The pandas library is the go-to tool for data manipulation and cleaning in Python. It provides powerful, flexible data structures like DataFrames and Series that make handling structured data (such as tables and spreadsheets) very efficient. Pandas allows you to easily manipulate, clean, and preprocess data by offering features like handling missing values, merging datasets, filtering data, and performing group-by operations. It's a vital tool in data analysis and is widely used alongside other libraries like NumPy for numerical computations and Matplotlib/Seaborn for visualization. The other options are incorrect because: • Option 1 (NumPy) is primarily used for numerical operations and is excellent for working with arrays, but it does not offer data manipulation functions specific to tables or datasets. • Option 3 (Matplotlib) is used for data visualization, not data manipulation. • Option 4 (Seaborn) is a statistical visualization library built on top of Matplotlib, useful for creating beautiful plots, but not focused on data cleaning. • Option 5 (Scikit-learn) is a machine learning library that focuses on model building and not data manipulation.
The bright yellow colour of the labels the insecticide container indicates that product is:
When a cell is fully turgid, which of the following will become zero?
Toned milk should contain minimum ____% fat and ___% SNF.
Which of the following is not a function of Auxin?
Which state has the highest share of Cotton production in terms of area?
Keratin protein is found in
_____ are the non-nutritive substances usually added to basal feed in small quantity for the fortification in order to improve feed efficiency and produ...
Grain moisture content(%) for storage of pulse should be in the range of
An irrigation project is called major irrigation project when CCA is
Lichens, the pioneer organisms that initiate ecological succession are actually a symbiotic association of
