Analyzing the Meteorological data

The dataset has the data for past 10years from 2006–04–01 00:00:00.000 +0200 to 2016–09–09 23:00:00.000 +0200. It corresponds to Finland, a country in Northern Europe. If you want to try using the dataset go to the below drive link and download: https://drive.google.com/open?id=1ScF_1a-bkHi1qe8Rn78uxK6_5QwUD9B .

We need to find the average temperature for the month starting from April 2006 to 2016 and average humidity for the same period.

STEPS INVOLVED:

STEP 1: Import the dataset

STEP 2: Print the Data

STEP 3: Check the type of the data

STEP 4: Cleaning the data

STEP 5: Check Whether the dataset has any invalid data

STEP 6: Finally Visualize the Data

Let’s start the above mentioned steps

IMPORT THE DATA

You can download the dataset from the given link https://drive.google.com/open?id=1ScF_1a-bkHi1qe8Rn78uxK6_5QwUD9B .

Before importing the data first import the libraries and then import the data. Here we use pandas library for creating the ‘DATAFRAME’

PRINT THE DATA:

To print the data you can use . head() method , this method print 5 random data from the dataset by default if you want to print the first five data you have mention .head(5) or else how many data you want to print. Likewise you can use .tail() to print the data from last.

PRINT THE DATA TYPES AND OTHER DATASET RELATED THINGS:

Column method is used to find the name of the columns in the dataset.

Shape method helps us to find how many rows and columns are availabe in the dataset.

dtype method is used to find the data type.

CLEANING THE DATA:

use drop() method to remove the unwanted columns

CHECK THE DATASET HAS THE PROPER FORMAT OR NOT:

Here I thought the format of the date is not proper so we need to create a date and time object by using the to_datetime() function. Then set the name of the index using set index .

Now since we have been given hourly data, we need to resample it monthly.

VISUALIZING THE DATA

Here I’m going to visualize the data from January to December(2006–2016).

JANUARY

FEBRUARY

MARCH

APRIL

MAY

JUNE

JULY

AUGUST

SEPTEMBER

OCTOBER

NOVEMBER

DECEMBER

INSIGHTS FROM THE ABOVE VISUALIZATION:

From September to March Large difference in Apparent Temperature but no changes in Humidity and in April to August minor changes in temperature but here also no difference in humidity. exactly, November, December, January high changes than other months.

CONCLUSION

In this blog we learned to analyze and visualize using the meteorological dataset in Matplotlib . I hope it will be useful for you . If you find any Mistakes please let me know . We discuss about the seaborn and other visualization process in another blog, Thank you. Have a Nice day.