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Data analytics_Python functions with examples.md.html
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Data analytics_Python functions with examples.md.html
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<hr>
<p class="has-line-data" data-line-start="1" data-line-end="2"><strong><em>A structured and detailed list covering a wide range of functions across various libraries commonly used in data analytics. This list will include functions from libraries like NumPy, pandas, matplotlib, seaborn, scikit-learn, and more. Each entry will include a brief description and an example where applicable.</em></strong></p>
<hr>
<h3 class="code-line" data-line-start="5" data-line-end="6"><a id="NumPy_Functions_5"></a>NumPy Functions</h3>
<ol>
<li class="has-line-data" data-line-start="7" data-line-end="13">
<p class="has-line-data" data-line-start="7" data-line-end="8"><strong><code>np.array()</code></strong> - Create a NumPy array.</p>
<pre><code class="has-line-data" data-line-start="9" data-line-end="12" class="language-python"><span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np
arr = np.array([<span class="hljs-number">1</span>, <span class="hljs-number">2</span>, <span class="hljs-number">3</span>])
</code></pre>
</li>
<li class="has-line-data" data-line-start="13" data-line-end="18">
<p class="has-line-data" data-line-start="13" data-line-end="14"><strong><code>np.zeros()</code></strong> - Create an array filled with zeros.</p>
<pre><code class="has-line-data" data-line-start="15" data-line-end="17" class="language-python">arr = np.zeros((<span class="hljs-number">3</span>, <span class="hljs-number">3</span>))
</code></pre>
</li>
<li class="has-line-data" data-line-start="18" data-line-end="23">
<p class="has-line-data" data-line-start="18" data-line-end="19"><strong><code>np.ones()</code></strong> - Create an array filled with ones.</p>
<pre><code class="has-line-data" data-line-start="20" data-line-end="22" class="language-python">arr = np.ones((<span class="hljs-number">2</span>, <span class="hljs-number">2</span>))
</code></pre>
</li>
<li class="has-line-data" data-line-start="23" data-line-end="28">
<p class="has-line-data" data-line-start="23" data-line-end="24"><strong><code>np.eye()</code></strong> - Create an identity matrix.</p>
<pre><code class="has-line-data" data-line-start="25" data-line-end="27" class="language-python">arr = np.eye(<span class="hljs-number">3</span>)
</code></pre>
</li>
<li class="has-line-data" data-line-start="28" data-line-end="33">
<p class="has-line-data" data-line-start="28" data-line-end="29"><strong><code>np.arange()</code></strong> - Create an array with a range of values.</p>
<pre><code class="has-line-data" data-line-start="30" data-line-end="32" class="language-python">arr = np.arange(<span class="hljs-number">0</span>, <span class="hljs-number">10</span>, <span class="hljs-number">2</span>)
</code></pre>
</li>
<li class="has-line-data" data-line-start="33" data-line-end="38">
<p class="has-line-data" data-line-start="33" data-line-end="34"><strong><code>np.linspace()</code></strong> - Create an array with evenly spaced values.</p>
<pre><code class="has-line-data" data-line-start="35" data-line-end="37" class="language-python">arr = np.linspace(<span class="hljs-number">0</span>, <span class="hljs-number">1</span>, <span class="hljs-number">5</span>)
</code></pre>
</li>
<li class="has-line-data" data-line-start="38" data-line-end="43">
<p class="has-line-data" data-line-start="38" data-line-end="39"><strong><code>np.random.rand()</code></strong> - Generate random values between 0 and 1.</p>
<pre><code class="has-line-data" data-line-start="40" data-line-end="42" class="language-python">arr = np.random.rand(<span class="hljs-number">2</span>, <span class="hljs-number">3</span>)
</code></pre>
</li>
<li class="has-line-data" data-line-start="43" data-line-end="48">
<p class="has-line-data" data-line-start="43" data-line-end="44"><strong><code>np.random.randint()</code></strong> - Generate random integers.</p>
<pre><code class="has-line-data" data-line-start="45" data-line-end="47" class="language-python">arr = np.random.randint(<span class="hljs-number">0</span>, <span class="hljs-number">10</span>, (<span class="hljs-number">2</span>, <span class="hljs-number">3</span>))
</code></pre>
</li>
<li class="has-line-data" data-line-start="48" data-line-end="53">
<p class="has-line-data" data-line-start="48" data-line-end="49"><strong><code>np.mean()</code></strong> - Compute the mean of an array.</p>
<pre><code class="has-line-data" data-line-start="50" data-line-end="52" class="language-python">mean = np.mean([<span class="hljs-number">1</span>, <span class="hljs-number">2</span>, <span class="hljs-number">3</span>, <span class="hljs-number">4</span>])
</code></pre>
</li>
<li class="has-line-data" data-line-start="53" data-line-end="58">
<p class="has-line-data" data-line-start="53" data-line-end="54"><strong><code>np.median()</code></strong> - Compute the median of an array.</p>
<pre><code class="has-line-data" data-line-start="55" data-line-end="57" class="language-python">median = np.median([<span class="hljs-number">1</span>, <span class="hljs-number">2</span>, <span class="hljs-number">3</span>, <span class="hljs-number">4</span>])
</code></pre>
</li>
<li class="has-line-data" data-line-start="58" data-line-end="63">
<p class="has-line-data" data-line-start="58" data-line-end="59"><strong><code>np.std()</code></strong> - Compute the standard deviation of an array.</p>
<pre><code class="has-line-data" data-line-start="60" data-line-end="62" class="language-python">std_dev = np.std([<span class="hljs-number">1</span>, <span class="hljs-number">2</span>, <span class="hljs-number">3</span>, <span class="hljs-number">4</span>])
</code></pre>
</li>
<li class="has-line-data" data-line-start="63" data-line-end="68">
<p class="has-line-data" data-line-start="63" data-line-end="64"><strong><code>np.sum()</code></strong> - Compute the sum of array elements.</p>
<pre><code class="has-line-data" data-line-start="65" data-line-end="67" class="language-python">total = np.sum([<span class="hljs-number">1</span>, <span class="hljs-number">2</span>, <span class="hljs-number">3</span>, <span class="hljs-number">4</span>])
</code></pre>
</li>
<li class="has-line-data" data-line-start="68" data-line-end="73">
<p class="has-line-data" data-line-start="68" data-line-end="69"><strong><code>np.prod()</code></strong> - Compute the product of array elements.</p>
<pre><code class="has-line-data" data-line-start="70" data-line-end="72" class="language-python">product = np.prod([<span class="hljs-number">1</span>, <span class="hljs-number">2</span>, <span class="hljs-number">3</span>, <span class="hljs-number">4</span>])
</code></pre>
</li>
<li class="has-line-data" data-line-start="73" data-line-end="78">
<p class="has-line-data" data-line-start="73" data-line-end="74"><strong><code>np.min()</code></strong> - Compute the minimum of an array.</p>
