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<p>Learn how to build interactive data dashboards using Streamlit. This guide provides a complete step-by-step approach, from creating a basic UI to deploying the app.</p>
<h2>Introduction to Streamlit</h2>
<p>Streamlit is an open-source framework for building quick data apps with Python.</p>
<h3>Why Streamlit?</h3>
<ul>
<li>Easy for building data-driven apps.</li>
<li>Simple integration with Python libraries like Pandas and Matplotlib.</li>
</ul>
<h3>Installation</h3>
<p>To install Streamlit, run the following command:</p>
<pre><code class="hljs language-bash">pip install streamlit
</code></pre>
<h3>Your First Streamlit App</h3>
<p>Here's how to create a simple "Hello, World!" app:</p>
<pre><code class="hljs language-python"><span class="hljs-keyword">import</span> streamlit <span class="hljs-keyword">as</span> st
st.title(<span class="hljs-string">"Hello, Streamlit!"</span>)
st.write(<span class="hljs-string">"This is your first Streamlit app."</span>)
</code></pre>
<p>Run the app with:</p>
<pre><code class="hljs language-bash">streamlit run hello_world.py
</code></pre>
<h2>Building the Basic UI</h2>
<p>Create a basic layout using Streamlit's built-in functions like <code>st.title()</code>, <code>st.header()</code>, and <code>st.file_uploader()</code>.</p>
<pre><code class="hljs language-python"><span class="hljs-keyword">import</span> streamlit <span class="hljs-keyword">as</span> st
st.title(<span class="hljs-string">"Interactive Data Dashboard"</span>)
st.header(<span class="hljs-string">"1. Load your Dataset"</span>)
uploaded_file = st.file_uploader(<span class="hljs-string">"Choose a CSV file"</span>, <span class="hljs-built_in">type</span>=<span class="hljs-string">"csv"</span>)
<span class="hljs-keyword">if</span> uploaded_file:
st.write(<span class="hljs-string">"File successfully uploaded."</span>)
</code></pre>
<h2>Loading and Displaying Data</h2>
<p>Once a file is uploaded, use <code>pandas</code> to load and display the data.</p>
<pre><code class="hljs language-python"><span class="hljs-keyword">import</span> pandas <span class="hljs-keyword">as</span> pd
<span class="hljs-keyword">if</span> uploaded_file:
df = pd.read_csv(uploaded_file)
st.subheader(<span class="hljs-string">"2. Data Preview"</span>)
st.dataframe(df.head())
</code></pre>
<h3>Adding Filters</h3>
<p>Allow users to filter data by a specific column.</p>
<pre><code class="hljs language-python"><span class="hljs-keyword">if</span> uploaded_file:
column_to_filter = st.selectbox(<span class="hljs-string">"Select column to filter"</span>, df.columns)
selected_value = st.selectbox(<span class="hljs-string">f"Select <span class="hljs-subst">{column_to_filter}</span> value"</span>, df[column_to_filter].unique())
filtered_data = df[df[column_to_filter] == selected_value]
st.write(<span class="hljs-string">f"Filtered data for <span class="hljs-subst">{column_to_filter}</span> = <span class="hljs-subst">{selected_value}</span>"</span>)
st.dataframe(filtered_data)
</code></pre>
<h2>Data Visualization</h2>
<h3>Matplotlib</h3>
<p>Use Matplotlib to display static charts.</p>
<pre><code class="hljs language-python"><span class="hljs-keyword">import</span> matplotlib.pyplot <span class="hljs-keyword">as</span> plt
<span class="hljs-keyword">if</span> uploaded_file:
fig, ax = plt.subplots()
filtered_data[column_to_filter].value_counts().plot(kind=<span class="hljs-string">'bar'</span>, ax=ax)
st.pyplot(fig)
</code></pre>
<h3>Plotly</h3>
<p>For interactive visualizations, use Plotly.</p>
<pre><code class="hljs language-python"><span class="hljs-keyword">import</span> plotly.express <span class="hljs-keyword">as</span> px
<span class="hljs-keyword">if</span> uploaded_file:
fig = px.line(filtered_data, x=<span class="hljs-string">"date_column"</span>, y=<span class="hljs-string">"value_column"</span>, title=<span class="hljs-string">"Trend over Time"</span>)
st.plotly_chart(fig)
</code></pre>
<h2>Advanced Interactivity & State Management</h2>
<p>Enhance user experience with checkboxes and buttons.</p>
