ID 26 - Group by occupation and count the occurrences

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Syntax

COUNT COUNTROWS ROW SUM SUMMARIZE

Related

45 - 3-Year Moving Average of Sales 27 - Group by occupation and count the occurrences AND Filter occupations with count greater than 5000 37 - Ranks customers by total sales grouped product 34 - Split and Group By

SELECT occupation, COUNT(*) 
FROM customer 
GROUP BY occupation;

EVALUATE
SUMMARIZE(
    customer,
    customer[occupation],
    "Count", COUNTROWS(customer)
)

import pandas as pd

# Sample DataFrame
data = {
    'occupation': ['Engineer', 'Doctor', 'Engineer', 'Artist', 'Doctor']
}

df = pd.DataFrame(data)

# Group by occupation and count the occurrences
df_result = df.groupby('occupation').size().reset_index(name='Count')

# Convert to list of lists
result = df_result.values.tolist()
print(result)