<pre><code class="has-line-data" data-line-start="75" data-line-end="77" class="language-python">min_val = np.min([<span class="hljs-number">1</span>, <span class="hljs-number">2</span>, <span class="hljs-number">3</span>, <span class="hljs-number">4</span>])
</code></pre>
</li>
<li class="has-line-data" data-line-start="78" data-line-end="83">
<p class="has-line-data" data-line-start="78" data-line-end="79"><strong><code>np.max()</code></strong> - Compute the maximum of an array.</p>
<pre><code class="has-line-data" data-line-start="80" data-line-end="82" class="language-python">max_val = np.max([<span class="hljs-number">1</span>, <span class="hljs-number">2</span>, <span class="hljs-number">3</span>, <span class="hljs-number">4</span>])
</code></pre>
</li>
<li class="has-line-data" data-line-start="83" data-line-end="88">
<p class="has-line-data" data-line-start="83" data-line-end="84"><strong><code>np.dot()</code></strong> - Compute the dot product of two arrays.</p>
<pre><code class="has-line-data" data-line-start="85" data-line-end="87" class="language-python">result = np.dot([<span class="hljs-number">1</span>, <span class="hljs-number">2</span>], [<span class="hljs-number">3</span>, <span class="hljs-number">4</span>])
</code></pre>
</li>
<li class="has-line-data" data-line-start="88" data-line-end="94">
<p class="has-line-data" data-line-start="88" data-line-end="89"><strong><code>np.transpose()</code></strong> - Transpose an array.</p>
<pre><code class="has-line-data" data-line-start="90" data-line-end="93" class="language-python">arr = np.array([[<span class="hljs-number">1</span>, <span class="hljs-number">2</span>], [<span class="hljs-number">3</span>, <span class="hljs-number">4</span>]])
transposed = np.transpose(arr)
</code></pre>
</li>
<li class="has-line-data" data-line-start="94" data-line-end="101">
<p class="has-line-data" data-line-start="94" data-line-end="95"><strong><code>np.concatenate()</code></strong> - Concatenate two or more arrays.</p>
<pre><code class="has-line-data" data-line-start="96" data-line-end="100" class="language-python">arr1 = np.array([<span class="hljs-number">1</span>, <span class="hljs-number">2</span>])
arr2 = np.array([<span class="hljs-number">3</span>, <span class="hljs-number">4</span>])
concatenated = np.concatenate((arr1, arr2))
</code></pre>
</li>
<li class="has-line-data" data-line-start="101" data-line-end="107">
<p class="has-line-data" data-line-start="101" data-line-end="102"><strong><code>np.reshape()</code></strong> - Reshape an array.</p>
<pre><code class="has-line-data" data-line-start="103" data-line-end="106" class="language-python">arr = np.array([<span class="hljs-number">1</span>, <span class="hljs-number">2</span>, <span class="hljs-number">3</span>, <span class="hljs-number">4</span>])
reshaped = np.reshape(arr, (<span class="hljs-number">2</span>, <span class="hljs-number">2</span>))
</code></pre>
</li>
<li class="has-line-data" data-line-start="107" data-line-end="113">
<p class="has-line-data" data-line-start="107" data-line-end="108"><strong><code>np.split()</code></strong> - Split an array into multiple sub-arrays.</p>
<pre><code class="has-line-data" data-line-start="109" data-line-end="112" class="language-python">arr = np.array([<span class="hljs-number">1</span>, <span class="hljs-number">2</span>, <span class="hljs-number">3</span>, <span class="hljs-number">4</span>])
split = np.split(arr, <span class="hljs-number">2</span>)
</code></pre>
</li>
<li class="has-line-data" data-line-start="113" data-line-end="118">
<p class="has-line-data" data-line-start="113" data-line-end="114"><strong><code>np.where()</code></strong> - Return indices where conditions are true.</p>
<pre><code class="has-line-data" data-line-start="115" data-line-end="117" class="language-python">indices = np.where([<span class="hljs-number">1</span>, <span class="hljs-number">2</span>, <span class="hljs-number">3</span>] > <span class="hljs-number">1</span>)
</code></pre>
</li>
<li class="has-line-data" data-line-start="118" data-line-end="123">
<p class="has-line-data" data-line-start="118" data-line-end="119"><strong><code>np.unique()</code></strong> - Find unique elements in an array.</p>
<pre><code class="has-line-data" data-line-start="120" data-line-end="122" class="language-python">unique_elements = np.unique([<span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">2</span>, <span class="hljs-number">2</span>])
</code></pre>
</li>
<li class="has-line-data" data-line-start="123" data-line-end="128">
<p class="has-line-data" data-line-start="123" data-line-end="124"><strong><code>np.histogram()</code></strong> - Compute the histogram of a dataset.</p>
<pre><code class="has-line-data" data-line-start="125" data-line-end="127" class="language-python">hist, bin_edges = np.histogram([<span class="hljs-number">1</span>, <span class="hljs-number">2</span>, <span class="hljs-number">2</span>, <span class="hljs-number">3</span>], bins=<span class="hljs-number">3</span>)
</code></pre>
</li>
<li class="has-line-data" data-line-start="128" data-line-end="133">
<p class="has-line-data" data-line-start="128" data-line-end="129"><strong><code>np.corrcoef()</code></strong> - Compute the correlation coefficient matrix.</p>
<pre><code class="has-line-data" data-line-start="130" data-line-end="132" class="language-python">corr_matrix = np.corrcoef([<span class="hljs-number">1</span>, <span class="hljs-number">2</span>, <span class="hljs-number">3</span>], [<span class="hljs-number">4</span>, <span class="hljs-number">5</span>, <span class="hljs-number">6</span>])
</code></pre>
</li>
<li class="has-line-data" data-line-start="133" data-line-end="138">
<p class="has-line-data" data-line-start="133" data-line-end="134"><strong><code>np.polyfit()</code></strong> - Fit a polynomial to data.</p>
<pre><code class="has-line-data" data-line-start="135" data-line-end="137" class="language-python">coeffs = np.polyfit([<span class="hljs-number">1</span>, <span class="hljs-number">2</span>, <span class="hljs-number">3</span>], [<span class="hljs-number">4</span>, <span class="hljs-number">5</span>, <span class="hljs-number">6</span>], <span class="hljs-number">1</span>)
</code></pre>
</li>
</ol>
<h3 class="code-line" data-line-start="138" data-line-end="139"><a id="pandas_Functions_138"></a>pandas Functions</h3>
<ol start="26">
<li class="has-line-data" data-line-start="140" data-line-end="146">
<p class="has-line-data" data-line-start="140" data-line-end="141"><strong><code>pd.DataFrame()</code></strong> - Create a DataFrame.</p>
<pre><code class="has-line-data" data-line-start="142" data-line-end="145" class="language-python"><span class="hljs-keyword">import</span> pandas <span class="hljs-keyword">as</span> pd
df = pd.DataFrame({<span class="hljs-string">'A'</span>: [<span class="hljs-number">1</span>, <span class="hljs-number">2</span>], <span class="hljs-string">'B'</span>: [<span class="hljs-number">3</span>, <span class="hljs-number">4</span>]})
</code></pre>
</li>
<li class="has-line-data" data-line-start="146" data-line-end="151">
<p class="has-line-data" data-line-start="146" data-line-end="147"><strong><code>pd.Series()</code></strong> - Create a Series.</p>