<pre><code class="hljs language-python"><span class="hljs-keyword">if</span> st.checkbox(<span class="hljs-string">"Show raw data"</span>):
st.subheader(<span class="hljs-string">"Raw Data"</span>)
st.dataframe(df)
</code></pre>
<p>Use session state to store data across interactions.</p>
<pre><code class="hljs language-python"><span class="hljs-keyword">if</span> <span class="hljs-string">'filtered_data'</span> <span class="hljs-keyword">not</span> <span class="hljs-keyword">in</span> st.session_state:
st.session_state[<span class="hljs-string">'filtered_data'</span>] = df
<span class="hljs-keyword">if</span> st.button(<span class="hljs-string">'Update Data'</span>):
st.session_state[<span class="hljs-string">'filtered_data'</span>] = filtered_data
</code></pre>
<h2>Deploying the Data Dashboard</h2>
<p>Once the app is ready, deploy it on Streamlit Cloud.</p>
<ol>
<li>Push the code to GitHub.</li>
<li>Go to <a href="https://share.streamlit.io/">Streamlit Cloud</a>.</li>
<li>Deploy with a single click.</li>
</ol>
<h2>Mini Project: Interactive Data Dashboard</h2>
<p>Here's the full code to build an interactive data dashboard:</p>
<pre><code class="hljs language-python"><span class="hljs-keyword">import</span> streamlit <span class="hljs-keyword">as</span> st
<span class="hljs-keyword">import</span> pandas <span class="hljs-keyword">as</span> pd
<span class="hljs-keyword">import</span> matplotlib.pyplot <span class="hljs-keyword">as</span> plt
<span class="hljs-keyword">import</span> plotly.express <span class="hljs-keyword">as</span> px
st.title(<span class="hljs-string">"Interactive Data Dashboard"</span>)
uploaded_file = st.file_uploader(<span class="hljs-string">"Upload a CSV"</span>, <span class="hljs-built_in">type</span>=<span class="hljs-string">"csv"</span>)
<span class="hljs-keyword">if</span> uploaded_file:
df = pd.read_csv(uploaded_file)
st.subheader(<span class="hljs-string">"Data Preview"</span>)
st.dataframe(df.head())
column_to_filter = st.selectbox(<span class="hljs-string">"Filter by column"</span>, df.columns)
selected_value = st.selectbox(<span class="hljs-string">f"Select <span class="hljs-subst">{column_to_filter}</span> value"</span>, df[column_to_filter].unique())
filtered_data = df[df[column_to_filter] == selected_value]
st.subheader(<span class="hljs-string">f"Filtered Data for <span class="hljs-subst">{column_to_filter}</span> = <span class="hljs-subst">{selected_value}</span>"</span>)
st.dataframe(filtered_data)
fig, ax = plt.subplots()
filtered_data[column_to_filter].value_counts().plot(kind=<span class="hljs-string">'bar'</span>, ax=ax)
st.pyplot(fig)
fig = px.line(filtered_data, x=<span class="hljs-string">"date_column"</span>, y=<span class="hljs-string">"value_column"</span>, title=<span class="hljs-string">"Trend over Time"</span>)
st.plotly_chart(fig)
<span class="hljs-keyword">if</span> st.checkbox(<span class="hljs-string">"Show raw data"</span>):
st.subheader(<span class="hljs-string">"Raw Data"</span>)
st.dataframe(df)
</code></pre>
<h2>Conclusion</h2>
<p>In this guide, you've learned how to build a complete data dashboard using Streamlit. You can enhance it further with database integration, authentication, and more advanced filtering options.</p></div><div class="mt-4 flex flex-wrap gap-1"><span class="text-xs bg-[#1C2B3A] text-[#5565E8] px-2 py-0.5 rounded">Streamlit</span><span class="text-xs bg-[#1C2B3A] text-[#5565E8] px-2 py-0.5 rounded">Data Apps</span><span class="text-xs bg-[#1C2B3A] text-[#5565E8] px-2 py-0.5 rounded">Python</span></div><div class="mt-8 pt-6 border-t border-[#1E2D3D]"><h2 class="text-xl font-bold text-[#E5E9F0] mb-3">Related Posts</h2><div class="grid grid-cols-1 sm:grid-cols-2 gap-4"><a class="block" href="https://blog.g1mishra.dev/admob-ads-in-expo-react-native-app"><div class="bg-[#011221] border border-light rounded p-3 hover:bg-[#011627] transition-colors h-full flex flex-col"><h3 class="text-lg font-semibold text-[#E5E9F0] mb-1">How to Add AdMob Ads to Your Expo React Native App</h3><p class="text-[#607B96] text-sm mb-2 flex-grow">Step-by-step guide to implementing Google AdMob ads in 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