Is bilinear resampling to a coarser resolution is similar to weighted spatial averaging? Question: I am using the xESMF python package to resample NDVI (greeness) data from 500 * 500 m to 1 * 1 degree.To clarify, I’m increasing the scale of the data. The package offers several techniques, including bilinear and conservative. I’m wondering …
Issues resampling raster to the resolution of another raster Question: I am trying to take a population raster and resample+reproject it to match the shape and resolution of a precipitation raster. Data Links: Population Data: https://figshare.com/ndownloader/files/10257111 Precipitation Data: https://www.ncei.noaa.gov/data/nclimgrid-monthly/access/nclimgrid_prcp.nc The Population Data is a series of rasters per decade of 5 different population models covering …
Resampling Hourly Data into Half Hourly in Pandas Question: I have the following DataFrame called prices: DateTime PriceAmountGBP 0 2022-03-27 23:00:00 202.807890 1 2022-03-28 00:00:00 197.724150 2 2022-03-28 01:00:00 191.615328 3 2022-03-28 02:00:00 188.798436 4 2022-03-28 03:00:00 187.706682 … … … 19 2023-01-24 18:00:00 216.915400 20 2023-01-24 19:00:00 197.050516 21 2023-01-24 20:00:00 168.227992 22 2023-01-24 …
Python dataframe – resample timestamps, group by hour, but keep the start and end datetime Question: I have a DataFrame containing timestamps and values. list = [‘2020-04-22 13:29:00+00:00′,’2020-04-22 13:31:00+00:00′,’2020-04-22 13:32:00+00:00′,’2020-04-22 13:33:00+00:00′,’2020-04-22 13:34:00+00:00′,’2020-04-22 13:35:00+00:00′,’2020-04-22 13:36:00+00:00′,’2020-04-22 13:54:00+00:00′,’2020-04-22 13:55:00+00:00′,’2020-04-22 13:56:00+00:00′,’2020-04-22 13:57:00+00:00′,’2020-04-22 13:58:00+00:00′,’2020-04-22 13:59:00+00:00′,’2020-04-22 14:00:00+00:00′,’2020-04-22 14:01:00+00:00′,’2020-04-22 14:02:00+00:00′,’2020-04-22 14:03:00+00:00′,’2020-04-22 14:04:00+00:00′,’2020-04-22 14:05:00+00:00′,’2020-04-22 14:06:00+00:00′,’2020-04-22 14:49:00+00:00′,’2020-04-22 14:50:00+00:00′,’2020-04-22 14:51:00+00:00′,’2020-04-22 14:52:00+00:00′,’2020-04-22 14:53:00+00:00′,’2020-04-22 14:54:00+00:00′,’2020-04-22 14:55:00+00:00′,’2020-04-22 14:56:00+00:00’,’2020-04-22 …
Python resample by day & get weekstart data Question: I have monthly data. When I apply resampling by day and cubic interpolation, there is a function to find the month end data import pandas as pd df=pd.read_excel(input_file, sheet_name=’mthly’, usecols=’A:D’, na_values=’ND’, index_col=0, header=0) df.index.names = [‘Period’] df.index = pd.to_datetime(df.index) q= pd.Series(df[series], index=df.index) d = q.resample(‘D’).interpolate(method=’cubic’) m=d[d.index.is_month_end] …
Pandas: easier way to sample interpolated time series data at given times (e.g. every full day) Question: Regularly I run into the problem that I have time series data that I want to interpolate and resample at given times. I have a solution, but it feels like "too labor intensive", e.g. I guess there should …
SMOTE – could not convert string to float Question: I think I’m missing something in the code below. from sklearn.model_selection import train_test_split from imblearn.over_sampling import SMOTE # Split into training and test sets # Testing Count Vectorizer X = df[[‘Spam’]] y = df[‘Value’] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=40) X_resample, y_resampled = …
How can I speed up xarray resample (much slower than pandas resample) Question: Here is an MWE for resampling a time series in xarray vs. pandas. The 10Min resample takes 6.8 seconds in xarray and 0.003 seconds in pandas. Is there some way to get the Pandas speed in xarray? Pandas resample seems to be …
Pandas monthly resample maintaining first date Question: when resampling a time series into a monthly series, pandas changes the initial date of my time series with the start of the month. From: 2020-01-12 0.730439 2020-01-13 0.559328 … 2021-06-29 0.188461 2021-06-30 0.750668 To: 2020-01-01 8.613978 2020-02-01 14.614601 … … 2021-05-01 11.936765 2021-06-01 13.758198 Instead of the …
Is there a way to prevent dtype from changing from Int64 to float64 when reindexing/upsampling a time-series? Question: I am using pandas 0.17.0 and have a df similar to this one: df.head() Out[339]: A B C DATE_TIME 2016-10-08 13:57:00 in 5.61 1 2016-10-08 14:02:00 in 8.05 1 2016-10-08 14:07:00 in 7.92 0 2016-10-08 14:12:00 in …
