Load Binary Data in Python with Numpy & Pandas

Published: 28 June 2023
on channel: Cloud Data Science

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In this Python tutorial, you'll learn how to efficiently load binary data in Python using two powerful libraries: Numpy and Pandas. Binary data is a common format for storing large datasets, and understanding how to read and manipulate it is crucial for data scientists, programmers, and anyone working with complex data structures.

🔍 Key Topics Covered:
✅ Introduction to binary data and its significance in data processing
✅ Installing Numpy and Pandas libraries in Python
✅ Loading binary data using Numpy's fromfile and frombuffer function
✅ Understanding data types and endianness in binary files
✅ Importing binary data into Pandas DataFrame for easy data manipulation
✅ Exploring advanced techniques for efficient binary data loading

💻 By the end of this tutorial, you'll have a solid understanding of how to work with binary data in Python using Numpy and Pandas. You'll be equipped with the necessary knowledge to tackle real-world data processing challenges and unleash the full potential of your data analysis projects.

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Timestamps:
0:00 - Intro to binary formats
1:33 - Creating a sample .bin file
2:17 - Loading the binary file with NumPy/Pandas

Code used in this video:
import struct
fmt = 'HHi5s' # add less than sign (angle bracket) before the H
p1 = struct.pack(fmt, 6, 1, 7, b'seven')
p2 = struct.pack(fmt, 6, 1, 8, b'eight')
p3 = struct.pack(fmt, 6, 1, 9, b'nine')
p4 = struct.pack(fmt, 20, 1, 10, b'ten')
body = p1 + p2 + p3 + p4
with open('example-binary.bin', 'wb') as f:
f.write(body)
import numpy as np
import pandas as pd
Defines a np.dtype that matches our binary record layout
dt = np.dtype([
('body_length', 'u2'), # add less than sign (angle bracket) before the u
('msg_type', 'u2'), # add less than sign (angle bracket) before the u
('number', 'i4'), # add less than sign (angle bracket) before the u
('name', 'S5')
])
with open('example-binary.bin', 'rb') as f:
b = f.read() # Reads in the binary file as bytes
np_data = np.frombuffer(b, dt) # Creates a NumPy array
df = pd.DataFrame(np_data)
df['name'] = df['name'].str.decode('utf-8')
df

📚 Relevant Tags:
#pythontutorial #BinaryDataProcessing #numpytutorial #pandastutorial #dataanalysis #datascience #pythonprogramming #datamanipulation #dataprocessing

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