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</li></ul></nav></div></div></aside><main class="docMainContainer_TBSr"><div class="container padding-top--md padding-bottom--lg"><div class="row"><div class="col docItemCol_VOVn"><div class="docItemContainer_Djhp"><article><nav class="theme-doc-breadcrumbs breadcrumbsContainer_Z_bl" aria-label="Breadcrumbs"><ul class="breadcrumbs"><li class="breadcrumbs__item"><a aria-label="Home page" class="breadcrumbs__link" href="/"><svg viewBox="0 0 24 24" class="breadcrumbHomeIcon_YNFT"><path d="M10 19v-5h4v5c0 .55.45 1 1 1h3c.55 0 1-.45 1-1v-7h1.7c.46 0 .68-.57.33-.87L12.67 3.6c-.38-.34-.96-.34-1.34 0l-8.36 7.53c-.34.3-.13.87.33.87H5v7c0 .55.45 1 1 1h3c.55 0 1-.45 1-1z" fill="currentColor"></path></svg></a></li><li class="breadcrumbs__item"><span class="breadcrumbs__link">Components reference</span></li><li class="breadcrumbs__item"><span class="breadcrumbs__link">Core components</span></li><li class="breadcrumbs__item"><span class="breadcrumbs__link">Utilities</span></li><li class="breadcrumbs__item breadcrumbs__item--active"><span class="breadcrumbs__link">Python Interpreter</span></li></ul></nav><div class="tocCollapsible_ETCw theme-doc-toc-mobile tocMobile_ITEo"><button type="button" class="clean-btn tocCollapsibleButton_TO0P">On this page</button></div><div class="theme-doc-markdown markdown"><header><h1>Python Interpreter</h1></header><style>[data-ch-theme="github-dark"] { --ch-t-colorScheme: dark;--ch-t-foreground: #c9d1d9;--ch-t-background: #0d1117;--ch-t-lighter-inlineBackground: #0d1117e6;--ch-t-editor-background: #0d1117;--ch-t-editor-foreground: #c9d1d9;--ch-t-editor-lineHighlightBackground: #6e76811a;--ch-t-editor-rangeHighlightBackground: #ffffff0b;--ch-t-editor-infoForeground: #3794FF;--ch-t-editor-selectionBackground: #264F78;--ch-t-focusBorder: #1f6feb;--ch-t-tab-activeBackground: #0d1117;--ch-t-tab-activeForeground: #c9d1d9;--ch-t-tab-inactiveBackground: #010409;--ch-t-tab-inactiveForeground: #8b949e;--ch-t-tab-border: #30363d;--ch-t-tab-activeBorder: #0d1117;--ch-t-editorGroup-border: #30363d;--ch-t-editorGroupHeader-tabsBackground: #010409;--ch-t-editorLineNumber-foreground: #6e7681;--ch-t-input-background: #0d1117;--ch-t-input-foreground: #c9d1d9;--ch-t-input-border: #30363d;--ch-t-icon-foreground: #8b949e;--ch-t-sideBar-background: #010409;--ch-t-sideBar-foreground: #c9d1d9;--ch-t-sideBar-border: #30363d;--ch-t-list-activeSelectionBackground: #6e768166;--ch-t-list-activeSelectionForeground: #c9d1d9;--ch-t-list-hoverBackground: #6e76811a;--ch-t-list-hoverForeground: #c9d1d9; }</style>
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<!-- -->
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<p>This component allows you to execute Python code with imported packages.</p>
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<p>The <strong>Python Interpreter</strong> component can only import packages that are already installed in your Langflow environment.
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If you encounter an <code>ImportError</code> when trying to use a package, you need to install it first.</p>
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<p>To install custom packages, see <a href="/install-custom-dependencies">Install custom dependencies</a>.</p>
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<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="use-the-python-interpreter-in-a-flow">Use the Python Interpreter in a flow<a href="#use-the-python-interpreter-in-a-flow" class="hash-link" aria-label="Direct link to Use the Python Interpreter in a flow" title="Direct link to Use the Python Interpreter in a flow"></a></h2>
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<ol>
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<li>To use this component in a flow, in the <strong>Global Imports</strong> field, add the packages you want to import as a comma-separated list, such as <code>math,pandas</code>.
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At least one import is required.</li>
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<li>In the <strong>Python Code</strong> field, enter the Python code you want to execute. Use <code>print()</code> to see the output.</li>
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<li>Optional: Enable <strong>Tool Mode</strong>, and then connect the <strong>Python Interpreter</strong> component to an <strong>Agent</strong> component as a tool.
