Pandas - Explanation on apply function being slow

Question

Apply function seems to work very slow with a large dataframe (about 1~3 million rows).

I have checked related questions here, like Speed up Pandas apply function, a

Answer 1

Concerning your first question, I can't say exactly why this instance is slow. But generally, apply does not take advantage of vectorization. Also, apply returns a new Series or DataFrame object, so with a very large DataFrame, you have considerable IO overhead (I cannot guarantee this is the case 100% of the time since Pandas has loads of internal implementation optimization).

For your first method, I assume you are trying to fill a 'value' column in df using the p_dict as a lookup table. It is about 1000x faster to use pd.merge:

import string, sys

import numpy as np
import pandas as pd

##
# Part 1 - filling a column by a lookup table
##
def f1(col, p_dict):
    return [p_dict[p_dict['ID'] == s]['value'].values[0] for s in col]

# Testing
n_size = 1000
np.random.seed(997)
p_dict = pd.DataFrame({'ID': [s for s in string.ascii_uppercase], 'value': np.random.randint(0,n_size, 26)})
df = pd.DataFrame({'p_id': [string.ascii_uppercase[i] for i in np.random.randint(0,26, n_size)]})

# Apply the f1 method  as posted
%timeit -n1 -r5 temp = df.apply(f1, args=(p_dict,))
>>> 1 loops, best of 5: 832 ms per loop

# Using merge
np.random.seed(997)
df = pd.DataFrame({'p_id': [string.ascii_uppercase[i] for i in np.random.randint(0,26, n_size)]})
%timeit -n1 -r5 temp = pd.merge(df, p_dict, how='inner', left_on='p_id', right_on='ID', copy=False)

>>> 1000 loops, best of 5: 826 µs per loop

Concerning the second task, we can quickly add a new column to p_dict that calculates a mean where the time window starts at min_week_num and ends at the week for that row in p_dict. This requires that p_dict is sorted by ascending order along the WEEK column. Then you can use pd.merge again.

I am assuming that min_week_num is 0 in the following example. But you could easily modify rolling_growing_mean to take a different value. The rolling_growing_mean method will run in O(n) since it conducts a fixed number of operations per iteration.

n_size = 1000
np.random.seed(997)
p_dict = pd.DataFrame({'WEEK': range(52), 'value': np.random.randint(0, 1000, 52)})
df = pd.DataFrame({'WEEK': np.random.randint(0, 52, n_size)})

def rolling_growing_mean(values):
    out = np.empty(len(values))
    out[0] = values[0]
    # Time window for taking mean grows each step
    for i, v in enumerate(values[1:]):
        out[i+1] = np.true_divide(out[i]*(i+1) + v, i+2)
    return out

p_dict['Means'] = rolling_growing_mean(p_dict['value'])

df_merged = pd.merge(df, p_dict, how='inner', left_on='WEEK', right_on='WEEK')