Handling time series data with Pandas
Pandas is arguably the most important library in Python. Learning to use its methods well is paramount, and it will serve you well as you use Python for any of your other projects. In addition to time series analysis, many more functions can be performed with Pandas including:
- DataFrame manipulation with integrated indexing
- Methods to read data from a variety of different file formats and write data into in-memory data structures
- Data sorting
- Data filtering
- Missing value imputation
- Reshaping and pivoting datasets
- Label-based slicing, indexing, and creation of subsets
- Efficient column insertion and deletion
- Group by operations on datasets
- Merging and joining of datasets
In this section, we will use it to convert a sequence of numbers into time series data and visualize it. Pandas provides options to add timestamps, organize data, and then efficiently operate on it.
Create a new Python file and...
