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add filter
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‎Chapter3/filter.ipynb‎

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"source": [
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"df.loc[df.groupby(\"type\")[\"type\"].transform(\"size\") > 1]"
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{
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"cell_type": "markdown",
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"id": "7afb3774",
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"source": [
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"### df.filter: Filter Columns Based on a Subset of Their Names"
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]
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},
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{
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"cell_type": "markdown",
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"id": "54f71d1b",
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"source": [
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"If you want to filter columns of a pandas DataFrame based on a subset of their names, use `DataFrame.filter`. In the example below, we only choose the columns that contain the word \"cat\". "
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]
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"cell_type": "code",
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"execution_count": 6,
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>cat1</th>\n",
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" <th>cat2</th>\n",
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" <th>num1</th>\n",
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" cat1 cat2 num1\n",
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"output_type": "execute_result"
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}
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],
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"source": [
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"import pandas as pd\n",
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"\n",
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"df = pd.DataFrame({\"cat1\": [\"a\", \"b\"], \"cat2\": [\"b\", \"c\"], \"num1\": [1, 2]})\n",
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"df \n"
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]
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>cat1</th>\n",
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" <th>cat2</th>\n",
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"source": [
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"df.filter(like='cat', axis=1)"
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‎docs/Chapter3/filter.html‎

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4.6.3. Filter a pandas DataFrame by Value Counts
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<li class="toc-h2 nav-item toc-entry">
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<a class="reference internal nav-link" href="#df-filter-filter-columns-based-on-a-subset-of-their-names">
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4.6.4. df.filter: Filter Columns Based on a Subset of Their Names
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<div class="section" id="df-filter-filter-columns-based-on-a-subset-of-their-names">
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<h2><span class="section-number">4.6.4. </span>df.filter: Filter Columns Based on a Subset of Their Names<a class="headerlink" href="#df-filter-filter-columns-based-on-a-subset-of-their-names" title="Permalink to this headline"></a></h2>
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<p>If you want to filter columns of a pandas DataFrame based on a subset of their names, use <code class="docutils literal notranslate"><span class="pre">DataFrame.filter</span></code>. In the example below, we only choose the columns that contain the word “cat”.</p>
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
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<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="s2">&quot;cat1&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;a&quot;</span><span class="p">,</span> <span class="s2">&quot;b&quot;</span><span class="p">],</span> <span class="s2">&quot;cat2&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;b&quot;</span><span class="p">,</span> <span class="s2">&quot;c&quot;</span><span class="p">],</span> <span class="s2">&quot;num1&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]})</span>
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<span class="n">df</span>
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<th>cat1</th>
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">like</span><span class="o">=</span><span class="s1">&#39;cat&#39;</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
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<th>cat1</th>
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