data wranglingcleantransformmergereshape much of the programming work in data analysis and modeling is spent on data preparationloadingcleaningtransformingand rearranging sometimes the way that data is stored in files or databases is not the way you need it for data processing application many people choose to do ad hoc processing of data from one form to another using general purpose programminglike pythonperlror javaor unix text processing tools like sed or awk fortunatelypandas along with the python standard library provide you with high-levelflexibleand high-performance set of core manipulations and algorithms to enable you to wrangle data into the right form without much trouble if you identify type of data manipulation that isn' anywhere in this book or elsewhere in the pandas libraryfeel free to suggest it on the mailing list or github site indeedmuch of the design and implementation of pandas has been driven by the needs of real world applications combining and merging data sets data contained in pandas objects can be combined together in number of built-in wayspandas merge connects rows in dataframes based on one or more keys this will be familiar to users of sql or other relational databasesas it implements database join operations pandas concat glues or stacks together objects along an axis combine_first instance method enables splicing together overlapping data to fill in missing values in one object with values from another will address each of these and give number of examples they'll be utilized in examples throughout the rest of the book www it-ebooks info