<pre><code class="has-line-data" data-line-start="148" data-line-end="150" class="language-python">series = pd.Series([<span class="hljs-number">1</span>, <span class="hljs-number">2</span>, <span class="hljs-number">3</span>])
</code></pre>
</li>
<li class="has-line-data" data-line-start="151" data-line-end="156">
<p class="has-line-data" data-line-start="151" data-line-end="152"><strong><code>pd.read_csv()</code></strong> - Read a CSV file into a DataFrame.</p>
<pre><code class="has-line-data" data-line-start="153" data-line-end="155" class="language-python">df = pd.read_csv(<span class="hljs-string">'data.csv'</span>)
</code></pre>
</li>
<li class="has-line-data" data-line-start="156" data-line-end="161">
<p class="has-line-data" data-line-start="156" data-line-end="157"><strong><code>pd.read_excel()</code></strong> - Read an Excel file into a DataFrame.</p>
<pre><code class="has-line-data" data-line-start="158" data-line-end="160" class="language-python">df = pd.read_excel(<span class="hljs-string">'data.xlsx'</span>)
</code></pre>
</li>
<li class="has-line-data" data-line-start="161" data-line-end="166">
<p class="has-line-data" data-line-start="161" data-line-end="162"><strong><code>pd.to_datetime()</code></strong> - Convert a series to datetime.</p>
<pre><code class="has-line-data" data-line-start="163" data-line-end="165" class="language-python">df[<span class="hljs-string">'date'</span>] = pd.to_datetime(df[<span class="hljs-string">'date'</span>])
</code></pre>
</li>
<li class="has-line-data" data-line-start="166" data-line-end="173">
<p class="has-line-data" data-line-start="166" data-line-end="167"><strong><code>pd.concat()</code></strong> - Concatenate DataFrames.</p>
<pre><code class="has-line-data" data-line-start="168" data-line-end="172" class="language-python">df1 = pd.DataFrame({<span class="hljs-string">'A'</span>: [<span class="hljs-number">1</span>, <span class="hljs-number">2</span>]})
df2 = pd.DataFrame({<span class="hljs-string">'B'</span>: [<span class="hljs-number">3</span>, <span class="hljs-number">4</span>]})
result = pd.concat([df1, df2], axis=<span class="hljs-number">1</span>)
</code></pre>
</li>
<li class="has-line-data" data-line-start="173" data-line-end="180">
<p class="has-line-data" data-line-start="173" data-line-end="174"><strong><code>pd.merge()</code></strong> - Merge DataFrames.</p>
<pre><code class="has-line-data" data-line-start="175" data-line-end="179" class="language-python">df1 = pd.DataFrame({<span class="hljs-string">'key'</span>: [<span class="hljs-string">'A'</span>, <span class="hljs-string">'B'</span>], <span class="hljs-string">'value'</span>: [<span class="hljs-number">1</span>, <span class="hljs-number">2</span>]})
df2 = pd.DataFrame({<span class="hljs-string">'key'</span>: [<span class="hljs-string">'A'</span>, <span class="hljs-string">'C'</span>], <span class="hljs-string">'value'</span>: [<span class="hljs-number">3</span>, <span class="hljs-number">4</span>]})
result = pd.merge(df1, df2, on=<span class="hljs-string">'key'</span>)
</code></pre>
</li>
<li class="has-line-data" data-line-start="180" data-line-end="185">
<p class="has-line-data" data-line-start="180" data-line-end="181"><strong><code>pd.groupby()</code></strong> - Group data by a column.</p>
<pre><code class="has-line-data" data-line-start="182" data-line-end="184" class="language-python">grouped = df.groupby(<span class="hljs-string">'A'</span>)
</code></pre>
</li>
<li class="has-line-data" data-line-start="185" data-line-end="190">
<p class="has-line-data" data-line-start="185" data-line-end="186"><strong><code>pd.pivot_table()</code></strong> - Create a pivot table.</p>
<pre><code class="has-line-data" data-line-start="187" data-line-end="189" class="language-python">pivot = pd.pivot_table(df, values=<span class="hljs-string">'B'</span>, index=<span class="hljs-string">'A'</span>, columns=<span class="hljs-string">'C'</span>)
</code></pre>
</li>
<li class="has-line-data" data-line-start="190" data-line-end="195">
<p class="has-line-data" data-line-start="190" data-line-end="191"><strong><code>df.head()</code></strong> - Return the first n rows of a DataFrame.</p>
<pre><code class="has-line-data" data-line-start="192" data-line-end="194" class="language-python">head = df.head(<span class="hljs-number">5</span>)
</code></pre>
</li>
<li class="has-line-data" data-line-start="195" data-line-end="200">
<p class="has-line-data" data-line-start="195" data-line-end="196"><strong><code>df.tail()</code></strong> - Return the last n rows of a DataFrame.</p>
<pre><code class="has-line-data" data-line-start="197" data-line-end="199" class="language-python">tail = df.tail(<span class="hljs-number">5</span>)
</code></pre>
</li>
<li class="has-line-data" data-line-start="200" data-line-end="205">
<p class="has-line-data" data-line-start="200" data-line-end="201"><strong><code>df.describe()</code></strong> - Generate descriptive statistics.</p>
<pre><code class="has-line-data" data-line-start="202" data-line-end="204" class="language-python">stats = df.describe()
</code></pre>
</li>
<li class="has-line-data" data-line-start="205" data-line-end="210">
<p class="has-line-data" data-line-start="205" data-line-end="206"><strong><code>df.info()</code></strong> - Get summary of a DataFrame.</p>
<pre><code class="has-line-data" data-line-start="207" data-line-end="209" class="language-python">info = df.info()
</code></pre>
</li>
<li class="has-line-data" data-line-start="210" data-line-end="215">
<p class="has-line-data" data-line-start="210" data-line-end="211"><strong><code>df.drop()</code></strong> - Drop rows or columns.</p>
<pre><code class="has-line-data" data-line-start="212" data-line-end="214" class="language-python">df_dropped = df.drop(<span class="hljs-string">'A'</span>, axis=<span class="hljs-number">1</span>)
</code></pre>
</li>
<li class="has-line-data" data-line-start="215" data-line-end="220">
<p class="has-line-data" data-line-start="215" data-line-end="216"><strong><code>df.fillna()</code></strong> - Fill missing values.</p>
<pre><code class="has-line-data" data-line-start="217" data-line-end="219" class="language-python">df_filled = df.fillna(<span class="hljs-number">0</span>)
</code></pre>
</li>
<li class="has-line-data" data-line-start="220" data-line-end="225">
<p class="has-line-data" data-line-start="220" data-line-end="221"><strong><code>df.dropna()</code></strong> - Drop missing values.</p>
<pre><code class="has-line-data" data-line-start="222" data-line-end="224" class="language-python">df_dropped_na = df.dropna()
</code></pre>
</li>
<li class="has-line-data" data-line-start="225" data-line-end="230">
<p class="has-line-data" data-line-start="225" data-line-end="226"><strong><code>df.isnull()</code></strong> - Check for missing values.</p>
<pre><code class="has-line-data" data-line-start="227" data-line-end="229" class="language-python">nulls = df.isnull()
</code></pre>
</li>
<li class="has-line-data" data-line-start="230" data-line-end="235">
