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2019-01-05.html
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<title>利用pandas、matplotlib和wordcloud进行数据分析</title>
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<div class="post">
<div class="post_body">
<div class=title_container>
<h1> 利用pandas、matplotlib和wordcloud进行数据分析 </h1>
</div>
<blockquote class="blockquote_lines_1">
<p class="md_block">
<span class="md_line md_line_start md_line_end">开发环境还是和之前一样，需要安装pandas，numpy，matplotlib，scipy，jieba, wordcloud库，安装方法可以自行百度。</span>
</p>
</blockquote>
<h2 id="toc_0" class="h16 md_first_h">首先介绍两个自己写的函数，下面会用到，一个是分词，另一个是返回一个dataframe的函数</h2>
<div class="codehilite code_lang_python highlight"><pre><span></span><span class="k">def</span> <span class="nf">cut_zh</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sql</span><span class="p">,</span> <span class="n">cut</span><span class="o">=</span><span class="bp">False</span><span class="p">):</span>
<span class="k">with</span> <span class="n">connect</span><span class="p">()</span> <span class="k">as</span> <span class="n">cur</span><span class="p">:</span>
<span class="n">cur</span><span class="o">.</span><span class="n">execute</span><span class="p">(</span><span class="n">sql</span><span class="p">)</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">cur</span><span class="o">.</span><span class="n">fetchall</span><span class="p">()</span>
<span class="n">words</span> <span class="o">=</span> <span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">word</span><span class="p">:</span> <span class="n">word</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">result</span><span class="p">)</span>
<span class="n">words</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">words</span><span class="p">)</span>
<span class="k">if</span> <span class="n">cut</span><span class="p">:</span>
<span class="n">pattern</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="sa">r</span><span class="s1">'[\u4e00-\u9fa5_a-zA-Z0-9]+'</span><span class="p">)</span>
<span class="n">words</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">findall</span><span class="p">(</span><span class="n">pattern</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">result</span><span class="p">))</span>
<span class="n">words</span> <span class="o">=</span> <span class="s1">' '</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">words</span><span class="p">)</span>
<span class="n">jieba</span><span class="o">.</span><span class="n">load_userdict</span><span class="p">(</span><span class="s2">"source/dict.txt"</span><span class="p">)</span>
<span class="n">words</span> <span class="o">=</span> <span class="n">jieba</span><span class="o">.</span><span class="n">cut</span><span class="p">(</span><span class="n">words</span><span class="p">)</span>
<span class="n">words</span> <span class="o">=</span> <span class="nb">filter</span><span class="p">(</span><span class="k">lambda</span> <span class="n">word</span><span class="p">:</span> <span class="n">word</span> <span class="o">!=</span> <span class="s1">' '</span><span class="p">,</span> <span class="n">words</span><span class="p">)</span>
<span class="n">words</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">words</span><span class="p">)</span>
<span class="k">return</span> <span class="n">words</span>
</pre></div>
<!--block_code_end-->
<ol>
<li class="md_li"><span> 先连接数据库，查找到需要的数据，并把数据返回一个list列表。
</span></li>
<li class="md_li"><span> 正则表达式提取汉字英文数字。
</span></li>
<li class="md_li"><span>判断如果是汉字，可以用jieba进行分词。<a class="md_compiled" href="https://github.com/hosiet/jieba">jieba官方文档</a>
</span></li>
<li class="md_li"><span>最后返回也是一个list列表。
</span></li>
</ol>
<hr>
<div class="codehilite code_lang_python highlight"><pre><span></span><span class="k">def</span> <span class="nf">make_df</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">words</span><span class="p">,</span> <span class="n">stopword</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
<span class="n">my_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'segment'</span><span class="p">:</span> <span class="n">words</span><span class="p">})</span>
<span class="k">if</span> <span class="n">stopword</span><span class="p">:</span>
<span class="n">stopwords</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">stopword</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s1">'stopword'</span><span class="p">],</span>
<span class="n">encoding</span><span class="o">=</span><span class="s1">'utf-8'</span><span class="p">)</span>
<span class="n">my_df</span> <span class="o">=</span> <span class="n">my_df</span><span class="p">[</span><span class="o">~</span><span class="n">my_df</span><span class="o">.</span><span class="n">segment</span><span class="o">.</span><span class="n">isin</span><span class="p">(</span><span class="n">stopwords</span><span class="o">.</span><span class="n">stopword</span><span class="p">)]</span>
<span class="n">my_df</span> <span class="o">=</span> <span class="n">my_df</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'segment'</span><span class="p">])[</span><span class="s1">'segment'</span><span class="p">]</span><span class="o">.</span><span class="n">agg</span><span class="p">({</span><span class="s1">'count'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">size</span><span class="p">})</span>
<span class="n">my_df</span> <span class="o">=</span> <span class="n">my_df</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span><span class="o">.</span><span class="n">sort_values</span><span class="p">([</span><span class="s1">'count'</span><span class="p">],</span> <span class="n">ascending</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
<span class="k">return</span> <span class="n">my_df</span>
</pre></div>
<!--block_code_end-->
<ol>
<li class="md_li"><span>通过上一个函数返回的list，把他进行词频统计并返回一个dataframe。
</span></li>
<li class="md_li"><span>stopword是停用词，一个txt文本文件，比如<code>stopword = + 'source/com.txt'</code>，这个文件里面有一些没用的词，比如 <code>这些 ，那么 ， 嗯，啊，哦</code>，把这些字写在里面，分析的时候就会排除。
</span></li>
<li class="md_li"><span>剩下的两个函数就是计算每个词出现的次数，并且重新排列。
</span></li>
</ol>
<hr>
<h2 id="toc_1" class="h16">制作词云</h2>
