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Pandas dataframes are 2 dimensional and tabular, and is particularly suited to data which is heterogenous and in columns, like an SQL table or Excel spreadsheet. Central to the pandas package are the special data structures called pandas Series and DataFrames. It is designed to make working with “relational” or “labeled” data easy and intuitive. ![]() Pandas, however, is capable of handling data of lots of different types. NumPy is useful when you are working with data that is all numeric. You are already familiar with the data analyis library numpy and the numpy array. In this lesson, we will cover pandas dataframes and some basic analysis. ![]() The key feature of pandas is the dataframe. Pandas is another Python package which is very popular for data analysis.
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