"Data analysis using Python" reading notes--fifth Chapter pandas Introduction

Pandas is the preferred library for subsequent content in this book. The pandas can meet the following requirements:

• Data structure with automatic or explicit data alignment by axis. This prevents many common errors caused by data misalignment and data from different data sources (indexed differently).
• Integrated time series capabilities
• Data structures that can handle time series data as well as non-time series
• Mathematical operations and simplicity (such as summing an axis) can be performed according to different metadata (axis number)
• Flexible handling of missing data
• Merge and other relational operations that appear in common databases, such as SQL-based

Pandas Data Structure Introduction

Two data structures: series and dataframe. A series is an object that is similar to a numpy array, which consists of a set of data (various numpy data types) and a set of data labels (that is, indexes) associated with it. Indexes and values can be specified by index and value, respectively. If you do not specify an index, 0 to N-1 indexes are created automatically.

Data analysis using Python-Reading notes-fifth Chapter pandas introduction