<div class="codehilite code_lang_python highlight"><pre><span></span><span class="k">def</span> <span class="nf">draw_wc</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">words</span><span class="p">,</span> <span class="n">stopword</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s1">''</span><span class="p">):</span>
<span class="k">if</span> <span class="n">stopword</span><span class="p">:</span>
<span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">make_df</span><span class="p">(</span><span class="n">words</span><span class="p">,</span> <span class="n">stopword</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">make_df</span><span class="p">(</span><span class="n">words</span><span class="p">)</span>
<span class="n">bg_pic</span> <span class="o">=</span> <span class="n">imread</span><span class="p">(</span><span class="s1">'source/luhan.jpg'</span><span class="p">)</span>
<span class="n">wordcloud</span> <span class="o">=</span> <span class="n">WordCloud</span><span class="p">(</span><span class="n">background_color</span><span class="o">=</span><span class="s1">'black'</span><span class="p">,</span> <span class="n">max_font_size</span><span class="o">=</span><span class="mi">110</span><span class="p">,</span>
<span class="n">mask</span><span class="o">=</span><span class="n">bg_pic</span><span class="p">,</span> <span class="n">min_font_size</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">'RGBA'</span><span class="p">,</span>
<span class="n">font_path</span><span class="o">=</span><span class="s1">'source/simhei.ttf'</span><span class="p">)</span>
<span class="n">word_frequence</span> <span class="o">=</span> <span class="p">{</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">values</span><span class="p">}</span>
<span class="n">wordcloud</span> <span class="o">=</span> <span class="n">wordcloud</span><span class="o">.</span><span class="n">fit_words</span><span class="p">(</span><span class="n">word_frequence</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="n">title</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">wordcloud</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s2">"off"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
<!--block_code_end-->
<ol>
<li class="md_li"><span>参数首先只看words，就是需要准备好的数据，然后把数据转换成dataframe格式。
</span></li>
<li class="md_li"><span>bg_pic 是制作词云的背景图，可以自己上传。
</span></li>
<li class="md_li"><span>初始化一个WordCloud对象，具体参数意义参考这里：<a class="md_compiled" href="http://www.jianshu.com/p/7d00f8833cf4">wordcloud文档</a>
</span></li>
<li class="md_li"><span>word_frequence 是统计词的一组字典，就是下面一个函数的参数。
</span></li>
<li class="md_li"><span>通过fit_fitwords函数展示已经分好的词频。（还有另外两个函数，可以在文档中查看）
</span></li>
<li class="md_li"><span>下面的代码就是用plot显示的过程了
</span></li>
</ol>
<hr>
<p class="md_block">
<span class="md_line md_line_start md_line_end">先贴一下<a class="md_compiled" href="https://matplotlib.org/api/pyplot_api.html">matplotlib官方文档</a>，详细的参数在这里都有解释</span>
</p>
<h2 id="toc_2" class="h16">制作饼图</h2>
<div class="codehilite code_lang_python highlight"><pre><span></span><span class="k">def</span> <span class="nf">draw_pie</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">words</span><span class="p">,</span> <span class="n">stopword</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s1">''</span><span class="p">):</span>
<span class="k">if</span> <span class="n">stopword</span><span class="p">:</span>
<span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">make_df</span><span class="p">(</span><span class="n">words</span><span class="p">,</span> <span class="n">stopword</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">make_df</span><span class="p">(</span><span class="n">words</span><span class="p">)</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">10</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
<span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="n">count</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">count</span> <span class="ow">in</span> <span class="n">data</span><span class="p">]</span>
<span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="n">name</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">data</span><span class="p">]</span>
<span class="n">expl</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="mi">0</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)))</span>
<span class="n">expl</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.1</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="n">title</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">pie</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">autopct</span><span class="o">=</span><span class="s1">'</span><span class="si">%1.0f%%</span><span class="s1">'</span><span class="p">,</span> <span class="n">pctdistance</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span> <span class="n">shadow</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span>
<span class="n">startangle</span><span class="o">=</span><span class="mi">60</span><span class="p">,</span> <span class="n">explode</span><span class="o">=</span><span class="n">expl</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">'equal'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
<!--block_code_end-->
<ol>
<li class="md_li"><span>前四行判断用不用停用词，和之前一样。
</span></li>
<li class="md_li"><span>由于饼图只考虑前几条数据，所以筛选前十个。
</span></li>
<li class="md_li"><span>x是从df里选择的每个词出现的次数那一列。
</span></li>
<li class="md_li"><span>y则是标签那一列。
</span></li>
<li class="md_li"><span>expl是一个列表，用于pie的参数，比如[1,0,0,0,0]，表示饼图分成五份，第一个图分割出来。（视觉效果，不写也可以，列表数量一定要和数据数量一样）
</span></li>
<li class="md_li"><span>下面的详细参数可以参考文档。
</span></li>
</ol>
<hr>
<h2 id="toc_3" class="h16">制作柱状图</h2>
<div class="codehilite code_lang_python highlight"><pre><span></span><span class="k">def</span> <span class="nf">draw_bar</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">words</span><span class="p">,</span> <span class="n">stopword</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s1">''</span><span class="p">):</span>
<span class="k">if</span> <span class="n">stopword</span><span class="p">:</span>
<span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">make_df</span><span class="p">(</span><span class="n">words</span><span class="p">,</span> <span class="n">stopword</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">make_df</span><span class="p">(</span><span class="n">words</span><span class="p">)</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">30</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
