In [1]:
# using pandas to  load in data from a URL
import pandas as pd
url="https://docs.google.com/spreadsheets/d/e/2PACX-1vQcpVvVioO23cndDwr1UmKhndrSq6ES6ZUKZ4fkBBqIAavd1_coVPO_yeOye-Ub-cAWlkX3psJvOU8o/pub?output=csv"
df = pd.read_csv(url)

After loading the Data, explore the data to understand its structure and characteristics¶

In [3]:
# print the first 10 rows od the data set
df.head()
Out[3]:
date meantemp humidity wind_speed meanpressure
0 2013-01-01 10.000000 84.500000 0.000000 1015.666667
1 2013-01-02 7.400000 92.000000 2.980000 1017.800000
2 2013-01-03 7.166667 87.000000 4.633333 1018.666667
3 2013-01-04 8.666667 71.333333 1.233333 1017.166667
4 2013-01-05 6.000000 86.833333 3.700000 1016.500000
In [9]:
# shape will print the count of (rows, columns) within the dataset
df.shape
Out[9]:
(1462, 5)
In [4]:
# Get basic statistics of the data
df.describe()
Out[4]:
meantemp humidity wind_speed meanpressure
count 1462.000000 1462.000000 1462.000000 1462.000000
mean 25.495521 60.771702 6.802209 1011.104548
std 7.348103 16.769652 4.561602 180.231668
min 6.000000 13.428571 0.000000 -3.041667
25% 18.857143 50.375000 3.475000 1001.580357
50% 27.714286 62.625000 6.221667 1008.563492
75% 31.305804 72.218750 9.238235 1014.944901
max 38.714286 100.000000 42.220000 7679.333333
In [5]:
# Check for missing values in the Dataset
df.isnull().sum()
Out[5]:
date            0
meantemp        0
humidity        0
wind_speed      0
meanpressure    0
dtype: int64