Peaks (2 points)

Data concerning the world highest mountains are stored in file peaks.csv. Download it and examine its contents.

We have already read this file obtaining the corresponding DataFrame, dfpeaks. The following example shows the names of all the columns of it and a subdataframe example:

>>> list(dfpeaks.columns)
['Mountain', 'Height', 'Country', 'Prominence', 'Range', 'Parent mountain', 'First ascent', 'Ascents', 'Failed attempts']

>>> dfpeaks[['Mountain', 'Height', 'Country', 'First ascent', 'Ascents']][3:8]
       Mountain  Height        Country  First ascent  Ascents
3        Lhotse    8516  Nepal + China          1956       26
4        Makalu    8485  Nepal + China          1955       45
5       Cho Oyu    8188  Nepal + China          1954       79
6  Dhaulagiri I    8167          Nepal          1960       51
7       Manaslu    8163          Nepal          1956       49

Save the following functions into file peaks.py and design it by using the pandas library.

1. Write function max_ascents() that takes a DataFrame, dfpeaks, as that described, the name of a country (str) and a year (int). This function considers only the subset of mountains that belong to the given country and such that its first ascent has been in the given year or in a subsequent year, and returns a tuple with two items: the name of the mountain with the maximum number of ascents (str) and this maximum number of ascents (int) (1 point). Examples:

   >>> max_ascents(dfpeaks, 'Nepal + China', 1955)
   ('Makalu', 45)
   >>> max_ascents(dfpeaks, 'Nepal + China', 1956)
   ('Lhotse', 26)
   >>> max_ascents(dfpeaks, 'India', 1955)
   ('Saser Kangri I / K22', 6)
   

   Note

   You have more tests in file test-max_ascents.txt

   Hint

   Use indexing, selection and methods max and idxmax.

2. Write function mount_range() that takes a DataFrame, dfpeaks, as that described, and returns another DataFrame resulting from grouping by mountain range (column 'Range'). The index of this DataFrame is all the mountain ranges in the given DataFrame and it has three columns with, respectively, the maximum mountain height and the total number of ascents and failed attempts for each mountain range (1 point). Example:

   >>> d1 = mount_range(dfpeaks)
   >>> type(d1)
   <class 'pandas.core.frame.DataFrame'>
   
   >>> d1.head()
                       Height  Ascents  Failed attempts
   Range                                               
   Annapurna Himalaya    8091       70              112
   Assam Himalaya        7782        2                2
   Baltoro Karakoram     8611      181              108
   Batura Karakoram      7795       10               12
   Daxue Shan            7556        6                7
   

   Note

   You have more tests in file test-range.txt

   Hint

   Use methods groupby and agg.

Note

To run these functions you don’t actually need to read the given data file. But you need to download and save it into your working folder because the given testfiles read from it to create the DataFrame.