Learn the power of Pandas Grouper with our practical example. Explore how to group and aggregate your data efficiently using the 'Grouper' function. Enhance your data analysis skills and discover the flexibility of Pandas in handling time-based data.
-----------------------------------------------------
Data Used in the Video:
dates = [
'Jan/01/2023', 'Jan/02/2023', 'Jan/03/2023', 'Jan/04/2023', 'Jan/05/2023',
'Feb/01/2023', 'Feb/02/2023', 'Feb/03/2023', 'Feb/04/2023', 'Feb/05/2023',
'Mar/01/2023', 'Mar/02/2023', 'Mar/03/2023', 'Mar/04/2023', 'Mar/05/2023'
]
df = pd.DataFrame( {
'Date': dates,
'Product': ['A'] * 15, # Product name 'A'
'Sold_Quantity': [random.randint(1, 50) for _ in range(15)]
})
------------------------------------
Get Answers to:
What is Grouper in Pandas?
#pythonpandas #python #pythonprogramming
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20 окт 2023
