How to Read .nc NetCDF Files in Python: Two Practical Methods

Introduction

The author, a Python enthusiast, answers a fan's question about reading .nc files, which are based on the netCDF (Network Common Data Form) format used to store multi‑dimensional scientific data.

1. Basics of .nc Files

.nc files can store a series of arrays, making them ideal for long‑term scientific observations such as meteorology, hydrology, or temperature data. Using a single .nc file avoids the need to manage many CSV or TXT files.

2. Solution

Method 1: Conventional Reading

First install the required library: pip install netCDF4 If the download is slow, use a mirror source:

pip install -i https://pypi.tuna.tsinghua.edu.cn/simple netCDF4

Then read the file with the following script (replace the file path with your own):

# -*- coding: utf-8 -*-
import netCDF4
from netCDF4 import Dataset
nc_obj = Dataset('D:\\tem_e0025_2.nc')
# Inspect variables
print(nc_obj)
print('---------------------------------------')
print(nc_obj.variables.keys())
# Access variable data
lat = nc_obj.variables['lat'][:]
lon = nc_obj.variables['lon'][:]
lev = nc_obj.variables['lev'][:]
time = nc_obj.variables['time'][:]
tem = nc_obj.variables['tem'][:]
# Write selected data to a text file
with open('ppm_lat.txt', 'a') as file:
    file.write('lat,lon,time,tem
')
    for i in range(len(lat)):
        file.write(f"{lat[i]},{lon[i]},{lev[i]},{time[i]},{tem[i]}
")

Method 2: Direct Value Extraction

An alternative approach provided by the fan uses direct indexing:

# -*- coding: utf-8 -*-
import netCDF4
from netCDF4 import Dataset
A_temp = Dataset(r'E:\PythonCrawler\有趣的代码\数据分析\最小二乘法数据分析代码和数据\Temperature_20211031.nc')
lat = A_temp['latitude'][:].data
lon = A_temp['longitude'][:].data
temp = A_temp['T'][:].data
depth = A_temp['depth'][:].data
print(len(lat))
print(len(lon))

Both methods demonstrate how to list variables, read their attributes, and extract the actual data values.

Conclusion

The article successfully answered the fan's question by presenting two workable ways to read .nc files in Python, enabling further analysis such as regression or clustering. Readers are encouraged to adapt the code to their own datasets.