Search results
Results from the WOW.Com Content Network
Pandas (styled as pandas) is a software library written for the Python programming language for data manipulation and analysis.In particular, it offers data structures and operations for manipulating numerical tables and time series.
Download as PDF; Printable version; ... Data analysis is the process of inspecting, ... Pandas – Python library for data analysis.
Orange is an open-source software package released under GPL and hosted on GitHub.Versions up to 3.0 include core components in C++ with wrappers in Python.From version 3.0 onwards, Orange uses common Python open-source libraries for scientific computing, such as numpy, scipy and scikit-learn, while its graphical user interface operates within the cross-platform Qt framework.
Wes McKinney is an American software developer and businessman. He is the creator and "Benevolent Dictator for Life" (BDFL) of the open-source pandas package for data analysis in the Python programming language, and has also authored three versions of the reference book Python for Data Analysis.
Exploratory data analysis is an analysis technique to analyze and investigate the data set and summarize the main characteristics of the dataset. Main advantage of EDA is providing the data visualization of data after conducting the analysis.
Python script Yes Extraction and analysis tool, handles corrupt and malicious PDF documents. PDFedit: GNU GPL: Yes Yes BSD Yes Software to view or edit the internal structures of PDF documents, and merge them. Pdftk: GNU GPL: Yes Yes Yes FreeBSD, Solaris Yes Command-line tools to edit and convert documents; supports filling of PDF forms with ...
De facto standard for matrix/tensor operations in Python. Pandas, a library for data manipulation and analysis. SageMath is a large mathematical software application which integrates the work of nearly 100 free software projects and supports linear algebra, combinatorics, numerical mathematics, calculus, and more. [12]
A variety of data re-sampling techniques are implemented in the imbalanced-learn package [1] compatible with the scikit-learn Python library. The re-sampling techniques are implemented in four different categories: undersampling the majority class, oversampling the minority class, combining over and under sampling, and ensembling sampling.