get_group Method¶

The get_group() method retrieves a specific group from a GroupBy object by its key.

Basic Usage¶

Access a single group by name.

1. Get Single Group¶

import pandas as pd

data = {
    'day': ['1/1/20', '1/2/20', '1/1/20', '1/2/20', '1/1/20', '1/2/20'],
    'city': ['NY', 'NY', 'SF', 'SF', 'LA', 'LA'],
    'temperature': [21, 14, 25, 32, 36, 42],
    'humidity': [31, 15, 36, 22, 16, 29],
}
df = pd.DataFrame(data)

dg = df.groupby("city")
print(dg.get_group("NY"))

      day city  temperature  humidity
0  1/1/20   NY           21        31
1  1/2/20   NY           14        15

2. Returns DataFrame¶

The result is a DataFrame containing only rows for that group.

3. Original Index Preserved¶

Row indices from the original DataFrame are kept.

Multiple Group Keys¶

Access groups with compound keys.

1. Tuple Key¶

grouped = df.groupby(['city', 'day'])
ny_jan1 = grouped.get_group(('NY', '1/1/20'))
print(ny_jan1)

2. Key Must Match¶

# Must provide all grouping columns
# grouped.get_group('NY')  # Error: need both city and day

3. Order Matters¶

# Tuple order must match groupby column order
grouped.get_group(('NY', '1/1/20'))  # Correct
# grouped.get_group(('1/1/20', 'NY'))  # Wrong order

Use Cases¶

When to use get_group.

1. Inspect Specific Group¶

# Debug or examine one group
ny_data = df.groupby('city').get_group('NY')
print(ny_data.describe())

2. Filter by Group¶

# Alternative to boolean indexing
# These are equivalent:
df[df['city'] == 'NY']
df.groupby('city').get_group('NY')

3. Compare Groups¶

grouped = df.groupby('city')
ny = grouped.get_group('NY')
sf = grouped.get_group('SF')

print(f"NY mean: {ny['temperature'].mean()}")
print(f"SF mean: {sf['temperature'].mean()}")

Error Handling¶

Handle missing groups.

1. KeyError for Missing¶

try:
    df.groupby('city').get_group('Tokyo')
except KeyError:
    print("Group 'Tokyo' not found")

2. Check Available Groups¶

grouped = df.groupby('city')
print(list(grouped.groups.keys()))
# ['LA', 'NY', 'SF']

3. Safe Access¶

grouped = df.groupby('city')
if 'Tokyo' in grouped.groups:
    tokyo_data = grouped.get_group('Tokyo')
else:
    print("No data for Tokyo")

Performance¶

get_group vs boolean indexing.

1. Single Access¶

# Similar performance for single access
df[df['city'] == 'NY']
df.groupby('city').get_group('NY')

2. Multiple Accesses¶

# GroupBy is faster for multiple accesses
grouped = df.groupby('city')  # Create once
ny = grouped.get_group('NY')
sf = grouped.get_group('SF')
la = grouped.get_group('LA')

3. Best Practice¶

# Create GroupBy object once, reuse for multiple operations