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The process of cleansing and integrating chaotic and complicated data sets for easy access and analysis is known as data wrangling. With the amount of data and data sources continually rising and expanding, it is becoming increasingly important to organise vast amounts of data for analysis.

Data wrangling entails converting data into multiple formats and analysing it so that it can be combined with other data to produce significant insights. Data gathering, data visualisation, and statistical model training for prediction are also included.

Prior to data wrangling, data preprocessing is done. Data Preprocessing data is prepared directly after the data is received from the data source in this situation. Data cleansing and aggregation are conducted during the early transformations.

Data wrangling's fundamental goal is to make raw data usable. To put it another way, putting data into a shape.

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