| In R I Want | In Python I Use |
|---|---|
| Base R | numpy |
| dplyr/tidyr | pandas |
| ggplot2 | matplotlib/seaborn |
Let’s import the numpy package:
Python
import numpy as npIn Python, we assign variables with =, not
<-
Python
x = 10
x## 10The arithmetic operations (+, -,
*, /) are the exact same
Python
x * 2
x + 2
x / 2
x - 2
x ** 2 # square
x % 2 # remainderComments also begin with a #:
Python
# This is a commentHelp files are called the same way
Python
help(min)
?minPython lists are like R lists, in that they can have the
different types. You create Python lists with brackets
[]
Python
x = ["hello", 1, True]
x## ['hello', 1, True]NumPy Arrays are the Python equivalent to R vectors (where each
element is the same type). You use the array() method of
the numpy package to create a numpy array (note that you give it a list
as input)
Python
vec = np.array([2, 3, 5, 1])
vec## array([2, 3, 5, 1])You can do vectorized operations on NumPy arrays
Python
vec + 2
vec - 2
vec * 2
vec / 2
2 / vecTwo vectors of the same size can be added/subtracted/multiplied/divided:
Python
x = np.array([1, 2, 3, 4])
y = np.array([5, 6, 7, 8])
x + y
x - y
x / y
x * y
x ** yYou extract individual elements just like in R, using brackets
[]
Python
vec[0]
vec[0:2]Extract arbitrary elements by passing an index array:
Python
ind = np.array([0, 2])
vec[ind]
# or
vec[np.array([0, 2])]Key Difference: Python starts counting
from 0, not 1. So the first element of a vector is
vec[0], not vec[1].
Combine two arrays via np.concatenate() (notice the
use of brackets here)
Python
x = np.array([1, 2, 3, 4])
y = np.array([5, 6, 7, 8])
np.concatenate([x, y])## array([1, 2, 3, 4, 5, 6, 7, 8])In R, we have functions operate on objects
(e.g. log(x), sort(x), etc).
Python also has functions that operate on objects. But objects usually have functions associated with them directly. You access these functions by a period after the object name. These functions are called “methods”. Use tab completion to scroll through the available methods of an object.
Python
vec.sort() # sort
vec.min() # minimum
vec.max() # maximum
vec.mean() # mean
vec.sum() # sum
vec.var() # varianceBut there are still loads of useful functions that operate on objects.
Python
np.sort(vec)
np.min(vec)
np.max(vec)
np.mean(vec)
np.sum(vec)
np.var(vec)
np.size(vec)
np.exp(vec)
np.log(vec)Python uses True and False. It uses the
same comparison operators as R
Python
vec > 3
vec < 3
vec == 3
vec != 3
vec <= 3
vec >= 3The logical operators are: Key Difference: “Not” uses a different character.
& And
| Or
~ Not
Python
np.array([True, True, False, False]) & np.array([True, False, True, False])## array([ True, False, False, False])np.array([True, True, False, False]) | np.array([True, False, True, False])## array([ True, True, True, False])~np.array([True, True, False, False])## array([False, False, True, True])You subset a vector using Booleans as you would in R
Python
vec[vec <= 3]## array([2, 3, 1])When you are dealing with single logicals, instead of arrays of
logicals, use and, or, and not
instead
Python
True and False## FalseTrue or False## Truenot True## FalseExercise: Consider two vectors \[ y = (1, 7, 1, 2, 8, 2)\\ x = (4, 6, 2, 7, 8, 2) \] Calculate their inner product: \[ y_1x_1 + y_2x_2 + y_3x_3 + y_4x_4 + y_5x_5 + y_6x_6 \] Do this using vectorized operations.
Exercise: Provide two ways of extracting the 2nd and 5th elements of this vector
Python
x = np.array([4, 7, 8, 1, 2])Exercise: Extract all elements from the previous vector between 5 and 8 (inclusive). Use predicates.