Distribute Missing Values in Pandas DataFrame Column with Python

If you want to test how Maschine Learning algorithms perform with missing values you may need a script to distribute a fixed percentage of missing values in a feature. This script randomly distributes missing values in a single data set’s column.


import numpy as np
from pandas import Series,DataFrame
import pandas as pd
import csv
import random
from __future__ import division

INPUT_FILE_NAME = 'bank-full-preprocessed-raw.csv'
OUTPUT_FILE_NAME = 'bank-full-preprocessed-raw-selected-attribute-duration-small-fraction.csv'

random.seed(2009) # seed
missing_value = "?"

dframe = pd.read_csv(INPUT_FILE_NAME, sep=',')

Script to randomly distribute missing values in selected attribute
with given percentage.

Example: distribute_selected("job", 0.03)
Script distributes 3% of missing values in job attribute.
def distribute_selected(attribute, percentage):

print "attribute name:", attribute
# number of rows to change
nrtbc = percentage*len(dframe)
print "number of rows with missing values:", int(nrtbc) , " of total:", len(dframe)
# make changes
for j in range(1,int(nrtbc)):
# find a random row
randomrownr = random.randint(1,len(dframe))
#print "random_rownr:", randomrownr
# change value @ randomrownr to ?
dframe.loc[dframe.index==randomrownr, attribute]=missing_value

distribute_selected("duration", 0.10)
dframe.to_csv(OUTPUT_FILE_NAME, sep=',', encoding='utf-8', index=False, quoting = csv.QUOTE_NONNUMERIC)

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