From 558e97e4dbdb6f8a2d30763b19e23fe9415a4ea2 Mon Sep 17 00:00:00 2001 From: "b.ghazlane" Date: Thu, 8 Apr 2021 00:05:30 +0200 Subject: [PATCH] fix: correct the ressources URL --- one_md_per_day_format/piscine/Week1/day1.md | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/one_md_per_day_format/piscine/Week1/day1.md b/one_md_per_day_format/piscine/Week1/day1.md index b63d92b..872ff54 100644 --- a/one_md_per_day_format/piscine/Week1/day1.md +++ b/one_md_per_day_format/piscine/Week1/day1.md @@ -26,7 +26,7 @@ Save one notebook per day or one per exercise. Use markdown to divide your noteb ## Ressources - https://medium.com/fintechexplained/why-should-we-use-NumPy-c14a4fb03ee9 -- https://docs.scipy.org/doc/NumPy-1.15.0/reference/ +- https://numpy.org/doc/ - https://jakevdp.github.io/PythonDataScienceHandbook/ # Exercice 1 Your first NumPy array @@ -124,7 +124,7 @@ NumPy proposes a lot of options to generate random data. In statistics, assumpti - Normal: The normal distribution is the most important probability distribution in statistics because it fits many natural phenomena.For example, if you need to generate a data sample that represents **Heights of 14 Year Old Girls** it can be done using the normal distribution. In that case, we need two parameters: the mean (1m51) and the standard deviation (0.0741m). NumPy provides `randn` to generate normal distribution (among other) -https://docs.scipy.org/doc/NumPy-1.15.0/reference/routines.random.html +https://numpy.org/doc/stable/reference/random/generator.html 1. Set the seed to 888 2. Generate a **one-dimensional** array of size 100 with a normal distribution @@ -210,7 +210,7 @@ The goal of this exercise is to learn to concatenate and reshape arrays. The easiest way is to use `array.reshape(10,10)`. -https://jakevdp.github.io/PythonDataScienceHandbook/02.02-the-basics-of-NumPy-arrays.html +https://jakevdp.github.io/PythonDataScienceHandbook/ (section: The Basics of NumPy Arrays) --- @@ -233,7 +233,7 @@ The goal of this exercise is to learn to access values of n-dimensional arrays e [1, 1, 1, 1, 1, 1, 1, 1, 1]], dtype=int8) ``` -https://jakevdp.github.io/PythonDataScienceHandbook/02.05-computation-on-arrays-broadcasting.html +https://jakevdp.github.io/PythonDataScienceHandbook/ (section: Computation on Arrays: Broadcasting) ## Correction @@ -320,7 +320,6 @@ This question is validated if, without having used a for loop or having filled t [ 8. 5. 8.]] ``` -https://jakevdp.github.io/PythonDataScienceHandbook/02.02-the-basics-of-NumPy-arrays.html ---