Obtaining Time Series Datasets in Python
This article provides a guide to obtaining time series datasets in Python, including from sources such as Yahoo Finance and Quandl.
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Time series datasets are important for machine learning. This article provides a guide to obtaining time series datasets in Python. It explains how to use the Quandl API to download time series data, as well as how to use the Statsmodels library to perform time series analysis. It also covers the use of the Pandas library for data manipulation and the Matplotlib library for data visualization.
Quandl is a platform for financial, economic, and alternative data that serves investment professionals. It provides a free API for downloading time series datasets. The API allows users to access and download data from Quandl in Python. The Quandl API also allows users to filter and manipulate data, as well as to access metadata and descriptions of the datasets.
The Statsmodels library is a Python package for performing statistical analysis. It provides a range of tools for analyzing time series data, including the ability to create and fit models, and to perform hypothesis tests. It also provides functions for performing regression analysis and for plotting time series data.
The Pandas library is a popular data manipulation library for Python. It provides a range of tools for manipulating time series datasets, including the ability to filter and sort data, as well as to calculate statistics. The Matplotlib library is a popular data visualization library for Python. It provides a range of tools for plotting time series data, including line plots, scatter plots, and histograms.
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