas.core.groupby.DataFrameGroupBy.sample
• pandas.core.groupby.DataFrameGroupBy.sem
• pandas.core.groupby.DataFrameGroupBy.shift
• pandas.core.groupby.DataFrameGroupBy.size
• pandas.core.groupby.DataFrameGroupBy.skew
• pandas.core.groupby.DataFrameGroupBy.std
• pandas.core.groupby.DataFrameGroupBy.sum
• pandas.core.groupby.DataFrameGroupBy.var
• pandas.core.groupby.DataFrameGroupBy.tail
• pandas.core.groupby.DataFrameGroupBy.take
• pandas.core.groupby.DataFrameGroupBy.value_counts
• pandas.core.groupby.SeriesGroupBy.all
• pandas.core.groupby.SeriesGroupBy.any
• pandas.core.groupby.SeriesGroupBy.bfill
• pandas.core.groupby.SeriesGroupBy.corr
• pandas.core.groupby.SeriesGroupBy.count
• pandas.core.groupby.SeriesGroupBy.cov
• pandas.core.groupby.SeriesGroupBy.cumcount
• pandas.core.groupby.SeriesGroupBy.cummax
• pandas.core.groupby.SeriesGroupBy.cummin
• pandas.core.groupby.SeriesGroupBy.cumprod
• pandas.core.groupby.SeriesGroupBy.cumsum
• pandas.core.groupby.SeriesGroupBy.describe
• pandas.core.groupby.SeriesGroupBy.diff
• pandas.core.groupby.SeriesGroupBy.ffill
• pandas.core.groupby.SeriesGroupBy.fillna
• pandas.core.groupby.SeriesGroupBy.first
• pandas.core.groupby.SeriesGroupBy.head
• pandas.core.groupby.SeriesGroupBy.last
• pandas.core.groupby.SeriesGroupBy.idxmax
• pandas.core.groupby.SeriesGroupBy.idxmin
• pandas.core.groupby.SeriesGroupBy.is_monotonic_increasing
• pandas.core.groupby.SeriesGroupBy.is_monotonic_decreasing
• pandas.core.groupby.SeriesGroupBy.max
• pandas.core.groupby.SeriesGroupBy.mean
• pandas.core.groupby.SeriesGroupBy.median
• pandas.core.groupby.SeriesGroupBy.min
• pandas.core.groupby.SeriesGroupBy.ngroup
• pandas.core.groupby.SeriesGroupBy.nlargest
• pandas.core.groupby.SeriesGroupBy.nsmallest
• pandas.core.groupby.SeriesGroupBy.nth
• pandas.core.groupby.SeriesGroupBy.nunique
• pandas.core.groupby.SeriesGroupBy.unique
• pandas.core.groupby.SeriesGroupBy.ohlc
• pandas.core.groupby.SeriesGroupBy.pct_change
• pandas.core.groupby.SeriesGroupBy.prod
• pandas.core.groupby.SeriesGroupBy.quantile
• pandas.core.groupby.SeriesGroupBy.rank
• pandas.core.groupby.SeriesGroupBy.resample
• pandas.core.groupby.SeriesGroupBy.rolling
• pandas.core.groupby.SeriesGroupBy.sample
• pandas.core.groupby.SeriesGroupBy.sem
• pandas.core.groupby.SeriesGroupBy.shift
• pandas.core.groupby.SeriesGroupBy.size
• pandas.core.groupby.SeriesGroupBy.skew
• pandas.core.groupby.SeriesGroupBy.std
• pandas.core.groupby.SeriesGroupBy.sum
• pandas.core.groupby.SeriesGroupBy.var
• pandas.core.groupby.SeriesGroupBy.tail
• pandas.core.groupby.SeriesGroupBy.take
• pandas.core.groupby.SeriesGroupBy.value_counts
• pandas.core.groupby.DataFrameGroupBy.boxplot
• pandas.core.groupby.DataFrameGroupBy.hist
• pandas.core.groupby.SeriesGroupBy.hist
• pandas.core.groupby.DataFrameGroupBy.plot
• pandas.core.groupby.SeriesGroupBy.plot

