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<div class="prompt input_prompt">In [2]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">re</span>
<span class="kn">from</span> <span class="nn">bokeh.plotting</span> <span class="k">import</span> <span class="o">*</span>
<span class="n">output_notebook</span><span class="p">()</span>
</pre></div>
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<span id="e72d9970-314c-4706-a60a-cd6203f0e94f">Loading BokehJS ...</span>
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<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [3]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">plen</span><span class="p">(</span><span class="n">x</span><span class="p">):</span> <span class="nb">print</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="k">def</span> <span class="nf">format_column</span><span class="p">(</span><span class="n">c</span><span class="p">):</span>
<span class="k">return</span> <span class="n">c</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">' '</span><span class="p">,</span> <span class="s1">'_'</span><span class="p">)</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
<span class="n">df</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="s1">'salaries.csv'</span><span class="p">)</span>
<span class="n">df</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="n">format_column</span><span class="p">(</span><span class="n">c</span><span class="p">)</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">]</span>
<span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area"><div class="prompt output_prompt">Out[3]:</div>
<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>timestamp</th>
<th>employer</th>
<th>location</th>
<th>job_title</th>
<th>years_at_employer</th>
<th>years_of_experience</th>
<th>annual_base_pay</th>
<th>signing_bonus</th>
<th>annual_bonus</th>
<th>annual_stock_value/bonus</th>
<th>gender</th>
<th>additional_comments</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>3/21/2016 12:54:49</td>
<td>NaN</td>
<td>Raleigh, NC</td>
<td>Software Developer</td>
<td>NaN</td>
<td>18</td>
<td>122000</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>Male</td>
<td>NaN</td>
</tr>
<tr>
<th>1</th>
<td>3/21/2016 12:58:52</td>
<td>Opower</td>
<td>San Francisco, CA</td>
<td>Systems Engineer</td>
<td>2</td>
<td>13</td>
<td>125000</td>
<td>5000</td>
<td>0</td>
<td>5000 shares</td>
<td>Male</td>
<td>Don't work here.</td>
</tr>
<tr>
<th>2</th>
<td>3/21/2016 12:58:57</td>
<td>NaN</td>
<td>San Diego CA</td>
<td>Systems engineer</td>
<td>1</td>
<td>1 of employment</td>
<td>83,000</td>
<td>0</td>
<td>$50.00</td>
<td>0</td>
<td>Male</td>
<td>NaN</td>
</tr>
<tr>
<th>3</th>
<td>3/21/2016 12:58:58</td>
<td>Walmart</td>
<td>Bentonville, AR</td>
<td>Senior Developer</td>
<td>8</td>
<td>15</td>
<td>65,000</td>
<td>NaN</td>
<td>5,000</td>
<td>3,000</td>
<td>Male</td>
<td>NaN</td>
</tr>
<tr>
<th>4</th>
<td>3/21/2016 12:59:11</td>
<td>Vertical Knowledge</td>
<td>Cleveland, OH</td>
<td>Software Engineer</td>
<td>1</td>
<td>4</td>
<td>86000</td>
<td>5000</td>
<td>6000</td>
<td>0</td>
<td>Male</td>
<td>NaN</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [14]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">create_standard_location</span><span class="p">(</span><span class="n">df</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">standardize</span><span class="p">(</span><span class="n">loc</span><span class="p">):</span>
<span class="k">if</span> <span class="n">pd</span><span class="o">.</span><span class="n">isnull</span><span class="p">(</span><span class="n">loc</span><span class="p">):</span>
<span class="k">return</span> <span class="s1">''</span>
<span class="k">return</span> <span class="n">loc</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">','</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span>
<span class="n">df</span><span class="p">[</span><span class="s1">'std_location'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">location</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">standardize</span><span class="p">)</span>
<span class="k">return</span> <span class="n">df</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">create_standard_location</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">create_clean_salaries</span><span class="p">(</span><span class="n">df</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">clean_individual_salary</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Clean the salary data</span>
<span class="sd"> 1. reject some malformed/difficult to parse data</span>
<span class="sd"> 2. try to detect currency and return the type</span>
<span class="sd"> """</span>
<span class="n">final_salary</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="n">salary_type</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="k">if</span> <span class="n">pd</span><span class="o">.</span><span class="n">notnull</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="n">salary_type</span> <span class="o">=</span> <span class="s1">'USD'</span> <span class="c1"># default</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">sub</span><span class="p">(</span><span class="s2">"[\s,]"</span><span class="p">,</span> <span class="s2">""</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">sub</span><span class="p">(</span><span class="s2">"\.[0-9]*"</span><span class="p">,</span> <span class="s2">""</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
