{"id":62023,"date":"2018-07-03T20:50:31","date_gmt":"2018-07-03T11:50:31","guid":{"rendered":"https:\/\/www.sejuku.net\/blog\/?p=62023"},"modified":"2024-05-06T11:52:57","modified_gmt":"2024-05-06T02:52:57","slug":"placeholder5","status":"publish","type":"post","link":"https:\/\/www.sejuku.net\/blog\/62023","title":{"rendered":"Pandas\u3067\u30c7\u30fc\u30bf\u578b\u3092\u78ba\u8a8d\u3059\u308bdtype\/dtypes\u3068\u578b\u5909\u63db\u3092\u884c\u3046astype"},"content":{"rendered":"<p>\u3053\u306e\u8a18\u4e8b\u3067\u306f\u3001<span style=\"color: #0000ff;\"><strong>Pandas\u306eSeries\u3084DataFrame\u306e\u8981\u7d20\u306e\u30c7\u30fc\u30bf\u578b<\/strong><\/span>\u3068\u3001<span style=\"color: #0000ff;\"><strong>Series\u578b\u306e\u8981\u7d20\u306e\u578b\u5909\u63db<\/strong><\/span>\u3092\u3059\u308b<strong><span style=\"color: #008000;\">astype\u30e1\u30bd\u30c3\u30c9<\/span><\/strong>\u306b\u3064\u3044\u3066\u7d39\u4ecb\u3057\u307e\u3059\u3002<\/p>\n<p>DataFrame\u306f\u975e\u5e38\u306b\u67d4\u8edf\u306a\u30af\u30e9\u30b9\u306a\u306e\u3067\u3001\u305d\u308c\u305e\u308c\u306e\u5217\u304c\u5225\u3005\u306e\u30c7\u30fc\u30bf\u578b\u3092\u3082\u3063\u3066\u3044\u308b\u3053\u3068\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n<p>\u306a\u306e\u3067DataFrame\u5168\u4f53\u306b\u5bfe\u3057\u3066\u578b\u5909\u63db\u3092\u4e00\u62ec\u3067\u884c\u3046\u306e\u3067\u306f\u306a\u304f\u3001\u5217\u3054\u3068\u306b\u578b\u5909\u63db\u3092\u304a\u3053\u306a\u3044\u307e\u3059\u3002<\/p>\n<p>\u3053\u306e\u8a18\u4e8b\u3067<\/p>\n<ul>\n<li><span style=\"color: #0000ff;\"><strong>Pandas\u306e\u30c7\u30fc\u30bf\u578b\u3068Python\u30fbNumPy\u306e\u30c7\u30fc\u30bf\u578b\u3068\u306e\u5bfe\u5fdc<\/strong><\/span><\/li>\n<li><span style=\"color: #0000ff;\"><strong>Series\u3001DataFrame\u306edtype\u306e\u78ba\u8a8d<\/strong><\/span><\/li>\n<li><span style=\"color: #0000ff;\"><strong>astype\u306b\u3088\u308b\u30ad\u30e3\u30b9\u30c8\u5909\u66f4<\/strong><\/span><\/li>\n<\/ul>\n<p>\u3092\u7406\u89e3\u30fb\u7fd2\u5f97\u3057\u3066\u3001Pandas\u306b\u304a\u3051\u308b\u578b\u306e\u6271\u3044\u3092\u5b66\u3073\u307e\u3057\u3087\u3046\u3002<\/p>\n<h2>Pandas\u306e\u30c7\u30fc\u30bf\u578b<\/h2>\n<p><span style=\"color: #0000ff;\"><strong>Python\u306e\u30c7\u30fc\u30bf\u578b<\/strong><\/span>\u3092\u5143\u306b\u3001\u3053\u308c\u306b\u5bfe\u5fdc\u3059\u308b<strong><span style=\"color: #0000ff;\">Pandas\u306e\u4ee3\u8868\u7684\u306a\u30c7\u30fc\u30bf\u578b<\/span><\/strong>\u3068<strong><span style=\"color: #0000ff;\">NumPy\u306e\u30c7\u30fc\u30bf\u578b<\/span><\/strong>\u3092\u78ba\u8a8d\u3057\u307e\u3057\u3087\u3046\u3002<\/p>\n<table class=\"colwidths-given table table-condensed docutils\" border=\"1\">\n<thead valign=\"bottom\">\n<tr>\n<th class=\"head\">Pandas dtype<\/th>\n<th class=\"head\">Python type<\/th>\n<th class=\"head\">NumPy type<\/th>\n<th class=\"head\">Usage<\/th>\n<\/tr>\n<\/thead>\n<tbody