<p class="has-line-data" data-line-start="230" data-line-end="231"><strong><code>df.notnull()</code></strong> - Check for non-missing values.</p>
<pre><code class="has-line-data" data-line-start="232" data-line-end="234" class="language-python">not_nulls = df.notnull()
</code></pre>
</li>
<li class="has-line-data" data-line-start="235" data-line-end="240">
<p class="has-line-data" data-line-start="235" data-line-end="236"><strong><code>df.apply()</code></strong> - Apply a function along an axis.</p>
<pre><code class="has-line-data" data-line-start="237" data-line-end="239" class="language-python">result = df.apply(np.sum, axis=<span class="hljs-number">0</span>)
</code></pre>
</li>
<li class="has-line-data" data-line-start="240" data-line-end="245">
<p class="has-line-data" data-line-start="240" data-line-end="241"><strong><code>df.map()</code></strong> - Map values using a function or dictionary.</p>
<pre><code class="has-line-data" data-line-start="242" data-line-end="244" class="language-python">df[<span class="hljs-string">'A'</span>] = df[<span class="hljs-string">'A'</span>].map(<span class="hljs-keyword">lambda</span> x: x*<span class="hljs-number">2</span>)
</code></pre>
</li>
<li class="has-line-data" data-line-start="245" data-line-end="250">
<p class="has-line-data" data-line-start="245" data-line-end="246"><strong><code>df.sort_values()</code></strong> - Sort DataFrame by values.</p>
<pre><code class="has-line-data" data-line-start="247" data-line-end="249" class="language-python">sorted_df = df.sort_values(by=<span class="hljs-string">'A'</span>)
</code></pre>
</li>
<li class="has-line-data" data-line-start="250" data-line-end="255">
<p class="has-line-data" data-line-start="250" data-line-end="251"><strong><code>df.set_index()</code></strong> - Set the DataFrame index.</p>
<pre><code class="has-line-data" data-line-start="252" data-line-end="254" class="language-python">df_indexed = df.set_index(<span class="hljs-string">'A'</span>)
</code></pre>
</li>
<li class="has-line-data" data-line-start="255" data-line-end="260">
<p class="has-line-data" data-line-start="255" data-line-end="256"><strong><code>df.reset_index()</code></strong> - Reset the DataFrame index.</p>
<pre><code class="has-line-data" data-line-start="257" data-line-end="259" class="language-python">df_reset = df.reset_index()
</code></pre>
</li>
<li class="has-line-data" data-line-start="260" data-line-end="262">
<p class="has-line-data" data-line-start="260" data-line-end="261"><strong><code>df.rename()</code></strong></p>
</li>
</ol>
<ul>
<li class="has-line-data" data-line-start="262" data-line-end="267">Rename columns or indices.<pre><code class="has-line-data" data-line-start="264" data-line-end="266" class="language-python">df_renamed = df.rename(columns={<span class="hljs-string">'A'</span>: <span class="hljs-string">'Alpha'</span>})
</code></pre>
</li>
</ul>
<ol start="50">
<li class="has-line-data" data-line-start="267" data-line-end="272">
<p class="has-line-data" data-line-start="267" data-line-end="268"><strong><code>df.query()</code></strong> - Query the DataFrame.</p>
<pre><code class="has-line-data" data-line-start="269" data-line-end="271" class="language-python">result = df.query(<span class="hljs-string">'A > 2'</span>)
</code></pre>
</li>
<li class="has-line-data" data-line-start="272" data-line-end="277">
<p class="has-line-data" data-line-start="272" data-line-end="273"><strong><code>df.to_csv()</code></strong> - Write DataFrame to a CSV file.</p>
<pre><code class="has-line-data" data-line-start="274" data-line-end="276" class="language-python">df.to_csv(<span class="hljs-string">'output.csv'</span>)
</code></pre>
</li>
<li class="has-line-data" data-line-start="277" data-line-end="282">
<p class="has-line-data" data-line-start="277" data-line-end="278"><strong><code>df.to_excel()</code></strong> - Write DataFrame to an Excel file.</p>
<pre><code class="has-line-data" data-line-start="279" data-line-end="281" class="language-python">df.to_excel(<span class="hljs-string">'output.xlsx'</span>)
</code></pre>
</li>
<li class="has-line-data" data-line-start="282" data-line-end="287">
<p class="has-line-data" data-line-start="282" data-line-end="283"><strong><code>df.corr()</code></strong> - Compute pairwise correlation of columns.</p>
<pre><code class="has-line-data" data-line-start="284" data-line-end="286" class="language-python">correlation = df.corr()
</code></pre>
</li>
<li class="has-line-data" data-line-start="287" data-line-end="292">
<p class="has-line-data" data-line-start="287" data-line-end="288"><strong><code>df.cumsum()</code></strong> - Compute cumulative sum.</p>
<pre><code class="has-line-data" data-line-start="289" data-line-end="291" class="language-python">cumsum = df.cumsum()
</code></pre>
</li>
<li class="has-line-data" data-line-start="292" data-line-end="297">
<p class="has-line-data" data-line-start="292" data-line-end="293"><strong><code>df.duplicated()</code></strong> - Detect duplicate rows.</p>
<pre><code class="has-line-data" data-line-start="294" data-line-end="296" class="language-python">duplicates = df.duplicated()
</code></pre>
</li>
<li class="has-line-data" data-line-start="297" data-line-end="302">
<p class="has-line-data" data-line-start="297" data-line-end="298"><strong><code>df.drop_duplicates()</code></strong> - Drop duplicate rows.</p>
<pre><code class="has-line-data" data-line-start="299" data-line-end="301" class="language-python">df_unique = df.drop_duplicates()
</code></pre>
</li>
<li class="has-line-data" data-line-start="302" data-line-end="307">
<p class="has-line-data" data-line-start="302" data-line-end="303"><strong><code>df.groupby().agg()</code></strong> - Aggregate data after grouping.</p>
<pre><code class="has-line-data" data-line-start="304" data-line-end="306" class="language-python">agg = df.groupby(<span class="hljs-string">'A'</span>).agg({<span class="hljs-string">'B'</span>: <span class="hljs-string">'sum'</span>})
</code></pre>
</li>
<li class="has-line-data" data-line-start="307" data-line-end="312">
<p class="has-line-data" data-line-start="307" data-line-end="308"><strong><code>df.rolling()</code></strong> - Create a rolling window.</p>
<pre><code class="has-line-data" data-line-start="309" data-line-end="311" class="language-python">rolling_mean = df[<span class="hljs-string">'A'</span>].rolling(window=<span class="hljs-number">3</span>).mean()
</code></pre>
</li>
<li class="has-line-data" data-line-start="312" data-line-end="317">
<p class="has-line-data" data-line-start="312" data-line-end="313"><strong><code>df.expanding()</code></strong> - Create an expanding window.</p>
<pre><code class="has-line-data" data-line-start="314" data-line-end="316" class="language-python">expanding_sum = df[<span class="hljs-string">'A'</span>].expanding().sum()
</code></pre>
</li>
<li class="has-line-data" data-line-start="317" data-line-end="322">
<p class="has-line-data" data-line-start="317" data-line-end="318"><strong><code>df.ewm()</code></strong> - Apply exponential weighted functions.</p>
<pre><code class="has-line-data" data-line-start="319" data-line-end="321" class="language-python">ewm_mean = df[<span class="hljs-string">'A'</span>].ewm(span=<span class="hljs-number">3</span>).mean()
