def test_data_estat_data(self):
ESTAT_KEY = os.environ['ESTAT']
df = jpd.DataReader('0000030001', 'estat', appid=ESTAT_KEY)
exp = pd.DataFrame(
{
'value': [117060396, 89187409, 27872987, 5575989, 1523907],
'全国都道府県030001': ['全国', '全国市部', '全国郡部', '北海道', '青森県'],
'全域・集中の別030002': ['全域'] * 5,
'年齢５歳階級Ａ030002': ['総数'] * 5,
'男女Ａ030001': ['男女総数'] * 5
},
index=pd.DatetimeIndex(['1980-01-01'] * 5, name='時間軸(年次)'))
self.assert_frame_equal(df.head(), exp)
df = jpd.DataReader(['0000030001', '0000030002'],
'estat',
appid=ESTAT_KEY)
self.assertIsInstance(df, pd.DataFrame)
df = jpd.DataReader("0002180001", 'estat', appid=ESTAT_KEY)
exp = pd.DataFrame(
{
'value': [445007, 194243, 199623, 203464, 190711],
'全国・都道府県・大都市': ['全国'] * 5,
'性別': ['総数'] * 5,
'表章項目': ['都道府県（自都市）内移動者数'] * 5
},
index=pd.DatetimeIndex([
'2009-03-01', '2009-02-01', '2009-01-01', '2008-12-01',
'2008-11-01'
],
name='時間軸（月次）'))
self.assert_frame_equal(df.head(), exp)
def test_data_limit(self):
ESTAT_KEY = os.environ['ESTAT']
df = jpd.DataReader('0003280394', 'estat', appid=ESTAT_KEY)
assert len(df) == 100000
self.assertEqual(df['value'].dtype, np.float64)
df = jpd.DataReader('0003280394', 'estat', appid=ESTAT_KEY, limit=20)
assert len(df) == 20
self.assertEqual(df['value'].dtype, np.float64)
def _assert_target(self, target):
ESTAT_KEY = os.environ['ESTAT']
df = jpd.DataReader(target, 'estat', appid=ESTAT_KEY)
exp_columns = pd.Index([
'統計表ID', '政府統計名', '作成機関名', '提供統計名及び提供分類名', '統計表題名及び表番号', '提供周期',
'調査年月', '公開日', '小地域属性フラグ', '統計大分野名', '統計小分野名', '総件数', '最終更新日'
], )
self.assert_index_equal(df.columns, exp_columns)
target = df.head(n=3)
df = jpd.DataReader(target, 'estat', appid=ESTAT_KEY)
self.assertIsInstance(df, pd.DataFrame)
def test_data_position(self):
ESTAT_KEY = os.environ['ESTAT']
df = jpd.DataReader('0003280394', 'estat', appid=ESTAT_KEY, limit=100)
assert len(df) == 100
self.assertEqual(df['value'].dtype, np.float64)
df2 = jpd.DataReader('0003280394',
'estat',
appid=ESTAT_KEY,
startPosition=11,
limit=90)
tm.assert_frame_equal(df.iloc[10:], df2)
self.assertEqual(df2['value'].dtype, np.float64)
def test_data_estat_error(self):
with tm.assertRaises(ValueError):
# no app ID
jpd.DataReader('00200521', 'estat', appid=None)
ESTAT_KEY = os.environ['ESTAT']
with tm.assertRaises(ValueError):
# blank list
jpd.DataReader([], 'estat', appid=ESTAT_KEY)
with tm.assertRaises(ValueError):
# invalid type
jpd.DataReader(1, 'estat', appid=ESTAT_KEY)
def DataRead(id_, ):
"""
Read eStat datalist or data.