valign=\"top\">\n<tr>\n<td>object<\/td>\n<td>str<\/td>\n<td>string_, unicode_<\/td>\n<td>Text<\/td>\n<\/tr>\n<tr>\n<td>int64<\/td>\n<td>int<\/td>\n<td>int_, int8, int16, int32, int64, uint8, uint16, uint32, uint64<\/td>\n<td>Integer numbers<\/td>\n<\/tr>\n<tr>\n<td>float64<\/td>\n<td>float<\/td>\n<td>float_, float16, float32, float64<\/td>\n<td>Floating point numbers<\/td>\n<\/tr>\n<tr>\n<td>bool<\/td>\n<td>bool<\/td>\n<td>bool_<\/td>\n<td>True\/False values<\/td>\n<\/tr>\n<tr>\n<td>datetime64<\/td>\n<td><span class=\"caps\">NA<\/span><\/td>\n<td>datetime64[ns]<\/td>\n<td>Date and time values<\/td>\n<\/tr>\n<tr>\n<td>timedelta[ns]<\/td>\n<td><span class=\"caps\">NA<\/span><\/td>\n<td><span class=\"caps\">NA<\/span><\/td>\n<td>Differences between two datetimes<\/td>\n<\/tr>\n<tr>\n<td>category<\/td>\n<td><span class=\"caps\">NA<\/span><\/td>\n<td><span class=\"caps\">NA<\/span><\/td>\n<td>Finite list of text values<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u8868\u5f15\u7528\uff1a<a href=\"http:\/\/pbpython.com\/pandas_dtypes.html\">&#8220;Overview of Pandas Data Types&#8221; Posted by Chris Moffitt in articles<\/a><\/p>\n<p>float32\u306a\u3069\u306e\u7cbe\u5ea6\u306e\u9055\u3046\u578b\u306f\u3082\u3061\u308d\u3093Pandas\u306b\u3082\u3042\u308a\u307e\u3059\u3002<\/p>\n<p>\u3088\u308a\u7d30\u304b\u3044\u30c7\u30fc\u30bf\u578b\u306e\u4e00\u89a7\u306f\u3001\u4ee5\u4e0b\u306e\u30b5\u30a4\u30c8\u69d8\u3067\u7d39\u4ecb\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n<p>https:\/\/note.nkmk.me\/python-pandas-dtype-astype\/<\/p>\n<h2>Pandas.DataFrame\u306edtype\/dtypes<\/h2>\n<p>NumPy\u306e\u914d\u5217\u306b\u306fdtype\u3068\u3044\u3046\u5c5e\u6027\u304c\u3042\u308a\u307e\u3057\u305f\u3002<\/p>\n<p>\u3057\u304b\u3057DataFrame\u3067\u306f\u3001<span style=\"color: #0000ff;\"><strong>\u5217\u3054\u3068\u306b\u5225\u306e\u30c7\u30fc\u30bf\u578b<\/strong><\/span>\u3092\u6301\u3064\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n<p>\u3088\u3063\u3066DataFrame\u81ea\u4f53\u306b\u306fdtype\u5c5e\u6027\u306f\u3042\u308a\u307e\u305b\u3093\u3002<\/p>\n<p>\u305d\u306e\u304b\u308f\u308a<span style=\"color: #0000ff;\"><strong><span style=\"color: #008000;\">dtypes<\/span><\/strong><\/span>\u3068\u3044\u3046<span style=\"color: #0000ff;\"><strong>\u5217\u3054\u3068\u306bdtype\u3092\u8868\u793a\u3059\u308b\u6a5f\u80fd<\/strong><\/span>\u304c\u7528\u610f\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n<pre class=\"lang:default decode:true\">import numpy as np\r\nimport pandas as pd\r\n\r\ndata = {\r\n    \"\u540d\u524d\":[\"\u592a\u90ce\",\"\u6b21\u90ce\",\"\u4e09\u90ce\"],\r\n    \"\u8eab\u9577\":[180.0, 170.0, 160.0],\r\n    \"\u5e74\u9f62\":[30,25,20],\r\n}\r\na = pd.DataFrame(data)\r\n<\/pre>\n<p>dtypes\u3067DataFrame\u306e\u5404\u5217\u306e\u5c5e\u6027\u3092\u78ba\u8a8d\u3057\u307e\u3057\u3087\u3046\u3002<\/p>\n<pre class=\"lang:default decode:true\">a.dtypes\r\n\r\n# \u7d50\u679c\r\n\u540d\u524d     object\r\n\u8eab\u9577    float64\r\n\u5e74\u9f62      int64\r\ndtype: object<\/pre>\n<p>\u3061\u306a\u307f\u306bDataFrame\u306edtype\u306b\u30a2\u30af\u30bb\u30b9\u3059\u308b\u3068Error\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n<pre