Data Analysis-Pandas

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Pandas:

Pandas allows for fast analysis and data cleaning and preparation, it excels in performance and productivity, it also has built-in visualization features, it can work with data from a wide variety of sources.

Pandas is seriously a game changer when it comes to cleaning, transforming, manipulating and analyzing data.

Series:

Pandas Series is a one-dimensional labeled array capable of holding data of any type (integer, string, float, python objects, etc.). The axis labels are collectively called index. 

Pandas Series is nothing but a column in an excel sheet. Labels need not be unique but must be a hashable type. 




DataFrames:

DataFrame is a 2-dimensional labeled data structure with columns of potentially different types. You can think of it like a spreadsheet or SQL table, or a dictionary of Series objects. It is generally the most commonly used pandas object.




















Missing Data:
If we have any missing data in the dataframe then how to deal with that.




GroupBy:
GroupBy method is used to group rows of data together and call aggregate function (any function that take many values and then spits out or output a single value such as taking the sum or average of the bunch of the values).






Merging Joining and Concatenating :







Operations :








Data Input and Output :






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Location: Jamalpur, Bihar, India

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