These are the conclusions by Zhao (Zhao, 2018) based on the Divvy dataset from 2013 to 2017 (https://www.divvybikes.com/system-data ) from Chicago, Illinois, United States. This notebook analysis was gathered from Kaggle (https://www.kaggle.com/), and the weather analysis was done from Wunderground.com (https://www.wunderground.com/). The study correlated the weather and biking behavior of Chicago residents, noting the variables of temperature and weather conditions in that area. This data and analysis is free and can be downloaded directly from the source at Kaggle (https://www.kaggle.com/yingwurenjian/chicago-divvy-bicycle-sharing-data).
Note. The visual display of the biking patterns in the city of Chicago data (Zhao, 2018), and I used the instrument Colab Notebook for this lab.
References
Zhao, Jifu. (2018). Chicago Divvy Bicycle Sharing Data. Retrieved online from Kaggle at https://www.kaggle.com/yingwurenjian/chicago-divvy-bicycle-sharing-data