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數(shù)據(jù)集怎么用（opencv數(shù)據(jù)集怎么用）

發(fā)布時間：2023-04-13 13:09:46 稿源：創(chuàng)意嶺閱讀： 74

大家好！今天讓創(chuàng)意嶺的小編來大家介紹下關(guān)于數(shù)據(jù)集怎么用的問題，以下是小編對此問題的歸納整理，讓我們一起來看看吧。

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本文目錄:

1、在sas中我想要使用數(shù)據(jù)集的某一行數(shù)字或者某一列數(shù)字怎么弄？
2、linnerud(體能訓(xùn)練)數(shù)據(jù)集怎么引用
3、finereport 里面數(shù)據(jù)集如何使用另外一個數(shù)據(jù)集的select出來的字段做查詢條件
4、【2020-05-31】如何查看并使用R的內(nèi)置數(shù)據(jù)集

數(shù)據(jù)集怎么用（opencv數(shù)據(jù)集怎么用）

一、在sas中我想要使用數(shù)據(jù)集的某一行數(shù)字或者某一列數(shù)字怎么弄？

使用某一列很好辦。某一列其實就是一個變量，如果你只想保留這個一個變量的話：

Data new;

set old(keep=a);

run;

至于如何使用某一行么，當(dāng)時我也遇到過這個問題，我用了比較笨的辦法，希望能夠幫助到你。其實就是想辦法將數(shù)據(jù)進行轉(zhuǎn)置。這樣使用某一行就變成了上述查詢某一列的問題了。

proc transpose data=old out=new;

run;

當(dāng)然直接保留某一行也是可以的

比如你只想選取第n個觀察值;

Data new;

set old;

if _N_=n;

run;

PS:上述代碼中 new 為輸出的數(shù)據(jù)集，old 為原始數(shù)據(jù)集， a 為你所需要的某一列的變量名稱

二、linnerud(體能訓(xùn)練)數(shù)據(jù)集怎么引用

以load_iris為例。

# 導(dǎo)入是必須的

from sklearn.datasets import load_iris

iris = load_iris()

iris # iris的所有信息，包括數(shù)據(jù)集、標(biāo)簽集、各字段名等

這個輸出太長太亂，而且后邊也有，我就不復(fù)制過來了

iris.keys() # 數(shù)據(jù)集關(guān)鍵字

dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names'])

descr = iris['DESCR']

data = iris['data']

feature_names = iris['feature_names']

target = iris['target']

target_names = iris['target_names']

descr

'Iris Plants Database\n====================\n\nNotes\n-----\nData Set Characteristics:\n :Number of Instances: 150 (50 in each of three classes)\n :Number of Attributes: 4 numeric, predictive attributes and the class\n :Attribute Information:\n - sepal length in cm\n - sepal width in cm\n - petal length in cm\n - petal width in cm\n - class:\n - Iris-Setosa\n - Iris-Versicolour\n - Iris-Virginica\n :Summary Statistics:\n\n ============== ==== ==== ======= ===== ====================\n Min Max Mean SD Class Correlation\n ============== ==== ==== ======= ===== ====================\n sepal length: 4.3 7.9 5.84 0.83 0.7826\n sepal width: 2.0 4.4 3.05 0.43 -0.4194\n petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)\n petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)\n ============== ==== ==== ======= ===== ====================\n\n :Missing Attribute Values: None\n :Class Distribution: 33.3% for each of 3 classes.\n :Creator: R.A. Fisher\n :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n :Date: July, 1988\n\nThis is a copy of UCI ML iris datasets.\nhttp://archive.ics.uci.edu/ml/datasets/Iris\n\nThe famous Iris database, first used by Sir R.A Fisher\n\nThis is perhaps the best known database to be found in the\npattern recognition literature. Fisher's paper is a classic in the field and\nis referenced frequently to this day. (See Duda & Hart, for example.) The\ndata set contains 3 classes of 50 instances each, where each class refers to a\ntype of iris plant. One class is linearly separable from the other 2; the\nlatter are NOT linearly separable from each other.\n\nReferences\n----------\n - Fisher,R.A. "The use of multiple measurements in taxonomic problems"\n Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to\n Mathematical Statistics" (John Wiley, NY, 1950).\n - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.\n (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.\n - Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System\n Structure and Classification Rule for Recognition in Partially Exposed\n Environments". IEEE Transactions on Pattern Analysis and Machine\n Intelligence, Vol. PAMI-2, No. 1, 67-71.\n - Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE Transactions\n on Information Theory, May 1972, 431-433.\n - See also: 1988 MLC Proceedings, 54-64. Cheeseman et al"s AUTOCLASS II\n conceptual clustering system finds 3 classes in the data.\n - Many, many more ...\n'

三、finereport 里面數(shù)據(jù)集如何使用另外一個數(shù)據(jù)集的select出來的字段做查詢條件

這個的話可以綁定數(shù)據(jù)列，將數(shù)據(jù)列拖拽至單元格，并設(shè)置單元格的屬性

數(shù)據(jù)集怎么用（opencv數(shù)據(jù)集怎么用）