Manish Dhasmana's repositories
Machine-Learning-with-Python
Python code for common Machine Learning Algorithms
IDF_analysis
Script for generation of IDF curves
scientific-visualization-book
An open access book on scientific visualization using python and matplotlib
Data-Science-For-Beginners
10 Weeks, 20 Lessons, Data Science for All!
python-for-environmental-science
Repo for the Jupyter notebooks for a programming course at the Justus-Liebig-University Giessen
Graph-Analysis-with-NetworkX
:sparkler: Network/Graph Analysis with NetworkX in Python. Topics range from network types, statistics, link prediction measures, and community detection.
ChangePoint
Python Script for detecting change point
EstimateTrend
It estimate the gridwise trend from the nc files.
pangeo_tutorial
Tutorial created by Chelle Gentemann for Ocean Sciences 2020 Meeting
ClimaticZones
This repo contains the shapefiles of differnt climatic zones across India and world.
Example_pycharm
Example of pycharm
Network-Science-with-Python-and-NetworkX-Quick-Start-Guide
Network Science with Python and NetworkX Quick Start Guide, published by Packt
Work-with-DEM-data-using-Python-from-Simple-to-Complicated
Work with DEM data using Python from Simple to Complicated. Many python packages will be touched such as GDAL, numpy, xarray, rasterio, folium, cartopy, geopandas etc.
Python-Practical-Application-on-Climate-Variability-Studies
This tutorial is a companion volume of Matlab versionm but add more. Main objective is the transference of know-how in practical applications and management of statistical tools commonly used to explore meteorological time series, focusing on applications to study issues related with the climate variability and climate change. This tutorial starts with some basic statistic for time series analysis as estimation of means, anomalies, standard deviation, correlations, arriving the estimation of particular climate indexes (Niño 3), detrending single time series and decomposition of time series, filtering, interpolation of climate variables on regular or irregular grids, leading modes of climate variability (EOF or HHT), signal processing in the climate system (spectral and wavelet analysis). In addition, this tutorial also deals with different data formats such as CSV, NetCDF, Binary, and matlab'mat, etc. It is assumed that you have basic knowledge and understanding of statistics and Python.
Non-Linear-Time-Series-Analysis
This repository is intended to serve as a standalone package for all non linear time series based analysis
Data-Analysis-with-R
Using gglot2, tidyr, dplyr, ggmap, choroplethr, shiny, logistic regression, clustering models and more
python_for_climate_scientists
A python course intended to provide a thorough grounding for those working in the earth sciences
ISLR-python
An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code
HadISD_v2
Code to create HadISD v2
railways
Indian Railways Data