[Maschinelles Lernen] Datenvorverarbeitung: Konvertieren Sie kategoriale Daten in numerische Werte-Python-Tutorial-php.cn

Bei der Durchführung einer Python-Datenanalyse muss zuerst die Datenvorverarbeitung durchgeführt werden.

Manchmal muss man mit einigen nicht numerischen Daten umgehen. Nun, worüber ich heute sprechen möchte, ist der Umgang mit diesen Daten.

Es gibt wahrscheinlich drei Methoden, die ich bisher kenne:

1, verwenden Sie LabelEncoder für eine schnelle Konvertierung;

2. Kategorien durch Zuordnung in numerische Werte umwandeln. Der Anwendungsbereich dieser Methode ist jedoch begrenzt

3. Konvertieren Sie mit der get_dummies-Methode.

<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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