Data grouping partitions a pandas DataFrame by one or more key columns so each group can be counted, summarized, visualized, or monitored separately. section 12.3.1 section 14.3.1
Common grouped operations include:
- Count records in each group with groupby(...).size(), then use reset_index(name='count') to produce a tabular result. section 12.3.1
- Compute several descriptive statistics with groupby(...).agg(['min', 'max', 'mean', 'median', 'std', 'var']). section 5.2.1
- Calculate subgroup means for an x-bar chart, then compare them with an overall mean and three-standard-deviation control limits. section 14.3.1
Counting simulated values
results1 = (df2.groupby('uniform_distribution') .size() .reset_index(name='count')) The resulting table has one row per unique value and a count column. The same pattern compares values generated by randint() or random choice with replacement. section 12.3.1 section 12.3.2
>>> results1 = df2.groupby('uniform_distribution') \
>>> .size() \
>>> .reset_index(name = 'count')
>>> print(results1)
uniform_distribution count
0 1 109
1 2 116
2 3 110
3 4 110
4 5 101
5 6 112
6 7 130
7 8 104
8 9 108
Inspect the complete grouped-count expression and its nine-row output. section 12.3.1
Grouping supports decisions about differences between categories and stability across repeated samples. Grouped statistics can reveal distinct class characteristics, while grouped means can identify assignable process variation. section 5.2.1 section 14.3.1