class=\"lang:default decode:true \">a.dtype\r\n\r\n# \u30a8\u30e9\u30fc\u30e1\u30c3\u30bb\u30fc\u30b8\r\n\r\n\r\n---------------------------------------------------------------------------\r\nAttributeError                            Traceback (most recent call last)\r\n&lt;ipython-input-25-b3cdf8323901&gt; in &lt;module&gt;\r\n----&gt; 1 a.dtype\r\n\r\n~\/anaconda3\/lib\/python3.7\/site-packages\/pandas\/core\/generic.py in __getattr__(self, name)\r\n   4374             if self._info_axis._can_hold_identifiers_and_holds_name(name):\r\n   4375                 return self[name]\r\n-&gt; 4376             return object.__getattribute__(self, name)\r\n   4377 \r\n   4378     def __setattr__(self, name, value):\r\n\r\nAttributeError: 'DataFrame' object has no attribute 'dtype'<\/pre>\n<p>DataFrame\u306edtype\u306f\u5217\u3054\u3068\u306b\u78ba\u8a8d\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3057\u305f\u3002<\/p>\n<p>DataFrame\u306e<span style=\"color: #0000ff;\"><strong>\u5404\u5217\u306fpd.Series\u578b<\/strong><\/span>\u306b\u306a\u3063\u3066\u3044\u3066\u3001<span style=\"color: #0000ff;\"><strong>Series\u306fdtype\u5c5e\u6027\u3092\u6301\u3063\u3066<\/strong><\/span>\u3044\u307e\u3059\u3002<\/p>\n<p>\u5b9f\u969b\u306b\u30b5\u30f3\u30d7\u30eb\u306eDataFrame\u306e\u5404\u5217\u306b\u3064\u3044\u3066\u3001dtype\u3092\u78ba\u8a8d\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n<pre class=\"lang:default decode:true \">print(a[\"\u540d\u524d\"].dtype)\r\nprint(a[\"\u8eab\u9577\"].dtype)\r\nprint(a[\"\u5e74\u9f62\"].dtype)\r\n\r\n# \u7d50\u679c\r\nobject\r\nfloat64\r\nint64\r\n<\/pre>\n<h2>astype\u3067\u30c7\u30fc\u30bf\u578b\u3092\u5909\u66f4<\/h2>\n<p>DataFrame\u306e\u5217\u3084Series\u306e<strong><span style=\"color: #0000ff;\">\u30c7\u30fc\u30bf\u578b\u3092\u5909\u66f4\u3059\u308b\u30e1\u30bd\u30c3\u30c9<\/span><\/strong>\u3068\u3057\u3066\u3001<strong><span style=\"color: #008000;\">astype<\/span><\/strong>\u304c\u7528\u610f\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n<p>\u4f7f\u3044\u65b9\u306fNumPy\u306eastype\u3068\u307b\u307c\u540c\u3058\u3067\u3059\u3002<\/p>\n<p>astype\u306e\u5f15\u6570\u306f<\/p>\n<ul>\n<li><span style=\"color: #0000ff;\"><strong>Python\u306e\u30c7\u30fc\u30bf\u578b<\/strong><\/span><\/li>\n<li><span style=\"color: #0000ff;\"><strong>NumPy\u306e\u30c7\u30fc\u30bf\u578b<\/strong><\/span><\/li>\n<li><span style=\"color: #0000ff;\"><strong>\u6587\u5b57\u5217\u3068\u3057\u3066Pandas\u3067\u4f7f\u3048\u308b\u30c7\u30fc\u30bf\u578b\u3092\u6307\u5b9a<\/strong><\/span><\/li>\n<\/ul>\n<p>\u306e3\u3064\u306e\u65b9\u6cd5\u304c\u4f7f\u3048\u307e\u3059\u3002<\/p>\n<h3>Python\u306e\u30c7\u30fc\u30bf\u578b\u3067\u6307\u5b9a<\/h3>\n<pre class=\"lang:default decode:true\">a[\"\u5e74\u9f62\"].astype(float)\r\n\r\n# \u51fa\u529b\r\n0    30.0\r\n1    25.0\r\n2    20.0\r\nName: \u5e74\u9f62, dtype: float64\r\n<\/pre>\n<p>\u307e\u305f\u3001<span style=\"color: #ff0000;\"><strong>astype\u30e1\u30bd\u30c3\u30c9\u306f\u30aa\u30ea\u30b8\u30ca\u30eb\u306e\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u3092\u5909\u66f4\u3057\u306a\u3044<\/strong><\/span>\u306e\u3067\u3001\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u81ea\u4f53\u3092\u5909\u66f4\u3057\u305f\u3044\u5834\u5408\u306f<\/p>\n<pre class=\"lang:default decode:true \">a[\"\u5e74\u9f62\"]=a[\"\u5e74\u9f62\"].astype(float)<\/pre>\n<p>\u306e\u3088\u3046\u306b\u65b0\u3057\u3044\u5024\u3067\u5143\u306e\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u3092\u4e0a\u66f8\u304d\u3057\u307e\u3057\u3087\u3046\u3002<\/p>\n<h3>NumPy\u306e\u30c7\u30fc\u30bf\u578b\u3067\u6307\u5b9a<\/h3>\n<pre class=\"lang:default decode:true\">a[\"\u5e74\u9f62\"].astype(np.float32)\r\n\r\n# \u51fa\u529b\r\n0    30.0\r\n1    25.0\r\n2    20.0\r\nName: \u5e74\u9f62, dtype: float32<\/pre>\n<h3>\u6587\u5b57\u5217\u3068\u3057\u3066Pandas\u3067\u4f7f\u3048\u308b\u30c7\u30fc\u30bf\u578b\u3092\u6307\u5b9a<\/h3>\n<pre class=\"lang:default decode:true\">a[\"\u5e74\u9f62\"].astype(\"float16\")\r\n\r\n# \u51fa\u529b\r\n0    30.0\r\n1    25.0\r\n2    20.0\r\nName: \u5e74\u9f62, dtype: float16<\/pre>\n<h2>\u307e\u3068\u3081<\/h2>\n<p>\u3053\u306e\u8a18\u4e8b\u3067\u306f\u3001<\/p>\n<ul>\n<li><span style=\"color: #0000ff;\"><strong>Pandas\u306edtype\/dtypes\u306e\u78ba\u8a8d<\/strong><\/span><\/li>\n<li><span style=\"color: #0000ff;\"><strong>astype\u306b\u3088\u308b\u30c7\u30fc\u30bf\u578b\u306e\u5909\u66f4<\/strong><\/span><\/li>\n<\/ul>\n<p>\u306b\u3064\u3044\u3066\u7d39\u4ecb\u3057\u307e\u3057\u305f\u3002<\/p>\n<p>dtype\u304cSeries\u3054\u3068\u306b\u6c7a\u3081\u3089\u308c\u3066\u3044\u3066\u3001DataFrame\u3067\u306f\u8907\u6570\u306e\u578b\u3092\u6301\u3063\u3066\u3044\u308b\u5834\u5408\u304c\u3042\u308b\u3053\u3068\u306b\u6ce8\u610f\u3057\u307e\u3057\u3087\u3046\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u3053\u306e\u8a18\u4e8b\u3067\u306f\u3001Pandas\u306eSeries\u3084DataFrame\u306e\u8981\u7d20\u306e\u30c7\u30fc\u30bf\u578b\u3068\u3001Series\u578b\u306e\u8981\u7d20\u306e\u578b\u5909\u63db\u3092\u3059\u308bastype\u30e1\u30bd\u30c3\u30c9\u306b\u3064\u3044\u3066\u7d39\u4ecb\u3057\u307e\u3059\u3002 DataFrame\u306f\u975e\u5e38\u306b\u67d4\u8edf\u306a\u30af\u30e9\u30b9\u306a\u306e\u3067\u3001\u305d\u308c\u305e\u308c\u306e\u5217\u304c\u5225\u3005 [&hellip;]<\/p>\n","protected":false},"author":4,"featured_media":76459,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"swell_btn_cv_data":"","footnotes":""},"categories":[2350],"tags":[2016],"class_list":["post-62023","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-python-study","tag-ai-python-pandas"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Pandas\u3067\u30c7\u30fc\u30bf\u578b\u3092\u78ba\u8a8d\u3059\u308bdtype\/dtypes\u3068\u578b\u5909\u63db\u3092\u884c\u3046astype | \u4f8d\u30a8\u30f3\u30b8\u30cb\u30a2\u30d6\u30ed\u30b0<\/title>\n<meta name=\"description\" content=\"\u3053\u306e\u8a18\u4e8b\u3067\u306f\u300c 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