<span class="k">if</span> <span class="s1">'€'</span> <span class="ow">in</span> <span class="n">x</span> <span class="ow">or</span> <span class="s1">'eur'</span> <span class="ow">in</span> <span class="n">x</span><span class="p">:</span>
<span class="n">salary_type</span> <span class="o">=</span> <span class="s1">'Euro'</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">sub</span><span class="p">(</span><span class="s1">r'[€(eur)(euro)]'</span><span class="p">,</span> <span class="s1">''</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
<span class="k">if</span> <span class="s1">'$'</span> <span class="ow">in</span> <span class="n">x</span> <span class="ow">or</span> <span class="s1">'usd'</span> <span class="ow">in</span> <span class="n">x</span><span class="p">:</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">sub</span><span class="p">(</span><span class="s1">r'[\$(usd)]'</span><span class="p">,</span> <span class="s1">''</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
<span class="k">if</span> <span class="s1">'£'</span> <span class="ow">in</span> <span class="n">x</span> <span class="ow">or</span> <span class="s1">'gbp'</span> <span class="ow">in</span> <span class="n">x</span><span class="p">:</span>
<span class="n">salary_type</span> <span class="o">=</span> <span class="s1">'GBP'</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">'[£(gbp)]'</span><span class="p">,</span> <span class="s1">''</span><span class="p">)</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">final_salary</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">except</span><span class="p">:</span>
<span class="c1">#print("convert to numeric failure: '{}'".format(x))</span>
<span class="n">final_salary</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="n">salary_type</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="k">return</span> <span class="p">(</span><span class="n">final_salary</span><span class="p">,</span> <span class="n">salary_type</span><span class="p">)</span>
<span class="n">clean_salary_ufunc</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">frompyfunc</span><span class="p">(</span><span class="n">clean_individual_salary</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="n">final_salary</span><span class="p">,</span> <span class="n">salary_type</span> <span class="o">=</span> <span class="n">clean_salary_ufunc</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">annual_base_pay</span><span class="p">)</span>
<span class="n">df</span><span class="p">[</span><span class="s1">'salary'</span><span class="p">]</span> <span class="o">=</span> <span class="n">final_salary</span>
<span class="n">df</span><span class="p">[</span><span class="s1">'salary_type'</span><span class="p">]</span> <span class="o">=</span> <span class="n">salary_type</span>
<span class="k">return</span> <span class="n">df</span>
<span class="k">def</span> <span class="nf">drop_unreasonable_salaries</span><span class="p">(</span><span class="n">df</span><span class="p">):</span>
<span class="n">reasonable</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'salary'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span> <span class="o">></span> <span class="mi">20000</span> <span class="ow">and</span> <span class="n">x</span> <span class="o"><</span> <span class="mi">360000</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"resonable salaries:"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">reasonable</span><span class="o">.</span><span class="n">value_counts</span><span class="p">())</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">reasonable</span><span class="p">]</span>
<span class="k">return</span> <span class="n">df</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">create_clean_salaries</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
<span class="n">plen</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">drop_unreasonable_salaries</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
<span class="n">plen</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
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<pre>2187
resonable salaries:
True 1934
False 253
Name: salary, dtype: int64
1934
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">loc</span>
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<pre><pandas.core.indexing._LocIndexer at 0x7f919088ca58></pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">create_sex</span><span class="p">(</span><span class="n">df</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">convert_gender</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">if</span> <span class="n">pd</span><span class="o">.</span><span class="n">isnull</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">return</span> <span class="n">x</span>
<span class="k">elif</span> <span class="s1">'female'</span> <span class="ow">in</span> <span class="n">x</span><span class="o">.</span><span class="n">lower</span><span class="p">():</span>
<span class="k">return</span> <span class="s1">'F'</span>
<span class="k">elif</span> <span class="s1">'male'</span> <span class="ow">in</span> <span class="n">x</span><span class="o">.</span><span class="n">lower</span><span class="p">():</span>
<span class="k">return</span> <span class="s1">'M'</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="s1">'other'</span>
