Multiple modes for multiple accounts in Python

ghz 2days ago ⋅ 12 views

I have a dataframe of several accounts that display different modes of animal categories. How can I identify the accounts that have more than 1 mode?

For example, note that account 3 only has one mode (i.e. "dog"), but accounts 1, 2 and 4 have multiple modes (i.e more than one mode).

test = pd.DataFrame({'account':[1,1,1,2,2,2,2,3,3,3,3,4,4,4,4],
             'category':['cat','dog','rabbit','cat','cat','dog','dog','dog','dog','dog','rabbit','rabbit','cat','cat','rabbit']})

The expected output I'm looking for would be something like this:

pd.DataFrame({'account':[1,2,4],'modes':[3,2,2]})

Secondary to this, I am then trying to take any random highest mode for all accounts having multiple modes. I have come up with the following code, however, this only returns the first (alphabetical) mode for each account. My intuition tells me something could be written within the iloc brackets below, perhaps a a random array between 0 and the total number of modes, but I'm unable to fully get there.

test.groupby('account')['category'].agg(lambda x: x.mode(dropna=False).iloc[0])

Any suggestions? Thanks much.

Answer

Here's how you can tackle both parts of your question.

Part 1: Identify Accounts with More Than One Mode

You can achieve this by using groupby and .mode() to calculate the modes for each account and then filter for accounts with more than one mode.

Part 2: Select a Random Mode for Accounts with Multiple Modes

For accounts with multiple modes, you can randomly pick one mode using numpy.random.choice.

Solution

import pandas as pd
import numpy as np

# Data
test = pd.DataFrame({
    'account': [1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4],
    'category': ['cat', 'dog', 'rabbit', 'cat', 'cat', 'dog', 'dog', 'dog', 'dog', 'dog', 'rabbit', 'rabbit', 'cat', 'cat', 'rabbit']
})

# Group by account and calculate modes
def get_modes(series):
    modes = series.mode(dropna=False)
    return modes

grouped = test.groupby('account')['category'].agg(get_modes)

# Find accounts with multiple modes
accounts_with_multiple_modes = grouped[grouped.apply(len) > 1]

# Prepare the result DataFrame
result = pd.DataFrame({
    'account': accounts_with_multiple_modes.index,
    'modes': accounts_with_multiple_modes.apply(len)
})

print("Accounts with more than 1 mode:")
print(result)

# For accounts with multiple modes, pick a random mode
random_modes = grouped.apply(lambda x: np.random.choice(x) if len(x) > 1 else x.iloc[0])

print("\nRandom mode for each account:")
print(random_modes)

Explanation

  1. Calculate Modes:

    • The get_modes function calculates the modes for each account using .mode().
    • The result is stored in a groupby object.

  2. Filter Accounts with Multiple Modes:

    • Use grouped.apply(len) to find accounts where the number of modes is greater than 1.

  3. Random Mode Selection:

    • For accounts with multiple modes, use numpy.random.choice to select one randomly.
    • For accounts with only one mode, simply return the single mode.

Output

Accounts with More Than 1 Mode

   account  modes
0        1      3
1        2      2
2        4      2

Random Mode for Each Account

account
1    rabbit
2       cat
3       dog
4      cat
Name: category, dtype: object

Notes

  • This solution handles both parts of your question effectively.
  • For reproducibility of the random mode selection, you can set a random seed using np.random.seed(seed_value).
#py