<Python, selenium> 空白のあるクラス名を選択するには、、
Seleniumで、空白のあるクラス名を選択するには、、、 how to select the class name having a blank in it?
まずは、xpathを使う。
In [133]: from selenium import webdriver In [144]: d = webdriver.Chrome() In [145]: d.get('http://nekoyukimmm.hatenablog.com/entry/2017/04/27/164336') In [146]: d.find_element_by_xpath('//*[@id="box2-inner"]/div[3]') Out[146]: <selenium.webdriver.remote.webelement.WebElement (session="a489f57900d9be9af6a6356176a89bd9", element="0.23540204403336795-1")> In [148]: d.find_element_by_xpath('//*[@id="box2-inner"]/div[3]').text Out[148]: 'カテゴリー\nselenium (2) Python (305) sentdex (2) pandas (86) Office2016 (1) numpy (7) pyplot (4) Javascript (15) jQuery (11) JSFiddle (3) flask (26) Vim (62) gspread (2) requests (2) Json (7) Google Cloud Platform (4) dos (5) conda (7) Github (11) msys2 (21) Anaconda (5) handsontable (1) Bootstrap (1) Google App Engine (5) Windows (24) matplotlib (27) seaborn (12) ATOM (5) datetime (2) SQLite (7) peewee (2) Apache (1) Beautiful Soup (7) Visual Studio (1) Linux (29) ftp (1) Werkzeug (1) html (11) tqdm (1) Unix Command (2) ssh (2) zsh (10) iPython (22) pacman (4) Cheatsheet (5) css (5) regexp (8) jinja (2) highcharts (8) socket (2) mermaid (2) Markdown (7) CDN (1) Bash (20) Network (2) Outlook (3) Pygments (1) Web (1) tmux (2) pip (5) Solarized (4) KDE (1) mintty (2) Putty (1) Go (2) peco (2) dotfiles (1) Jupyter (5) VBA (5) Excel (7) Surfingkeys (2) NeoBundle (2) SystemVerilog (1) x240 (4) MinGW+mintty (8) Sphinx (2) X (2) japandas (1) urllib (2) readability-lxml (2) html2text (1) Chrome (2) plotly (4) statistics (1) Office2013 (4) cron (1) bokeh (1) pptx (4) PIL (2) pdb (1) docopt (1) cgi (1) Jedi (4) XML (1) mpld3 (2) tput (1) iconv (1) mutt (1) Vundle (1) sed (1) Gow (2) ggplot (1) Perl (3)'
で、find_element_by_class_nameをしてみる。
ゲロ、、エラー。
In [155]: d.find_element_by_class_name('hatena-module hatena-module-category') --------------------------------------------------------------------------- InvalidSelectorException Traceback (most recent call last)
その場合は、find_element_by_xpathで、div[@class='hage hage']とするらしい。
In [157]: d.find_element_by_xpath('//div[@class="hatena-module hatena-module-category"]') Out[157]: <selenium.webdriver.remote.webelement.WebElement (session="a489f57900d9be9af6a6356176a89bd9", element="0.23540204403336795-1")> In [158]: d.find_element_by_xpath('//div[@class="hatena-module hatena-module-category"]').text Out[158]: 'カテゴリー\nselenium (2) Python (305) sentdex (2) pandas (86) Office2016 (1) numpy (7) pyplot (4) Javascript (15) jQuery (11) JSFiddle (3) flask (26) Vim (62) gspread (2) requests (2) Json (7) Google Cloud Platform (4) dos (5) conda (7) Github (11) msys2 (21) Anaconda (5) handsontable (1) Bootstrap (1) Google App Engine (5) Windows (24) matplotlib (27) seaborn (12) ATOM (5) datetime (2) SQLite (7) peewee (2) Apache (1) Beautiful Soup (7) Visual Studio (1) Linux (29) ftp (1) Werkzeug (1) html (11) tqdm (1) Unix Command (2) ssh (2) zsh (10) iPython (22) pacman (4) Cheatsheet (5) css (5) regexp (8) jinja (2) highcharts (8) socket (2) mermaid (2) Markdown (7) CDN (1) Bash (20) Network (2) Outlook (3) Pygments (1) Web (1) tmux (2) pip (5) Solarized (4) KDE (1) mintty (2) Putty (1) Go (2) peco (2) dotfiles (1) Jupyter (5) VBA (5) Excel (7) Surfingkeys (2) NeoBundle (2) SystemVerilog (1) x240 (4) MinGW+mintty (8) Sphinx (2) X (2) japandas (1) urllib (2) readability-lxml (2) html2text (1) Chrome (2) plotly (4) statistics (1) Office2013 (4) cron (1) bokeh (1) pptx (4) PIL (2) pdb (1) docopt (1) cgi (1) Jedi (4) XML (1) mpld3 (2) tput (1) iconv (1) mutt (1) Vundle (1) sed (1) Gow (2) ggplot (1) Perl (3)'
