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There are a lot of exercises and XP I earned there on this free course, I will show you some examples of things I learn there, but impossible to put all here, it is not the intent of this post and it is too much:
- 4700 XP Earned
- 1 Courses Completed
- 57 Exercises Aced
1. Python List
- Create a list:
mySimpleList = [12, 43, 54, 34, 90] #Simple list with same type
myWeirdList = ['a', 43, 54, 'c', 90] #Different types of items
- Print a list
print(myList) # Knowing that the list is already created
- List of list
countries = [["Cameroon", "CM"],
["Nigeria", "NG"],
["France", "FR"],
["Gabon", "GA"]]
- Type of a variable
To print the type of variable, just hit this:
print(type(myVariable))
- Index
list[4] = list[-2] = 5
- Subsetting list
A subsetting always return a list. Here the first index is included in the result and the last is not.
list[1:4] = [2, 3, 4] # From index 1 to index 3 included
list[:4] = [1, 2, 3, 4] # From the start to index 3 included
list[1:] = [2, 3, 4, 5, 6] # From index 1 to the end
2. Numpy Array
- Install Numpy: pip3 install numpy
- Import Numpy
There is some manner to import python package/function, let’s focus on these two for this post:
import numpy # Here we will address numpy array with numpy.array
import numpy as np # Address numpy array with np.array
- From list to numpy Array
# Using countries list declared up there
countries_np_array = np.array(countries)
- subsetting np array
countries_np_array[:, 1] = array([‘CM’, ‘NG’, ‘FR’, ‘GA’])
# Return all country code, all rows and the second column
age_array = np.array([2, 4, 6, 8])
age_selector = age_array >= 4
# result array([False, True, True, True], dtype=bool)
# Now use this selector to index the new array
print(age_array[age_selector])
# result array([4, 6, 8])
Note: Numpy does not allow multiple types on an array and will force all type to be same.
age_array = np.array([True, 4, False, 8])
print(age_array)
#result array([1, 4, 0, 8])
- Operation over collections
age = [2, 4, 6, 8]
div = [2, 2, 2, 4]
age_array = np.array(age) # Numpy array of ages
div_array = np.array(div) # Numpy array of divs
print(age/div)
# divide python list
# Traceback (most recent call last):
# File “<stdin>”, line 1, in <module>
#TypeError: unsupported operand type(s) for /: ‘list’ and ‘list’
print(age_array/div_array) # Will compute without issue on each item
array([ 1., 2., 3., 2.]) # result
Data Science deals with a lot of information to analyze, sort and do others things on, then it needs to do mathematical operations over collections quickly.
3. Little stats with Numpy
- Average: np.mean(your_numpy_array_or_axis)
- Median: np.median(your_numpy_array_or_axis)
- Standard Deviation: np.std(your_numpy_array_or_axis)
Supposed we have an array representing the grade of 3 students of a class in two courses(French and English):
import numpy as np
student_grades = np.array([[12, 16], [15.5, 9], [5, 16]])
# Average of student's grade in French
# Here we select all the rows and the french axis(the first column)
french_average = np.average(student_grades[:, 0])
print(french_average)
# result: 10.833333333333334
# Standart deviation of student's grade in English
# We select all the rows and the english axis(the second column)
english_std = np.std(student_grades[:, 1])
print(english_std)
# result: 3.299831645537221
