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README.md

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+---
+title: Simple Data Dashboard
+emoji: 📊
+colorFrom: purple
+colorTo: blue
+sdk: streamlit
+python_version: 3.11
+app_file: app.py
+pinned: false
+---
+
+# Simple Data Dashboard (Streamlit)
+
+A lightweight Streamlit dashboard for exploring a CSV file: preview, summary stats, filtering, and a quick line chart.
+
+## Features
+- Upload any `.csv` file
+- Preview the first rows
+- Summary statistics (`describe()`)
+- Filter rows by selecting:
+  - a column
+  - a value from that column
+- Generate a simple line chart using selected X and Y columns (on the filtered data)
+
+## Tech Stack
+- Python
+- Streamlit
+- Pandas / NumPy
+- Matplotlib (imported; Streamlit chart used for the plot)
+
+## Run Locally
+
+### 1) Create a virtual environment (recommended)
+```bash
+python -m venv .venv
+source .venv/bin/activate   # macOS/Linux
+# .venv\Scripts\activate  # Windows
+```
+
+### 2) Install dependencies
+```bash
+pip install streamlit pandas numpy matplotlib
+```
+
+### 3) Start the app
+```bash
+streamlit run app.py
+```
+
+Then open the local URL shown in the terminal (usually http://localhost:8501).
+
+## Project Structure (suggested for GitHub)
+```
+simple-data-dashboard/
+  main.py
+  README.md
+  requirements.txt
+```
+
+### Example `requirements.txt`
+```txt
+streamlit
+pandas
+numpy
+matplotlib
+```
+
+## Tips
+- For best results, pick numeric columns for the Y-axis.
+- If your X column is not unique or not sorted, the line chart may look messy—try using an index-like column.
+
+## License
+Add a license if you plan to publish publicly (MIT is common for small demo projects).

