One of the hypotheses that the scientific community is working on is the option that the SARS-CoV-2 coronavirus is less transmissible in the presence of a warm and humid climate, a possibility that could reduce the incidence of COVID-19 disease as the spring progresses, the summer months get closer and it becomes warmer. For the time being, this is only a hypothesis, since although there are preliminary studies that point in that direction, there is still not enough scientific evidence to say that the virus survives worse in heat and that the pandemic could be attenuated by the arrival of higher temperatures or a more humid climate.

Some respiratory viruses, such as influenza, are known to spread more during the cold-climate months, and the other known coronavirus generally survives worse in higher temperatures and greater humidity than in colder or drier environments. There are some reasons for the seasonality of viruses in temperate regions, but the information is still lacking as to whether this theory can be applied to the new coronavirus.

Photo by Nicolas Cool on Unsplash

Data Overview :

The rising average temperature of Earth’s climate system, called global warming, is driving changes in rainfall patterns, extreme weather, arrival of seasons, and more. Collectively, global warming and its effects are known as climate change. While there have been prehistoric periods of global warming, observed changes since the mid-20th century have been unprecedented in rate and scale. So a dataset on the temperature of major cities of the world will help analyze the same. Also weather information is helpful for a lot of data science tasks like sales forecasting, logistics etc. The data is available for research and non-commercial purposes only.

license :

Content : Daily level average temperature values is present in city_temperature.csv file

Acknowledgements :

University of Dayton for making this dataset available in the first place!

Data Preparing

1. Importing the required libraries

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt

## !pip install plotly
## !pip install chart_studio

import plotly.tools as tls
import plotly as py
import plotly.graph_objs as go
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
from chart_studio import plotly as py
from plotly.offline import iplot

%matplotlib inline

#eda #timeseries #weather #python #plot