Numpy

ブロードキャスト

2017年12月3日 by 河副太智 Leave a Comment

<span role="presentation"><span class="cm-keyword">import</span> <span class="cm-variable">numpy</span> <span class="cm-keyword">as</span> <span class="cm-variable">np</span></span>

				1

						<span role="presentation"><span class="cm-keyword">import</span> <span class="cm-variable">numpy</span> <span class="cm-keyword">as</span> <span class="cm-variable">np</span></span>

				1

<span role="presentation"><span class="cm-comment"># Numpy配列xを生成</span></span>

				1

						<span role="presentation"><span class="cm-comment"># Numpy配列xを生成</span></span>

<span role="presentation"><span class="cm-variable">x</span> = <span class="cm-variable">np</span>.<span class="cm-property">arange</span>(<span class="cm-number">20</span>)</span>

				1

						<span role="presentation"><span class="cm-variable">x</span> = <span class="cm-variable">np</span>.<span class="cm-property">arange</span>(<span class="cm-number">20</span>)</span>

<span role="presentation"><span class="cm-variable">x</span> = <span class="cm-variable">x</span>.<span class="cm-property">reshape</span>(<span class="cm-number">4</span>, <span class="cm-number">5</span>)</span>

				1

						<span role="presentation"><span class="cm-variable">x</span> = <span class="cm-variable">x</span>.<span class="cm-property">reshape</span>(<span class="cm-number">4</span>, <span class="cm-number">5</span>)</span>

<span role="presentation">​
&gt;&gt;&gt;</span>

				1
2

						<span role="presentation">​
&gt;&gt;&gt;</span>

				1

[[ 0 1 2 3 4]
 [ 5 6 7 8 9]
 [10 11 12 13 14]
 [15 16 17 18 19]]

				1
2
3
4

						[[ 0 1 2 3 4]
 [ 5 6 7 8 9]
 [10 11 12 13 14]
 [15 16 17 18 19]]

<span role="presentation">

</span>

				1
2
3
4

						<span role="presentation">
 
</span>

<span role="presentation"><span class="cm-comment"># xの全ての要素に10を足す</span></span>

				1

						<span role="presentation"><span class="cm-comment"># xの全ての要素に10を足す</span></span>

<span role="presentation"><span class="cm-variable">x</span> = <span class="cm-variable">x</span> <span class="cm-operator">+</span> <span class="cm-number">10

&gt;&gt;&gt;
[[10 11 12 13 14]
 [15 16 17 18 19]
 [20 21 22 23 24]
 [25 26 27 28 29]]

</span></span>

				1
2
3
4
5
6
7
8
9
10
11
12

						<span role="presentation"><span class="cm-variable">x</span> = <span class="cm-variable">x</span> <span class="cm-operator">+</span> <span class="cm-number">10
 
&gt;&gt;&gt;
[[10 11 12 13 14]
 [15 16 17 18 19]
 [20 21 22 23 24]
 [25 26 27 28 29]]
 
</span></span>

				1

<span role="presentation">​</span>

				1

						<span role="presentation">​</span>

<span role="presentation"><span class="cm-comment"># 0から4の整数値をもつ1×4のNumpy配列yを生成</span></span>

				1

						<span role="presentation"><span class="cm-comment"># 0から4の整数値をもつ1×4のNumpy配列yを生成</span></span>

<span role="presentation"><span class="cm-variable">y</span> = <span class="cm-variable">np</span>.<span class="cm-property">array</span>([<span class="cm-variable">np</span>.<span class="cm-property">arange</span>(<span class="cm-number">5</span>)])</span>

				1

						<span role="presentation"><span class="cm-variable">y</span> = <span class="cm-variable">np</span>.<span class="cm-property">array</span>([<span class="cm-variable">np</span>.<span class="cm-property">arange</span>(<span class="cm-number">5</span>)])</span>

<span role="presentation">​&gt;&gt;&gt;[[0 1 2 3 4]]</span>

				1

						<span role="presentation">​&gt;&gt;&gt;[[0 1 2 3 4]]</span>

				1

<span role="presentation">​</span>

				1

						<span role="presentation">​</span>

<span role="presentation"><span class="cm-comment"># xのn番目の列のすべての行からnだけ引く</span></span>

				1

						<span role="presentation"><span class="cm-comment"># xのn番目の列のすべての行からnだけ引く</span></span>

