decorouz

# When analyzing survey data, it is common for missing values to be represented by various placeholders 
# such as "DK", "NO RESPONSE", "Missing/DK", and "REFUSED". During exploratory data analysis (EDA), 
# it is standard practice to examine instances of missing values using the command df.isna().sum(). 
# However, this command does not account for the aforementioned placeholders. 
# Consequently, it is beneficial to employ a utility function that computes the count of these specific 
# representations of missing values.

def count_special_values(df: pd.DataFrame)->pd.DataFrame:
    """
    Counts the occurrences of specific special values (DK, REFUSED, NO RESPONSE, Missing/DK) 

decorouz

def convert_int_to_word(num: int) -> str:
    """Convert integer from zero to trillion to its words representation"""
    d = { 0 : 'zero', 1 : 'one', 2 : 'two', 3 : 'three', 4 : 'four', 5 : 'five',
          6 : 'six', 7 : 'seven', 8 : 'eight', 9 : 'nine', 10 : 'ten',
          11 : 'eleven', 12 : 'twelve', 13 : 'thirteen', 14 : 'fourteen',
          15 : 'fifteen', 16 : 'sixteen', 17 : 'seventeen', 18 : 'eighteen',
          19 : 'nineteen', 20 : 'twenty',
          30 : 'thirty', 40 : 'forty', 50 : 'fifty', 60 : 'sixty',
          70 : 'seventy', 80 : 'eighty', 90 : 'ninety' }

decorouz

import numpy as np
import pandas as pd

rng = np.random.default_rng(1)

# Create a values from normal distribution with mean 0 and variance 1
data = rng.standard_normal((127, 5))
missing = rng.choice([0, np.nan], p=[0.7, 0.3], size=data.shape) # 30% missing data
data += missing