<script type="text/javascript">
<!--
document.write('<div id="oa_widget"></div>');
document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=undefined&type=result"></script>');
-->
</script>

COPY SCRIPT

For further information contact us at helpdesk@openaire.eu

Minimalist Data Wrangling with Python

Name: Minimalist Data Wrangling with Python
Creator: Gagolewski, Marek
Keywords: pandas, matplotlib, outliers, scipy, data frames, data cleansing, missing values, classification, vectors, matrices

integration_instructionsResearch softwarekeyboard_double_arrow_right Software 23 May 2022Publisher:Zenodo

Authors: Gagolewski, Marek;

doi: 10.5281/zenodo.6572447 , 10.5281/zenodo.6597307 , 10.5281/zenodo.14886678 , 10.5281/zenodo.6637086 , 10.5281/zenodo.7018473 , 10.5281/zenodo.7608376

Minimalist Data Wrangling with Python

- Summary
- Subjects
- Related research
  (1)
- Metrics

Abstract

2022-05-23 (v0.3.1): More lightweight mathematical notation. Some equalities related to the mathematical functions we rely on (the natural logarithm, cosine, etc.). A way to compute the most correlated pair of variables. A note on modifying elements in an array and on adding new rows and columns. An example seasonal plot in the time series chapter. Solutions to the SQL exercises added; using {command}pd.testing.assert_frame_equal instead of {command}DataFrame.equals to ignore small round-off errors. More details on file paths.

Please cite this book as below.

Keywords

pandas, matplotlib, outliers, scipy, data frames, data cleansing, missing values, classification, vectors, matrices, numpy, regression, scikit-learn, data science, text processing, time series, data wrangling, Python, clustering

1 Research products, page 1 of 1

Minimalist Data Wrangling with Python
2022IsVersionOf

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	0
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Average
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average

Usage byUsageCounts

visibility	views	3
download	downloads	1

3
views
1
downloads
Powered by

Found an issue? Give us feedback

visibility

download

Average