It provides about 70% of what I want to do day-to-day. Pandas plots provides the "basics to easily create decent looking plots" from data frames. Many excellent plotting tools are built on top of Matplotlib. (If you are frustrated by Matplotlib and haven't read Effectively Using Matplotlib by Chris Moffitt, go read it.) Matplotlib-Based Libraries Much of that frustration would be alleviated if it were recognized as a library of lower level plotting primitives on which other tools can be built. ![]() " Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms." Native Matplotlib is the cause of frustration to many data analysts due to the complex syntax. I often want to facet these on various categorical variables and layer them on a common grid. In particular, I make a lot of bar charts (including histograms), line plots (including time series), scatter plots, and density plots from data in Pandas data frames. I spend a significant amount of my time making simple plots to understand complex data sets (exploratory data analysis) and help others understand them (presentations). Plotting is an essential component of data analysis. Smoothed Line Plot and Scatter Plot Layered.Scatter Plot and Regression Line with 95% Confidence Interval Layered. ![]() Scatter Plot with Points Sized by Continuous Value.John Tukey in The Future of Data Analysis Contents The simple graph has brought more information to the data analyst's mind than any other device. Python Plotting for Exploratory Data Analysis
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