{"id":340249,"date":"2021-02-07T19:43:37","date_gmt":"2021-02-07T11:43:37","guid":{"rendered":"http:\/\/4563.org\/?p=340249"},"modified":"2021-02-07T19:43:37","modified_gmt":"2021-02-07T11:43:37","slug":"pandas-%e8%8a%b1%e4%ba%86-7-%e5%88%86-32-%e7%a7%92%e7%94%9f%e6%88%90%e4%b8%80%e5%bc%a045639-rows-x-7-columns%e7%9a%84%e8%a1%a8%ef%bc%8c%e5%ae%9e%e5%9c%a8%e6%98%af%e5%a4%aa%e5%a4%aa%e5%a4%aa%e6%85%a2","status":"publish","type":"post","link":"http:\/\/4563.org\/?p=340249","title":{"rendered":"pandas \u82b1\u4e86 7 \u5206 32 \u79d2\u751f\u6210\u4e00\u5f20[45639 rows x 7 columns]\u7684\u8868\uff0c\u5b9e\u5728\u662f\u592a\u592a\u592a\u6162\u4e86\uff0c\u6c42\u66f4\u5feb\u7684\u65b9\u6cd5"},"content":{"rendered":"
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pandas \u82b1\u4e86 7 \u5206 32 \u79d2\u751f\u6210\u4e00\u5f20[45639 rows x 7 columns]\u7684\u8868\uff0c\u5b9e\u5728\u662f\u592a\u592a\u592a\u6162\u4e86\uff0c\u6c42\u66f4\u5feb\u7684\u65b9\u6cd5 <\/h1>\n

<\/p>\n

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\u8cc7\u6df1\u5927\u4f6c : yaleyu <\/span> <\/i> 3<\/span> <\/div>\n
<\/div>\n<\/p><\/div>\n<\/p><\/div>\n<\/p><\/div>\n
<\/p>\n

\u539f\u59cb\u6570\u636e\u7ed3\u6784\u5982\u4e0b\uff0c\u603b\u5171 7000 \u591a\u6761\uff0c\u60f3\u6241\u5e73\u5316\u7136\u540e\u653e\u5230 pd \u91cc\u9762\u505a\u8fdb\u4e00\u6b65\u7684\u5904\u7406<\/p>\n

>>> len(funds) 7597 >>> funds[0] {'000005': {'company': '\u5609\u5b9e', 'name': '\u5609\u5b9e\u589e\u5f3a\u4fe1\u7528\u5b9a\u671f\u503a\u5238', 'type': '\u5b9a\u5f00\u503a\u5238', 'earning': {'1m': 0.1, '3m': 1.0, '6m': 0.85, '1y': 2.59}, 'hold_stocks': [{'code': '601966', 'name': '\u73b2\u73d1\u8f6e\u80ce', 'percentage': '0.51%', 'volume': 0.67, 'value': 23.48}, {'code': '002745', 'name': '\u6728\u6797\u68ee', 'percentage': '0.47%', 'volume': 1.5, 'value': 21.92}]}} >>> funds[-1] {'970008': {'company': '\u534e\u5b89', 'name': '\u534e\u5b89\u8bc1\u5238\u6c47\u8d62\u589e\u5229\u4e00\u5e74\u6301\u6709\u6df7\u5408 C', 'type': '\u6df7\u5408\u578b', 'earning': {'1m': -0.55, '3m': -0.58, '6m': 0.67, '1y': 0}, 'hold_stocks': [{'code': '300692', 'name': '\u4e2d\u73af\u73af\u4fdd', 'percentage': '0.72%', 'volume': 31.81, 'value': 496.79}, {'code': '603012', 'name': '\u521b\u529b\u96c6\u56e2', 'percentage': '0.67%', 'volume': 72.28, 'value': 465.48}, {'code': '300197', 'name': '\u94c1\u6c49\u751f\u6001', 'percentage': '0.64%', 'volume': 138.93, 'value': 440.39}, {'code': '002562', 'name': '\u5144\u5f1f\u79d1\u6280', 'percentage': '0.23%', 'volume': 31.0, 'value': 160.27}]}} >>> <\/code><\/pre>\n
\u751f\u6210 df \u4ee3\u7801\u5982\u4e0b:<\/p>\n
df = pd.DataFrame(columns=['fund_code', 'fund_company', 'stock_code', 'stock_name', 'stock_percentage', 'stock_volume', 'stock_value']) i = 0 for fund in funds:     fund_code = list(fund.keys())[0]     fund_company = list(fund.values())[0]['company']     if list(fund.values())[0]['hold_stocks']:         for hold_stock in list(fund.values())[0]['hold_stocks']:             stock_code = hold_stock['code'].strip()             stock_name = hold_stock['name'].strip()             stock_percentage = float(hold_stock['percentage'].strip('%'))             stock_volume = hold_stock['volume']             stock_value = hold_stock['value']             df.loc[i] = [fund_code, fund_company, stock_code, stock_name, stock_percentage, stock_volume, stock_value]             i += 1     else:         df.loc[i] = [fund_code, fund_company, '', '', 0, 0, 0]  # \u4e0d\u6301\u4ed3\u4efb\u4f55\u80a1\u7968\u5219\u4ec5\u8bb0\u5f55\u57fa\u91d1\u4ee3\u7801\u548c\u57fa\u91d1\u516c\u53f8         i += 1 <\/code><\/pre>\n<\/p><\/div>\n