<span class="n">df</span><span class="p">[</span><span class="s1">'sex'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">gender</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">convert_gender</span><span class="p">)</span>
<span class="k">return</span> <span class="n">df</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">create_sex</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">dropna</span><span class="p">(</span><span class="n">subset</span><span class="o">=</span><span class="s1">'sex'</span><span class="o">.</span><span class="n">split</span><span class="p">())</span>
<span class="n">df</span><span class="o">.</span><span class="n">sex</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
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<pre>/home/jrenner/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:13: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
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<pre>M 1790
F 66
other 10
Name: sex, dtype: int64</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">cols</span> <span class="o">=</span> <span class="s1">'location job_title salary years_at_employer years_of_experience sex'</span><span class="o">.</span><span class="n">split</span><span class="p">()</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s1">'salary'</span><span class="p">,</span> <span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">drop_unreasonable_years</span><span class="p">(</span><span class="n">df</span><span class="p">):</span>
<span class="n">plen</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">dropna</span><span class="p">(</span><span class="n">subset</span><span class="o">=</span><span class="s1">'salary years_job years_experience'</span><span class="o">.</span><span class="n">split</span><span class="p">())</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">years_experience</span> <span class="o">></span> <span class="mi">0</span><span class="p">]</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">years_experience</span> <span class="o"><</span> <span class="mi">50</span><span class="p">]</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">years_experience</span> <span class="o"><</span> <span class="mi">50</span><span class="p">]</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">years_job</span> <span class="o">>=</span> <span class="mi">0</span><span class="p">]</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">years_job</span> <span class="o"><=</span> <span class="mi">50</span><span class="p">]</span>
<span class="n">plen</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
<span class="k">return</span> <span class="n">df</span>
<span class="n">df</span><span class="p">[</span><span class="s1">'years_job'</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_numeric</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">years_at_employer</span><span class="p">,</span> <span class="n">errors</span><span class="o">=</span><span class="s1">'coerce'</span><span class="p">)</span>
<span class="n">df</span><span class="p">[</span><span class="s1">'years_experience'</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_numeric</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">years_of_experience</span><span class="p">,</span> <span class="n">errors</span><span class="o">=</span><span class="s1">'coerce'</span><span class="p">)</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">drop_unreasonable_years</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
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<pre>1866
1668
</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="n">cols</span><span class="p">]</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
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<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>location</th>
<th>job_title</th>
<th>salary</th>
<th>years_at_employer</th>
<th>years_of_experience</th>
<th>sex</th>
</tr>
</thead>
<tbody>
<tr>
<th>677</th>
<td>san francisco</td>
<td>CTO Google rail product</td>
<td>356000</td>
<td>5</td>
<td>12</td>
<td>M</td>
</tr>
<tr>
<th>1524</th>
<td>NaN</td>
<td>General Manager</td>
<td>350000</td>
<td>0.5</td>
<td>20</td>
<td>M</td>
</tr>
<tr>
<th>488</th>
<td>Los Gatos</td>
<td>Senior Software Engineer</td>
<td>350000</td>
<td>1</td>
<td>10</td>
<td>M</td>
</tr>
<tr>
<th>104</th>
<td>Jersey City, NJ</td>
<td>Consultant, CTO as needed</td>
<td>325000</td>
<td>1</td>
<td>8</td>
<td>M</td>
</tr>
<tr>
<th>510</th>
<td>San Francisco</td>
<td>CTO</td>
<td>310000</td>
<td>2</td>
<td>10</td>
<td>M</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="n">cols</span><span class="p">]</span><span class="o">.</span><span class="n">tail</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
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<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>location</th>
<th>job_title</th>
<th>salary</th>
<th>years_at_employer</th>
<th>years_of_experience</th>
<th>sex</th>
</tr>
</thead>
<tbody>
<tr>
<th>1121</th>
<td>Madrid</td>
<td>Support</td>
<td>22500</td>
<td>15</td>
<td>15</td>
<td>M</td>
</tr>
<tr>
<th>1148</th>
<td>Cluj-Napoca, Romania</td>
<td>Software Engineer</td>
<td>21500</td>
<td>2</td>
<td>2</td>
<td>M</td>
</tr>
<tr>
<th>577</th>
<td>Bangalore, India</td>
<td>Developer</td>
<td>21496</td>
<td>0.5</td>
<td>3</td>
<td>M</td>
</tr>
<tr>
<th>1216</th>
<td>Nottingham UK</td>
<td>Jnr Software Developer</td>
<td>20500</td>
<td>1</td>
<td>1</td>
<td>M</td>
</tr>
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<th>841</th>
