Graphic logs are the most common way geologists characterize and communicate the composition and variability of clastic and carbonate sedimentary successions; with a simple drawing, a graphic log imparts complex geological concepts (e.g., Bouma turbidite sequence, shoreface parasequence). The term 'graphic log' originates from a geologist graphically drawing (i.e., 'logging') an outcrop or core with thickness/depth on the y axis, while the x axis usually represents grain size. Graphic logs can be drawn at vastly different scales, from the characterization of every bed in sections 10s of meters thick to a rough description of lithology over 1000s of meters, making comprehensive, quantitative comparison difficult.Many geologists carefully hand-draw graphic logs at fine-scale in a field notebook, and then digitally retrace them in drawing software. However, this detailed data (e.g., thickness, grain size) that may have taken days or weeks to collect is often never captured in a machine-readable, tabular format. So, while tens of thousands of meters of graphic logs exist to quantify lithologic heterogeneity and stacking patterns within and between depositional environments, this data is rarely digital and available for analysis. Despite this, geologists have long been attempting to quantify graphic log data to better distinguish stacking patterns, depositional processes, and depositional environments to aid in prediction of stratigraphic architecture and earth-resource distribution.We present litholog, an open-source software package in Python that stores, plots, and analyzes graphic-log data. We also include software in R and Matlab that digitize hand-drawn graphic logs into a tabular format readable by litholog. We discuss the diversity of graphic log data, the implementation of graphic log data in a digital, structured, tabular format; finally, we recommend methods and provide a template for standardizing collection of this important type of stratigraphic data. It is our hope that these software packages, combined with advances in 'big data' analytics and machine-learning algorithms, will lead to new discoveries in sedimentary geology.