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For example, connect a <strong>Python Interpreter</strong> component and a <a href="/calculator"><strong>Calculator</strong> component</a> as tools for an <strong>Agent</strong> component, and then test how it chooses different tools to solve math problems.
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<img decoding="async" loading="lazy" alt="Python Interpreter and Calculator components connected to an Agent component" src="/assets/images/component-python-interpreter-1ef9e7dec643a216fd5e83c2139d09c9.png" width="3988" height="2928" class="img_ev3q"></li>
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<li>Ask the agent an easier math question.
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The <strong>Calculator</strong> tool can add, subtract, multiple, divide, or perform exponentiation.
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The agent executes the <code>evaluate_expression</code> tool to correctly answer the question.</li>
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</ol>
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<p>Result:</p>
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<div class="ch-codeblock not-prose" data-ch-theme="github-dark"><div class="ch-code-wrapper ch-code" data-ch-measured="false"><code class="ch-code-scroll-parent"><br><div><span class="ch-code-line-number">_<!-- -->10</span><div style="display:inline-block;margin-left:16px"><span>Executed evaluate_expression</span></div></div><div><span class="ch-code-line-number">_<!-- -->10</span><div style="display:inline-block;margin-left:16px"><span>Input:</span></div></div><div><span class="ch-code-line-number">_<!-- -->10</span><div style="display:inline-block;margin-left:16px"><span>{</span></div></div><div><span class="ch-code-line-number">_<!-- -->10</span><div style="display:inline-block;margin-left:16px"><span> "expression": "2+5"</span></div></div><div><span class="ch-code-line-number">_<!-- -->10</span><div style="display:inline-block;margin-left:16px"><span>}</span></div></div><div><span class="ch-code-line-number">_<!-- -->10</span><div style="display:inline-block;margin-left:16px"><span>Output:</span></div></div><div><span class="ch-code-line-number">_<!-- -->10</span><div style="display:inline-block;margin-left:16px"><span>{</span></div></div><div><span class="ch-code-line-number">_<!-- -->10</span><div style="display:inline-block;margin-left:16px"><span> "result": "7"</span></div></div><div><span class="ch-code-line-number">_<!-- -->10</span><div style="display:inline-block;margin-left:16px"><span>}</span></div></div><br></code></div></div>
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<ol start="5">
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<li>Give the agent complete Python code.
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This example creates a Pandas DataFrame table with the imported <code>pandas</code> packages, and returns the square root of the mean squares.</li>
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</ol>
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<div class="ch-codeblock not-prose" data-ch-theme="github-dark"><div class="ch-code-wrapper ch-code" data-ch-measured="false"><code class="ch-code-scroll-parent"><br><div><span class="ch-code-line-number">_<!-- -->12</span><div style="display:inline-block;margin-left:16px"><span>import pandas as pd</span></div></div><div><span class="ch-code-line-number">_<!-- -->12</span><div style="display:inline-block;margin-left:16px"><span>import math</span></div></div><div><span class="ch-code-line-number">_<!-- -->12</span><div style="display:inline-block;margin-left:16px"><span></span></div></div><div><span class="ch-code-line-number">_<!-- -->12</span><div style="display:inline-block;margin-left:16px"><span># Create a simple DataFrame</span></div></div><div><span class="ch-code-line-number">_<!-- -->12</span><div style="display:inline-block;margin-left:16px"><span>df = pd.DataFrame({</span></div></div><div><span class="ch-code-line-number">_<!-- -->12</span><div style="display:inline-block;margin-left:16px"><span> 'numbers': [1, 2, 3, 4, 5],</span></div></div><div><span class="ch-code-line-number">_<!-- -->12</span><div style="display:inline-block;margin-left:16px"><span> 'squares': [x**2 for x in range(1, 6)]</span></div></div><div><span class="ch-code-line-number">_<!-- -->12</span><div style="display:inline-block;margin-left:16px"><span>})</span></div></div><div><span class="ch-code-line-number">_<!-- -->12</span><div style="display:inline-block;margin-left:16px"><span></span></div></div><div><span class="ch-code-line-number">_<!-- -->12</span><div style="display:inline-block;margin-left:16px"><span># Calculate the square root of the mean</span></div></div><div><span class="ch-code-line-number">_<!-- -->12</span><div style="display:inline-block;margin-left:16px"><span>result = math.sqrt(df['squares'].mean())</span></div></div><div><span class="ch-code-line-number">_<!-- -->12</span><div style="display:inline-block;margin-left:16px"><span>print(f"Square root of mean squares: {result}")</span></div></div><br></code></div></div>
