Pandas groupby, then sort within groups

Filtering top values within groups

Suppose you want to pick the cream of the crop, the best of the best, the top values within each group. For this, nlargest() is your go-to function:

# Looking for the top performers?
top_entries = df.groupby('group_col', group_keys=False).apply(lambda x: x.nlargest(3, 'sort_col'))
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This snippet pulls the top 3 entries per group, sorted by 'sort_col'. A toast to the high achievers! 🍾

Aggregate, then select

When faced with grouped data, you may need to tally up (or aggregate) each group before selecting certain results. Here's how:

# Count'em up
agg_df = df.groupby('group_col').agg({'data': 'sum'})

# Now pick the interesting ones
top_agg = agg_df.groupby('group_col').head(3)
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Once we sum it all up, we use head() to select the top 3 groups. It's a pick-and-choose kind of world, isn't it? 😊

Custom operations via apply

For the complex queries that keep you awake at night, use apply() with custom functions:

def custom_sort(g):
    # Does this function make my code look big?
    return g.sort_values('data', ascending=False).head(3)

sorted_custom_df = df.groupby('group_col').apply(custom_sort)
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This function, custom_sort, aids you in both sorting and selection. Custom-tailored, just like your Sunday best! 👔

Efficient data preparation for analysis

Getting your data structured makes your analysis run smoother than a well-oiled machine:

# A bit of light cleaning
groups = df.groupby(['job', 'source'])

# A bit of heavy lifting
sorted_groups = groups.apply(lambda x: x.sort_values('metrics', ascending=False))
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First, we group by 'job' and 'source', then sort by 'metrics' within these groups. Voila! Your insightful data, served fresh and piping hot!

Lambdas for rapid, flexible operations

For quick adjustments on the fly or custom needs, embrace the power of lambda functions within groupby:

# In need of speed?
sorted_with_lambda = df.groupby('group_col').apply(lambda x: x.sort_values('data').nlargest(3, 'other_col'))
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Here, we sort within each group and also select the top 3 entries based on other_col. It's like a two-for-one deal!

Practical scenarios

For real-life situations, imagine you're filtering out the top-selling products within each category of an e-commerce dataset:

# Who are the sellers smashing the Sales Record?
sales_df.groupby('category').apply(lambda x: x.nlargest(3, 'sales')).reset_index(drop=True)
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This code fetches the top 3 products per category, providing targeted insights. Who knew data analysis could be this thrilling?! 🎢