{"html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Fnekoyukimmm.hatenablog.com%2Fentry%2F2015%2F07%2F29%2F173809\" title=\"&lt;Python, pandas&gt; DataFlame.copy() - \u306d\u3053\u3086\u304d\u306e\u30e1\u30e2\" class=\"embed-card embed-blogcard\" scrolling=\"no\" frameborder=\"0\" style=\"display: block; width: 100%; height: 190px; max-width: 500px; margin: 10px 0px;\"></iframe>","author_url":"https://blog.hatena.ne.jp/nekoyukimmm/","type":"rich","description":"\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0DataFlame\u306e\u30b3\u30d4\u30fc\u306f\u3001=\u3060\u3051\u3067\u306f\u3060\u3081\u3089\u3057\u3044\u3002\u3002\u3002\u521d\u3081\u3066\u3057\u3063\u305f\u3002\u3002\u3002 In [207]: df = pd.DataFrame(data=[(0,1),(2,3)]) In [208]: df Out[208]: 0 1 0 0 1 1 2 3 In [209]: df1 = df In [210]: df1.ix[0,0]=99 In [211]: df1 Out[211]: 0 1 0 99 1 1 2 3 In [212]: df Out[212]: 0 1 0 99 1 1 2 3 \u3068\u3001\u3044\u3046\u3053\u3068\u3067\u3001=\u306f\u30b3\u30d4\u30fc\u3067\u306a\u304f\u3001\u30ea\u30f3\u30af\u3092\u4f5c\u6210\u3057\u3066\u3044\u308b\u3002 \u30b3\u30d4\u30fc\u3092\u4f5c\u6210\u3059\u308b\u306e\u306f\u3001.co\u2026","published":"2015-07-29 17:38:09","author_name":"nekoyukimmm","width":"100%","version":"1.0","provider_name":"Hatena Blog","blog_title":"\u306d\u3053\u3086\u304d\u306e\u30e1\u30e2","blog_url":"https://nekoyukimmm.hatenablog.com/","provider_url":"https://hatena.blog","title":"<Python, pandas> DataFlame.copy()","height":"190","url":"https://nekoyukimmm.hatenablog.com/entry/2015/07/29/173809","image_url":null,"categories":["pandas","Python"]}