 \u5927\u4f6c\u6709\u8a71\u8aaa<\/b> (19<\/span>)        <\/div>\n
 <\/div>\n<\/p><\/div>\n<\/p><\/div>\n
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     \u8cc7\u6df1\u5927\u4f6c : bigtan <\/span>  <\/div>\n
     <\/i>            <\/span> <\/div>\n<\/p><\/div>\n
                                                                 pd.Dataframe.from_dict                                                            <\/div>\n<\/p><\/div>\n<\/li>\n
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     \u8cc7\u6df1\u5927\u4f6c : bigtan <\/span>  <\/div>\n
     <\/i>            <\/span> <\/div>\n<\/p><\/div>\n
                                                                 \u4f60\u4e0d\u662f\u751f\u6210\u4e86\u4e00\u5f20\u8868\uff0c\u800c\u662f\u51e0\u4e07\u5f20                                                            <\/div>\n<\/p><\/div>\n<\/li>\n
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     \u8cc7\u6df1\u5927\u4f6c : binux <\/span>  <\/div>\n
     <\/i>            <\/span> <\/div>\n<\/p><\/div>\n
                                                                 \u4e00\u6b21\u628a\u6570\u636e\u5582\u7ed9 dataframe                                                            <\/div>\n<\/p><\/div>\n<\/li>\n
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     \u8cc7\u6df1\u5927\u4f6c : lv2016 <\/span>  <\/div>\n
     <\/i>            <\/span> <\/div>\n<\/p><\/div>\n
                                                                 \u76f4\u63a5\u4fee\u6539 dataframe \u786e\u5b9e\u5f88\u6162\uff0c\u5efa\u8bae\u5148\u751f\u6210 list\uff0c\u518d\u8f6c dataframe                                                            <\/div>\n<\/p><\/div>\n<\/li>\n
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     \u8cc7\u6df1\u5927\u4f6c : WinG <\/span>  <\/div>\n
     <\/i>            <\/span> <\/div>\n<\/p><\/div>\n
                                                                 \u91cf\u5316\u7092\u80a1???                                                            <\/div>\n<\/p><\/div>\n<\/li>\n
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     \u4e3b<\/span> \u8cc7\u6df1\u5927\u4f6c : yaleyu <\/span>  <\/div>\n
     <\/i>            <\/span> <\/div>\n<\/p><\/div>\n
                                                                 @lv2016 \u8bd5\u8fc7\u5148\u6241\u5e73\u5316 dict \u6210 list\uff0c\u518d\u8d4b\u503c\u7ed9 df\uff0c\u65f6\u95f4\u7a0d\u665a\u7f29\u77ed\u4e86\u4e00\u4e22\u4e22\uff0c\u4e0d\u8fc7\u4e0d\u662f\u5f88\u660e\u663e\u3002                                                            <\/div>\n<\/p><\/div>\n<\/li>\n
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     \u4e3b<\/span> \u8cc7\u6df1\u5927\u4f6c : yaleyu <\/span>  <\/div>\n
     <\/i>            <\/span> <\/div>\n<\/p><\/div>\n
                                                                 @bigtan \u8c22\u8c22\uff0c\u6211\u7814\u7a76\u4e00\u4e0b\u8fd9\u4e2a\uff0c\u65b0\u5e74\u5feb\u4e50\u54c8\u3002                                                            <\/div>\n<\/p><\/div>\n<\/li>\n
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     \u8cc7\u6df1\u5927\u4f6c : princelai <\/span>  <\/div>\n
