chore(i18n,learn): processed translations (#44851)

2022-01-21 01:00:18 +05:30
parent f866718a3d
commit 5c868af2b8
1696 changed files with 159426 additions and 69 deletions
--- a/curriculum/challenges/japanese/08-data-analysis-with-python/numpy/accessing-and-changing-elements,-rows,-columns.md
+++ b/curriculum/challenges/japanese/08-data-analysis-with-python/numpy/accessing-and-changing-elements,-rows,-columns.md
@@ -0,0 +1,54 @@
+---
+id: 5e9a0a8e09c5df3cc3600ed4
+title: '要素、行、列へのアクセスと変更'
+challengeType: 11
+videoId: v-7Y7koJ_N0
+bilibiliIds:
+  aid: 590517748
+  bvid: BV1Eq4y1f7Fa
+  cid: 409025392
+dashedName: accessing-and-changing-elements-rows-columns
+---
+
+# --question--
+
+## --text--
+
+次の両方の Numpy 配列について 3 列目の値を 20 に変更するコードはどれですか？
+
+```py
+a = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]])
+
+# Output:
+# [[ 1  2  20  4  5]
+# [ 6  7 20  9 10]]
+```
+
+## --answers--
+
+```python
+a[:, 3] = 20
+```
+
+---
+
+```python
+a[2, :] = 20
+```
+
+---
+
+```python
+a[:, 2] = 20
+```
+
+---
+
+```python
+a[1, 2] = 20
+```
+
+## --video-solution--
+
+3
+
--- a/curriculum/challenges/japanese/08-data-analysis-with-python/numpy/basics-of-numpy.md
+++ b/curriculum/challenges/japanese/08-data-analysis-with-python/numpy/basics-of-numpy.md
@@ -0,0 +1,49 @@
+---
+id: 5e9a0a8e09c5df3cc3600ed3
+title: Numpy の基本
+challengeType: 11
+videoId: f9QrZrKQMLI
+bilibiliIds:
+  aid: 763014202
+  bvid: BV1K64y1a7bu
+  cid: 409025169
+dashedName: basics-of-numpy
+---
+
+# --question--
+
+## --text--
+
+次のコードは何を表示しますか？
+
+```python
+b = np.array([[1.0,2.0,3.0],[3.0,4.0,5.0]])
+print(b)
+```
+
+## --answers--
+
+```python
+[[1.0 2.0 3.0]
+[3.0 4.0 5.0]]
+```
+
+---
+
+```python
+[[1. 2. 3.]
+[3. 4. 5.]]
+```
+
+---
+
+```python
+[[1. 3.]
+[2. 4.]
+[3. 5.]
+```
+
+## --video-solution--
+
+2
+
--- a/curriculum/challenges/japanese/08-data-analysis-with-python/numpy/copying-arrays-warning.md
+++ b/curriculum/challenges/japanese/08-data-analysis-with-python/numpy/copying-arrays-warning.md
@@ -0,0 +1,48 @@
+---
+id: 5e9a0a8e09c5df3cc3600ed7
+title: 配列のコピーに関する注意
+challengeType: 11
+videoId: iIoQ0_L0GvA
+bilibiliIds:
+  aid: 633008569
+  bvid: BV1Bb4y127fb
+  cid: 409026161
+dashedName: copying-arrays-warning
+---
+
+# --question--
+
+## --text--
+
+次のコードを実行した後、`a` の値は何になりますか？
+
+```py
+import numpy as np
+
+a = np.array([1, 2, 3, 4, 5])
+b = a
+b[2] = 20
+```
+
+## --answers--
+
+```python
+[1 2 3 4 5]
+```
+
+---
+
+```python
+[1 2 20 4 5]
+```
+
+---
+
+```python
+[1 20 3 4 5]
+```
+
+## --video-solution--
+
+2
+
--- a/curriculum/challenges/japanese/08-data-analysis-with-python/numpy/initialize-array-problem.md
+++ b/curriculum/challenges/japanese/08-data-analysis-with-python/numpy/initialize-array-problem.md
@@ -0,0 +1,65 @@
+---
+id: 5e9a0a8e09c5df3cc3600ed6
+title: 配列の初期化の問題
+challengeType: 11
+videoId: 0jGfH8BPfOk
+bilibiliIds:
+  aid: 763027834
+  bvid: BV1w64y1a7eo
+  cid: 409025878
+dashedName: initialize-array-problem
+---
+
+# --question--
+
+## --text--
+
+次のような配列を生成する別の方法は何ですか？
+
+```py
+[[0. 0. 0. 0. 0. 0. 0.]
