Data Interview Qs - Rain in the US

InterviewQs

Rain in the US

Data Analysis Python Pandas Data Manipulation Data Visualization External Dataset

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Hi, 

Suppose you have the following dataset, which contains information about a year's worth of US weather. Using Pandas, plot the top 10 locations that recieved the most rain in 2016. You will want to utilize the temperature field to flag when precipitation is rain (if it's too cold, the precipitation will be snow).

To help get you started, below is code to load the dataset into a Pandas dataframe. You can also make a copy of this Google Colab notebook.

        

%matplotlib inline

import pandas as pd

import matplotlib.pyplot as plt

import numpy as np

import seaborn as sns

data = pd.read_csv("https://raw.githubusercontent.com/erood/interviewqs.com_code_snippets/master/Datasets/weather.csv")

data.head()

Data.Precipitation Date.Full Date.Month Date.Week of Date.Year Station.City ...
0 0.00 2016-01-03 1 3 2016 Birmingham ...
1 0.00 2016-01-03 1 3 2016 Huntsville ...
2 0.16 2016-01-03 1 3 2016 Mobile ...
3 0.00 2016-01-03 1 3 2016 Montgomery ...
4 0.01 2016-01-03 1 3 2016 Anchorage ...

Solution will be written in Python for premium users.



 

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