In this post, I am sharing Python libraries that, I think, you should master for spatial analysis, economics, and econometrics. These tools are essential for handling geospatial data, performing econometric modeling, and extracting meaningful insights from complex datasets. Libraries like GeoPandas enable efficient spatial data manipulation and mapping, while PySAL provides advanced spatial econometrics capabilities. Statsmodels is crucial for statistical modeling and hypothesis testing, and pandas remains foundational for data cleaning and transformation. In addition, NumPy supports efficient numerical computing, and SciPy extends this with a broad set of scientific and statistical routines that are often used in econometric workflows.…
Everything you need to know about hexadecimal color code
This post describes complete code to create 72 maps from 24 Excel sheets in distributed in 4 Excel files in R. I map of US to demonstrate the visualization process.
In this post, I am cleaning data available in very messy .txt file and saving clean data as .csv file in local directory for the future use. This post is partially inspired by a request from a student to help in cleaning their research project data.To accomplish objective metioned above, I am using .txt file from 2022 Release – North American Breeding Bird Survey Dataset (1996-2021). This data was published on 2022-07-05 and accessed on May 19, 2023. You can fild “SpeciesList.txt” data by scrolling through the midway of the webpage. Open this file in new tab by clicking file…
You can see this figure in my previous post as well. In this post I have taken a different approach of loading data in R. You can manually enter data in R using the base R package which I demonstrated in this post. The code below should work without modification and generate figure at the end of the post.
