Best course data science using pandas, Top 10 pandas course

Explore pandas data structures: series, data frame, and panel, their one, two, and three dimensional containers, and how they store homogeneous or heterogeneous data with mutable values and index-based access.

learn to create pandas series, a one-dimensional data structure that stores any data type and uses index or labels to access elements. explore constructors with data, index, dtype, and copy.

Explore the basic functionalities of series and data frames in pandas, including access, dtype, empty, ndim, size, values, head, tail, and axis, with practical examples.

Explore how to apply a function to pandas objects, including series and dataframes, using pipe for the whole frame, axis-based apply for columns or rows, and applymap for elementwise operations.

Learn to sort dataframes in pandas by index, columns, and values, control order with ascending or descending, and sort by single or multiple columns using sort_index and sort_values.

Learn how to customize the number of rows and columns displayed in the binders data structure using get option, set option, reset option, describe option, and context-specific options.

Master indexing and selection in Bandar's data structures using indexing and attribute operators, log function, and boolean expressions to query data frames, series, and panels.

Learn how to apply statistical functions on pandas structures—series and dataframes—to analyze data behavior, using percentage change, correlation, and ranking along different axes.

Apply pandas window functions to smooth data using rolling, expanding, and exponentially weighted moving averages. Learn to specify window parameters, compute means, and handle noisy or large data efficiently.

Explore how pandas aggregation functions work with rolling, expanding, and exponentially weighted moving averages, applying functions like mean and sum across single, multiple, or selected dataframe columns.

Learn to handle missing data in pandas by using isnull and notnull checks, and re indexing. Apply fill, forward fill, backward fill, drop, and replace to prepare data before modeling.

Learn how to concatenate data frames in pandas using concat and append, control axis and keys, manage ignore index and join behavior, and distinguish sources with indicators.