![]() Match simple Index on level of MultiIndex otherwise select subset of. When forward filling or backfilling, the maximum size gap (in absolute numeric distance) to fill for inexact matches. ![]() When forward filling or backfilling, the maximum size gap (in number of elements) to fill. Use fill_value="missing" (the default behavior) when you want absent labels to have null values in the result. Substitute value to use when introducing missing data by reindexing. Interpolation (fill) method "ffill" fills forward, while "bfill" fills backward. You can alternately do reindex(index=new_labels) or reindex(columns=new_labels). The axis to reindex, whether "index" (rows) or "columns". Use the passed sequence as the new column labels. Use the passed sequence as the new index labels. An Index will be used exactly as is without any copying. Can be Index instance or any other sequence-like Python data structure. Table 5.3: reindex function arguments Argument ReindexingĪn important method on pandas objects is reindex, which means to create a new object with the values rearranged to align with the new index. This book is not intended to serve as exhaustive documentation for the pandas library instead, we'll focus on familiarizing you with heavily used features, leaving the less common (i.e., more esoteric) things for you to learn more about by reading the online pandas documentation. In the chapters to come, we will delve more deeply into data analysis and manipulation topics using pandas. This section will walk you through the fundamental mechanics of interacting with the data contained in a Series or DataFrame. Returns True if the Index has no duplicate valuesĬompute the array of unique values in the Index Returns True if each element is greater than or equal to the previous element ![]() Table 5.2: Some Index methods and properties Method/PropertyĬoncatenate with additional Index objects, producing a new IndexĬompute Boolean array indicating whether each value is contained in the passed collectionĬompute new Index with element at Index i deletedĬompute new Index by deleting passed valuesĬompute new Index by inserting element at Index i If a DataFrame’s index and columns have their name attributes set, these will also be displayed: Like the “2D ndarray” case except masked values are missing in the DataFrame result The DataFrame’s indexes are used unless different ones are passed Treated as the “dictionary of arrays” caseĮach value becomes a column indexes from each Series are unioned together to form the result’s row index if no explicit index is passedĮach inner dictionary becomes a column keys are unioned to form the row index as in the “dictionary of Series” caseĮach item becomes a row in the DataFrame unions of dictionary keys or Series indexes become the DataFrame’s column labels ![]() Table 5.1: Possible data inputs to the DataFrame constructor TypeĪ matrix of data, passing optional row and column labelsĮach sequence becomes a column in the DataFrame all sequences must be the same length The vibrant pandas developer and user communities have been a key part of its success. The developer community has grown to over 2,500 distinct contributors, who've been helping build the project as they used it to solve their day-to-day data problems. Since becoming an open source project in 2010, pandas has matured into a quite large library that's applicable in a broad set of real-world use cases. NumPy, by contrast, is best suited for working with homogeneously typed numerical array data. While pandas adopts many coding idioms from NumPy, the biggest difference is that pandas is designed for working with tabular or heterogeneous data. pandas adopts significant parts of NumPy's idiomatic style of array-based computing, especially array-based functions and a preference for data processing without for loops. pandas is often used in tandem with numerical computing tools like NumPy and SciPy, analytical libraries like statsmodels and scikit-learn, and data visualization libraries like matplotlib. ![]() It contains data structures and data manipulation tools designed to make data cleaning and analysis fast and convenient in Python. Pandas will be a major tool of interest throughout much of the rest of the book. The code examples are MIT licensed and can be found on GitHub or Gitee. The content from this website may not be copied or reproduced. If you find the online edition of the book useful, please consider ordering a paper copy or a DRM-free eBook to support the author. If you encounter any errata, please report them here. This Open Access web version of Python for Data Analysis 3rd Edition is now available as a companion to the print and digital editions. ![]()
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