<td>Paris, France</td>
<td>Research Engineer</td>
<td>20400</td>
<td>0</td>
<td>1</td>
<td>M</td>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">salary_type</span> <span class="o">==</span> <span class="s1">'USD'</span><span class="p">]</span>
<span class="n">loc</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">location</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="s1">''</span><span class="p">)</span>
<span class="n">sv_set</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'bay area'</span><span class="p">,</span> <span class="s1">'san jose'</span><span class="p">,</span> <span class="s1">'san francisco'</span><span class="p">,</span> <span class="s1">'mountain view'</span><span class="p">}</span>
<span class="n">is_silicon</span> <span class="o">=</span> <span class="n">loc</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span><span class="o">.</span><span class="n">isin</span><span class="p">(</span><span class="n">sv_set</span><span class="p">)</span>
<span class="n">sv_df</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">is_silicon</span><span class="p">]</span>
<span class="n">other_df</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="o">~</span><span class="n">is_silicon</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"silicon valley:"</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">sv_df</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"other:"</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">other_df</span><span class="p">))</span>
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<pre>silicon valley: 158
other: 1473
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<h1 id="Basic-Salary-Info">Basic Salary Info<a class="anchor-link" href="#Basic-Salary-Info">¶</a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">show_salary_info</span><span class="p">():</span>
<span class="n">dlist</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">def</span> <span class="nf">add</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">name</span><span class="p">):</span>
<span class="n">mean</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">salary</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="n">median</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">salary</span><span class="o">.</span><span class="n">median</span><span class="p">()</span>
<span class="n">quants</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">salary</span><span class="o">.</span><span class="n">quantile</span><span class="p">([</span><span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.25</span><span class="p">,</span> <span class="mf">0.75</span><span class="p">,</span> <span class="mf">0.9</span><span class="p">,</span> <span class="mf">0.99</span><span class="p">]))</span>
<span class="n">d</span> <span class="o">=</span> <span class="p">{</span>
<span class="s1">'name'</span> <span class="p">:</span> <span class="n">name</span><span class="p">,</span>
<span class="s1">'mean'</span> <span class="p">:</span> <span class="n">mean</span><span class="p">,</span>
<span class="s1">'median'</span><span class="p">:</span> <span class="n">median</span><span class="p">,</span>
<span class="s1">'1%'</span> <span class="p">:</span> <span class="n">quants</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
<span class="s1">'10%'</span> <span class="p">:</span> <span class="n">quants</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
<span class="s1">'25%'</span> <span class="p">:</span> <span class="n">quants</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span>
<span class="s1">'75%'</span> <span class="p">:</span> <span class="n">quants</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span>
<span class="s1">'90%'</span> <span class="p">:</span> <span class="n">quants</span><span class="p">[</span><span class="mi">4</span><span class="p">],</span>
<span class="s1">'99%'</span> <span class="p">:</span> <span class="n">quants</span><span class="p">[</span><span class="mi">5</span><span class="p">],</span>
<span class="p">}</span>
<span class="n">dlist</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">d</span><span class="p">)</span>
<span class="n">sv_male_df</span> <span class="o">=</span> <span class="n">sv_df</span><span class="p">[</span><span class="n">sv_df</span><span class="o">.</span><span class="n">sex</span> <span class="o">==</span> <span class="s1">'M'</span><span class="p">]</span>
<span class="n">sv_female_df</span> <span class="o">=</span> <span class="n">sv_df</span><span class="p">[</span><span class="n">sv_df</span><span class="o">.</span><span class="n">sex</span> <span class="o">==</span> <span class="s1">'F'</span><span class="p">]</span>
<span class="n">other_male_df</span> <span class="o">=</span> <span class="n">other_df</span><span class="p">[</span><span class="n">other_df</span><span class="o">.</span><span class="n">sex</span> <span class="o">==</span> <span class="s1">'M'</span><span class="p">]</span>
<span class="n">other_female_df</span> <span class="o">=</span> <span class="n">other_df</span><span class="p">[</span><span class="n">other_df</span><span class="o">.</span><span class="n">sex</span> <span class="o">==</span> <span class="s1">'F'</span><span class="p">]</span>
<span class="n">add</span><span class="p">(</span><span class="n">sv_df</span><span class="p">,</span> <span class="s1">'Silicon Valley'</span><span class="p">)</span>