     <\/i>            <\/span> <\/div>\n<\/p><\/div>\n
                                                                 \u4e0a\u8bf4\u4e86\uff0c\u6bcf\u6b21\u64cd\u4f5c df \u662f\u5f88\u6162\u7684\uff0c\u53ef\u4ee5\u64cd\u4f5c json \u53d8\u4e3a\u9002\u5408\u7684\u683c\u5f0f\uff0c\u7136\u540e\u4e00\u6b21\u6027\u7ed9 pandas<\/p>\n
    “`
    from pandas import json_normalize<\/p>\n
    funds = [{‘000005’: {‘company’: ‘\u5609\u5b9e’, ‘name’: ‘\u5609\u5b9e\u589e\u5f3a\u4fe1\u7528\u5b9a\u671f\u503a\u5238’, ‘type’: ‘\u5b9a\u5f00\u503a\u5238’, ‘earning’: {‘1m’: 0.1, ‘3m’: 1.0, ‘6m’: 0.85, ‘1y’: 2.59},
     ‘hold_stocks’: [{‘code’: ‘601966’, ‘name’: ‘\u73b2\u73d1\u8f6e\u80ce’, ‘percentage’: ‘0.51%’, ‘volume’: 0.67, ‘value’: 23.48},
     {‘code’: ‘002745’, ‘name’: ‘\u6728\u6797\u68ee’, ‘percentage’: ‘0.47%’, ‘volume’: 1.5, ‘value’: 21.92}]}},
     {‘970008’: {‘company’: ‘\u534e\u5b89’, ‘name’: ‘\u534e\u5b89\u8bc1\u5238\u6c47\u8d62\u589e\u5229\u4e00\u5e74\u6301\u6709\u6df7\u5408 C’, ‘type’: ‘\u6df7\u5408\u578b’, ‘earning’: {‘1m’: -0.55, ‘3m’: -0.58, ‘6m’: 0.67, ‘1y’: 0},
     ‘hold_stocks’: [{‘code’: ‘300692’, ‘name’: ‘\u4e2d\u73af\u73af\u4fdd’, ‘percentage’: ‘0.72%’, ‘volume’: 31.81, ‘value’: 496.79},
     {‘code’: ‘603012’, ‘name’: ‘\u521b\u529b\u96c6\u56e2’, ‘percentage’: ‘0.67%’, ‘volume’: 72.28, ‘value’: 465.48},
     {‘code’: ‘300197’, ‘name’: ‘\u94c1\u6c49\u751f\u6001’, ‘percentage’: ‘0.64%’, ‘volume’: 138.93, ‘value’: 440.39},
     {‘code’: ‘002562’, ‘name’: ‘\u5144\u5f1f\u79d1\u6280’, ‘percentage’: ‘0.23%’, ‘volume’: 31.0, ‘value’: 160.27}]}}]<\/p>\n
    new_funds = []
    for fund in funds:
     for k, v in fund.items():
     v.update({‘code’: k})
     new_funds.append(v)
    df = json_normalize(new_funds, ‘hold_stocks’, [‘company’, ‘name’, ‘type’, ‘code’], meta_prefix=’fund_’, record_prefix=’stock_’)<\/p>\n
    “`                                                            <\/p><\/div>\n<\/p><\/div>\n<\/li>\n
  • \n
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     \u8cc7\u6df1\u5927\u4f6c : princelai <\/span>  <\/div>\n
     <\/i>            <\/span> <\/div>\n<\/p><\/div>\n
                                                                 \u7f29\u8fdb\u4e71\u4e86\uff0cnew_funds.append \u662f\u5728\u4e24\u5c42\u5faa\u73af\u91cc\uff0cjson_normalize \u662f\u5728\u6700\u5916\u5c42\uff0c\u5faa\u73af\u5b8c\u6bd5\u624d\u53bb\u6267\u884c\u7684                                                            <\/div>\n<\/p><\/div>\n<\/li>\n
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     \u8cc7\u6df1\u5927\u4f6c : Escapist367 <\/span>  <\/div>\n
     <\/i>            <\/span> <\/div>\n<\/p><\/div>\n
                                                                 df_list=[]
    i = 0
    for fund in funds:
     fund_code = list(fund.keys())[0]
     fund_company = list(fund.values())[0][‘company’]
     if list(fund.values())[0][‘hold_stocks’]:
     for hold_stock in list(fund.values())[0][‘hold_stocks’]:
     stock_code = hold_stock[‘code’].strip()
     stock_name = hold_stock[‘name’].strip()
     stock_percentage = float(hold_stock[‘percentage’].strip(‘%’))
     stock_volume = hold_stock[‘volume’]
     stock_value = hold_stock[‘value’]
     df_list.append([fund_code, fund_company, stock_code, stock_name, stock_percentage, stock_volume, stock_value])
     i += 1
     else:
