Pandas provides fast and flexible data structures for relational data analysis. Handles missing data (NaN, NA, NaT) in both floating and non-floating point data. Offers automatic data alignment and flexible group by functionality. Supports merging, joining, reshaping, and pivoting of data sets. Includes robust IO tools for various file formats and databases
Two-dimensional, size-mutable tabular data structure. Contains labeled axes (rows and columns). Can be thought of as dict-like container for Series objects. Primary pandas data structure
Converts various data types to pandas datetime objects. Requires minimum "year", "month", "day" columns in DataFrame. Supports various input types including scalars, arrays, Series, and DataFrame
Pandas provides Series and DataFrame classes for handling different data types. DataFrame is a two-dimensional data structure with rows and columns. IPython automatically enables tab completion for DataFrame columns
Anaconda distribution provides easiest installation for new Python users. Miniconda recommended for experienced users with conda package manager. Installation possible via pip from PyPI with pip>=19.3. Source installation available through contributing guide
Pandas .drop() method removes rows or columns from DataFrames. Method returns new DataFrame unless inplace parameter is True