<span class="n">add</span><span class="p">(</span><span class="n">other_df</span><span class="p">,</span> <span class="s1">'Non-Silicon Valley'</span><span class="p">)</span>
<span class="n">add</span><span class="p">(</span><span class="n">sv_male_df</span><span class="p">,</span> <span class="s1">'SV_Males'</span><span class="p">)</span>
<span class="n">add</span><span class="p">(</span><span class="n">other_male_df</span><span class="p">,</span> <span class="s1">'Non_SV_Males'</span><span class="p">)</span>
<span class="n">add</span><span class="p">(</span><span class="n">sv_female_df</span><span class="p">,</span> <span class="s1">'SV_Females'</span><span class="p">)</span>
<span class="n">add</span><span class="p">(</span><span class="n">other_female_df</span><span class="p">,</span> <span class="s1">'Non_SV_Females'</span><span class="p">)</span>
<span class="n">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="n">dlist</span><span class="p">)</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'name mean median 1</span><span class="si">% 10%</span><span class="s1"> 25</span><span class="si">% 75%</span><span class="s1"> 90</span><span class="si">% 99%</span><span class="s1">'</span><span class="o">.</span><span class="n">split</span><span class="p">()]</span>
<span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">'mean'</span><span class="p">,</span> <span class="s1">'median'</span><span class="p">]</span> <span class="o">+</span> <span class="p">[</span><span class="n">c</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">columns</span> <span class="k">if</span> <span class="s1">'%'</span> <span class="ow">in</span> <span class="n">c</span><span class="p">]:</span>
<span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
<span class="n">s</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">style</span>
<span class="n">s</span><span class="o">.</span><span class="n">background_gradient</span><span class="p">(</span><span class="n">cmap</span><span class="o">=</span><span class="s1">'coolwarm'</span><span class="p">)</span>
<span class="k">return</span> <span class="n">s</span>
<span class="c1"># print("SV Salary Quantiles:")</span>
<span class="c1"># print(sv_df.salary.quantile([0.01, 0.1, 0.25, 0.5, 0.75, 0.9, 0.99]))</span>
<span class="c1"># print("Non_SV Salary Quantiles:")</span>
<span class="c1"># print(other_df.salary.quantile([0.01, 0.1, 0.25, 0.5, 0.75, 0.9, 0.99]))</span>
<span class="n">show_salary_info</span><span class="p">()</span>
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<table id="T_9978de2e_efe1_11e5_b2f4_000f6006a350" None>
<thead>
<tr>
<th class="blank">
<th class="col_heading level0 col0">name
<th class="col_heading level0 col1">mean
<th class="col_heading level0 col2">median
<th class="col_heading level0 col3">1%
<th class="col_heading level0 col4">10%
<th class="col_heading level0 col5">25%
<th class="col_heading level0 col6">75%
<th class="col_heading level0 col7">90%
<th class="col_heading level0 col8">99%
</tr>
</thead>
<tbody>
<tr>
<th id="T_9978de2e_efe1_11e5_b2f4_000f6006a350" class="row_heading level8 row0">
0
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row0_col0" class="data row0 col0">
Silicon Valley
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row0_col1" class="data row0 col1">
134720
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row0_col2" class="data row0 col2">
130000
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row0_col3" class="data row0 col3">
59560
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row0_col4" class="data row0 col4">
95000
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row0_col5" class="data row0 col5">
110000
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row0_col6" class="data row0 col6">
155750
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row0_col7" class="data row0 col7">
175000
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row0_col8" class="data row0 col8">
270100
</tr>
<tr>
<th id="T_9978de2e_efe1_11e5_b2f4_000f6006a350" class="row_heading level8 row1">
1
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row1_col0" class="data row1 col0">
Non-Silicon Valley
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row1_col1" class="data row1 col1">
99759
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row1_col2" class="data row1 col2">
95000
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row1_col3" class="data row1 col3">
25000
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row1_col4" class="data row1 col4">
47000
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row1_col5" class="data row1 col5">
70000
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row1_col6" class="data row1 col6">
125000
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row1_col7" class="data row1 col7">
150000
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row1_col8" class="data row1 col8">
242799
</tr>
<tr>
<th id="T_9978de2e_efe1_11e5_b2f4_000f6006a350" class="row_heading level8 row2">
2
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row2_col0" class="data row2 col0">
SV_Males
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row2_col1" class="data row2 col1">
137120
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row2_col2" class="data row2 col2">
132500
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row2_col3" class="data row2 col3">
66290
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row2_col4" class="data row2 col4">
99400