"""
id_len = len(id_)
if (id_len != 8) and (id_len != 10):
raise ValueError('Length of id must be either 8 or 10.')
data = jpd.DataReader(id_, 'estat', appid=keys.jpd_key)
# in case the data large than the limit 100_000 once
n = 1
while len(data) % 100_000 == 0:
n = n + 100_000
data_more = jpd.DataReader(id_, 'estat', appid=keys.jpd_key, startPosition=n)
data = pd.concat([data, data_more], sort=False, ignore_index=True)
print('.', end=' ')
def test_data_yahoojp_en(self):
idx = pd.DatetimeIndex(['2014-10-01', '2014-10-02', '2014-10-03'],
name='Date')
columns = jpd.io.data._ohlc_columns_en
expected = pd.DataFrame(
{
'Open': [6450, 6370, 6231],
'High': [6559, 6423, 6309],
'Low': [6435, 6256, 6217],
'Close': [6500, 6275, 6290],
'Volume': [14482100, 15240200, 10280100],
'Adj Close': [6500, 6275, 6290]
},
index=idx,
columns=columns)
df = jpd.DataReader(7203,
'yahoojp',
start='2014-10-01',
end='2014-10-05',
en=True)
tm.assert_frame_equal(df, expected)
_check_plot_works(df.plot, kind='ohlc')
def test_data_estat_data_numeric(self):
ESTAT_KEY = os.environ['ESTAT']
df = jpd.DataReader('0003109612', 'estat', appid=ESTAT_KEY)
self.assertEqual(df['value'].dtype, np.float64)
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from spyre import server
import pandas as pd
pd.options.display.mpl_style = 'default'
# あらかじめデータを取得しておく
# 終値のみを取得し、一つのDataFrameに結合
import japandas as jpd
toyota = jpd.DataReader(7203, 'yahoojp', start='2015-01-01')[[u'終値']]
toyota.columns = [u'トヨタ']
honda = jpd.DataReader(7267, 'yahoojp', start='2015-01-01')[[u'終値']]
honda.columns = [u'ホンダ']
df = toyota.join(honda)
# spyre.server.App を継承したクラスを作る
class StockExample(server.App):
title = u"株価のプロット"
# 左側のペインに表示する UI 要素を辞書のリストで指定
# ここではドロップダウン一つだけを表示
inputs = [{
"input_type":
'dropdown',
# ドロップダウン自体の表示ラベル
"label":
'Frequency',
def test_to_yahoojp(self):
idx = pd.DatetimeIndex(['2014-10-01', '2014-10-02', '2014-10-03'],
name='日付')
columns = jpd.io.data._ohlc_columns_jp
expected = pd.DataFrame(
{
'始値': [6450, 6370, 6231],
'高値': [6559, 6423, 6309],
'安値': [6435, 6256, 6217],
'終値': [6500, 6275, 6290],
'出来高': [14482100, 15240200, 10280100],
'調整後終値*': [6500, 6275, 6290]
},
index=idx,
columns=columns)
df = jpd.DataReader(7203,
'yahoojp',
start='2014-10-01',
end='2014-10-05')
tm.assert_frame_equal(df, expected)
_check_plot_works(df.plot, kind='ohlc')
df = jpd.DataReader(7203,
'yahoojp',
start='2014-10-01',
end='2014-10-05',
interval='d')
tm.assert_frame_equal(df, expected)
df = jpd.DataReader(7203,
'yahoojp',
start='2014-10-01',
end='2014-10-05',
interval='v')
tm.assert_frame_equal(df, expected)
idx = pd.DatetimeIndex(['2014-10-06', '2014-10-14'], name='日付')
expected = pd.DataFrame(
{
'始値': [6370, 6050],
'高値': [6455, 6069],
'安値': [6145, 5710],
'終値': [6220, 5731],
'出来高': [51266400, 50540700],
'調整後終値*': [6220, 5731]
},
index=idx,
columns=columns)
df = jpd.DataReader(7203,
'yahoojp',
start='2014-10-01',
end='2014-10-15',
interval='w')
tm.assert_frame_equal(df, expected)
idx = pd.DatetimeIndex(['2014-10-01', '2014-11-01', '2014-12-01'],
name='日付')
expected = pd.DataFrame(
{
'始値': [6450, 6830, 7360],
'高値': [6559, 7314, 7873],
'安値': [5710, 6696, 7107],
'終値': [6498, 7314, 7558],
'出来高': [233353400, 232048700, 244583800],
'調整後終値*': [6498, 7314, 7558]
},
index=idx,
columns=columns)
df = jpd.DataReader(7203,
'yahoojp',
start='2014-10-01',
end='2014-12-31',
interval='m')
tm.assert_frame_equal(df, expected)
