How to Optimize GPS Data Extraction with Python: A Step‑by‑Step Tutorial

1. Introduction

Hello, I'm PiPi. In the previous article we used Python to import data and split columns, achieving the expected result. This article continues the optimization, as shown in the figure below.

2. Implementation

Teacher Yuliang provided further guidance, illustrated in the next figure.

The following code implements the optimization:

with open("./GpsSnr.txt", "r", encoding="utf-8") as f:
    txt = f.readlines()
regex = r"\((\d+,\d+\.\d+)\)"
temp = [re.findall(regex, x) for x in txt]
df = pd.DataFrame(temp)
# This saves latitude and longitude together
# df.to_excel('GpsSnr1.xlsx', index=False)

# Split latitude and longitude
data = []
for i in df.columns:
    data.append(df[i].str.split(',', expand=True))
df = pd.concat(data, axis=1)
df.columns = [a+str(b) for a in ['gps','glnoss','beidou'] for b in range(10)]
# print(df.head())
df.to_excel('GpsSnr2.xlsx', index=False)

The third figure shows part of the guidance.

Separating the data into three columns makes further analysis easier, such as filtering records where the 'beidou' column falls within a specific range. Splitting into 30 columns would require more complex column handling.

3. Conclusion

This article presents a Python automation solution for processing GPS data, providing detailed analysis and code implementation to help readers solve the problem.