Summary

1. Pandas is the most popular library in the scientific Python ecosystem for doing data analysis.

2. A Series type object is pandus data structure that represents a one-dimensional array-like object containing an array of data (of any NumPy supported data type) and an associated array of data labels, called its index

3.A Series type object can be created using panda.Series) where data may be specified as a sequence of any type or ndarrays.

4. Series object’s indexes are not necessarily from 0 to n-1 always.

5.You can use numpy.NaN to add missing data. Common Series attributes are: index, values, drype, shape, nbytes, ndim, size, itemsize, hasnans, empty etc.

6.A slice object is created from Series object using a syntax of

7.Vector operations mean that if you apply a function or expression then it is individually applied on each item of the object.

8.When you perform arithmetic operations on two Series type objects, the data is aligned on the basis of maching indexes and then performed arithmetic: for non-overlapping indexes, the arithmetic operations

9.A DataFrame is a two-dimensional labeled array like, pandas data structure that stores an ordered collection.

A dataframe object is created uning pandas.DataFrame() and the values to it are passed in a two dimensional sequence such as a 2D adarray or dictionary having listvindarrays/dictionary/Series etc as values or anotherdataframe object.

10.Common dataframe objects include: index, columns, axes, dtypes, size, shape, values, empty, ndim.

11.Trampose of a dataframe is available through non-existing column, it will add a new column.

12.A column can be deleted using del command.