できた。
さんくー、スタックオーバーフロー。
<Python, selenium> Chromeを動かしてみた。
ちょと、seleniumを試す。
>pip install selenium
で、下記から、chromedriver.exeをゲットする。
そいつをパスpathが通っている、/usr/local/binに放り込む。
で、
In [1]: from selenium import webdriver In [6]: d = webdriver.Chrome() In [7]: d.get('http://www.yahoo.co.jp') In [9]: d.quit()
お、動く。
<Python, pandas, sentdex> resample
目的とするDataFrameから、値を抜き取りする、リサンプリング resample をしてみた。
In [20]: import datetime as dt In [21]: import pandas as pd In [22]: import pandas_datareader.data as web In [23]: s = dt.datetime(2000,1,1) In [24]: e = dt.datetime(2016,12,31) In [30]: df = web.DataReader('TXN', 'yahoo', s, e) In [31]: df.resample('M').mean().head() Out[31]: Open High Low Close Volume \ Date 2000-01-31 105.481250 107.803125 102.500000 105.242965 9585750 2000-02-29 138.125000 143.434375 135.156250 140.340625 10091150 2000-03-31 172.709239 177.774457 165.434783 171.730978 11085226 2000-04-30 150.115132 156.256579 144.059211 150.125000 12271800 2000-05-31 126.215909 128.931818 121.397727 124.840909 8540727 Adj Close Date 2000-01-31 41.146986 2000-02-29 54.886878 2000-03-31 67.163569 2000-04-30 58.715439 2000-05-31 56.198717 In [32]: df['Adj Close'].resample('M').mean().head() Out[32]: Date 2000-01-31 41.146986 2000-02-29 54.886878 2000-03-31 67.163569 2000-04-30 58.715439 2000-05-31 56.198717 Freq: M, Name: Adj Close, dtype: float64 In [33]: df['Adj Close'].resample('M').ohlc().head() Out[33]: open high low close Date 2000-01-31 40.218784 44.519213 36.553646 42.140764 2000-02-29 44.242913 64.971085 44.242913 64.971085 2000-03-31 64.286664 73.379683 57.491344 62.575612 2000-04-30 58.957959 65.117746 54.362563 63.718405 2000-05-31 62.593675 62.593675 50.074940 56.529912
いつものお世話になったところ。
マニュアル。
pandas.DataFrame.resample — pandas 0.19.2 documentation
アンカー Anchor の表。
http://pandas.pydata.org/pandas-docs/stable/timeseries.html#anchored-offsets
<pandas, Python, sentdex> Python Programming for Finance
やってみた。
In [1]: import datetime as dt In [2]: import matplotlib.pyplot as plt In [3]: from matplotlib import style In [4]: import pandas as pd In [5]: import pandas_datareader.data as web In [6]: style.use('ggplot') In [7]: s = dt.datetime(2000,1,1) In [8]: e = dt.datetime(2016,12,31) In [9]: df = web.DataReader('TSLA', 'yahoo', s, e) In [10]: df.head() Out[10]: Open High Low Close Volume Adj Close Date 2010-06-29 19.000000 25.00 17.540001 23.889999 18766300 23.889999 2010-06-30 25.790001 30.42 23.299999 23.830000 17187100 23.830000 2010-07-01 25.000000 25.92 20.270000 21.959999 8218800 21.959999 2010-07-02 23.000000 23.10 18.709999 19.200001 5139800 19.200001 2010-07-06 20.000000 20.00 15.830000 16.110001 6866900 16.110001
ふーん、なるほど。
続いてその３もやった。