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row2_col5" class="data row2 col5">
110750
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row2_col6" class="data row2 col6">
160500
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row2_col7" class="data row2 col7">
175000
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row2_col8" class="data row2 col8">
277100
</tr>
<tr>
<th id="T_9978de2e_efe1_11e5_b2f4_000f6006a350" class="row_heading level8 row3">
3
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row3_col0" class="data row3 col0">
Non_SV_Males
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row3_col1" class="data row3 col1">
100125
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row3_col2" class="data row3 col2">
97000
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row3_col3" class="data row3 col3">
25000
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row3_col4" class="data row3 col4">
48000
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row3_col5" class="data row3 col5">
70000
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row3_col6" class="data row3 col6">
125000
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row3_col7" class="data row3 col7">
150000
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row3_col8" class="data row3 col8">
239359
</tr>
<tr>
<th id="T_9978de2e_efe1_11e5_b2f4_000f6006a350" class="row_heading level8 row4">
4
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row4_col0" class="data row4 col0">
SV_Females
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row4_col1" class="data row4 col1">
99187
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row4_col2" class="data row4 col2">
107500
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row4_col3" class="data row4 col3">
56800
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row4_col4" class="data row4 col4">
73000
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row4_col5" class="data row4 col5">
87500
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row4_col6" class="data row4 col6">
111020
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row4_col7" class="data row4 col7">
120959
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row4_col8" class="data row4 col8">
124595
</tr>
<tr>
<th id="T_9978de2e_efe1_11e5_b2f4_000f6006a350" class="row_heading level8 row5">
5
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row5_col0" class="data row5 col0">
Non_SV_Females
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row5_col1" class="data row5 col1">
91765
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row5_col2" class="data row5 col2">
90000
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row5_col3" class="data row5 col3">
29480
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row5_col4" class="data row5 col4">
52400
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row5_col5" class="data row5 col5">
73000
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row5_col6" class="data row5 col6">
110000
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row5_col7" class="data row5 col7">
124200
<td id="T_9978de2e_efe1_11e5_b2f4_000f6006a350row5_col8" class="data row5 col8">
206799
</tr>
</tbody>
</table>
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<h1 id="Visualizations">Visualizations<a class="anchor-link" href="#Visualizations">¶</a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">F_ALPHA</span> <span class="o">=</span> <span class="mf">0.3</span>
<span class="k">def</span> <span class="nf">graph_salary_vs_experience</span><span class="p">():</span>
<span class="n">p</span> <span class="o">=</span> <span class="n">figure</span><span class="p">(</span><span class="n">title</span><span class="o">=</span><span class="s2">"Salary vs Years of Experience"</span><span class="p">,</span> <span class="n">y_axis_label</span><span class="o">=</span><span class="s1">'Salary (1k)'</span><span class="p">,</span> <span class="n">x_axis_label</span><span class="o">=</span><span class="s1">'Years Experience'</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">sv_df</span><span class="o">.</span><span class="n">years_experience</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">sv_df</span><span class="o">.</span><span class="n">salary</span> <span class="o">/</span> <span class="mi">1000</span>
<span class="n">p</span><span class="o">.</span><span class="n">scatter</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">size</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">fill_alpha</span><span class="o">=</span><span class="n">F_ALPHA</span><span class="p">,</span> <span class="n">line_alpha</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">fill_color</span><span class="o">=</span><span class="s1">'red'</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="s1">'Silicon Valley'</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">other_df</span><span class="o">.</span><span class="n">years_experience</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">other_df</span><span class="o">.</span><span class="n">salary</span> <span class="o">/</span> <span class="mi">1000</span>
<span class="n">p</span><span class="o">.</span><span class="n">scatter</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">size</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">fill_alpha</span><span class="o">=</span><span class="n">F_ALPHA</span><span class="p">,</span> <span class="n">line_alpha</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">fill_color</span><span class="o">=</span><span class="s1">'grey'</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="s1">'Other USA'</span><span class="p">)</span>