<span role="presentation"><span class="cm-variable">x</span> = <span class="cm-variable">x</span> <span class="cm-operator">-</span> <span class="cm-variable">y</span></span>

				1

						<span role="presentation"><span class="cm-variable">x</span> = <span class="cm-variable">x</span> <span class="cm-operator">-</span> <span class="cm-variable">y</span></span>

[[10 10 10 10 10]
 [15 15 15 15 15]
 [20 20 20 20 20]
 [25 25 25 25 25]]

[[10 10 10 10 10]

[15 15 15 15 15]

[20 20 20 20 20]

[25 25 25 25 25]]

slice [x:y] スライス　破壊的操作を防ぐcopy()

2017年12月3日 by 河副太智 Leave a Comment

import numpy as np

arr = np.arange(10)
print (arr)>>>[0 1 2 3 4 5 6 7 8 9]

print (arr[3])>>>3
#リストはゼロから開始しているので指定時にマイナス１は不要

print(arr[5:8])>>>[5 6 7]

arr[5:8] = 99
print(arr)>>>[ 0 1 2 3 4 99 99 99 8 9]

slice = arr[5:8]
slice[1] = 55555 #0から数えるのでほしい数マイナス１の数を入れる
print (arr)>>>[ 0 1 2 3 4 99 55555 99 8 9]

slice[:] = 11111
print (arr)>>>[ 0 1 2 3 4 11111 11111 11111 8 9]

破壊的操作となるため、もとのデータをそのままにしたい場合は
arr[5:8].copy()
とする

例　copy()を使用

1	例　copy()を使用

import numpy as np

arr = np.arange(10)
print (arr)

print (arr[0])#リストはゼロから開始しているので指定時にマイナス１は不要

print(arr[5:8].copy())
print(arr)

abc=arr[5:8].copy()
print(abc)

slice = arr.copy()
slice[:] = 11111
print (slice)

print(arr)

reshape

2017年11月29日 by 河副太智 Leave a Comment

<span role="presentation"><span class="cm-keyword">リストを2次元配列にする

import</span> <span class="cm-variable">numpy</span> <span class="cm-keyword">as</span> <span class="cm-variable">np</span></span>

				1
2
3

						<span role="presentation"><span class="cm-keyword">リストを2次元配列にする
 
import</span> <span class="cm-variable">numpy</span> <span class="cm-keyword">as</span> <span class="cm-variable">np</span></span>

<span role="presentation"><span class="cm-variable">a</span> = <span class="cm-variable">np</span>.<span class="cm-property">arange</span>(<span class="cm-number">15</span>)</span>

				1

						<span role="presentation"><span class="cm-variable">a</span> = <span class="cm-variable">np</span>.<span class="cm-property">arange</span>(<span class="cm-number">15</span>)</span>

<span role="presentation"><span class="cm-variable">b</span>=<span class="cm-variable">a</span>.<span class="cm-property">reshape</span>(<span class="cm-number">3</span>,<span class="cm-number">5</span>)</span>

				1

						<span role="presentation"><span class="cm-variable">b</span>=<span class="cm-variable">a</span>.<span class="cm-property">reshape</span>(<span class="cm-number">3</span>,<span class="cm-number">5</span>)</span>

<span role="presentation">​</span>

				1

						<span role="presentation">​</span>

<span role="presentation"><span class="cm-builtin">print</span><span class=" CodeMirror-matchingbracket">(</span><span class="cm-variable">b</span><span class=" CodeMirror-matchingbracket">)</span></span>

				1

						<span role="presentation"><span class="cm-builtin">print</span><span class=" CodeMirror-matchingbracket">(</span><span class="cm-variable">b</span><span class=" CodeMirror-matchingbracket">)</span></span>

<span role="presentation">​</span>

				1

						<span role="presentation">​</span>

[[ 0  1  2  3  4]
 [ 5  6  7  8  9]
 [10 11 12 13 14]]

[[ 0 1 2 3 4]

[ 5 6 7 8 9]

[10 11 12 13 14]]

arrange アレンジ

2017年11月29日 by 河副太智 Leave a Comment

#アレンジは変数内の数字のカンマなしリストを生成

import numpy as np
a = np.arange(15)

print(a)

[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14]