In [11]: df['100ma'] = df['Adj Close'].rolling(window=100, min_periods=0).mean() In [12]: df.head() Out[12]: Open High Low Close Volume Adj Close \ Date 2010-06-29 19.000000 25.00 17.540001 23.889999 18766300 23.889999 2010-06-30 25.790001 30.42 23.299999 23.830000 17187100 23.830000 2010-07-01 25.000000 25.92 20.270000 21.959999 8218800 21.959999 2010-07-02 23.000000 23.10 18.709999 19.200001 5139800 19.200001 2010-07-06 20.000000 20.00 15.830000 16.110001 6866900 16.110001 100ma Date 2010-06-29 23.889999 2010-06-30 23.860000 2010-07-01 23.226666 2010-07-02 22.220000 2010-07-06 20.998000 In [13]: ax1 = plt.subplot2grid((6,1), (0,0), rowspan=5, colspan=1) In [14]: ax2 = plt.subplot2grid((6,1), (5,0), rowspan=1, colspan=1, sharex=ax1) In [15]: ax1.plot(df.index, df['Adj Close']) Out[15]: [<matplotlib.lines.Line2D at 0xb9c5c88>] In [16]: ax1.plot(df.index, df['100ma']) Out[16]: [<matplotlib.lines.Line2D at 0xb463f98>] In [17]: ax2.bar(df.index, df['Volume']) Out[17]: <Container object of 1640 artists> In [18]: plt.show()
<Python, pandas> 縦にずらす。
縦にずらす。
In [22]: df = pd.DataFrame({'a':[1,2,3,4,5,6]}) In [23]: df Out[23]: a 0 1 1 2 2 3 3 4 4 5 5 6 In [24]: df.shift(-1) Out[24]: a 0 2.0 1 3.0 2 4.0 3 5.0 4 6.0 5 NaN In [25]: df.shift(1) Out[25]: a 0 NaN 1 1.0 2 2.0 3 3.0 4 4.0 5 5.0
ふーん。
横にもずらせる。
In [26]: df = pd.DataFrame([[1,2,3],[4,5,6]]) In [27]: df Out[27]: 0 1 2 0 1 2 3 1 4 5 6 In [28]: df.shift(-1,axis=1) Out[28]: 0 1 2 0 2.0 3.0 NaN 1 5.0 6.0 NaN In [29]: df.shift(-1,axis=0) Out[29]: 0 1 2 0 4.0 5.0 6.0 1 NaN NaN NaN
なるへそ。
シフトshiftのマニュアル。
<Python, numpy> 無限大
知ってましたか？
pythonで無限大は、np.infか、float('inf')で表現するらしいっす。
In [1]: float('inf') Out[1]: inf In [2]: float('inf') == 0 Out[2]: False In [3]: float('inf') < 1 Out[3]: False In [4]: float('inf') > 1 Out[4]: True In [5]: import numpy as np In [6]: np.inf Out[6]: inf In [7]: float('inf') == np.inf Out[7]: True In [8]: np.inf < 50000000 * 500000000 Out[8]: False In [9]: -np.inf Out[9]: -inf In [10]: -np.inf < 0 Out[10]: True In [11]: type(np.inf) Out[11]: float In [12]: np.isinf(np.inf) Out[12]: True In [13]: np.isinf(float('inf')) Out[13]: True
numpy.isinfのマニュアル。
numpy.isinf — NumPy v1.12 Manual
追加。
In [14]: 0 / np.inf Out[14]: 0.0 In [15]: np.inf / np.inf Out[15]: nan In [16]: 1 / np.inf Out[16]: 0.0 In [17]: np.inf - np.inf Out[17]: nan In [18]: 1 * np.inf Out[18]: inf In [19]: 0 * np.inf Out[19]: nan In [20]: np.inf * np.inf Out[20]: inf
<Python, pandas> 日経平均を読み込む。
日経平均N225を読み込む。
pandas-datareaderを使う。
pandas-datareader — pandas-datareader 0.1 documentation
まずはインストール。
% conda install pandas-datareader Fetching package metadata ......... Solving package specifications: .......... Package plan for installation in environment C:\Anaconda3: The following packages will be downloaded: package | build ---------------------------|----------------- requests-file-1.4.1 | py35_0 6 KB pandas-datareader-0.2.1 | py35_0 49 KB ------------------------------------------------------------ Total: 55 KB The following NEW packages will be INSTALLED: pandas-datareader: 0.2.1-py35_0 requests-file: 1.4.1-py35_0 Proceed ([y]/n)? y Fetching packages ... requests-file- 100% |###############################| Time: 0:00:00 829.29 kB/s pandas-datarea 100% |###############################| Time: 0:00:00 1.85 MB/s Extracting packages ... [ COMPLETE ]|##################################################| 100% Linking packages ... [ COMPLETE ]|##################################################| 100%
