How to read a CSV file with missing values into a NumPy array?

Last update on April 29 2025 12:54:25 (UTC/GMT +8 hours)

5. CSV with Missing Values Read

Write a NumPy program that reads a CSV file containing missing values into a NumPy array, handling the missing values appropriately.

Content of data.csv
------------------------- 
1,Jorah Liina,95.5
2,Basil Aisha,90.0
3,Helga Myrthe,80.0
4,Lehi Piero,91.5

Sample Solution:

Python Code:

import numpy as np

# Define the path to the CSV file
csv_file_path = 'data.csv'

# Read the CSV file into a NumPy array, handling missing values as NaN
data_array = np.genfromtxt(csv_file_path, delimiter=',', dtype=float, filling_values=np.nan)

# Print the resulting NumPy array
print(data_array)

Output:

[[10. nan 95.]
 [11. nan 90.]
 [12. nan 85.]
 [13. nan 96.]
 [14. nan 95.]]

Explanation:

Import NumPy Library: Import the NumPy library to handle arrays.
Define CSV File Path: Specify the path to the CSV file containing the data with missing values.
Read CSV File into NumPy Array: Use np.genfromtxt() to read the contents of the CSV file into a NumPy array. The delimiter parameter specifies the delimiter used in the CSV file, dtype=float ensures all data is treated as floating-point numbers, and filling_values=np.nan fills missing values with NaN.
Print NumPy Array: Output the resulting NumPy array to verify the data read from the CSV file, with missing values appropriately handled.

For more Practice: Solve these Related Problems:

Write a Numpy program to read a CSV file containing missing values and then fill these gaps with the column mean.
Write a Numpy program to load a CSV file with missing numeric entries and then interpolate the missing values using linear interpolation.
Write a Numpy program to import a CSV file with missing data and then apply a forward-fill technique to complete the array.
Write a Numpy program to read CSV data with missing entries and then use masking to compute statistics only on non-missing values.

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