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Pandas guide Pandas Guide Meher Krishna Patel Created on Octorber Last updated October More documents are freely available at PythonDSP CTable of contents Table of contents i Pandas Basic Introduction Data structures Series DataFrame Overview Reading ?les

Pandas Guide Meher Krishna Patel Created on Octorber Last updated October More documents are freely available at PythonDSP CTable of contents Table of contents i Pandas Basic Introduction Data structures Series DataFrame Overview Reading ?les Data operations Row and column selection Filter Data Sorting Null values String operations Count Values Plots Groupby Groupby with column-names Groupby with custom ?eld Unstack Merge Merge with di ?erent ?les Merge table with itself Index Creating index Multiple index Reset index Implement using Python-CSV library Read the ?le Display movies according to year operator iemgetter Replace empty string with collections Counter collections defaultdict Numpy Creating Arrays Boolean indexing Reshaping arrays Concatenating the data i CTable of contents Data processing Hierarchical indexing Creating multiple index Partial indexing Unstack the data Column indexing Swap and sort level Summary statistics by level File operations Reading ?les Writing data to a ?le Merge Many to one Inner and outer join Concatenating the data Data transformation Removing duplicates Replacing values Groupby and data aggregation Basics Iterating over group Data aggregation Time series Dates and times Generate series of time Convert string to dates Periods Time o ?sets Index data with time Application Basics Resampling Plotting the data Moving windows functions Reading multiple ?les Example Baby names trend Total boys and girls in year pivot table ii PythonDSP CTable of contents Note ? Created using Python- and Pandas- ? CSV ?les can be downloaded from below link https bitbucket org pythondsp pandasguide downloads Meher Krishna Patel CChapter Pandas Basic Introduction Data processing is important part of analyzing the data because data is not always available in desired format Various processing are required before analyzing the data such as cleaning restructuring or merging etc Numpy Scipy Cython and Panda are the tools available in python which can be used fast processing of the data Further Pandas are built on the top of Numpy Pandas provides rich set of functions to process various types of data Further working with Panda is fast easy and more expressive than other tools Pandas provides fast data processing as Numpy along with exible data manipulation techniques as spreadsheets and relational databases Lastly pandas integrates well with matplotlib library which makes it very handy tool for analyzing the data Note ? In chapter two important data structures i e Series and DataFrame are discussed ? Chapter shows the frequently used features of Pandas with example And later chapters include various other information about Pandas Data structures Pandas provides two very useful data structures to process the data i e Series and DataFrame which are discussed in this section Series The Series is a one-dimensional array that can store various data types including mix data types The row labels in a Series are called the index Any list tuple and dictionary can be converted in to Series using ? series ? method as shown below import pandas as pd converting tuple to Series h 'AA' ' - - ' s pd Series h type s continues on next