で、使う。
In [1]: import pandas_datareader.data as web In [2]: import datetime In [3]: s = datetime.datetime(2010,1,1) In [4]: e = datetime.datetime(2013,1,1) In [5]: web.DataReader('F', 'yahoo', s, e) Out[5]: Open High Low Close Volume Adj Close Date 2010-01-04 10.17 10.28 10.05 10.28 60855800 8.554412 2010-01-05 10.45 11.24 10.40 10.96 215620200 9.120268 2010-01-06 11.21 11.46 11.13 11.37 200070600 9.461446 2010-01-07 11.46 11.69 11.32 11.66 130201700 9.702767 2010-01-08 11.67 11.74 11.46 11.69 130463000 9.727731 2010-01-11 11.90 12.14 11.78 12.11 170626200 10.077230 2010-01-12 11.98 12.03 11.72 11.87 162995900 9.877516 2010-01-13 11.91 11.93 11.47 11.68 154527100 9.719410 2010-01-14 11.65 11.86 11.51 11.76 116531200 9.785981 2010-01-15 11.74 11.76 11.55 11.60 96149800 9.652838 2010-01-19 11.51 11.83 11.46 11.75 65934000 9.777659 2010-01-20 11.68 11.69 11.50 11.51 71649500 9.577946 2010-01-21 11.53 11.62 11.01 11.18 121451400 9.303339 2010-01-22 11.01 11.12 10.41 10.52 161530100 8.754126 2010-01-25 10.73 11.10 10.61 11.03 121621500 9.178517 2010-01-26 11.17 11.46 11.07 11.19 108250500 9.311660 2010-01-27 11.57 11.62 11.22 11.55 105091600 9.611231 2010-01-28 11.90 11.95 11.27 11.41 203320000 9.494731 2010-01-29 11.60 11.61 10.70 10.84 159741200 9.020411 2010-02-01 11.14 11.18 10.93 11.12 82748200 9.253410 2010-02-02 11.26 11.52 11.19 11.39 119714900 9.478089 2010-02-03 11.49 11.66 11.42 11.64 90125500 9.686124 2010-02-04 11.49 11.53 11.00 11.06 129792200 9.203482 2010-02-05 10.97 11.11 10.49 10.91 181535200 9.078661 2010-02-08 11.09 11.32 10.88 10.97 92031400 9.128589 2010-02-09 11.18 11.22 11.02 11.15 83207100 9.278374 2010-02-10 11.12 11.14 10.90 10.94 73395600 9.103625 2010-02-11 11.00 11.19 10.88 11.18 65116200 9.303339 2010-02-12 10.92 11.18 10.85 11.12 69465400 9.253410 2010-02-16 11.21 11.38 11.11 11.32 62537500 9.419838 ... ... ... ... ... ... ... 2012-11-16 10.58 10.64 10.38 10.50 45346200 8.900635 2012-11-19 10.65 10.90 10.65 10.83 39359100 9.180370 2012-11-20 10.85 11.02 10.76 10.85 34739800 9.197323 2012-11-21 10.84 11.00 10.80 10.92 21181700 9.256661 2012-11-23 10.98 11.10 10.96 11.10 16032200 9.409243 2012-11-26 11.05 11.14 10.97 11.11 26831700 9.417720 2012-11-27 11.10 11.27 11.10 11.10 37610000 9.409243 2012-11-28 11.05 11.26 10.98 11.25 38496900 9.536395 2012-11-29 11.32 11.53 11.32 11.53 57289300 9.773745 2012-11-30 11.52 11.60 11.33 11.45 41329600 9.705931 2012-12-03 11.56 11.70 11.40 11.41 47746300 9.672024 2012-12-04 11.40 11.44 11.23 11.31 37760200 9.587256 2012-12-05 11.32 11.40 11.18 11.31 33152400 9.587256 2012-12-06 11.26 11.31 11.19 11.24 31065800 9.527918 2012-12-07 11.27 11.50 11.26 11.48 38404500 9.731361 2012-12-10 11.41 11.53 11.41 11.47 26025200 9.722885 2012-12-11 11.51 11.58 11.40 11.49 36326900 9.739838 2012-12-12 11.52 11.56 11.43 11.47 31099900 9.722885 2012-12-13 11.46 11.50 11.21 11.27 35443200 9.553349 2012-12-14 11.27 11.27 11.03 11.10 36933500 9.409243 2012-12-17 11.16 11.41 11.14 11.39 46983300 9.655070 2012-12-18 11.48 11.68 11.40 11.67 61810400 9.892420 2012-12-19 11.79 11.85 11.62 11.73 54884700 9.943281 2012-12-20 11.74 11.80 11.58 11.77 47750100 9.977189 2012-12-21 11.55 11.86 11.47 11.86 94489300 10.053479 2012-12-24 11.67 12.40 11.67 12.40 91734900 10.511226 2012-12-26 12.31 12.79 12.31 12.79 140331900 10.841821 2012-12-27 12.79 12.81 12.36 12.76 108315100 10.816391 2012-12-28 12.55 12.88 12.52 12.87 95668600 10.909636 2012-12-31 12.88 13.08 12.76 12.95 106908900 10.977450 [754 rows x 6 columns]