+[0. 1. 1. 1. 1. 1. 0.]
+[0. 1. 1. 1. 1. 1. 0.]
+[0. 1. 1. 5. 1. 1. 0.]
+[0. 1. 1. 1. 1. 1. 0.]
+[0. 1. 1. 1. 1. 1. 0.]
+[0. 0. 0. 0. 0. 0. 0.]]
+```
+
+## --answers--
+
+```py
+output = np.ones((7, 7))
+
+z = np.zeros((5, 5))
+z[2, 2] = 5
+
+output[1:1, -1:-1] = z
+```
+
+---
+
+```py
+output = np.zeros((7,7))
+
+z = np.ones((5, 5))
+z[2, 2] = 5
+
+output[1:-1, 1:-1] = z
+```
+
+---
+
+```py
+output = np.ones((7, 7))
+
+z = np.zeros((5, 5))
+z[3, 3] = 5
+
+output[1:-1, 1:-1] = z
+```
+
+## --video-solution--
+
+2
+
--- a/curriculum/challenges/japanese/08-data-analysis-with-python/numpy/initializing-different-arrays.md
+++ b/curriculum/challenges/japanese/08-data-analysis-with-python/numpy/initializing-different-arrays.md
@@ -0,0 +1,48 @@
+---
+id: 5e9a0a8e09c5df3cc3600ed5
+title: 異なる配列の初期化
+challengeType: 11
+videoId: CEykdsKT4U4
+bilibiliIds:
+  aid: 718044756
+  bvid: BV1MQ4y1k7BB
+  cid: 409025638
+dashedName: initializing-different-arrays
+---
+
+# --question--
+
+## --text--
+
+次のコードは何を表示しますか？
+
+```py
+a = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]])
+
+print(np.full_like(a, 100))
+```
+
+## --answers--
+
+```py
+[[100 100 100 100 100]]
+```
+
+---
+
+```py
+[[100 100 100 100 100]
+[100 100 100 100 100]]
+```
+
+---
+
+```py
+[[ 1  2  3  4  5]
+[ 6  7 20  9 10]]
+```
+
+## --video-solution--
+
+2
+
--- a/curriculum/challenges/japanese/08-data-analysis-with-python/numpy/loading-data-and-advanced-indexing.md
+++ b/curriculum/challenges/japanese/08-data-analysis-with-python/numpy/loading-data-and-advanced-indexing.md
@@ -0,0 +1,60 @@
+---
+id: 5e9a0a8e09c5df3cc3600eda
+title: データの読み込みと高度なインデックス作成
+challengeType: 11
+videoId: tUdBZ7pF8Jg
+bilibiliIds:
+  aid: 720524642
+  bvid: BV1xQ4y1r7mu
+  cid: 409027117
+dashedName: loading-data-and-advanced-indexing
+---
+
+# --question--
+
+## --text--
+
+次の内容を含む `data.txt` という名前のファイルが与えられます。
+
+<pre>
+29,97,32,100,45
+15,88,5,75,22
+</pre>
+
+次の配列を生成するコードはどれですか？
+
+```py
+[29. 32. 45. 15.  5. 22.]
