> ## Documentation Index
> Fetch the complete documentation index at: https://genai.codewithsiva.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Working with Data

> Load CSV files, explore dataframes, and create simple charts in Streamlit

Streamlit is a great fit for data apps because it works naturally with Pandas, NumPy, and Matplotlib.

## Reading CSV files

Use Pandas to load data from a local file or an uploaded file.

```python theme={null}
import streamlit as st
import pandas as pd

@st.cache_data
def load_data():
    return pd.read_csv("data/sales.csv")

sales = load_data()
st.dataframe(sales.head())
```

## Displaying Pandas DataFrames

DataFrames are one of the easiest ways to show tabular data in Streamlit.

```python theme={null}
import streamlit as st
import pandas as pd

sales = pd.DataFrame({
    "Month": ["Jan", "Feb", "Mar"],
    "Sales": [120, 180, 150]
})

st.subheader("Sales Overview")
st.dataframe(sales)
```

## Filtering and searching data

You can combine widgets with Pandas to build simple search and filter tools.

```python theme={null}
import streamlit as st
import pandas as pd

sales = pd.DataFrame({
    "Product": ["Phone", "Laptop", "Headphones"],
    "Region": ["North", "South", "North"],
    "Sales": [100, 200, 50]
})

search = st.text_input("Search product")
region = st.selectbox("Region", ["All", "North", "South"])

filtered = sales[sales["Product"].str.contains(search, case=False, na=False)]
if region != "All":
    filtered = filtered[filtered["Region"] == region]

st.dataframe(filtered)
```

## Basic NumPy operations

NumPy is useful for simple calculations and arrays.

```python theme={null}
import streamlit as st
import numpy as np

values = np.array([1, 2, 3, 4, 5])
st.write("Sum:", values.sum())
st.write("Mean:", values.mean())
st.write("Sorted:", np.sort(values))
```

## Creating charts with Matplotlib

You can render charts inside your Streamlit app using Matplotlib.

```python theme={null}
import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt

sales = pd.DataFrame({
    "Month": ["Jan", "Feb", "Mar", "Apr"],
    "Sales": [120, 180, 150, 220]
})

fig, ax = plt.subplots()
ax.plot(sales["Month"], sales["Sales"], marker="o")
ax.set_title("Monthly Sales")

st.pyplot(fig)
```

## Interactive data exploration

Combine controls and tabular output to create a simple exploration experience.

```python theme={null}
import streamlit as st
import pandas as pd

sales = pd.DataFrame({
    "Product": ["Phone", "Laptop", "Headphones"],
    "Sales": [100, 200, 50],
    "Rating": [4, 5, 3]
})

show_only_top = st.checkbox("Show top rated only")
if show_only_top:
    sales = sales[sales["Rating"] >= 4]

st.dataframe(sales)
```

## What's next?

You can now move on to performance topics such as caching and multipage apps.

<Card title="Caching" icon="arrow-right" href="/streamlit/caching">
  Learn to speed up your app with cached functions
</Card>