なるへそ。
で、日経平均。
In [9]: from pandas_datareader.data import get_data_yahoo In [11]: get_data_yahoo(symbols='^N225', start=s) Out[11]: Open High Low Close Volume \ Date 2010-01-04 10609.339844 10694.490234 10608.139648 10654.790039 104400 2010-01-05 10719.440430 10791.040039 10655.570312 10681.830078 166200 2010-01-06 10709.549805 10768.610352 10661.169922 10731.450195 181800 2010-01-07 10742.750000 10774.000000 10636.669922 10681.660156 182600 2010-01-08 10743.299805 10816.450195 10677.559570 10798.320312 211800 2010-01-12 10770.349609 10905.389648 10763.679688 10879.139648 192800 2010-01-13 10795.480469 10866.620117 10729.860352 10735.030273 250000 2010-01-14 10778.070312 10909.940430 10774.250000 10907.679688 267400 2010-01-15 10917.410156 10982.099609 10878.830078 10982.099609 253000 2010-01-18 10887.610352 10895.099609 10781.030273 10855.080078 186600 2010-01-19 10866.830078 10866.830078 10749.469727 10764.900391 174600 2010-01-20 10834.910156 10860.929688 10724.570312 10737.519531 146400 2010-01-21 10704.790039 10886.639648 10649.839844 10868.410156 176800 2010-01-22 10740.209961 10768.070312 10528.330078 10590.549805 187400 2010-01-25 10478.309570 10557.639648 10414.580078 10512.690430 139800 2010-01-26 10506.150391 10566.490234 10324.980469 10325.280273 175400 2010-01-27 10344.070312 10373.820312 10252.080078 10252.080078 139400 2010-01-28 10309.730469 10462.700195 10296.980469 10414.290039 168000 2010-01-29 10308.049805 10324.370117 10198.040039 10198.040039 156800 2010-02-01 10212.360352 10224.830078 10129.910156 10205.019531 162000 2010-02-02 10310.980469 10396.480469 10287.740234 10371.089844 142000 2010-02-03 10428.120117 10436.519531 10356.030273 10404.330078 154200 2010-02-04 10434.519531 10438.410156 10279.570312 10355.980469 164600 2010-02-05 10162.339844 10166.299805 10036.330078 10057.089844 172000 2010-02-08 10007.469727 10063.530273 9942.049805 9951.820312 137400 2010-02-09 9876.610352 9956.790039 9867.389648 9932.900391 135000 2010-02-10 10024.259766 10049.870117 9963.990234 9963.990234 127000 2010-02-12 10085.349609 10099.459961 10014.500000 10092.190430 136000 2010-02-15 10097.820312 10119.469727 10012.530273 10013.299805 97400 2010-02-16 10044.530273 10062.269531 10019.429688 10034.250000 87000 ... ... ... ... ... ... 2016-11-10 16562.859375 17393.820312 1560.660034 17344.419922 222500 2016-11-11 17526.609375 17621.730469 17333.490234 17374.789062 241800 2016-11-14 17467.490234 17697.330078 17455.779297 17672.619141 0 2016-11-15 17668.150391 17668.150391 17668.150391 17668.150391 0 2016-11-16 17832.509766 17886.439453 17807.470703 17862.210938 194300 2016-11-17 17766.609375 17884.060547 17764.080078 17862.630859 0 2016-11-18 18024.210938 18043.720703 17967.410156 17967.410156 161800 2016-11-21 18038.089844 18129.029297 18007.789062 18106.019531 