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Kalau tidak pasti salah, jangan lupa menuliskan dia isi katanya, karena telah membicarakan dengannya tadi – “Tolong siapa itu” bahkan dia dekatinakan ini,.Data wrangling is of utmost importance in data science. There are a large number of data science apps available on the app stores, but not all of them support the same data wrangling methods. Here is a list of tools that is essential for every data scientist. ۱٫ Jupyter Notebooks Jupyter Notebooks were developed by another start-up called ipython, which was developed in the year 2002. Jupyter Notebooks are collections of code that implement several concepts. The concept of Jupyter Notebooks is very similar to the concept of wikis. Jupyter notebooks are persistent documents in which the user can edit the current version. We can install Jupyter by installing Anaconda. Anaconda is a special tool that provides all the backend tools for the Jupyter Notebooks. Jupyter Notebooks are very similar to the notebooks used in the field of mathematics. ۲٫ Pandas Pandas is a fast and scalable data analysis tool that has a lot of packages. Pandas is an open-source Python package that is used for data manipulation and feature extraction. Pandas is very easy to use and manipulate the data. We can use Pandas to create and manipulate dataframes. All the packages of Pandas are written in python, and we can use that for data wrangling. ۳٫ Numpy Numpy is an open source scientific Python package that performs matrix computation, vectorized operations, etc. We can use this package to perform statistical analysis, but we can also use it for data wrangling. Numpy will allow us to do data wrangling very quickly. ۴٫ Scipy Scipy is a library for scientific computation, linear algebra, and other standard mathematical packages. We can use scipy for any data wrangling exercise. We can calculate and manipulate matrices, vectors, and data frames. Pandas and numpy are only for manipulating data. Scipy provides more functionality for data wrangling. ۵٫ Matplotlib Matplotlib is a collection of modules and is



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