</code></pre>
</li>
</ol>
<h3 class="code-line" data-line-start="322" data-line-end="323"><a id="matplotlib_Functions_322"></a>matplotlib Functions</h3>
<ol start="61">
<li class="has-line-data" data-line-start="324" data-line-end="330">
<p class="has-line-data" data-line-start="324" data-line-end="325"><strong><code>plt.plot()</code></strong> - Plot y versus x as lines and/or markers.</p>
<pre><code class="has-line-data" data-line-start="326" data-line-end="329" class="language-python"><span class="hljs-keyword">import</span> matplotlib.pyplot <span class="hljs-keyword">as</span> plt
plt.plot([<span class="hljs-number">1</span>, <span class="hljs-number">2</span>, <span class="hljs-number">3</span>], [<span class="hljs-number">4</span>, <span class="hljs-number">5</span>, <span class="hljs-number">6</span>])
</code></pre>
</li>
<li class="has-line-data" data-line-start="330" data-line-end="335">
<p class="has-line-data" data-line-start="330" data-line-end="331"><strong><code>plt.scatter()</code></strong> - Create a scatter plot.</p>
<pre><code class="has-line-data" data-line-start="332" data-line-end="334" class="language-python">plt.scatter([<span class="hljs-number">1</span>, <span class="hljs-number">2</span>, <span class="hljs-number">3</span>], [<span class="hljs-number">4</span>, <span class="hljs-number">5</span>, <span class="hljs-number">6</span>])
</code></pre>
</li>
<li class="has-line-data" data-line-start="335" data-line-end="340">
<p class="has-line-data" data-line-start="335" data-line-end="336"><strong><code>plt.bar()</code></strong> - Create a bar plot.</p>
<pre><code class="has-line-data" data-line-start="337" data-line-end="339" class="language-python">plt.bar([<span class="hljs-string">'A'</span>, <span class="hljs-string">'B'</span>, <span class="hljs-string">'C'</span>], [<span class="hljs-number">3</span>, <span class="hljs-number">5</span>, <span class="hljs-number">7</span>])
</code></pre>
</li>
<li class="has-line-data" data-line-start="340" data-line-end="345">
<p class="has-line-data" data-line-start="340" data-line-end="341"><strong><code>plt.hist()</code></strong> - Create a histogram.</p>
<pre><code class="has-line-data" data-line-start="342" data-line-end="344" class="language-python">plt.hist([<span class="hljs-number">1</span>, <span class="hljs-number">2</span>, <span class="hljs-number">2</span>, <span class="hljs-number">3</span>, <span class="hljs-number">4</span>], bins=<span class="hljs-number">4</span>)
</code></pre>
</li>
<li class="has-line-data" data-line-start="345" data-line-end="350">
<p class="has-line-data" data-line-start="345" data-line-end="346"><strong><code>plt.boxplot()</code></strong> - Create a box plot.</p>
<pre><code class="has-line-data" data-line-start="347" data-line-end="349" class="language-python">plt.boxplot([<span class="hljs-number">1</span>, <span class="hljs-number">2</span>, <span class="hljs-number">2</span>, <span class="hljs-number">3</span>, <span class="hljs-number">4</span>])
</code></pre>
</li>
<li class="has-line-data" data-line-start="350" data-line-end="355">
<p class="has-line-data" data-line-start="350" data-line-end="351"><strong><code>plt.pie()</code></strong> - Create a pie chart.</p>
<pre><code class="has-line-data" data-line-start="352" data-line-end="354" class="language-python">plt.pie([<span class="hljs-number">3</span>, <span class="hljs-number">5</span>, <span class="hljs-number">7</span>], labels=[<span class="hljs-string">'A'</span>, <span class="hljs-string">'B'</span>, <span class="hljs-string">'C'</span>])
</code></pre>
</li>
<li class="has-line-data" data-line-start="355" data-line-end="360">
<p class="has-line-data" data-line-start="355" data-line-end="356"><strong><code>plt.title()</code></strong> - Set the title of the plot.</p>
<pre><code class="has-line-data" data-line-start="357" data-line-end="359" class="language-python">plt.title(<span class="hljs-string">'My Plot'</span>)
</code></pre>
</li>
<li class="has-line-data" data-line-start="360" data-line-end="365">
<p class="has-line-data" data-line-start="360" data-line-end="361"><strong><code>plt.xlabel()</code></strong> - Set the x-axis label.</p>
<pre><code class="has-line-data" data-line-start="362" data-line-end="364" class="language-python">plt.xlabel(<span class="hljs-string">'X Axis'</span>)
</code></pre>
</li>
<li class="has-line-data" data-line-start="365" data-line-end="370">
<p class="has-line-data" data-line-start="365" data-line-end="366"><strong><code>plt.ylabel()</code></strong> - Set the y-axis label.</p>
<pre><code class="has-line-data" data-line-start="367" data-line-end="369" class="language-python">plt.ylabel(<span class="hljs-string">'Y Axis'</span>)
</code></pre>
</li>
<li class="has-line-data" data-line-start="370" data-line-end="376">
<p class="has-line-data" data-line-start="370" data-line-end="371"><strong><code>plt.legend()</code></strong> - Show a legend on the plot.</p>
<pre><code class="has-line-data" data-line-start="372" data-line-end="375" class="language-python">plt.plot([<span class="hljs-number">1</span>, <span class="hljs-number">2</span>, <span class="hljs-number">3</span>], [<span class="hljs-number">4</span>, <span class="hljs-number">5</span>, <span class="hljs-number">6</span>], label=<span class="hljs-string">'Line'</span>)
plt.legend()
</code></pre>
</li>
<li class="has-line-data" data-line-start="376" data-line-end="381">
<p class="has-line-data" data-line-start="376" data-line-end="377"><strong><code>plt.grid()</code></strong> - Show grid lines.</p>
<pre><code class="has-line-data" data-line-start="378" data-line-end="380" class="language-python">plt.grid(<span class="hljs-keyword">True</span>)
</code></pre>
</li>
<li class="has-line-data" data-line-start="381" data-line-end="386">
<p class="has-line-data" data-line-start="381" data-line-end="382"><strong><code>plt.show()</code></strong> - Display the plot.</p>
<pre><code class="has-line-data" data-line-start="383" data-line-end="385" class="language-python">plt.show()
</code></pre>
</li>
<li class="has-line-data" data-line-start="386" data-line-end="391">
<p class="has-line-data" data-line-start="386" data-line-end="387"><strong><code>plt.savefig()</code></strong> - Save the plot to a file.</p>
<pre><code class="has-line-data" data-line-start="388" data-line-end="390" class="language-python">plt.savefig(<span class="hljs-string">'plot.png'</span>)
</code></pre>
</li>
<li class="has-line-data" data-line-start="391" data-line-end="396">
<p class="has-line-data" data-line-start="391" data-line-end="392"><strong><code>plt.xlim()</code></strong> - Set limits for x-axis.</p>
<pre><code class="has-line-data" data-line-start="393" data-line-end="395" class="language-python">plt.xlim(<span class="hljs-number">0</span>, <span class="hljs-number">10</span>)
</code></pre>
</li>
<li class="has-line-data" data-line-start="396" data-line-end="401">
<p class="has-line-data" data-line-start="396" data-line-end="397"><strong><code>plt.ylim()</code></strong> - Set limits for y-axis.</p>