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<p>The agent correctly chooses the <code>run_python_repl</code> tool to solve the problem.</p>
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<p>Result:</p>
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<div class="ch-codeblock not-prose" data-ch-theme="github-dark"><div class="ch-code-wrapper ch-code" data-ch-measured="false"><code class="ch-code-scroll-parent"><br><div><span class="ch-code-line-number">_<!-- -->12</span><div style="display:inline-block;margin-left:16px"><span>Executed run_python_repl</span></div></div><div><span class="ch-code-line-number">_<!-- -->12</span><div style="display:inline-block;margin-left:16px"><span></span></div></div><div><span class="ch-code-line-number">_<!-- -->12</span><div style="display:inline-block;margin-left:16px"><span>Input:</span></div></div><div><span class="ch-code-line-number">_<!-- -->12</span><div style="display:inline-block;margin-left:16px"><span></span></div></div><div><span class="ch-code-line-number">_<!-- -->12</span><div style="display:inline-block;margin-left:16px"><span>{</span></div></div><div><span class="ch-code-line-number">_<!-- -->12</span><div style="display:inline-block;margin-left:16px"><span> "python_code": "import pandas as pd\nimport math\n\n# Create a simple DataFrame\ndf = pd.DataFrame({\n 'numbers': [1, 2, 3, 4, 5],\n 'squares': [x**2 for x in range(1, 6)]\n})\n\n# Calculate the square root of the mean\nresult = math.sqrt(df['squares'].mean())\nprint(f\"Square root of mean squares: {result}\")"</span></div></div><div><span class="ch-code-line-number">_<!-- -->12</span><div style="display:inline-block;margin-left:16px"><span>}</span></div></div><div><span class="ch-code-line-number">_<!-- -->12</span><div style="display:inline-block;margin-left:16px"><span>Output:</span></div></div><div><span class="ch-code-line-number">_<!-- -->12</span><div style="display:inline-block;margin-left:16px"><span></span></div></div><div><span class="ch-code-line-number">_<!-- -->12</span><div style="display:inline-block;margin-left:16px"><span>{</span></div></div><div><span class="ch-code-line-number">_<!-- -->12</span><div style="display:inline-block;margin-left:16px"><span> "result": "Square root of mean squares: 3.3166247903554"</span></div></div><div><span class="ch-code-line-number">_<!-- -->12</span><div style="display:inline-block;margin-left:16px"><span>}</span></div></div><br></code></div></div>
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<p>If you don't include the package imports in the chat, the agent can still create the table using <code>pd.DataFrame</code>, because the <code>pandas</code> package is imported globally by the <strong>Python Interpreter</strong> component in the <strong>Global Imports</strong> field.</p>
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<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="python-interpreter-parameters">Python Interpreter parameters<a href="#python-interpreter-parameters" class="hash-link" aria-label="Direct link to Python Interpreter parameters" title="Direct link to Python Interpreter parameters"></a></h2>
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<table><thead><tr><th>Name</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td>global_imports</td><td>String</td><td>Input parameter. A comma-separated list of modules to import globally, such as <code>math,pandas,numpy</code>.</td></tr><tr><td>python_code</td><td>Code</td><td>Input parameter. The Python code to execute. Only modules specified in Global Imports can be used.</td></tr><tr><td>results</td><td>Data</td><td>Output parameter. The output of the executed Python code, including any printed results or errors.</td></tr></tbody></table></div></article><nav class="docusaurus-mt-lg pagination-nav" aria-label="Docs pages"><a class="pagination-nav__link pagination-nav__link--prev" href="/current-date"><div class="pagination-nav__sublabel">Previous</div><div class="pagination-nav__label">Current Date</div></a><a class="pagination-nav__link pagination-nav__link--next" href="/sql-database"><div class="pagination-nav__sublabel">Next</div><div class="pagination-nav__label">SQL Database</div></a></nav></div></div><div class="col col--3"><div class="tableOfContents_bqdL thin-scrollbar theme-doc-toc-desktop"><ul class="table-of-contents table-of-contents__left-border"><li><a href="#use-the-python-interpreter-in-a-flow" class="table-of-contents__link toc-highlight">Use the Python Interpreter in a flow</a></li><li><a href="#python-interpreter-parameters" class="table-of-contents__link toc-highlight">Python Interpreter parameters</a></li></ul></div></div></div></div></main></div></div></div><footer class="theme-layout-footer footer"><div class="container container-fluid"><div class="row footer__links"><div class="theme-layout-footer-column col footer__col"><div class="footer__title"></div><ul class="footer__items clean-list"><li class="footer__item"><div class="footer-links">
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