<span class="n">show</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
<span class="n">graph_salary_vs_experience</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">graph_salary_vs_years_at_job</span><span class="p">():</span>
<span class="n">p</span> <span class="o">=</span> <span class="n">figure</span><span class="p">(</span><span class="n">title</span><span class="o">=</span><span class="s2">"Salary vs Years at Job"</span><span class="p">,</span> <span class="n">y_axis_label</span><span class="o">=</span><span class="s1">'Salary (1k)'</span><span class="p">,</span> <span class="n">x_axis_label</span><span class="o">=</span><span class="s1">'Years at Job'</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">sv_df</span><span class="o">.</span><span class="n">years_job</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">sv_df</span><span class="o">.</span><span class="n">salary</span> <span class="o">/</span> <span class="mi">1000</span>
<span class="n">p</span><span class="o">.</span><span class="n">scatter</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">size</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">fill_alpha</span><span class="o">=</span><span class="n">F_ALPHA</span><span class="p">,</span> <span class="n">line_alpha</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">fill_color</span><span class="o">=</span><span class="s1">'red'</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="s1">'Silicon Valley'</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">other_df</span><span class="o">.</span><span class="n">years_job</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">other_df</span><span class="o">.</span><span class="n">salary</span> <span class="o">/</span> <span class="mi">1000</span>
<span class="n">p</span><span class="o">.</span><span class="n">scatter</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">size</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">fill_alpha</span><span class="o">=</span><span class="n">F_ALPHA</span><span class="p">,</span> <span class="n">line_alpha</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">fill_color</span><span class="o">=</span><span class="s1">'grey'</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="s1">'Other USA'</span><span class="p">)</span>
<span class="n">show</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
<span class="n">graph_salary_vs_years_at_job</span><span class="p">()</span>
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<div class="prompt input_prompt">In [26]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">graph_salary_vs_gender</span><span class="p">(</span><span class="n">df</span><span class="p">):</span>
<span class="n">male</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">sex</span> <span class="o">==</span> <span class="s1">'M'</span><span class="p">]</span>
<span class="n">female</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">sex</span> <span class="o">==</span> <span class="s1">'F'</span><span class="p">]</span>
<span class="n">other</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">sex</span> <span class="o">==</span> <span class="s1">'other'</span><span class="p">]</span>
<span class="n">p</span> <span class="o">=</span> <span class="n">figure</span><span class="p">(</span><span class="n">title</span><span class="o">=</span><span class="s2">"Salary vs Years Experience (by Gender)"</span><span class="p">,</span> <span class="n">y_axis_label</span><span class="o">=</span><span class="s1">'Salary (1k)'</span><span class="p">,</span> <span class="n">x_axis_label</span><span class="o">=</span><span class="s1">'Years Experience'</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">male</span><span class="o">.</span><span class="n">years_experience</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">male</span><span class="o">.</span><span class="n">salary</span> <span class="o">/</span> <span class="mi">1000</span>
<span class="n">p</span><span class="o">.</span><span class="n">scatter</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">size</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">fill_alpha</span><span class="o">=</span><span class="n">F_ALPHA</span><span class="p">,</span> <span class="n">line_alpha</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">fill_color</span><span class="o">=</span><span class="s1">'grey'</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="s1">'Male'</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">female</span><span class="o">.</span><span class="n">years_experience</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">female</span><span class="o">.</span><span class="n">salary</span> <span class="o">/</span> <span class="mi">1000</span>
<span class="n">p</span><span class="o">.</span><span class="n">scatter</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">size</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">fill_alpha</span><span class="o">=</span><span class="n">F_ALPHA</span><span class="p">,</span> <span class="n">line_alpha</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">fill_color</span><span class="o">=</span><span class="s1">'blue'</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="s1">'Female'</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">other</span><span class="o">.</span><span class="n">years_experience</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">other</span><span class="o">.</span><span class="n">salary</span> <span class="o">/</span> <span class="mi">1000</span>
