pandas: a Foundational Python Library for Data Analysis and Statistics

By Wes McKinney. 2011

1,660 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence (2020, background): “While both NumPy and Pandas [10] (Figure 1) provide abstractions over a collection of data points with operations that work on the dataset as a whole, Pandas extends NumPy by providing a data frame-like object supporting heterogeneous column types and row and column metadata.”
Compact graphical representation of phylogenetic data and metadata with GraPhlAn (2015, methodology): “Export2graphlan makes use of two external libraries: the pandas python library ( McKinney, 2012 ) and the BIOM library, only when BIOM files are given as input.”
Optuna: A Next-generation Hyperparameter Optimization Framework (2019, methodology): “ight purpose compatibility is also featured by select few frameworks like Hyperopt and GPyOt as well. The user of Optuna can also conduct more involved analysis by exporting the results in the pandas [21] dataframe, which is highly compatible with interactive analysis frameworks like Jupyter Noteboo”
Pandapower—An Open-Source Python Tool for Convenient Modeling, Analysis, and Optimization of Electric Power Systems (2017, methodology): “The tabular data structure is based on the Python library pandas [21].”
Evaluation of TanDEM-X DEMs on selected Brazilian sites: Comparison with SRTM, ASTER GDEM and ALOS AW3D30 (2017, methodology): “Statistical analyses were performed with the Python libraries Scipy, Numpy, Pandas, Seaborn and Matplotlib (Oliphant, 2006; Hunter, 2007; McKinney, 2011; The SciPy community, 2013; Waskom et al., 2016).”
Reusable workflows for gender prediction (2018, methodology): “…they can be combined into a workﬂow for building state of the art classiﬁcation models: Load Corpus From CSV widget takes a comma-separated values (CSV) ﬁle containing the corpus with texts and annotations and converts it into a Pan-das dataframe (McKinney, 2011) that enables further processing.”
In Search of a Lightweight "Good Enough" Offline Generative AI for Mobile Robots: Performance Benchmarking (2025, unknown): “The natural language processing pipeline implements pan-das for data manipulation [19], numpy for numerical operations [20], and nltk for tokenization and linguistic processing [21].”