What's the Weather Like?

Background

Created a Python script to visualize the weather of 500+ cities across the world of varying distance from the equator by utilizing Python library - citipy, and the OpenWeatherMap API, to create a representative model of weather across world cities.

The script does the following :

Randomly selects at least 500 unique (non-repeat) cities based on latitude and longitude.
Perform a weather check on each of the cities using a series of successive API calls.
Includes a print log of each city as it's being processed with the city number and city name.
Saves a CSV of all retrieved data and a PNG and JPEG image for each scatter plot.

Part I - WeatherPy

Created a series of scatter plots to showcase the following relationships:

Temperature (F) vs. Latitude
Humidity (%) vs. Latitude
Cloudiness (%) vs. Latitude
Wind Speed (mph) vs. Latitude

The note book "WeatherPy.ipynb" has detailed explanation on what the code is analyzing.

Created a linear regression on each relationship. Created seperate sets of plots for Northern Hemisphere and Southern Hemisphere:

Northern Hemisphere - Temperature (F) vs. Latitude
Southern Hemisphere - Temperature (F) vs. Latitude
Northern Hemisphere - Humidity (%) vs. Latitude
Southern Hemisphere - Humidity (%) vs. Latitude
Northern Hemisphere - Cloudiness (%) vs. Latitude
Southern Hemisphere - Cloudiness (%) vs. Latitude
Northern Hemisphere - Wind Speed (mph) vs. Latitude
Southern Hemisphere - Wind Speed (mph) vs. Latitude

After each pair of plots, find explanation on what the linear regression is modeling and other observations, if any.

Part II - VacationPy

Used jupyter-gmaps and the Google Places API for this fun exercise .

Created a heat map that displays the humidity for every city from Part I.
Narrowed down the DataFrame to find ideal weather condition. For example:
- A max temperature lower than 80 degrees but higher than 70.
- Wind speed less than 10 mph.
- Zero cloudiness.
- Dropped any rows that didn't contain all three conditions. We want to be sure the weather is ideal.
Using Google Places API to fetch the first hotel for each city located within 5000 meters of search coordinates.
Plotted the hotels on top of the humidity heatmap with each pin containing the Hotel Name, City, and Country.

Key notes:

Saved images in both 'png' and 'jpeg' formats.
Analysis was done using a Jupyter notebook.
Defined and used functions
Used citipy and OpenWeatherMap API and gmaps
Used Matplotlib or Pandas plotting libraries.
For Part I, Find written description of three observable trends based on the data at the top of notebook.
For Part II, included a screenshot of the heatmap and saved it to the folder.
Used proper labeling plots, including aspects like: Plot Titles (with date of analysis) and Axes Labels.
Used color maps.
For max intensity in the heat map, set the highest humidity found in the data set.

Execution:

Important : Please download the repository.
The script for Part I is in the Jupyter notebook 'WeatherPy.ipynb' which can be located in the folder WeatherPy.
- Find written description of three observable trends based on the data at the top of notebook.
The script for Part II is in the Jupyter notebook 'VacationPy.ipynb' which can be located in the folder WeatherPy.
- Find a screenshot of the heatmaps in the folder 'WeatherPy/Heat_Maps'
Both files have detailed comments explaining each segment.

Results:

Tested it multiple times.
All the segments of Jupyter notebook executed successfully generating the final report.
Successfully displays required output results, plots and maps.

eRRORS !!!! What ERRORS ????

REST ASSURED, the code runs error free. Just Follow these detailed instructions ....

v-malm / what-s-the-weather-like Goto Github PK