Rumah >pembangunan bahagian belakang >Tutorial Python >【机器学习】数据预处理之将类别数据转换为数值
在进行python数据分析的时候,首先要进行数据预处理。
有时候不得不处理一些非数值类别的数据,嗯, 今天要说的就是面对这些数据该如何处理。
目前了解到的大概有三种方法:
1,通过LabelEncoder来进行快速的转换;
2,通过mapping方式,将类别映射为数值。不过这种方法适用范围有限;
3,通过get_dummies方法来转换。
<span style="color: #008080"> 1</span> <span style="color: #0000ff">import</span><span style="color: #000000"> pandas as pd </span><span style="color: #008080"> 2</span> <span style="color: #0000ff">from</span> io <span style="color: #0000ff">import</span><span style="color: #000000"> StringIO </span><span style="color: #008080"> 3</span> <span style="color: #008080"> 4</span> csv_data = <span style="color: #800000">'''</span><span style="color: #800000">A,B,C,D </span><span style="color: #008080"> 5</span> <span style="color: #800000">1,2,3,4 </span><span style="color: #008080"> 6</span> <span style="color: #800000">5,6,,8 </span><span style="color: #008080"> 7</span> <span style="color: #800000">0,11,12,</span><span style="color: #800000">'''</span> <span style="color: #008080"> 8</span> <span style="color: #008080"> 9</span> df =<span style="color: #000000"> pd.read_csv(StringIO(csv_data)) </span><span style="color: #008080">10</span> <span style="color: #0000ff">print</span><span style="color: #000000">(df) </span><span style="color: #008080">11</span> <span style="color: #008000">#</span><span style="color: #008000">统计为空的数目</span> <span style="color: #008080">12</span> <span style="color: #0000ff">print</span><span style="color: #000000">(df.isnull().sum()) </span><span style="color: #008080">13</span> <span style="color: #0000ff">print</span><span style="color: #000000">(df.values) </span><span style="color: #008080">14</span> <span style="color: #008080">15</span> <span style="color: #008000">#</span><span style="color: #008000">丢弃空的</span> <span style="color: #008080">16</span> <span style="color: #0000ff">print</span><span style="color: #000000">(df.dropna()) </span><span style="color: #008080">17</span> <span style="color: #0000ff">print</span>(<span style="color: #800000">'</span><span style="color: #800000">after</span><span style="color: #800000">'</span><span style="color: #000000">, df) </span><span style="color: #008080">18</span> <span style="color: #0000ff">from</span> sklearn.preprocessing <span style="color: #0000ff">import</span><span style="color: #000000"> Imputer </span><span style="color: #008080">19</span> <span style="color: #008000">#</span><span style="color: #008000"> axis=0 列 axis = 1 行</span> <span style="color: #008080">20</span> imr = Imputer(missing_values=<span style="color: #800000">'</span><span style="color: #800000">NaN</span><span style="color: #800000">'</span>, strategy=<span style="color: #800000">'</span><span style="color: #800000">mean</span><span style="color: #800000">'</span>, axis=<span style="color: #000000">0) </span><span style="color: #008080">21</span> imr.fit(df) <span style="color: #008000">#</span><span style="color: #008000"> fit 构建得到数据</span> <span style="color: #008080">22</span> imputed_data = imr.transform(df.values) <span style="color: #008000">#</span><span style="color: #008000">transform 将数据进行填充</span> <span style="color: #008080">23</span> <span style="color: #0000ff">print</span><span style="color: #000000">(imputed_data) </span><span style="color: #008080">24</span> <span style="color: #008080">25</span> df = pd.DataFrame([[<span style="color: #800000">'</span><span style="color: #800000">green</span><span style="color: #800000">'</span>, <span style="color: #800000">'</span><span style="color: #800000">M</span><span style="color: #800000">'</span>, 10.1, <span style="color: #800000">'</span><span style="color: #800000">class1</span><span style="color: #800000">'</span><span style="color: #000000">], </span><span style="color: #008080">26</span> [<span style="color: #800000">'</span><span style="color: #800000">red</span><span style="color: #800000">'</span>, <span style="color: #800000">'</span><span style="color: #800000">L</span><span style="color: #800000">'</span>, 13.5, <span style="color: #800000">'</span><span style="color: #800000">class2</span><span style="color: #800000">'</span><span style="color: #000000">], </span><span style="color: #008080">27</span> [<span style="color: #800000">'</span><span style="color: #800000">blue</span><span style="color: #800000">'</span>, <span style="color: #800000">'</span><span style="color: #800000">XL</span><span style="color: #800000">'</span>, 15.3, <span style="color: #800000">'</span><span style="color: #800000">class1</span><span style="color: #800000">'</span><span style="color: #000000">]]) </span><span style="color: #008080">28</span> df.columns =[<span style="color: #800000">'</span><span style="color: #800000">color</span><span style="color: #800000">'</span>, <span style="color: #800000">'</span><span style="color: #800000">size</span><span style="color: #800000">'</span>, <span style="color: #800000">'</span><span style="color: #800000">price</span><span style="color: #800000">'</span>, <span style="color: #800000">'</span><span style="color: #800000">classlabel</span><span style="color: #800000">'</span><span style="color: #000000">] </span><span style="color: #008080">29</span> <span style="color: #0000ff">print</span><span style="color: #000000">(df) </span><span style="color: #008080">30</span> <span style="color: #008080">31</span> size_mapping = {<span style="color: #800000">'</span><span style="color: #800000">XL</span><span style="color: #800000">'</span>:3, <span style="color: #800000">'</span><span style="color: #800000">L</span><span style="color: #800000">'</span>:2, <span style="color: #800000">'</span><span style="color: #800000">M</span><span style="color: #800000">'</span>:1<span style="color: #000000">} </span><span style="color: #008080">32</span> df[<span style="color: #800000">'</span><span style="color: #800000">size</span><span style="color: #800000">'</span>] = df[<span style="color: #800000">'</span><span style="color: #800000">size</span><span style="color: #800000">'</span><span style="color: #000000">].map(size_mapping) </span><span style="color: #008080">33</span> <span style="color: #0000ff">print</span><span style="color: #000000">(df) </span><span style="color: #008080">34</span> <span style="color: #008080">35</span> <span style="color: #008000">#</span><span style="color: #008000"># 遍历Series</span> <span style="color: #008080">36</span> <span style="color: #0000ff">for</span> idx, label <span style="color: #0000ff">in</span> enumerate(df[<span style="color: #800000">'</span><span style="color: #800000">classlabel</span><span style="color: #800000">'</span><span style="color: #000000">]): </span><span style="color: #008080">37</span> <span style="color: #0000ff">print</span><span style="color: #000000">(idx, label) </span><span style="color: #008080">38</span> <span style="color: #008080">39</span> <span style="color: #008000">#</span><span style="color: #008000">1, 利用LabelEncoder类快速编码,但此时对color并不适合,</span> <span style="color: #008080">40</span> <span style="color: #008000">#</span><span style="color: #008000">看起来,好像是有大小的</span> <span style="color: #008080">41</span> <span style="color: #0000ff">from</span> sklearn.preprocessing <span style="color: #0000ff">import</span><span style="color: #000000"> LabelEncoder </span><span style="color: #008080">42</span> class_le =<span style="color: #000000"> LabelEncoder() </span><span style="color: #008080">43</span> color_le =<span style="color: #000000"> LabelEncoder() </span><span style="color: #008080">44</span> df[<span style="color: #800000">'</span><span style="color: #800000">classlabel</span><span style="color: #800000">'</span>] = class_le.fit_transform(df[<span style="color: #800000">'</span><span style="color: #800000">classlabel</span><span style="color: #800000">'</span><span style="color: #000000">].values) </span><span style="color: #008080">45</span> <span style="color: #008000">#</span><span style="color: #008000">df['color'] = color_le.fit_transform(df['color'].values)</span> <span style="color: #008080">46</span> <span style="color: #0000ff">print</span><span style="color: #000000">(df) </span><span style="color: #008080">47</span> <span style="color: #008080">48</span> <span style="color: #008000">#</span><span style="color: #008000">2, 映射字典将类标转换为整数</span> <span style="color: #008080">49</span> <span style="color: #0000ff">import</span><span style="color: #000000"> numpy as np </span><span style="color: #008080">50</span> class_mapping = {label: idx <span style="color: #0000ff">for</span> idx, label <span style="color: #0000ff">in</span> enumerate(np.unique(df[<span style="color: #800000">'</span><span style="color: #800000">classlabel</span><span style="color: #800000">'</span><span style="color: #000000">]))} </span><span style="color: #008080">51</span> df[<span style="color: #800000">'</span><span style="color: #800000">classlabel</span><span style="color: #800000">'</span>] = df[<span style="color: #800000">'</span><span style="color: #800000">classlabel</span><span style="color: #800000">'</span><span style="color: #000000">].map(class_mapping) </span><span style="color: #008080">52</span> <span style="color: #0000ff">print</span>(<span style="color: #800000">'</span><span style="color: #800000">2,</span><span style="color: #800000">'</span><span style="color: #000000">, df) </span><span style="color: #008080">53</span> <span style="color: #008080">54</span> <span style="color: #008080">55</span> <span style="color: #008000">#</span><span style="color: #008000">3,处理1不适用的</span> <span style="color: #008080">56</span> <span style="color: #008000">#</span><span style="color: #008000">利用创建一个新的虚拟特征</span> <span style="color: #008080">57</span> <span style="color: #0000ff">from</span> sklearn.preprocessing <span style="color: #0000ff">import</span><span style="color: #000000"> OneHotEncoder </span><span style="color: #008080">58</span> pf = pd.get_dummies(df[[<span style="color: #800000">'</span><span style="color: #800000">color</span><span style="color: #800000">'</span><span style="color: #000000">]]) </span><span style="color: #008080">59</span> df = pd.concat([df, pf], axis=1<span style="color: #000000">) </span><span style="color: #008080">60</span> df.drop([<span style="color: #800000">'</span><span style="color: #800000">color</span><span style="color: #800000">'</span>], axis=1, inplace=<span style="color: #000000">True) </span><span style="color: #008080">61</span> <span style="color: #0000ff">print</span>(df)
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