     df_list.append([fund_code, fund_company, ”, ”, 0, 0, 0]) # \u4e0d\u6301\u4ed3\u4efb\u4f55\u80a1\u7968\u5219\u4ec5\u8bb0\u5f55\u57fa\u91d1\u4ee3\u7801\u548c\u57fa\u91d1\u516c\u53f8
     i += 1<\/p>\n
    df = pd.DataFrame(df_list,columns=[‘fund_code’, ‘fund_company’, ‘stock_code’, ‘stock_name’, ‘stock_percentage’, ‘stock_volume’, ‘stock_value’])<\/p>\n
    ==================
    \u5728\u4f60\u57fa\u7840\u4e0a\u6700\u5c0f\u6539\u52a8\u3002
    \u4f60\u5148\u7528\u4e00\u4e2a list \u5b58\u653e\u6bcf\u884c\u7ed3\u679c\uff0c\u518d\u4e00\u6b21\u6027\u8f6c df \u5c31\u884c\u4e86\uff0c\u5927\u6982\u53ea\u8981 1s                                                            <\/div>\n<\/p><\/div>\n<\/li>\n
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     \u4e3b<\/span> \u8cc7\u6df1\u5927\u4f6c : yaleyu <\/span>  <\/div>\n
     <\/i>            <\/span> <\/div>\n<\/p><\/div>\n
                                                                 @princelai \u7f29\u8fdb\u4e71\u4e86\u6ca1\u4e8b\uff0c\u597d\u50cf V2 \u56de\u590d\u4e0d\u80fd\u7528 markdown \u683c\u5f0f\u3002<\/p>\n
    \u4f60\u8fd9\u4e2a\u592a\u725b\u4e86\uff0c\u65f6\u95f4\u4ece 7 \u5206\u591a\u949f\u7f29\u77ed\u5230 0.5 \u79d2\u4e86\uff0c\u4e0d\u8fc7\u6570\u636e\u4ece 45639 \u884c\u51cf\u5c11\u6210\u4e86 42138 \u884c\uff0c\u6211\u518d\u4ed4\u7ec6\u68c0\u67e5\u4e00\u4e0b\u54ea\u4e9b\u6570\u636e\u88ab\u629b\u5f03\u4e86\u3002<\/p>\n
    \u65b0\u5e74\u5feb\u4e50\u54c8\u3002                                                            <\/p><\/div>\n<\/p><\/div>\n<\/li>\n
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     \u8cc7\u6df1\u5927\u4f6c : billgreen1 <\/span>  <\/div>\n
     <\/i>            <\/span> <\/div>\n<\/p><\/div>\n
                                                                 @princelai \u8fd9\u4e2a\u8d5e\uff0c\u6211\u4e4b\u524d\u90fd\u4e0d\u4e86\u89e3\u8fd9\u4e2a\u51fd\u6570                                                            <\/div>\n<\/p><\/div>\n<\/li>\n
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     \u8cc7\u6df1\u5927\u4f6c : youthfire <\/span>  <\/div>\n
     <\/i>            <\/span> <\/div>\n<\/p><\/div>\n
                                                                 pandas \u901f\u5ea6\u8fd8\u662f\u5f88\u5feb\u7684\uff0c\u6211\u7684\u7ecf\u9a8c\u662f\u57fa\u672c\u4e0a\u767e\u4e07\u7ea7\u7684\uff0c\u8bba\u5206\u949f\u7684\u8bdd\u591a\u534a\u662f\u5d4c\u5957\u5199\u9519\u4e86\u5728\u4f4e\u6548\u6267\u884c\u3002\u6570\u636e\u5e93\u5904\u7406\u7684\u8bdd\u4f18\u52bf\u66f4\u660e\u663e\u3002                                                            <\/div>\n<\/p><\/div>\n<\/li>\n
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     \u8cc7\u6df1\u5927\u4f6c : winglight2016 <\/span>  <\/div>\n
     <\/i>            <\/span> <\/div>\n<\/p><\/div>\n
                                                                 \u4f60\u7528\u4e86\u4e24\u5c42 for \u7136\u540e\u8c03\u7528 loc \u65b9\u6cd5\uff0c\u5f53\u7136\u4f1a\u6162\u5440\u3002\u8bb0\u5f97\u51cf\u5c11 for \u5faa\u73af\u6b21\u6570\u4ee5\u53ca loc \u8c03\u7528\u6b21\u6570\u3002<\/p>\n
    pandas \u7684\u4f18\u52bf\u5728 dataframe \u7684\u6279\u5904\u7406\u4e0a\uff0c\u5c3d\u91cf\u628a\u8ba1\u7b97\u64cd\u4f5c\u653e\u5230 dataframe\/series \u7684\u539f\u751f\u65b9\u6cd5\u91cc\u8fdb\u884c\uff0c\u5927\u90e8\u5206\u9700\u6c42\u5e94\u8be5\u90fd\u80fd\u6ee1\u8db3\u3002                                                            <\/p><\/div>\n<\/p><\/div>\n<\/li>\n
