数据分析系列精彩浓缩

Posted 2021-02-01 jcjc

数据分析系列精彩浓缩（二）

那么我们有了UCI提供的datasets，我们怎么Perfect operation呢？

• First，we download a data file to the localhost , such as crx.data file

• we will use pure python operation crx.data file

• step are as follows

  • input : crx.data file

  • output : A 2-D list

  • it should look like

    >>> output
    [[data_0], [data_1], [data_2], ...] 

  • individual data example

    >>> data_[0]
    [‘b‘, 30.83, 0, ‘u‘, ‘g‘, ‘w‘, ‘v‘, 1.25, ‘t‘, ‘t‘, ‘01‘, ‘f‘, ‘g‘, ‘00202‘, 0, ‘+‘]

  • Mind the data types,Do‘t make all of them string.注意数据类型

• my code is as follows，for reference only

   file_name = "E:datacrx.data"
   data_ = open(file_name, ‘r‘)
      # print(data_)
      lines = data_.readlines()
      output = []
      # never use built-in names unless you mean to replace it
      for list_str in lines:
          str_list = list_str[:-1].split(",")
          # keep it
          # str_list.remove(str_list[len(str_list)-1])
          data = []
          for substr in str_list:
              if substr.isdigit():
                  if len(substr) > 1 and substr.startswith(‘0‘):
                      data.append(substr)
                  else:
                      substr = int(substr)
                      data.append(substr)
              else:
                  try:
                      current = float(substr)
                      data.append(current)
                  except ValueError as e:
                      if substr == ‘?‘:
                          substr = ‘missing‘
                      data.append(substr)
          output.append(data)
      return output

• 通过上面的操作，我们就可以感觉到已经做和数据相关的事情了，the importance of data types

ok back to the point , before you do anything

It is important for you to at least have a rough idea of what kind of data you are dealing with. For instance, if you have read through all the files in the data folder and the description on the website, you should at least know that:

• This dataset consists of 690 credit card applicants‘ personal information and whether or not they are approved for the credit card.

• Each data entry has 15 attributes, and data types of each attribute are on the website

  • we see that A2, A3, A8, A11, A14, A15 are continuous (number)

  • All others are categorical (choices)

• 37 cases (5%) have one or more missing values

• This dataset has 2 classes, positive and negative, meaning approved and declined

If you haven‘t already read through all these information, go back and try to capture and understand your dataset first

Here is the link:

https://archive.ics.uci.edu/ml/datasets/Credit+Approval

• 通过对数据文件和网站上的描述（By describing data folders and website )

• 我们已经了解了这些数据实际是干什么用的

• 也知道了python解析出来的每条数据对应的属性和分类

既然知道了这些数据的attribute and classify，那就期待进一步Perfect operation吧。。。

• Decmber 28.2018

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