From 45e1fc47b5b91fba87fd39f146d6af236a0809bb Mon Sep 17 00:00:00 2001
From: daviddoji <daviddoji@pm.me>
Date: Thu, 19 Aug 2021 19:27:05 +0200
Subject: [PATCH] Alternative solution to exercise 4

---
 100_Numpy_exercises_with_hints.md                | 4 ++--
 100_Numpy_exercises_with_hints_with_solutions.md | 6 +++---
 100_Numpy_exercises_with_solutions.md            | 4 ++--
 3 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/100_Numpy_exercises_with_hints.md b/100_Numpy_exercises_with_hints.md
index 5b8db89f..1457bc57 100644
--- a/100_Numpy_exercises_with_hints.md
+++ b/100_Numpy_exercises_with_hints.md
@@ -19,7 +19,7 @@ File automatically generated. See the documentation to update questions/answers/
 #### 3. Create a null vector of size 10 (★☆☆)
 `hint: np.zeros`
 #### 4. How to find the memory size of any array (★☆☆)
-`hint: size, itemsize`
+`hint: size, itemsize, nbytes`
 #### 5. How to get the documentation of the numpy add function from the command line? (★☆☆)
 `hint: np.info`
 #### 6. Create a null vector of size 10 but the fifth value which is 1 (★☆☆)
@@ -247,4 +247,4 @@ np.sqrt(-1) == np.emath.sqrt(-1)
 #### 99. Given an integer n and a 2D array X, select from X the rows which can be interpreted as draws from a multinomial distribution with n degrees, i.e., the rows which only contain integers and which sum to n. (★★★)
 `hint: np.logical_and.reduce, np.mod`
 #### 100. Compute bootstrapped 95% confidence intervals for the mean of a 1D array X (i.e., resample the elements of an array with replacement N times, compute the mean of each sample, and then compute percentiles over the means). (★★★)
-`hint: np.percentile`
\ No newline at end of file
+`hint: np.percentile`
diff --git a/100_Numpy_exercises_with_hints_with_solutions.md b/100_Numpy_exercises_with_hints_with_solutions.md
index 5a623960..de0f2978 100644
--- a/100_Numpy_exercises_with_hints_with_solutions.md
+++ b/100_Numpy_exercises_with_hints_with_solutions.md
@@ -33,11 +33,11 @@ Z = np.zeros(10)
 print(Z)
 ```
 #### 4. How to find the memory size of any array (★☆☆)
-`hint: size, itemsize`
+`hint: size, itemsize, nbytes`
 
 ```python
 Z = np.zeros((10,10))
-print("%d bytes" % (Z.size * Z.itemsize))
+print("%d bytes (%d bytes, using "nbytes")" % ((Z.size * Z.itemsize), Z.nbytes))
 ```
 #### 5. How to get the documentation of the numpy add function from the command line? (★☆☆)
 `hint: np.info`
@@ -1205,4 +1205,4 @@ idx = np.random.randint(0, X.size, (N, X.size))
 means = X[idx].mean(axis=1)
 confint = np.percentile(means, [2.5, 97.5])
 print(confint)
-```
\ No newline at end of file
+```
diff --git a/100_Numpy_exercises_with_solutions.md b/100_Numpy_exercises_with_solutions.md
index b332550f..7df4e6ce 100644
--- a/100_Numpy_exercises_with_solutions.md
+++ b/100_Numpy_exercises_with_solutions.md
@@ -37,7 +37,7 @@ print(Z)
 
 ```python
 Z = np.zeros((10,10))
-print("%d bytes" % (Z.size * Z.itemsize))
+print("%d bytes (%d bytes, using "nbytes")" % ((Z.size * Z.itemsize), Z.nbytes))
 ```
 #### 5. How to get the documentation of the numpy add function from the command line? (★☆☆)
 
@@ -1205,4 +1205,4 @@ idx = np.random.randint(0, X.size, (N, X.size))
 means = X[idx].mean(axis=1)
 confint = np.percentile(means, [2.5, 97.5])
 print(confint)
-```
\ No newline at end of file
+```