Learning pandas' groupby functionality can be approached systematically by grouping related methods together. Here's a structured way to learn these methods, organized by functionality and complexity:

1. Basic GroupBy Mechanics

Start with understanding how to create groups and inspect them:

• DataFrameGroupBy.__iter__, SeriesGroupBy.__iter__ (iteration over groups)
• DataFrameGroupBy.groups, SeriesGroupBy.groups (dictionary of groups)
• DataFrameGroupBy.indices, SeriesGroupBy.indices (group indices)
• DataFrameGroupBy.get_group, SeriesGroupBy.get_group (extract a specific group)
• pandas.Grouper (flexible grouping rules, e.g., time-based)
• pandas.NamedAgg (for named aggregation, e.g., agg(new_col=('old_col', 'sum')))

2. Core Aggregation Methods

Learn how to compute summaries/statistics on groups:

• Basic Aggregations:
• count(), size(), sum(), mean(), median(), min(), max()
• nunique(), first(), last(), prod(), var(), std(), sem(), skew()
• Advanced Aggregations:
• agg() / aggregate() (flexible single/multiple operations)
• describe() (summary stats)
• quantile() (percentiles)
• value_counts() (frequency counts)

3. Group-Wise Transformations

Methods that return data aligned with the original input:

• transform() (apply function to each group, returning same shape)
• rank(), cumsum(), cumprod(), cummin(), cummax() (cumulative operations)
• pct_change(), diff() (group-wise differences/percent changes)
• shift(), bfill(), ffill() (handling missing data within groups)

4. Filtering Groups

Select subsets of groups based on conditions:

• filter() (keep groups that meet a criterion)
• head(), tail(), nth() (select rows within groups)
• sample() (random sampling from groups)

5. Specialized Operations

• Index/Position Handling:
• idxmin(), idxmax() (return index of min/max)
• ngroup() (group IDs)
• cumcount() (counter within each group)
• Time-Series Specific:
• resample() (time-based regrouping)
• rolling() (rolling window operations)
• Correlation/Covariance:
• corr(), cov(), corrwith() (group-wise relationships)
• Unique Values:
• unique() (unique values per group)
• nlargest(), nsmallest() (top/bottom values per group)

6. Visualization

• plot(), hist(), boxplot() (visualizing grouped data)

7. Advanced Workflow Tools

• apply() (flexible apply-any-function)
• pipe() (method chaining with groupby)
• ohlc() (open-high-low-close for financial data)

Suggested Learning Order

1. Basic Mechanics (Section 1) → How groups are formed and inspected.
2. Aggregations (Section 2) → Summarizing data (most common use case).
3. Transformations (Section 3) → Modify data while preserving shape.
4. Filtering (Section 4) → Subset groups.
5. Specialized Ops (Section 5) → As needed for your use case.
6. Visualization/Advanced (Sections 6–7) → For deeper analysis.

Key Notes

• agg()/aggregate() are interchangeable; agg() is more commonly used.
• apply() is powerful but slower; prefer built-in methods (e.g., sum()) when possible.
• transform() vs. agg(): Use transform to broadcast results back to the original rows.

Pandas GroupBy: Basic Mechanics Study Notes

Understanding how to create, inspect, and extract groups in pandas.

1. DataFrameGroupBy.__iter__ & SeriesGroupBy.__iter__

Purpose: Iterate over groups in a GroupBy object.

Syntax:

python

for name, group in df.groupby('column'):
    # name = group key (e.g., unique value in 'column')
    # group = DataFrame/Series for that group

Example:

python

import pandas as pd

df = pd.DataFrame({
    'Animal': ['Dog', 'Cat', 'Dog', 'Cat'],
    'Speed': [40, 30, 35, 25]
})

for animal, group in df.groupby('Animal'):
    print(f"Animal: {animal}")
    print(group)

Output:

Animal: Cat
  Animal  Speed
1    Cat     30
3    Cat     25

Animal: Dog
  Animal  Speed
0    Dog     40
2    Dog     35

When to Use:

• When you need to manually process each group (e.g., custom aggregations, filtering).
• Useful for debugging or inspecting groups before applying operations.

