Dictionary is one of the data type in python. It uses to connect pieces of related information. It can store limitless amount of information. It is an unordered collection of data values, elements of dictionary does not have any order, since you cannot refer to an element by using an index.
A Dictionary in Python is a collection of key-value pair. Each key is connected to value associated with that key. Value in dictionary can be of any data type, i.e. it can be number, a string, a list or even another dictionary, it can be duplicate. …
Installing and using pandas
To use pandas you require NumPy on your system. If you are using Anaconda, then you already have Numpy and Pandas installed on it.
To import pandas library you use ‘import pandas’
We can check version of it you have ‘pandas.__version__’ under the ‘print’ statement. Then it prints the version you have installed on your system that you are going to use.
Instead of always using pandas keyword we can create object of it or we can say it as a alias, to make alias or object of pandas is- ‘import pandas as pd’. Here ‘pd’…
Pandas basically stands for panel data. It is core library for data manipulation and data analysis.
Pandas basically give you single and multidimensional data structure, and you can perform various types of data manipulation operations on these single and multidimensional data structures.
Single dimensional data structure in pandas is known as ‘Series’ object. Series object is one dimensional labeled array, and multidimensional data structure is the ‘DataFrame’.
Pandas is newer package built on top of NumPy, and provides the efficient implementation of ‘DataFrame’.
DataFram are multidimensional arrays with attached row and column labels with heterogeneous type data. It offers convenient storage interface for labeled data. Pandas implements number of powerful data operations to user of both database framework and spreadsheet programs.
Pandas, its Series and DataFrame objects, provide efficient access to these sort of ‘data munging’ tasks that occupy much of data scientist’s time