<pre><code class="has-line-data" data-line-start="398" data-line-end="400" class="language-python">plt.ylim(<span class="hljs-number">0</span>, <span class="hljs-number">10</span>)
</code></pre>
</li>
<li class="has-line-data" data-line-start="401" data-line-end="406">
<p class="has-line-data" data-line-start="401" data-line-end="402"><strong><code>plt.subplots()</code></strong> - Create a figure and a set of subplots.</p>
<pre><code class="has-line-data" data-line-start="403" data-line-end="405" class="language-python">fig, ax = plt.subplots(<span class="hljs-number">2</span>, <span class="hljs-number">2</span>)
</code></pre>
</li>
<li class="has-line-data" data-line-start="406" data-line-end="411">
<p class="has-line-data" data-line-start="406" data-line-end="407"><strong><code>plt.subplot()</code></strong> - Add a subplot to the figure.</p>
<pre><code class="has-line-data" data-line-start="408" data-line-end="410" class="language-python">plt.subplot(<span class="hljs-number">2</span>, <span class="hljs-number">2</span>, <span class="hljs-number">1</span>)
</code></pre>
</li>
<li class="has-line-data" data-line-start="411" data-line-end="416">
<p class="has-line-data" data-line-start="411" data-line-end="412"><strong><code>plt.tight_layout()</code></strong> - Adjust subplots to fit into figure area.</p>
<pre><code class="has-line-data" data-line-start="413" data-line-end="415" class="language-python">plt.tight_layout()
</code></pre>
</li>
<li class="has-line-data" data-line-start="416" data-line-end="421">
<p class="has-line-data" data-line-start="416" data-line-end="417"><strong><code>plt.axhline()</code></strong> - Add a horizontal line across the axis.</p>
<pre><code class="has-line-data" data-line-start="418" data-line-end="420" class="language-python">plt.axhline(y=<span class="hljs-number">0.5</span>, color=<span class="hljs-string">'r'</span>)
</code></pre>
</li>
<li class="has-line-data" data-line-start="421" data-line-end="426">
<p class="has-line-data" data-line-start="421" data-line-end="422"><strong><code>plt.axvline()</code></strong> - Add a vertical line across the axis.</p>
<pre><code class="has-line-data" data-line-start="423" data-line-end="425" class="language-python">plt.axvline(x=<span class="hljs-number">0.5</span>, color=<span class="hljs-string">'r'</span>)
</code></pre>
</li>
<li class="has-line-data" data-line-start="426" data-line-end="431">
<p class="has-line-data" data-line-start="426" data-line-end="427"><strong><code>plt.annotate()</code></strong> - Annotate a point in the plot.</p>
<pre><code class="has-line-data" data-line-start="428" data-line-end="430" class="language-python">plt.annotate(<span class="hljs-string">'Annotation'</span>, xy=(<span class="hljs-number">1</span>, <span class="hljs-number">2</span>), xytext=(<span class="hljs-number">2</span>, <span class="hljs-number">3</span>))
</code></pre>
</li>
<li class="has-line-data" data-line-start="431" data-line-end="436">
<p class="has-line-data" data-line-start="431" data-line-end="432"><strong><code>plt.errorbar()</code></strong> - Plot with error bars.</p>
<pre><code class="has-line-data" data-line-start="433" data-line-end="435" class="language-python">plt.errorbar([<span class="hljs-number">1</span>, <span class="hljs-number">2</span>, <span class="hljs-number">3</span>], [<span class="hljs-number">4</span>, <span class="hljs-number">5</span>, <span class="hljs-number">6</span>], yerr=<span class="hljs-number">0.1</span>)
</code></pre>
</li>
<li class="has-line-data" data-line-start="436" data-line-end="441">
<p class="has-line-data" data-line-start="436" data-line-end="437"><strong><code>plt.imshow()</code></strong> - Display an image.</p>
<pre><code class="has-line-data" data-line-start="438" data-line-end="440" class="language-python">plt.imshow([[<span class="hljs-number">1</span>, <span class="hljs-number">2</span>], [<span class="hljs-number">3</span>, <span class="hljs-number">4</span>]], cmap=<span class="hljs-string">'gray'</span>)
</code></pre>
</li>
<li class="has-line-data" data-line-start="441" data-line-end="446">
<p class="has-line-data" data-line-start="441" data-line-end="442"><strong><code>plt.contour()</code></strong> - Create a contour plot.</p>
<pre><code class="has-line-data" data-line-start="443" data-line-end="445" class="language-python">plt.contour([[<span class="hljs-number">1</span>, <span class="hljs-number">2</span>], [<span class="hljs-number">3</span>, <span class="hljs-number">4</span>]])
</code></pre>
</li>
<li class="has-line-data" data-line-start="446" data-line-end="451">
<p class="has-line-data" data-line-start="446" data-line-end="447"><strong><code>plt.contourf()</code></strong> - Create a filled contour plot.</p>
<pre><code class="has-line-data" data-line-start="448" data-line-end="450" class="language-python">plt.contourf([[<span class="hljs-number">1</span>, <span class="hljs-number">2</span>], [<span class="hljs-number">3</span>, <span class="hljs-number">4</span>]])
</code></pre>
</li>
</ol>
<h3 class="code-line" data-line-start="451" data-line-end="452"><a id="seaborn_Functions_451"></a>seaborn Functions</h3>
<ol start="86">
<li class="has-line-data" data-line-start="453" data-line-end="459">
<p class="has-line-data" data-line-start="453" data-line-end="454"><strong><code>sns.scatterplot()</code></strong> - Create a scatter plot.</p>
<pre><code class="has-line-data" data-line-start="455" data-line-end="458" class="language-python"><span class="hljs-keyword">import</span> seaborn <span class="hljs-keyword">as</span> sns
sns.scatterplot(x=<span class="hljs-string">'x'</span>, y=<span class="hljs-string">'y'</span>, data=df)
</code></pre>
</li>
<li class="has-line-data" data-line-start="459" data-line-end="464">
<p class="has-line-data" data-line-start="459" data-line-end="460"><strong><code>sns.lineplot()</code></strong> - Create a line plot.</p>
<pre><code class="has-line-data" data-line-start="461" data-line-end="463" class="language-python">sns.lineplot(x=<span class="hljs-string">'x'</span>, y=<span class="hljs-string">'y'</span>, data=df)
</code></pre>
</li>
<li class="has-line-data" data-line-start="464" data-line-end="469">
<p class="has-line-data" data-line-start="464" data-line-end="465"><strong><code>sns.barplot()</code></strong> - Create a bar plot.</p>
<pre><code class="has-line-data" data-line-start="466" data-line-end="468" class="language-python">sns.barplot(x=<span class="hljs-string">'x'</span>, y=<span class="hljs-string">'y'</span>, data=df)
</code></pre>
</li>
<li class="has-line-data" data-line-start="469" data-line-end="474">
<p class="has-line-data" data-line-start="469" data-line-end="470"><strong><code>sns.boxplot()</code></strong> - Create a box plot.</p>
<pre><code class="has-line-data" data-line-start="471" data-line-end="473" class="language-python">sns.boxplot(x=<span class="hljs-string">'x'</span>, y=<span class="hljs-string">'y'</span>, data=df)
</code></pre>
</li>
<li class="has-line-data" data-line-start="474" data-line-end="479">