<span class="n">x</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">))</span>
<span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="n">count</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">count</span> <span class="ow">in</span> <span class="n">data</span><span class="p">]</span>
<span class="n">label</span> <span class="o">=</span> <span class="p">[</span><span class="n">name</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">data</span><span class="p">]</span>
<span class="n">plt</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">tick_label</span><span class="o">=</span><span class="n">label</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'rgbycmk'</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">rotation</span><span class="o">=</span><span class="mi">30</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="n">title</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
<span class="k">for</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="n">plt</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="o">+</span><span class="mf">0.05</span><span class="p">,</span> <span class="s1">'</span><span class="si">%.0f</span><span class="s1">'</span> <span class="o">%</span> <span class="n">b</span><span class="p">,</span> <span class="n">ha</span><span class="o">=</span><span class="s1">'center'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
<!--block_code_end-->
<ol>
<li class="md_li"><span>和饼图差不多，x是数据有多少组，也就是有多少个柱。y是每个数据的词频（相当于y轴的值)，label是数据的名字。
</span></li>
<li class="md_li"><span>for循环是在每一个柱上面显示标签值，zip(x, y)是生成每一个的坐标。
</span></li>
</ol>
<div class="codehilite code_lang_python highlight"><pre><span></span><span class="k">def</span> <span class="nf">draw_barh</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">words</span><span class="p">,</span> <span class="n">stopword</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s1">''</span><span class="p">):</span>
<span class="k">if</span> <span class="n">stopword</span><span class="p">:</span>
<span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">make_df</span><span class="p">(</span><span class="n">words</span><span class="p">,</span> <span class="n">stopword</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">make_df</span><span class="p">(</span><span class="n">words</span><span class="p">)</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">30</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
<span class="n">x</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">))</span>
<span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="n">count</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">count</span> <span class="ow">in</span> <span class="n">data</span><span class="p">]</span>
<span class="n">label</span> <span class="o">=</span> <span class="p">[</span><span class="n">name</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">data</span><span class="p">]</span>
<span class="n">plt</span><span class="o">.</span><span class="n">barh</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">tick_label</span><span class="o">=</span><span class="n">label</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'rgbycmk'</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.2</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="n">title</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'人数'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
<span class="k">for</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="n">plt</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">a</span><span class="p">,</span> <span class="s1">'</span><span class="si">%.0f</span><span class="s1">'</span> <span class="o">%</span> <span class="n">b</span><span class="p">,</span> <span class="n">ha</span><span class="o">=</span><span class="s1">'left'</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s1">'center'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
<!--block_code_end-->
<ul>
<li class="md_li"><span>横向柱状图，和柱状图大同小异。注意x，y坐标就可以。
</span></li>
</ul>
<hr>
<h2 id="toc_4" class="h16">折线图</h2>
<div class="codehilite code_lang_python highlight"><pre><span></span><span class="k">def</span> <span class="nf">draw_plot_birth</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">words</span><span class="p">,</span> <span class="n">stopword</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s1">''</span><span class="p">):</span>
<span class="k">if</span> <span class="n">stopword</span><span class="p">:</span>
<span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">make_df</span><span class="p">(</span><span class="n">words</span><span class="p">,</span> <span class="n">stopword</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">make_df</span><span class="p">(</span><span class="n">words</span><span class="p">)</span>
<span class="n">x_sort</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s1">'segment'</span><span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="mi">42</span><span class="p">:</span><span class="mi">110</span><span class="p">]</span>
<span class="n">x</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">x_sort</span><span class="p">))</span>
<span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="n">name</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">x_sort</span><span class="p">]</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="s1">'b--'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="n">title</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'年份'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'数量'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="p">[</span><span class="n">i</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">x_sort</span><span class="p">],</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">90</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
<!--block_code_end-->
<ul>
<li class="md_li"><span> 这个折线图按月份排序，而不是按词频。x_sort可以改变排序方式
</span></li>
</ul>
<p class="md_block">
<span class="md_line md_line_start md_line_end"> xticks是x轴的标签名。</span>
</p>
</div>
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