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     \u8cc7\u6df1\u5927\u4f6c : lixuda <\/span>  <\/div>\n
     <\/i>            <\/span> <\/div>\n<\/p><\/div>\n
                                                                 pandas \u4e0d\u8981\u7528 for\uff0c\u7528 apply \u3002                                                            <\/div>\n<\/p><\/div>\n<\/li>\n
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     \u8cc7\u6df1\u5927\u4f6c : allAboutDbmss <\/span>  <\/div>\n
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                                                                 https:\/\/github.com\/modin-project\/modin
    \u6211\u6bcf\u5929\u90fd\u5411\u4eba\u63a8\u8350\u8fd9\u4e2a                                                            <\/div>\n<\/p><\/div>\n<\/li>\n
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     \u8cc7\u6df1\u5927\u4f6c : owenliang <\/span>  <\/div>\n
     <\/i>            <\/span> <\/div>\n<\/p><\/div>\n
                                                                 \u5b66\u5b66 pyspark \u3002                                                            <\/div>\n<\/p><\/div>\n<\/li>\n
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     \u8cc7\u6df1\u5927\u4f6c : fxrocks <\/span>  <\/div>\n
     <\/i>            <\/span> <\/div>\n<\/p><\/div>\n
                                                                 1 \u6b63\u89e3                                                            <\/div>\n<\/p><\/div>\n<\/li>\n
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     \u8cc7\u6df1\u5927\u4f6c : volvo007 <\/span>  <\/div>\n
     <\/i>            <\/span> <\/div>\n<\/p><\/div>\n
                                                                 \u8fd8\u6709\u4e2a\u51fd\u6570 
    pd.json_normalize\uff0c\u53ef\u4ee5\u628a\u534a\u7ed3\u6784\u5316\u7684 json \uff08\u6bd4\u5982\u6570\u7ec4\u5957\u5bf9\u8c61\u53c8\u5957\u6570\u7ec4\u800c\u4e14\u4e0d\u540c\u7ec4\u5d4c\u5957\u4e0d\u4e00\u6837\u7684\uff09\u8f6c\u4e3a\u5b57\u5178\uff0c\u800c\u4e14\u53ef\u4ee5\u6709\u9009\u62e9\u6027\u5730\u62bd\u53d6 json \u4e2d\u7684\u67d0\u4e9b\u5b57\u6bb5
    \u8d3c\u597d\u7528                                                            <\/div>\n<\/p><\/div>\n<\/li>\n
  • \n","protected":false},"excerpt":{"rendered":"
    pandas \u82b1\u4e86 7 \u5206 32 …<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[],"tags":[],"_links":{"self":[{"href":"http:\/\/4563.org\/index.php?rest_route=\/wp\/v2\/posts\/340249"}],"collection":[{"href":"http:\/\/4563.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/4563.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/4563.org\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/4563.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=340249"}],"version-history":[{"count":0,"href":"http:\/\/4563.org\/index.php?rest_route=\/wp\/v2\/posts\/340249\/revisions"}],"wp:attachment":[{"href":"http:\/\/4563.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=340249"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/4563.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=340249"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/4563.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=340249"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}