How to apply custom function to pandas data frame for each row [duplicate]

Question

I want to apply a custom function and create a derived column called population2050 that is based on two columns already present in my data frame.

import pandas as pd
import sqlite3
conn = sqlite3.connect('factbook.db')
query = "select * from facts where area_land =0;"
facts = pd.read_sql_query(query,conn)
print(list(facts.columns.values))

def final_pop(initial_pop,growth_rate):
    final = initial_pop*math.e**(growth_rate*35)
    return(final)

facts['pop2050'] = facts['population','population_growth'].apply(final_pop,axis=1)

When I run the above code, I get an error. Am I not using the 'apply' function correctly?

Answer 1

You were almost there:

facts['pop2050'] = facts.apply(lambda row: final_pop(row['population'],row['population_growth']),axis=1)

Using lambda allows you to keep the specific (interesting) parameters listed in your function, rather than bundling them in a 'row'.

Answer 2

Your function,

def function(x):
  // your operation
  return x

call your function as,

df['column']=df['column'].apply(function)

Answer 3

Apply will pass you along the entire row with axis=1. Adjust like this assuming your two columns are called initial_popand growth_rate

def final_pop(row):
    return row.initial_pop*math.e**(row.growth_rate*35)

Answer 4

You can achieve the same result without the need for DataFrame.apply(). Pandas series (or dataframe columns) can be used as direct arguments for NumPy functions and even built-in Python operators, which are applied element-wise. In your case, it is as simple as the following:

import numpy as np

facts['pop2050'] = facts['population'] * np.exp(35 * facts['population_growth'])

This multiplies each element in the column population_growth, applies numpy's exp() function to that new column (35 * population_growth) and then adds the result with population.