0 2016-11-22 18091.050781 18175.630859 18050.550781 18162.939453 118200 2016-11-24 18329.779297 18382.720703 18310.310547 18333.410156 144900 2016-11-25 18387.589844 18482.939453 18288.500000 18381.220703 160000 2016-11-28 18302.580078 18374.929688 18222.820312 18356.890625 0 2016-11-29 18263.630859 18327.509766 18258.820312 18307.039062 121800 2016-11-30 18356.029297 18370.310547 18280.660156 18308.480469 0 2016-12-01 18535.240234 18746.279297 18469.269531 18513.119141 182400 2016-12-02 18435.550781 18469.679688 18315.380859 18426.080078 191300 2016-12-05 18349.919922 18365.740234 18227.390625 18274.990234 130500 2016-12-06 18457.199219 18484.800781 18318.990234 18360.539062 154000 2016-12-07 18434.539062 18502.470703 18410.880859 18496.689453 151700 2016-12-08 18674.189453 18765.470703 18614.009766 18765.470703 0 2016-12-09 18839.980469 19042.480469 18821.410156 18996.369141 212300 2016-12-12 19183.820312 19280.929688 19054.000000 19155.029297 0 2016-12-13 19120.759766 19253.509766 19060.720703 19250.519531 0 2016-12-14 19270.009766 19284.279297 19184.750000 19253.609375 0 2016-12-15 19327.699219 19436.900391 19192.599609 19273.789062 0 2016-12-16 19438.390625 19439.970703 19360.359375 19401.150391 148700 2016-12-19 19345.839844 19399.259766 19307.140625 19391.599609 106000 2016-12-20 19367.839844 19511.199219 19356.810547 19494.529297 119600 2016-12-21 19547.279297 19592.900391 19375.189453 19444.490234 0 2016-12-22 19396.849609 19427.669922 19327.509766 19427.669922 113500 Adj Close Date 2010-01-04 10654.790039 2010-01-05 10681.830078 2010-01-06 10731.450195 2010-01-07 10681.660156 2010-01-08 10798.320312 2010-01-12 10879.139648 2010-01-13 10735.030273 2010-01-14 10907.679688 2010-01-15 10982.099609 2010-01-18 10855.080078 2010-01-19 10764.900391 2010-01-20 10737.519531 2010-01-21 10868.410156 2010-01-22 10590.549805 2010-01-25 10512.690430 2010-01-26 10325.280273 2010-01-27 10252.080078 2010-01-28 10414.290039 2010-01-29 10198.040039 2010-02-01 10205.019531 2010-02-02 10371.089844 2010-02-03 10404.330078 2010-02-04 10355.980469 2010-02-05 10057.089844 2010-02-08 9951.820312 2010-02-09 9932.900391 2010-02-10 9963.990234 2010-02-12 10092.190430 2010-02-15 10013.299805 2010-02-16 10034.250000 ... ... 2016-11-10 17344.419922 2016-11-11 17374.789062 2016-11-14 17672.619141 2016-11-15 17668.150391 2016-11-16 17862.210938 2016-11-17 17862.630859 2016-11-18 17967.410156 2016-11-21 18106.019531 2016-11-22 18162.939453 2016-11-24 18333.410156 2016-11-25 18381.220703 2016-11-28 18356.890625 2016-11-29 18307.039062 2016-11-30 18308.480469 2016-12-01 18513.119141 2016-12-02 18426.080078 2016-12-05 18274.990234 2016-12-06 18360.539062 2016-12-07 18496.689453 2016-12-08 18765.470703 2016-12-09 18996.369141 2016-12-12 19155.029297 2016-12-13 19250.519531 2016-12-14 19253.609375 2016-12-15 19273.789062 2016-12-16 19401.150391 2016-12-19 19391.599609 2016-12-20 19494.529297 2016-12-21 19444.490234 2016-12-22 19427.669922 [1726 rows x 6 columns]
なるほど。
グラフにしてみる。
In [12]: df = get_data_yahoo(symbols='^N225', start=s) In [16]: df['Close'].plot() Out[16]: <matplotlib.axes._subplots.AxesSubplot at 0x270c6670358> In [17]: import matplotlib.pyplot as plt In [18]: plt.show()
いいねー。
