np.genfromtxt() for handling missing data in NumPy - Mini Project: Build & Apply

Using np.genfromtxt() to Handle Missing Data

📖 Scenario: You work in a small store that tracks daily sales data in a CSV file. Sometimes, some sales numbers are missing because the cashier forgot to enter them. You want to load this data into Python and handle the missing values properly so you can analyze the sales.

🎯 Goal: Load the sales data from a CSV file using np.genfromtxt() so that missing values are handled as np.nan. Then, calculate the average sales ignoring the missing values.

📋 What You'll Learn

Use np.genfromtxt() to load data from a CSV string with missing values

Set the correct parameters to handle missing data as np.nan

Calculate the average sales ignoring missing values using np.nanmean()

Print the average sales as the final output

💡 Why This Matters

🌍 Real World

Stores, labs, and businesses often collect data with missing entries. Handling missing data correctly is important for accurate analysis.

💼 Career

Data scientists and analysts frequently use <code>np.genfromtxt()</code> to load imperfect datasets and prepare them for analysis.

1

Create the sales data CSV string

Create a variable called sales_data that contains this exact CSV string with missing values represented by empty fields:
10,20,30\n40,,60\n70,80,

NumPy

# Create the variable sales_data with the exact CSV string
# Your code here

Need a hint?

Use triple quotes or escaped newlines to create the multiline string exactly as shown.

2

Set the delimiter configuration

Create a variable called delimiter and set it to the string "," to specify the CSV delimiter.

NumPy

sales_data = "10,20,30\n40,,60\n70,80,"
# Create the variable delimiter and set it to ','
# Your code here

Need a hint?

The delimiter for CSV files is usually a comma.

3

Load the data using np.genfromtxt() handling missing values

Use np.genfromtxt() to load the data from sales_data with the delimiter variable. Set dtype to float and missing_values to an empty string "" so missing entries become np.nan. Store the result in a variable called sales_array.

NumPy

sales_data = "10,20,30\n40,,60\n70,80,"
delimiter = ","
# Load sales_data using np.genfromtxt with delimiter, dtype=float, missing_values=''
# Store result in sales_array
# Your code here

Need a hint?

Use sales_data.splitlines() to pass the CSV lines to np.genfromtxt().

4

Calculate and print the average sales ignoring missing values

Calculate the average of sales_array ignoring np.nan values using np.nanmean(). Store the result in average_sales. Then print average_sales.

NumPy

import numpy as np

sales_data = "10,20,30\n40,,60\n70,80,"
delimiter = ","
sales_array = np.genfromtxt(
    sales_data.splitlines(),
    delimiter=delimiter,
    dtype=float,
    missing_values="",
    filling_values=np.nan
)
# Calculate average_sales using np.nanmean on sales_array
# Print average_sales
# Your code here

Need a hint?

Use np.nanmean() to ignore np.nan values when calculating the average.