<span class="n">p</span><span class="o">.</span><span class="n">scatter</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">size</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">fill_alpha</span><span class="o">=</span><span class="n">F_ALPHA</span><span class="p">,</span> <span class="n">line_alpha</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">fill_color</span><span class="o">=</span><span class="s1">'red'</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="s1">'Other'</span><span class="p">)</span>
<span class="n">show</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
<span class="n">graph_salary_vs_gender</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
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<h1 id="Let's-try-to-see-salary-by-common-geographic-locations">Let's try to see salary by common geographic locations<a class="anchor-link" href="#Let's-try-to-see-salary-by-common-geographic-locations">¶</a></h1>
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<div class="prompt input_prompt">In [64]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">regional_salaries</span><span class="p">():</span>
<span class="n">res_list</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">grp</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'std_location'</span><span class="p">):</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">grp</span><span class="p">)</span> <span class="o"><</span> <span class="mi">8</span> <span class="ow">or</span> <span class="n">name</span> <span class="o">==</span> <span class="s1">''</span><span class="p">:</span>
<span class="k">continue</span>
<span class="n">res</span> <span class="o">=</span> <span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">grp</span><span class="o">.</span><span class="n">salary</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> <span class="o">/</span> <span class="mi">1000</span><span class="p">)</span>
<span class="n">res_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">res_list</span><span class="p">)</span>
<span class="n">p</span> <span class="o">=</span> <span class="n">figure</span><span class="p">()</span>
<span class="n">p</span><span class="o">.</span><span class="n">plot_width</span> <span class="o">=</span> <span class="mi">1000</span>
<span class="n">p</span><span class="o">.</span><span class="n">plot_height</span> <span class="o">=</span> <span class="mi">800</span>
<span class="k">def</span> <span class="nf">sort_func</span><span class="p">(</span><span class="n">tup</span><span class="p">):</span>
<span class="k">return</span> <span class="n">tup</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="n">res_list</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">key</span><span class="o">=</span><span class="n">sort_func</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</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">res_list</span><span class="p">))]</span>
<span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="n">r</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">res_list</span><span class="p">]</span>
<span class="n">p</span><span class="o">.</span><span class="n">scatter</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">texts</span> <span class="o">=</span> <span class="p">[</span><span class="n">r</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">res_list</span><span class="p">]</span>
<span class="n">p</span><span class="o">.</span><span class="n">text</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">texts</span><span class="p">,</span> <span class="n">angle</span><span class="o">=-</span><span class="mf">0.8</span><span class="p">)</span>
<span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">mean</span> <span class="ow">in</span> <span class="n">res_list</span><span class="p">:</span>
<span class="k">pass</span>
<span class="n">show</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
<span class="n">regional_salaries</span><span class="p">()</span>
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<pre>[('atlanta', 91.181727272727272), ('austin', 93.223684210526315), ('bay area', 134.91666666666666), ('berlin', 60.545454545454547), ('boston', 107.34480851063829), ('cambridge', 79.25), ('chicago', 97.824324324324323), ('denver', 97.763157894736835), ('houston', 127.0), ('london', 57.958333333333336), ('los angeles', 120.01851851851852), ('menlo park', 155.15384615384616), ('minneapolis', 87.44285714285715), ('mountain view', 136.18039999999999), ('new york', 125.16666666666667), ('new york city', 114.36666666666667), ('nyc', 119.0948275862069), ('palo alto', 135.45454545454544), ('philadelphia', 91.230769230769241), ('portland', 98.029411764705884), ('redmond', 122.32476190476191), ('remote', 128.79166666666669), ('salt lake city', 92.549999999999997), ('san diego', 93.57692307692308), ('san francisco', 133.69791411042945), ('san jose', 141.80769230769232), ('seattle', 120.27631578947368), ('sf', 146.59999999999999), ('sunnyvale', 123.25), ('sweden', 53.715375000000002), ('toronto', 97.390461538461537), ('vancouver', 93.400000000000006), ('washington', 143.4375), ('washington dc', 111.2)]
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