July 2018
Intermediate to advanced
596 pages
13h 35m
Japanese
Content preview from Pythonによるデータ分析入門 第2版 ―NumPy、pandasを使ったデータ処理
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132
4
章
NumPy
の基礎：配列とベクトル演算
しょう。まず思いつくのはブールインデックス参照を用いる方法で、
np.abs(walk) >=10
として
1,000
要素を持つ真偽値配列を得ることができます。この各要素には、それぞれの時刻の到達点が
10
もしく
は−
10
より大きいかどうかが格納されます。ただ、本当に知りたかったのは最初に
10
もしくは−
10
に
到達したときがいつか、つまりそのときの配列のインデックスがいくつか、ということです。そこで
argmax
を活用します。
argmax
は配列内の最大値のうち、一番若いインデックスを戻します。真偽値配
列の最大値は
True
そのものであるため、今回の目的に合致します。
In [257]: (np.abs(walk) >= 10).argmax()
Out[257]: 37
一点だけ、
argmax
を用いる方法が常に最適ではないことに注意してください。
argmax
を呼び出すと
配列要素の全スキャンが発生します。今回の例
では、配列内の要素の最大値が
True
であると事前にわ
かっていました。このため
True
が見つかった時点で処理を中断でき、全スキャンの必要はありません
でした。
4.7.1
多重ランダムウォーク
今回のゴールがランダムウォークの複数回の試行であり、例えば
5,000
回の試行であるとします。こ
れは上記のコードを手直しするだけで実現可能です。
numpy.random
モジュールの関数群は数値の
2
つ
組（
2-tuple
、
2
つの数字でできた組）を受け取ると、その
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