Note:

• Prefer built-in GroupBy methods (e.g., agg(), transform()) for efficiency.

*2. DataFrameGroupBy.groups & SeriesGroupBy.groups

Purpose: Returns a dictionary mapping group keys to row indices.

Syntax:

python

grouped = df.groupby('column')
grouped.groups  # Returns {group_key: [indices]}

Example:

python

grouped = df.groupby('Animal')
print(grouped.groups)

Output:

{'Cat': [1, 3], 'Dog': [0, 2]}

When to Use:

• To inspect which rows belong to each group.
• Useful for debugging or when you need direct access to group indices.

Note:

• The keys are the unique values in the grouping column.
• The values are lists of row indices (positions in the original DataFrame).

*3. DataFrameGroupBy.indices & SeriesGroupBy.indices

Purpose: Similar to .groups, but returns a dictionary of NumPy arrays (faster for large data).

Syntax:

python

grouped = df.groupby('column')
grouped.indices  # Returns {group_key: np.array(indices)}

Example:

python

print(grouped.indices)

Output:

{'Cat': array([1, 3]), 'Dog': array([0, 2])}

When to Use:

• When working with large datasets (NumPy arrays are more efficient than lists).
• If you need indices for further NumPy-based computations.

Note:

• Almost identical to .groups, but returns arrays instead of lists.

*4. DataFrameGroupBy.get_group & SeriesGroupBy.get_group

Purpose: Extract a single group as a DataFrame or Series.

Syntax:

python

grouped = df.groupby('column')
grouped.get_group('group_name')

Example:

python

dog_group = grouped.get_group('Dog')
print(dog_group)

Output:

  Animal  Speed
0    Dog     40
2    Dog     35

When to Use:

• When you need to work with a specific subset of data.
• Useful for debugging or isolating a group for further analysis.

Note:

• Raises KeyError if the group does not exist.

*5. pandas.Grouper

Purpose: Flexible grouping (especially for time-based grouping).

Syntax:

python

df.groupby(pd.Grouper(key='date_column', freq='D'))  # Daily grouping

Example:

python

df = pd.DataFrame({
    'Date': pd.date_range('2023-01-01', periods=4, freq='D'),
    'Value': [10, 20, 15, 25]
})

grouped = df.groupby(pd.Grouper(key='Date', freq='2D')).sum()
print(grouped)

Output:

            Value
Date
2023-01-01     30  # Jan 1 + Jan 2
2023-01-03     40  # Jan 3 + Jan 4

When to Use:

• For time-based resampling (e.g., daily, weekly, monthly).
• When grouping by a column with a custom frequency.

Note:

• Similar to resample(), but works inside groupby().

*6. pandas.NamedAgg

Purpose: Named aggregation (clear column naming in agg()).

Syntax:

python

df.groupby('column').agg(
    new_name=pd.NamedAgg(column='col', aggfunc='sum')
)
# Or (shorthand):
df.groupby('column').agg(new_name=('col', 'sum'))

Example:

python

result = df.groupby('Animal').agg(
    Avg_Speed=('Speed', 'mean'),
    Max_Speed=('Speed', 'max')
)
print(result)

Output:

       Avg_Speed  Max_Speed
Animal
Cat         27.5         30
Dog         37.5         40

When to Use:

• When you need readable column names after aggregation.
• Replaces the older dict syntax ({'col': ['mean', 'sum']}).

Note:

• Introduced in pandas 0.25+ for cleaner aggregation syntax.

Summary Table

Best Practices

✅ Use get_group() to inspect a single group.

✅ Prefer .indices for large datasets (faster than .groups).

✅ Use pd.Grouper for time-series grouping.

✅ Use NamedAgg for readable aggregation results.