<p class="has-line-data" data-line-start="474" data-line-end="475"><strong><code>sns.histplot()</code></strong> - Create a histogram.</p>
<pre><code class="has-line-data" data-line-start="476" data-line-end="478" class="language-python">sns.histplot(df[<span class="hljs-string">'x'</span>])
</code></pre>
</li>
<li class="has-line-data" data-line-start="479" data-line-end="484">
<p class="has-line-data" data-line-start="479" data-line-end="480"><strong><code>sns.heatmap()</code></strong> - Create a heatmap.</p>
<pre><code class="has-line-data" data-line-start="481" data-line-end="483" class="language-python">sns.heatmap(data=df.corr())
</code></pre>
</li>
<li class="has-line-data" data-line-start="484" data-line-end="489">
<p class="has-line-data" data-line-start="484" data-line-end="485"><strong><code>sns.pairplot()</code></strong> - Plot pairwise relationships in a dataset.</p>
<pre><code class="has-line-data" data-line-start="486" data-line-end="488" class="language-python">sns.pairplot(df)
</code></pre>
</li>
<li class="has-line-data" data-line-start="489" data-line-end="494">
<p class="has-line-data" data-line-start="489" data-line-end="490"><strong><code>sns.heatmap()</code></strong> - Plot a heatmap.</p>
<pre><code class="has-line-data" data-line-start="491" data-line-end="493" class="language-python">sns.heatmap(data=df.corr())
</code></pre>
</li>
<li class="has-line-data" data-line-start="494" data-line-end="499">
<p class="has-line-data" data-line-start="494" data-line-end="495"><strong><code>sns.distplot()</code></strong> - Plot a univariate distribution.</p>
<pre><code class="has-line-data" data-line-start="496" data-line-end="498" class="language-python">sns.distplot(df[<span class="hljs-string">'x'</span>])
</code></pre>
</li>
<li class="has-line-data" data-line-start="499" data-line-end="504">
<p class="has-line-data" data-line-start="499" data-line-end="500"><strong><code>sns.violinplot()</code></strong> - Create a violin plot.</p>
<pre><code class="has-line-data" data-line-start="501" data-line-end="503" class="language-python">sns.violinplot(x=<span class="hljs-string">'x'</span>, y=<span class="hljs-string">'y'</span>, data=df)
</code></pre>
</li>
<li class="has-line-data" data-line-start="504" data-line-end="509">
<p class="has-line-data" data-line-start="504" data-line-end="505"><strong><code>sns.kdeplot()</code></strong> - Create a Kernel Density Estimate plot.</p>
<pre><code class="has-line-data" data-line-start="506" data-line-end="508" class="language-python">sns.kdeplot(df[<span class="hljs-string">'x'</span>])
</code></pre>
</li>
<li class="has-line-data" data-line-start="509" data-line-end="514">
<p class="has-line-data" data-line-start="509" data-line-end="510"><strong><code>sns.jointplot()</code></strong> - Create a joint plot.</p>
<pre><code class="has-line-data" data-line-start="511" data-line-end="513" class="language-python">sns.jointplot(x=<span class="hljs-string">'x'</span>, y=<span class="hljs-string">'y'</span>, data=df)
</code></pre>
</li>
<li class="has-line-data" data-line-start="514" data-line-end="519">
<p class="has-line-data" data-line-start="514" data-line-end="515"><strong><code>sns.lmplot()</code></strong> - Create a scatter plot with a linear fit.</p>
<pre><code class="has-line-data" data-line-start="516" data-line-end="518" class="language-python">sns.lmplot(x=<span class="hljs-string">'x'</span>, y=<span class="hljs-string">'y'</span>, data=df)
</code></pre>
</li>
<li class="has-line-data" data-line-start="519" data-line-end="524">
<p class="has-line-data" data-line-start="519" data-line-end="520"><strong><code>sns.palplot()</code></strong> - Plot a color palette.</p>
<pre><code class="has-line-data" data-line-start="521" data-line-end="523" class="language-python">sns.palplot(sns.color_palette(<span class="hljs-string">"husl"</span>))
</code></pre>
</li>
<li class="has-line-data" data-line-start="524" data-line-end="525">
<p class="has-line-data" data-line-start="524" data-line-end="525"><strong><code>sns.set_style()</code></strong> - Set the aesthetic style of the plots.</p>
</li>
</ol>
<pre><code>```python
sns.set_style('whitegrid')
```
</code></pre>
<h3 class="code-line" data-line-start="529" data-line-end="530"><a id="scikitlearn_Functions_529"></a>scikit-learn Functions</h3>
<ol start="101">
<li class="has-line-data" data-line-start="531" data-line-end="532"><strong><code>sklearn.model_selection.train_test_split()</code></strong> - Split arrays or matrices into random train and test subsets.</li>
</ol>
<pre><code>```python
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
```
</code></pre>
<ol start="102">
<li class="has-line-data" data-line-start="537" data-line-end="538"><strong><code>sklearn.preprocessing.StandardScaler()</code></strong> - Standardize features by removing the mean and scaling to unit variance.</li>
</ol>
<pre><code>```python
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
```
</code></pre>
<ol start="103">
<li class="has-line-data" data-line-start="544" data-line-end="546"><strong><code>sklearn.preprocessing.MinMaxScaler()</code></strong> - Transform features by scaling each feature to a</li>
</ol>
<p class="has-line-data" data-line-start="546" data-line-end="552">given range.<br>
<code>python from sklearn.preprocessing import MinMaxScaler scaler = MinMaxScaler() X_scaled = scaler.fit_transform(X)</code></p>
<ol start="104">
<li class="has-line-data" data-line-start="553" data-line-end="554"><strong><code>sklearn.impute.SimpleImputer()</code></strong> - Impute missing values.</li>
</ol>
<pre><code>```python
from sklearn.impute import SimpleImputer
imputer = SimpleImputer(strategy='mean')
X_imputed = imputer.fit_transform(X)
```
</code></pre>
<ol start="105">
<li class="has-line-data" data-line-start="560" data-line-end="561"><strong><code>sklearn.feature_selection.SelectKBest()</code></strong> - Select features according to the k highest scores.</li>
</ol>
<pre><code>```python
from sklearn.feature_selection import SelectKBest, f_classif
selector = SelectKBest(score_func=f_classif, k=10)
X_new = selector.fit_transform(X, y)
```
</code></pre>
<ol start="106">
<li class="has-line-data" data-line-start="567" data-line-end="568"><strong><code>sklearn.feature_extraction.text.CountVectorizer()</code></strong> - Convert a collection of text documents to a matrix of token counts.</li>
</ol>
<pre><code>```python
from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(documents)
```
</code></pre>
<ol start="107">
<li class="has-line-data" data-line-start="574" data-line-end="575"><strong><code>sklearn.feature_extraction.text.TfidfVectorizer()</code></strong> - Convert a collection of text documents to a matrix of TF-IDF features.</li>
</ol>
<pre><code>```python
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(documents)
```
</code></pre>
<ol start="108">
<li class="has-line-data" data-line-start="581" data-line-end="582"><strong><code>sklearn.decomposition.PCA()</code></strong> - Perform Principal Component Analysis (PCA) for dimensionality reduction.</li>
</ol>
<pre><code>```python
from sklearn.decomposition import PCA
pca = PCA(n_components=2)
