code into a data_exploration.py file and print the answers for the questions above inside the script.

• Save the script to your computer so you can upload to the LEO assignments folder
• Remember to comment your code! (add comments explaining what your code does)

Part 2 - Data manipulation

The temperatures in our data are represented in Fahrenheit, hence,  need to convert the temperatures into Celsius.

• Select from the data columns USAF, YR--MODAHRMN, TEMP, MAX, MIN and assign them into a new variable called selected
• Remove all rows from selected that has NoData in column TEMP using dropna() -function
• Convert the Fahrenheit temperatures from TEMP into a new column Celsius using the conversion formula:

• 

• Round the values in Celsius to have 1 decimal place (don't create a new column --> update the current one)

add  codes into a data_exploration.py file.

• Save the script to your computer so you can upload to the LEO assignments folder
• Remember to comment your code! (add comments explaining what your code does)

Part 3 - Data selection 

 divide the data into separate subsets for different stations and save those DataFrames into disk.

• Divide the selection into two separate datasets:
  • Select all rows from selected DataFrame into variable called kumpula where the USAF code is 29980
  • Select all rows from selected DataFrame into variable called rovaniemi where the USAF code is 28450
• Save kumpula DataFrame into Kumpula_temps_May_Aug_2017.csv file (CSV format)
  • separate the columns with ,
  • use only 1 decimal in the floating point numbers
• Save rovaniemi DataFrame into Rovaniemi_temps_May_Aug_2017.csv file (CSV format)
  • separate the columns with ,
  • use only 1 decimal in the floating point numbers
• Save your csv files to your computer and upload to the LEO assignments folder

 add  codes into a data_exploration.py file.

• Save the script to your computer so you can upload to the LEO assignments folder
• Remember to comment your code! (add comments explaining what your code does)

Part 4 - Data analysis

In this part the aim is to understand how different the summer temperatures has been in Helsinki Kumpula and Rovaniemi. Using the data from Part 3 in kumpula and rovaniemi DataFrames answer the following questions (all temperature output in Part 4 should be in Celsius with one decimal place):

 

Part 4a

• What was the median temperature in Celsius (one decimal place) in each of the following locations:
  • Helsinki Kumpula?

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