Pandas Percentage of Total with GroupBy

Exploring Alternative Approaches and Performance Pitfalls

While the previous solution is simple and on-the-nose, there are other noteworthy methods that can be handy when you're handling piles of data or need the flexibility of choice.

Tidy Chaining for Easy Reading

Method chaining helps your code convey the flow of transformations in a clear and organized manner:

# Method chaining for people who like things neat
df['Percentage'] = (df.groupby('group_col')['value_col']
                      .transform('sum')
                      .pipe(lambda x: 100 * df['value_col'] / x))
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Optimizing for Large Datasets

When wrestling with enormous datasets, it might be productive to test different approaches to find which performs the best. Make %timeit your best friend:

# It's race time, folks!
%timeit df.groupby('group_col')['value_col'].transform('sum')
%timeit df.groupby('group_col')['value_col'].apply(lambda x: x.sum())


apply() could be a slow-poke. Why? It has a broader job description. So, use it judiciously!

Getting Deeper with GroupBy Aggregations

Our fast answer is neat and gets the job done. But let's dig a little deeper and address potential problems.

State-wide Aggregation for Percentages

Suppose you are a data warrior who needs to calculate state-level percentages for each category:

# Calculating state total sales. Move over, Excel!
df['state_total'] = df.groupby('state')['sales'].transform('sum')

# We are all about fair sharing here
df['office_percentage'] = (df['sales'] / df['state_total']) * 100


Pandas doing its thing to ensure that office percentages sum up to 100% state-wise. No office left behind!

Broadcasting with div

Pandas div is your trusted companion for correctly joining data for happier computations:

# Don't you love it when things align?
df['Percentage'] = df['sales'].div(df.groupby('state')['sales'].transform('sum'), level='state') * 100


Beware of apply

When using apply(), remember that it's powerful, but it might also enjoy sluggish Sundays. Yes, it can be slower, but it’s the go-to guy for customized tasks!

Advanced Techniques and Tricks for Enthusiasts

Let's brush up on a few advanced techniques and watch out for common booby traps.

Handling Zeroes like a Pro

Sometimes, a group might have zero sales. Now what? Make sure you handle it like a ninja, either by replacing NaN values or adding a tiny epsilon (the smallest number that can be represented):

# Watch out! Dividing by zero ahead!
df['Percentage'] = df.groupby('group_col')['value_col'].transform(lambda x: 100 * x / (x.sum() or np.finfo(float).eps))


Regality in Multiple Columns

You'll want to group on several columns to calculate nested percentages. Very regal indeed:

# Let's do the nested group dance
df['Percentage'] = df.groupby(['state', 'city'])['sales'].transform(lambda x: 100 * x / x.sum())


Behold! The percentage of sales for offices within a city in a state.

When to Ditch transform

If you need multiple aggregations at once or your grouping operation is playing truant by not producing a series aligned with the DataFrame index, transform is not your friend.