pandas are Python packages providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python. Pandas are mainly used for Pandas allow importing data from various file formats such as comma-separated values, JSON, Parquet, SQL database tables or queries, and Microsoft Excel.
Features of Pandas:
It is used for Data Frame objects and for data manipulation. It is also used for reading tools and writing data for data structures and different file formats. It aligns the data and integrates with the handling of missing data. Also reshapes the data and pivots data sets. It also does the label-based slicing, fancy indexing, and sub-setting of large data sets. It will take care of all merging and joining things. It provides data filtration. The library is highly optimized for performance, with critical code paths.
Pandas are mainly used for data analysis and associated manipulation of tabular data in Data frames. Pandas allow importing data from various file formats. Pandas allow various data manipulation operations such as merging, reshaping, selecting,] as well as data cleaning and data wrangling features. The development of pandas introduced into Python many comparable features of working with Data Frames. The panda’s library is built upon another library called NumPy which is oriented to efficiently working with arrays instead of the features of working on Data Frames.
Advantages:
It is fast and efficient for manipulating and analyzing data. Data from different file objects can be loaded. It is easy in handling the missing data (represented as NaN) in floating point as well as non-floating point data. It can alter columns that can be inserted and deleted from Data frames and higher dimensional objects. Also used for data set merging and joining. It is so flexible in reshaping and pivoting data sets.
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