Sample_Data_for_Plotting_and_Filtering.csv

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+Date,City,Temperature,Humidity,Wind Speed,Rainfall
+2023-01-01,Chicago,22.166129392299855,61.93649511543435,6.263552806187275,0
+2023-01-02,New York,13.28269599707907,61.28813196279497,4.748310171715538,0
+2023-01-03,Phoenix,28.870106264448683,55.40084471967545,12.64672909121614,15
+2023-01-04,New York,22.26673645955334,60.02155301041372,13.449880261249078,5
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+2023-01-06,Phoenix,31.33665579234697,73.22960858860029,11.013979834091764,5
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+2023-01-08,Los Angeles,23.993583025399108,50.35632259306495,10.775298134363133,5
+2023-01-09,Los Angeles,30.96758045207047,56.74813678541643,10.153407777162691,10
+2023-01-10,New York,21.16968310509066,63.80235389649399,7.413056755597967,0
+2023-01-11,New York,17.47367936052598,54.14980316044958,10.63789409805333,0
+2023-01-12,Los Angeles,31.280418635733515,66.6828878135053,8.198707807351441,5
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+2023-01-14,New York,22.942918304361605,65.73014921627954,12.127231595268691,0
+2023-01-15,Chicago,28.81768593129745,52.15219303357924,7.945732342335329,0
+2023-01-16,Los Angeles,22.14476553231419,61.67428049932768,9.569584767845562,0
+2023-01-17,Chicago,19.58080530671381,66.2251383305259,7.819780214083182,0
+2023-01-18,Houston,20.426997424859877,71.28984033436139,8.07682315140769,0
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+2023-01-21,Phoenix,22.99196807212576,79.43967605534046,11.666510573744464,0
+2023-01-22,Chicago,23.284182805780343,47.90882188149915,8.893333928147973,15
+2023-01-23,Los Angeles,22.291572885076903,42.375944399930574,11.106956531028331,0
+2023-01-24,Phoenix,33.709239659202886,70.14491563570775,8.40391048067789,0
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+2023-01-26,Los Angeles,26.886885795616408,53.905601095495925,12.148040626901441,0
+2023-01-27,Los Angeles,28.998914598313505,57.68692733421215,7.5512139991588505,0
+2023-01-28,Los Angeles,23.83083565593751,59.7611883151882,7.5407522478819295,0
+2023-01-29,Los Angeles,22.310851601307775,57.40437344508312,11.561052594705895,0
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+2023-01-31,Los Angeles,29.238100861368626,52.3334236028117,7.293771520140986,0
+2023-02-01,Chicago,23.8385638468339,69.05851849718222,11.602639117035668,0
+2023-02-02,New York,17.782895400605753,45.16463352800746,11.019408938016998,0
+2023-02-03,Los Angeles,24.794123287091082,64.67551340939997,10.303619550692627,0
+2023-02-04,Houston,20.67164606280725,53.818736345141566,10.055543945039776,0
+2023-02-05,Houston,29.7010690745091,41.93709129057614,11.872667348758739,0
+2023-02-06,Los Angeles,26.94574399012889,51.77333151743474,12.100174356560778,0
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+2023-02-08,Los Angeles,23.236745088040166,58.51768491921674,8.009956733967968,5
+2023-02-09,New York,26.75400089514104,64.9076923886483,13.94509186199778,0
+2023-02-10,Phoenix,30.68764543809697,80.63395176253289,8.394657041421633,15
+2023-02-11,Chicago,25.46355698227373,63.22294622361077,12.066604748930153,15
+2023-02-12,Houston,34.39951107349792,56.87223318765508,7.767000734463014,15
+2023-02-13,Los Angeles,24.240931734183278,52.60866717840071,9.500639625690859,0
+2023-02-14,New York,25.326344179958895,43.33302161211304,8.669177994816867,10
+2023-02-15,Los Angeles,20.77278457116569,60.04031027022848,9.565296112183901,15
+2023-02-16,Chicago,23.501980363912956,79.65728136547257,11.920675317313803,10
+2023-02-17,Chicago,22.547292548883416,64.73623388107006,9.961108266082663,0
+2023-02-18,Phoenix,25.56224540247249,66.31800635621929,11.272388609701336,0
+2023-02-19,Los Angeles,28.212822934533012,57.93827852321966,11.504828336779882,15
+2023-02-20,Chicago,28.301615595357752,65.23157562806085,10.08508624080603,15
+2023-02-21,Phoenix,33.73484322901722,74.44898403643246,6.513940363482025,5
+2023-02-22,Houston,22.78180911012742,53.29443460382488,15.392267354121945,0
+2023-02-23,Houston,32.07897323204049,71.63634660575994,8.255667216375011,0
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+2023-02-26,New York,34.24099712743694,68.7837004732068,8.84557055289277,0
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+2023-03-01,Phoenix,18.57857662729868,59.26255276728639,10.303622087141614,0
+2023-03-02,New York,27.78318701225198,75.61540459894039,10.958971257293056,0
+2023-03-03,Chicago,24.47152037782313,50.42521677431462,11.697968652638677,0
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+2023-03-08,Chicago,22.441171242102232,80.41147485067162,7.125539249868353,15
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app.py

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+
+
+import streamlit as st
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from streamlit import columns
+
+st.title("Simple Data Dashboard")
+
+uploaded_file = st.file_uploader("Choose a CSV File",type="csv")
+
+
+if uploaded_file is not None:
+    df = pd.read_csv(uploaded_file)
+
+    st.subheader("Data Preview")
+    st.write(df.head())
+
+    st.subheader("Data Summary")
+    st.write(df.describe())
+
+    st.subheader("Filter Data")
+    columns = df.columns.tolist()
+    selected_column = st.selectbox("Select column to filter by", columns)
+    unique_values = df[selected_column].unique()
+    selected_value = st.selectbox("Select value", unique_values)
+
+    filtered_df = df[df[selected_column] == selected_value]
+    st.write(filtered_df)
+
+    st.subheader("Plot Data")
+    x_column = st.selectbox("Select x-axis column", columns)
+    y_column = st.selectbox("Select y-axis column", columns)
+
+    if st.button("Generate Plot"):
+        st.line_chart(filtered_df.set_index(x_column)[y_column])
+else:
+    st.write("Waiting on file upload...")

requirements.txt

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+streamlit
+pandas
+numpy
+matplotlib