pandas里面的对于数据操作比如where,drop以及dropna等都会有一个属性:inplace,这个单词意思是原地,如果inplace=true代表数据本身要返回(原地数据也会被改变);如果inplace=false(默认)代表只是返回数据一个副本(copy,原数据并不会被改变)。
 
DataFrame里面的corr其实是(线性)相关性,什么是相关性?就是变量A的增长是否导致B的增长(减少)。
DataFrame在聚集操作(比如计算均值)的时候,是可以从两个维度来进行计算,axis=0是纵向,axis=1是横向;对于一个m行,n列的数据,df.mean(axis=0)返回的是一行n列数据,因为在纵向上进行聚集;如果df.mean(axis=1)则代表是横向聚集,于是是一列m行数据。
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" alt="" />
 
  num_attribs=list(housing_num) #num_attribs返回的是列名
 
  TypeError Traceback (most recent call last) <ipython-input-178-70bca072c4f7> in <module>() 1 from sklearn.model_selection import GridSearchCV 2 param_grid={ ----> 3 {'n_estimators':[3, 10, 30], 'max_features':[2,4,6,8]},{'bootstrap':[False], 'n_estimators':[3,10], 'max_features':[2,3,4]} 4 } 5 TypeError: unhashable type: 'dict'
  这个是因为param_grid应该是[],而不是{};故爆此异常。
 
关于pyton里面的zip
  zip的操作是用于两个array位置匹配组合成
  a = [1,2,3]
  b = [4,5,6]
  c = [4,5,6,7,8]
  zipped = zip(a,b) # 打包为元组的列表
  >>> [(1, 4), (2, 5), (3, 6)]
  zip(a,c) # 元素个数与最短的列表一致
  >>> [(1, 4), (2, 5), (3, 6)]
  zip(*zipped) # 与 zip 相反,可理解为解压,返回二维矩阵式
  >>> [(1, 2, 3), (4, 5, 6)]
 
dataframe里面drop并不改变原始数据集,只是返回了操作后的数据集,如下例:strat_train_set其实并没有删除median_house_value列,但是housing确实没有该列的数据(那是因为inplace默认为false,所以所有的操作只是在数据的副本中进行,同时返回副本)。
housing = strat_train_set.drop("median_house_value", axis=1)
housing_labels = strat_train_set["median_house_value"].copy()
 
注意dataframe的where条件,第一个谓词判断(条件判断),第二个是如果谓词判断不满足(条件返回为false),则替换。第三个参数则是代表数据是否要覆盖当前数据集,如果True则是覆盖当前数据及,如果为false则不修改当前数据集,而是创建一个拷贝,然后对于拷贝数据集进行修改;如果设置为False需要接收返回值,因为下面的例子中“inplace=True”,所以修改是发生在当前数据集的,所以不需要接收返回值。
housing["income_cat"].where(housing["income_cat"] > 5, 5.0, inplace=True)
 
StratifierShuffleSplit,其实是StratifierKFloder和ShufflerSplit的组成,是交叉验证的实现。看一下下面的代码,注意这里train_index,以及test_index返回的其实是数组,这里编译器的处理和Java不同,java是逐个遍历,逐个处理,但是对于python而言,是一次性获取所有的值,然后把数组扔给housing,让housing去遍历然后返回值给strat_train_set以及strat_test_set。
 split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)
for train_index, test_index in split.split(housing, housing["income_cat"]):
  print(str(train_index) + ";" + str(test_index) + "\n")
  strat_train_set = housing.loc[train_index]
  strat_test_set = housing.loc[test_index]

 关于交叉预期(corst_val_predict)

  cross_val_predict里面有一个参数是cv,代表的含义就是cross-validation,交叉验证。

关于numpy.rand与numpy.randn

  前者是无规律的随机数(当时是伪随机数),后者则是符合正态分布的随机数,所以在构造随机数的时候,为了获得更好的扩散性,通常都会选择randn,randn相对而言数据分布会更加分散一些,而rand产生的随机数则比较紧凑。
 
 
 
 

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