Geospatial data visualization GeoPandas Merging GeoDataFrames and Pandas DataFrames Choropleth maps color code geographic regions according to numeric values. They are an impactful way to display variation in a variable between regions. The Python package GeoPandas combines pandas and Shapely, a package for manipulating points, lines and polygons. It also depends on Fiona for file… Read More A choropleth paints a thousand words
DataFrame filtering & subsetting. Dictionary from lists DataFrame.query() Relational plots using .relplot() Categorical plots using .catplot() Distribution, swarm and scatterplots Ensuring global food security is number 2 of the 17 UN Sustainable development Goals that are part of the 2030 Agenda for Sustainable Development. The worlds population is currently 7.8 billion, 60 years ago it… Read More Visualizing land use with Seaborn
Exploratory Data Analysis Data visualization Matplotlib Tidy Data In part I we took raw csv files, merged, cleaned and shaped them. Now we’re ready for the fun part, exploratory data analysis (EDA). Using different data visualizations we can start to answer questions such as where is copper mined? How much is mined? and reveal other… Read More Raw Materials & Raw Data, II
Import data to Pandas DataFrame Merge DataFrames pd.merge() Data cleaning & shaping – missing values, duplicates, dictionary, for loop etc. Copper is the third most consumed industrial metal in the world, with 75% of copper used in electrical wires, telecommunication cables and electronics. Amazingly there is about 30 miles of copper wire in a electronic… Read More Raw Materials & Raw Data, I
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