1	[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14]

fillna欠損値と直前のデータを補完

2017年11月29日 by 河副太智 Leave a Comment

import numpy as np
from numpy import nan as NA
import pandas as pd

aa = pd.DataFrame(np.random.rand(10,4))

#一部のデータNanにする
sample_data_frame.iloc[1,0] = NA
sample_data_frame.iloc[2,2] = NA
sample_data_frame.iloc[5:,3] = NA

import numpy as np

from numpy import nan as NA

import pandas as pd

aa = pd.DataFrame(np.random.rand(10,4))

#一部のデータNanにする

sample_data_frame.iloc[1,0] = NA

sample_data_frame.iloc[2,2] = NA

sample_data_frame.iloc[5:,3] = NA

fillna()を用いると、引数として与えた数をNaNの部分に代入、
この例では0を代入

sample_data_frame.fillna(0)

1 2	sample_data_frame.fillna(0)

methodをにffillを指定し、上の値がそのままコピーされる

<span class="cm-variable">sample_data_frame</span>.<span class="cm-property">fillna</span>(<span class="cm-variable">method</span>=<span class="cm-string">"ffill"</span>)

1 2	<span class="cm-variable">sample_data_frame</span>.<span class="cm-property">fillna</span>(<span class="cm-variable">method</span>=<span class="cm-string">"ffill"</span>)

#欠損値処理
df['Fare'] = df['Fare'].fillna(df['Fare'].median())#Fareの平均値を代入
df['Age'] = df['Age'].fillna(df['Age'].median())#Ageの平均値を代入
df['Embarked'] = df['Embarked'].fillna('S')#Embarkedの値はQ,O,Sのどれかなので代表的なSを代入
df=df.dropna(subset=['Age'])#AgeのカラムにNanがある物を削除

#欠損値処理

df['Fare'] = df['Fare'].fillna(df['Fare'].median())#Fareの平均値を代入

df['Age'] = df['Age'].fillna(df['Age'].median())#Ageの平均値を代入

df['Embarked'] = df['Embarked'].fillna('S')#Embarkedの値はQ,O,Sのどれかなので代表的なSを代入

df=df.dropna(subset=['Age'])#AgeのカラムにNanがある物を削除

dropna() データを欠損させ、その行を削除

2017年11月29日 by 河副太智 Leave a Comment

<span class="cm-keyword">import</span> <span class="cm-variable">numpy</span> <span class="cm-keyword">as</span> <span class="cm-variable">np</span>
<span class="cm-keyword">from</span> <span class="cm-variable">numpy</span> <span class="cm-keyword">import</span> <span class="cm-variable">nan</span> <span class="cm-keyword">as</span> <span class="cm-variable">NA</span>
<span class="cm-keyword">import</span> <span class="cm-variable">pandas</span> <span class="cm-keyword">as</span> <span class="cm-variable">pd</span>

<span class="cm-variable">sample_data_frame</span> = <span class="cm-variable">pd</span>.<span class="cm-property">DataFrame</span>(<span class="cm-variable">np</span>.<span class="cm-property">random</span>.<span class="cm-property">rand</span>(<span class="cm-number">10</span>,<span class="cm-number">4</span>))

<span class="cm-comment">#一部のデータをわざと欠損</span>
<span class="cm-variable">sample_data_frame</span>.<span class="cm-property">iloc</span>[<span class="cm-number">1</span>,<span class="cm-number">0</span>] = <span class="cm-variable">NA</span>


<span class="cm-variable">sample_data_frame</span>

import numpy as np

from numpy import nan as NA

import pandas as pd

sample_data_frame = pd.DataFrame(np.random.rand(10,4))

#一部のデータをわざと欠損

sample_data_frame.iloc[1,0] = NA

sample_data_frame

dropnaを用いると、NaNのある行を全て取り除く

<span class="cm-variable">sample_data_frame</span>.<span class="cm-property">dropna</span>()

1 2	<span class="cm-variable">sample_data_frame</span>.<span class="cm-property">dropna</span>()

ペアワイズ削除

<span class="cm-variable">sample_data_frame</span>[[<span class="cm-number">0</span>,<span class="cm-number">1</span>]].<span class="cm-property">dropna</span>()
</code><code class="cm-s-ipython language-python">

sample_data_frame[[0,1]].dropna()

</code><code class="cm-s-ipython language-python">