I'm VERY new to xarray, and I tried to import a satellite netcdf files into python using xarray using this file: https://tropomi.gesdisc.eosdis.nasa.gov/data//S5P_TROPOMI_Level2/S5P_L2__NO2____HiR.1/2020/003/S5P_OFFL_L2__NO2____20200103T170946_20200103T185116_11525_01_010302_20200105T100506.nc
This is the code I used:
import xarray as xr
import numpy as np
import pandas as pd
tropomi = xr.open_dataset('test2.nc', engine = 'netcdf4')
tropomi
Output:
But the output does not present any variables, and has 53 attributes - why is this happening?
Thanks!
I figured it out. When you open the file without a group defined, you get the global attributes with no variables. You need to include a group='PRODUCT' to get the data products, like this:
tropomi = xr.open_dataset('test2.nc', group='PRODUCT')
Related
I'm looking to turn a geojason into a pandas dataframe that I can work with using python. However, for some reason, the geojason package will not install on my computer.
So wanted to know how I could turn a geojason file into a dataframe witout using the geojason package.
This is what I have so far
import json
import pandas as pd
with open('Local_Authority_Districts_(December_2020)_UK_BGC.geojson') as f:
data = json.load(f)
Here is a link to the geojason that I'm working with. I'm new to python so any help would be much appreciated. https://drive.google.com/file/d/1V4WljiJcASqq9ksh8CHM_2nBC0K2PR18/view?usp=sharing
You could use geopandas. It's as easy as this:
import geopandas as gpd
gdf = gpd.read_file('Local_Authority_Districts_(December_2020)_UK_BGC.geojson')
You can turn the resulting geodataframe into a regular dataframe with:
df = pd.DataFrame(gdf)
I am new to Python, coming from MATLAB. In MATLAB, I used to create a variable table (copy from excel to MATLAB) in MATLAB and save it as a .mat file and whenever I needed the data from the MATLAB, I used to import it using:
A = importdata('Filename.mat');
[Filename is 38x5 table, see the attached photo]
Is there a way I can do this in Python? I have to work with about 35 such tables and loading everytime from excel is not the best way.
In order to import excel tables into your python environment you have to install pandas.
Check out the detailed guideline.
import pandas as pd
xl = pd.ExcelFile('myFile.xlsx')
I hope this helps.
Use pandas:
import pandas as pd
dataframe = pd.read_csv("your_data.csv")
dataframe.head() # prints out first rows of your data
Or from Excel:
dataframe = pd.read_excel('your_excel_sheet.xlsx')
I am trying to load a large number of data files from the same folder in Python. The ultimate goal here is to simply choose which file I would like to use in calculations, rather than individually opening files.
Here is what I have. This seems to work in opening the data in the files, but I am having a hard time choosing a specific file I want to work with (and assigning a value to each column in each file).
import astropy
import numpy as np
import matplotlib.pyplot as plt
dir = '/S34_east_tfa/'
import glob, os
os.chdir(dir)
for file in glob.glob("*.data"):
data = np.loadtxt(file)
print (data)
Time = data[:,0]
Use a python dictionary, instead of overwriting the results in data variable inside your loop.
data_dict = dict()
for file in glob.glob("*.data"):
data_dict[file] = np.loadtxt(file)
Is this what you were looking for?
I trying reads data for dataset with help of geopandas, but interpreter wright:
File
"/home/divinitytoffee/PycharmProjects/Radar/venv/lib/python3.5/site-packages/geopandas/datasets/init.py",
line 33, in get_path raise alueError(msg) ValueError: The dataset
'resource/RAVL_vLuki/rd0a0h.00d' is not available
import geopandas as gpd
import fiona.ogrext
import pandas as pd
gpd_data = gpd.gpd.read_file(gpd.datasets.get_path('resource/RAVL_vLuki/rd0a0h.00d'))
Actually the question, how to fix?
Data are presented in the form of a *.00d
The geopandas.datasets.get_path is meant to return the path of a few datasets that are included in the geopandas library itself (eg for examples).
When reading your own file, you need to pass the path directly to read_file:
gpd_data = gpd.gpd.read_file('resource/RAVL_vLuki/rd0a0h.00d')
Do you want to import it as a data frame?
I don't know if this will help.
import geopandas as gpd
gpd_data = gpd.gpd.GeoDataFrame.from_file('resource/RAVL_vLuki/rd0a0h.00d')
I have .dat file that I want to use in my script which draws scatter graph with data input from that .dat file. I have been manually converting .dat files to .csv for this purpose but I find it not satisfactory.
This is what I am using currently.
import pandas as pd import matplotlib.pyplot as plt import numpy as np
filename=raw_input('Enter filename ')
csv = pd.read_csv(filename)
data=csv[['deformation','stress']]
data=data.astype(float)
x=data['deformation']
y=data['stress']
plt.scatter(x,y,s=0.5)
fit=np.polyfit(x,y,15)
p=np.poly1d(fit)
plt.plot(x,p(x),"r--")
plt.show()
Programmer friend told me that it would be more convenient to convert it to JSON and use it as such. How would I go about this?
try using the numpy read feature
import numpy as np
yourArray = np.fromfile('YourData.dat',dtype=dtype)
yourArray = np.loadtxt('YourData.dat')
loadtxt is more flexible than fromfile