+```
+
+## --answers--
+
+```py
+filedata = np.genfromtxt('data.txt', delimiter=',')
+output = np.any(filedata < 50)
+
+print(output)
+```
+
+---
+
+```py
+filedata = np.genfromtxt('data.txt', delimiter=',')
+output = np.all(filedata < 50, axis=1)
+
+print(output)
+```
+
+---
+
+```py
+filedata = np.genfromtxt('data.txt', delimiter=',')
+output = filedata[filedata < 50]
+
+print(output)
+```
+
+## --video-solution--
+
+3
+
--- a/curriculum/challenges/japanese/08-data-analysis-with-python/numpy/mathematics.md
+++ b/curriculum/challenges/japanese/08-data-analysis-with-python/numpy/mathematics.md
@@ -0,0 +1,53 @@
+---
+id: 5e9a0a8e09c5df3cc3600ed8
+title: 算術演算
+challengeType: 11
+videoId: 7txegvyhtVk
+bilibiliIds:
+  aid: 890533226
+  bvid: BV1KP4y1h733
+  cid: 409026503
+dashedName: mathematics
+---
+
+# --question--
+
+## --text--
+
+次のコードを実行した後、`b` の値は何になりますか？
+
+```py
+import numpy as np
+
+a = np.array(([1, 2, 3, 4, 5], [6, 7, 8, 9, 10]))
+b = np.max(a, axis=1).sum()
+```
+
+## --answers--
+
+```py
+10
+```
+
+---
+
+```py
+7
+```
+
+---
+
+```py
+5
+```
+
+---
+
+```py
+15
+```
+
+## --video-solution--
+
+4
+
--- a/curriculum/challenges/japanese/08-data-analysis-with-python/numpy/reorganizing-arrays.md
+++ b/curriculum/challenges/japanese/08-data-analysis-with-python/numpy/reorganizing-arrays.md
@@ -0,0 +1,53 @@
+---
+id: 5e9a0a8e09c5df3cc3600ed9
+title: 配列の再編成
+challengeType: 11
+videoId: VNWAQbEM-C8
+bilibiliIds:
+  aid: 548035655
+  bvid: BV1fq4y1N7aC
+  cid: 409026755
+dashedName: reorganizing-arrays
+---
+
+# --question--
+
+## --text--
+
+次の配列を生成するコードはどれですか？
+
+```py
+[[1. 1.]
+[1. 1.]
+[1. 1.]
+[1. 1.]]
+```
+
+## --answers--
+
+```py
+a = np.ones((2, 4))
+b = a.reshape((4, 2))
+print(b)
+```
+
+---
+
+```py
+a = np.ones((2, 4))
+b = a.reshape((2, 4))
+print(b)
+```
+
+---
+
+```py
+a = np.ones((2, 4))
+b = a.reshape((8, 1))
+print(b)
+```
+
+## --video-solution--
+
+1
+
--- a/curriculum/challenges/japanese/08-data-analysis-with-python/numpy/what-is-numpy.md
+++ b/curriculum/challenges/japanese/08-data-analysis-with-python/numpy/what-is-numpy.md
@@ -0,0 +1,38 @@
+---
+id: 5e9a0a8e09c5df3cc3600ed2
+title: Numpy とは
+challengeType: 11
+videoId: 5Nwfs5Ej85Q
+bilibiliIds:
+  aid: 293086867
+  bvid: BV1Tf4y1E7QZ
+  cid: 409024791
+dashedName: what-is-numpy
+---
+
+# --question--
+
+## --text--
+
+Numpy の配列が通常の Python のリストよりも高速なのはなぜですか？
+
+## --answers--
+
+Numpy はオブジェクトを繰り返し処理する際に型チェックを行わないから。
+
+---
+
+Numpy は固定の型を使用するから。
+
+---
+
+Numpy は連続したメモリを使用するから。
+
+---
+
+上記のすべて。
+
+## --video-solution--
+
+4
+