import pandas as pd
from sklearn import svm, metrics, model_selection, datasets

# アヤメのCSVデータを読み込む
iris = datasets.load_iris()

# 学習用とテスト用に分離する
train_data, test_data, train_label, test_label = model_selection.train_test_split(iris.data, iris.target, test_size=0.2, train_size=0.8, shuffle = True)

# データの学習
clf = svm.SVC()
clf.fit(train_data, train_label)

# データを予測
pre = clf.predict(test_data)

# 正答率を求める
ac_score = metrics.accuracy_score(test_label, pre)
print('正解率：', ac_score)

import pandas as pd                 # 追加
from sklearn import svm, metrics    # metrics追加

# XORの演算と結果
xor_data = [
    [0, 0, 0],
    [0, 1, 1],
    [1, 0, 1],
    [1, 1, 0]
]

# 学習させるためにデータとラベルに分ける

# [変更前]
#data = []
#label = []
#for row in xor_data:
#    data.append([row[0], row[1]])
#    label.append(row[2])

# [変更後]
xor_df = pd.DataFrame(xor_data)
data  = xor_df.iloc[:,0:2]    # データ
label = xor_df.iloc[:,2]      # ラベル

# データの学習
clf = svm.SVC()
clf.fit(data, label)

# データを予測
pre = clf.predict(data)
print('予測結果:', pre)

# 正解と合っているか結果を確認

# [変更前]
#cnt_ok = 0
#total = 0
#for idx, ans in enumerate(label):
#    if pre[idx] == ans:
#        cnt_ok += 1
#    total += 1
#print('正解率：{} / {}'.format(cnt_ok, cnt_ok / total))

# [変更後]
ac_score = metrics.accuracy_score(label, pre)
print('正解率：', ac_score)

from sklearn import svm

# XORの演算と結果
xor_data = [
    [0, 0, 0],
    [0, 1, 1],
    [1, 0, 1],
    [1, 1, 0]
]

# 学習させるためにデータとラベルに分ける
data = []
label = []

for row in xor_data:
    data.append([row[0], row[1]])
    label.append(row[2])

# データの学習
clf = svm.SVC()
clf.fit(data, label)

# データを予測
pre = clf.predict(data)
print('予測結果:', pre)

# 正解と合っているか結果を確認
cnt_ok = 0
total = 0

for idx, ans in enumerate(label):
    if pre[idx] == ans:
        cnt_ok += 1
    total += 1

print('正解率：{} / {}'.format(cnt_ok, cnt_ok / total))

import pandas as pd

book = pd.read_excel('data.xlsx', sheet_name='Sheet1')
print(book)
print()

print(book.sort_values(by='データ１', ascending=False))

import csv

with open('test.csv', 'w') as f:
    writer = csv.writer(f, delimiter=',', quotechar='"',lineterminator='\n')
    writer.writerow(['ID', '商品名', '値段'])
    writer.writerow(['10', 'SDカード',          '500'])
    writer.writerow(['20', 'マウス',            '300'])
    writer.writerow(['30', 'マジック（赤,青）', '150'])

import csv

with open('test.csv', 'r') as f:
    reader = csv.reader(f, delimiter=',', quotechar='"')
    for cells in reader:
        print(cells)

import yaml

data = [
    {"name":"Yamada", "age":"33", "gender":"man"},
    {"name":"Sato",   "age":"58", "gender":"woman"},
    {"name":"Kato",   "age":"42", "gender":"man"},
    {"name":"Nishi",  "age":"22", "gender":"man"}
]

# PythonのデータをYAMLに変換
str = yaml.dump(data)
print(str)
print('--- --- ---')

# YAMLをPythonデータに変換
data1 = yaml.load(str)

# Pythonデータを表示
for p in data1:
    print(p)

pip install pyyaml

import yaml

str = '''
Date: 2020-04-15
PriceList:
    -
        item_id: 1
        name: Banana
        color: yellow
        price: 150
    -
        item_id: 2
        name: Orange
        color: orange
        price: 300
    -
        item_id: 3
        name: Apple
        color: red
        price: 200
'''
# YAMLを解析
data = yaml.load(str)

print(data['Date'])
print()

for d1 in data['PriceList']:
    print(d1)

import urllib.request as req
import os.path, random
import json

# JSONをダウンロード
url = 'http://api.aoikujira.com/hyakunin/get.php?fmt=json'
savename = 'hyakunin.json'
if not os.path.exists(savename):
    req.urlretrieve(url, savename)

# JSONファイルを解析
data = json.load(open(savename, 'r', encoding='utf-8'))

# ランダムに一首表示
for i in range(3):
    r = random.choice(data)
    print(r['kami'], r['simo'],)

from bs4 import BeautifulSoup
import urllib.request as req
import os.path

# XMLをダウンロード
url = 'http://archive.city.yokohama.lg.jp/somu/org/kikikanri/data/shelter.xml'
savename = 'shelter.xml'
if not os.path.exists(savename):
    req.urlretrieve(url, savename)

# BeautifulSoupでXMLを解析
xml = open(savename, 'r', encoding='utf-8').read()
soup = BeautifulSoup(xml, 'html.parser')

# データを各区ごとに確
info = {}
for i in soup.find_all('shelter'):
    name = i.find('name').string
    ward = i.find('ward').string
    addr = i.find('address').string
    note = i.find('notes').string
    if not (ward in info):
        info[ward] = []
    info[ward].append(name)

# 区ごとに防災拠点を表示
for ward in info.keys():
    print('+', ward)
    for name in info[ward]:
        print('| - ', name)

pip install beautifulsoup4

<td class="stoksPrice">108.460000</td>

from bs4 import BeautifulSoup
import urllib.request as req

url = 'http://stocks.finance.yahoo.co.jp/stocks/detail/?code=usdjpy'
res = req.urlopen(url)

soup = BeautifulSoup(res, 'html.parser')

price = soup.select_one('.stoksPrice').string
print('usdjpy=', price)