X_pca = pca.fit_transform(X)
```
</code></pre>
<ol start="109">
<li class="has-line-data" data-line-start="588" data-line-end="589"><strong><code>sklearn.cluster.KMeans()</code></strong> - K-means clustering.</li>
</ol>
<pre><code>```python
from sklearn.cluster import KMeans
kmeans = KMeans(n_clusters=3)
kmeans.fit(X)
```
</code></pre>
<ol start="110">
<li class="has-line-data" data-line-start="595" data-line-end="596"><strong><code>sklearn.linear_model.LinearRegression()</code></strong> - Linear regression.</li>
</ol>
<pre><code>```python
from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(X_train, y_train)
```
</code></pre>
<ol start="111">
<li class="has-line-data" data-line-start="602" data-line-end="603"><strong><code>sklearn.ensemble.RandomForestClassifier()</code></strong> - Random Forest classifier.</li>
</ol>
<pre><code>```python
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier()
model.fit(X_train, y_train)
```
</code></pre>
<ol start="112">
<li class="has-line-data" data-line-start="609" data-line-end="610"><strong><code>sklearn.ensemble.GradientBoostingClassifier()</code></strong> - Gradient Boosting classifier.</li>
</ol>
<pre><code>```python
from sklearn.ensemble import GradientBoostingClassifier
model = GradientBoostingClassifier()
model.fit(X_train, y_train)
```
</code></pre>
<ol start="113">
<li class="has-line-data" data-line-start="616" data-line-end="617"><strong><code>sklearn.svm.SVC()</code></strong> - Support Vector Classification.</li>
</ol>
<pre><code>```python
from sklearn.svm import SVC
model = SVC()
model.fit(X_train, y_train)
```
</code></pre>
<ol start="114">
<li class="has-line-data" data-line-start="623" data-line-end="624"><strong><code>sklearn.metrics.accuracy_score()</code></strong> - Compute the accuracy of a model.</li>
</ol>
<pre><code>```python
from sklearn.metrics import accuracy_score
accuracy = accuracy_score(y_test, y_pred)
```
</code></pre>
<ol start="115">
<li class="has-line-data" data-line-start="629" data-line-end="630"><strong><code>sklearn.metrics.confusion_matrix()</code></strong> - Compute the confusion matrix.</li>
</ol>
<pre><code>```python
from sklearn.metrics import confusion_matrix
matrix = confusion_matrix(y_test, y_pred)
```
</code></pre>
<ol start="116">
<li class="has-line-data" data-line-start="635" data-line-end="636"><strong><code>sklearn.metrics.classification_report()</code></strong> - Generate a classification report.</li>
</ol>
<pre><code>```python
from sklearn.metrics import classification_report
report = classification_report(y_test, y_pred)
```
</code></pre>
<ol start="117">
<li class="has-line-data" data-line-start="641" data-line-end="642"><strong><code>sklearn.metrics.roc_auc_score()</code></strong> - Compute the area under the receiver operating characteristic curve (ROC AUC).</li>
</ol>
<pre><code>```python
from sklearn.metrics import roc_auc_score
auc = roc_auc_score(y_test, y_prob)
```
</code></pre>
<ol start="118">
<li class="has-line-data" data-line-start="647" data-line-end="648"><strong><code>sklearn.metrics.roc_curve()</code></strong> - Compute Receiver Operating Characteristic (ROC) curve.</li>
</ol>
<pre><code>```python
from sklearn.metrics import roc_curve
fpr, tpr, thresholds = roc_curve(y_test, y_prob)
```
</code></pre>
<ol start="119">
<li class="has-line-data" data-line-start="653" data-line-end="654"><strong><code>sklearn.model_selection.GridSearchCV()</code></strong> - Perform grid search to find the best parameters for a model.</li>
</ol>
<pre><code>```python
from sklearn.model_selection import GridSearchCV
grid = GridSearchCV(model, param_grid)
grid.fit(X_train, y_train)
```
</code></pre>
<ol start="120">
<li class="has-line-data" data-line-start="660" data-line-end="661"><strong><code>sklearn.model_selection.cross_val_score()</code></strong> - Evaluate a model using cross-validation.</li>
</ol>
<pre><code>```python
from sklearn.model_selection import cross_val_score
scores = cross_val_score(model, X, y, cv=5)
```
</code></pre>
<h3 class="code-line" data-line-start="666" data-line-end="667"><a id="Other_Useful_Functions_666"></a>Other Useful Functions</h3>
<ol start="121">
<li class="has-line-data" data-line-start="668" data-line-end="669"><strong><code>datetime.datetime.now()</code></strong> - Get the current date and time.</li>
</ol>
<pre><code>```python
from datetime import datetime
now = datetime.now()
```
</code></pre>
<ol start="122">
<li class="has-line-data" data-line-start="674" data-line-end="675"><strong><code>datetime.timedelta()</code></strong> - Create a time difference.</li>
</ol>
<pre><code>```python
from datetime import timedelta
delta = timedelta(days=5)
```
</code></pre>
<ol start="123">
<li class="has-line-data" data-line-start="680" data-line-end="681"><strong><code>itertools.chain()</code></strong> - Chain multiple iterables.</li>
</ol>
<pre><code>```python
from itertools import chain
result = list(chain([1, 2], [3, 4]))
```
</code></pre>
<ol start="124">
<li class="has-line-data" data-line-start="686" data-line-end="687"><strong><code>itertools.combinations()</code></strong> - Create combinations of elements.</li>
</ol>
<pre><code>```python
from itertools import combinations
combs = list(combinations([1, 2, 3], 2))
```
</code></pre>
<ol start="125">
<li class="has-line-data" data-line-start="692" data-line-end="693"><strong><code>itertools.permutations()</code></strong> - Create permutations of elements.</li>
</ol>
<pre><code>```python
from itertools import permutations
perms = list(permutations([1, 2, 3], 2))
```
</code></pre>
<ol start="126">
<li class="has-line-data" data-line-start="698" data-line-end="699"><strong><code>functools.reduce()</code></strong> - Apply a function cumulatively to the items of an iterable.</li>
</ol>
<pre><code>```python
from functools import reduce
result = reduce(lambda x, y: x + y, [1, 2, 3])
```
</code></pre>
<ol start="127">
<li class="has-line-data" data-line-start="704" data-line-end="705"><strong><code>operator.itemgetter()</code></strong> - Create a function to get an item from a sequence.</li>
</ol>
<pre><code>```python
from operator import itemgetter
getter = itemgetter(1)
result = getter([1, 2, 3])
```
</code></pre>
<ol start="128">
<li class="has-line-data" data-line-start="711" data-line-end="712"><strong><code>pandas.plotting.scatter_matrix()</code></strong> - Create a scatter matrix plot.</li>
</ol>
<pre><code>```python
from pandas.plotting import scatter_matrix
scatter_matrix(df)
```
</code></pre>
<ol start="129">
<li class="has-line-data" data-line-start="717" data-line-end="718"><strong><code>scipy.stats.describe()</code></strong> - Compute various descriptive statistics.</li>
</ol>
<pre><code>```python
from scipy.stats import describe
stats = describe([1, 2, 3, 4, 5])
```
</code></pre>
<ol start="130">
<li class="has-line-data" data-line-start="723" data-line-end="724"><strong><code>scipy.optimize.minimize()</code></strong> - Minimize a function.</li>
</ol>
<pre><code>```python
from scipy.optimize import minimize
result = minimize(lambda x: x**2, 0)
```
</code></pre>
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