> ## Documentation Index
> Fetch the complete documentation index at: https://wiki.latch.bio/llms.txt
> Use this file to discover all available pages before exploring further.
# Add Custom Plots from Any Library
> Latch Plots allows you to install and import any libraries to generate custom plots and display them in the notebook view.
In addition to [interactive, no-code Plot cell](/plots/plotting/plot-types), Latch Plots allows developers to programmatically use any library and generate plots of interest.
There are two main ways to programmatically create custom plots:
1. Display an image using libraries like `seaborn` or `matplotlib`
2. Display a figure from `plotly`
## Option 1: Display a plot image from `seaborn` or `matplotlib`
### Step 1: Install the Python packages
To install your Python libraries, first click on **Custom Data Analysis** to create a Python cell. Paste in the following Python code to install packages:
```python theme={null}
import os
os.system("/opt/mamba/envs/plots-faas/bin/pip install matplotlib")
```
### Step 2: Display the plot as an image
To display a `matplotlib` or `seaborn` figure, first assign the figure to a variable, e.g. `fig`
```python theme={null}
import pandas as pd
import matplotlib.pyplot as plt
# Load the dataset
df = pd.read_csv("https://latch-public.s3.us-west-2.amazonaws.com/plots/test-data/iris.csv")
# Create the scatter plot
plt.figure(figsize=(10, 6))
print(df)
# Store the matplotlib figure in a `fig`variable
fig = plt.scatter(df['sepal.length'], df['sepal.width'], c='blue', label='Sepal')
plt.scatter(df['petal.length'], df['petal.width'], c='green', label='Petal')
```
Click on the `fig` variable to display the image in the output of the cell.
## Option 2: Display a custom, interactive Plotly figure
First, click on **Custom Data Analysis** to create a new Python cell. Next, import the Plotly Express library and create your plot of interest.
In the example below, we created a scatter plot for the iris dataset, and save the Plotly object as a Python variable called `fig`.
```python theme={null}
import plotly.express as px
df = px.data.iris()
fig = px.scatter(
df,
x="sepal_length",
y="sepal_width",
template="simple_white"
)
```
Select the `fig` variable to display the interactive Plotly figure in the output of the cell.
## Option 2: Display a custom, interactive Plotly figure
First, click on **Custom Data Analysis** to create a new Python cell. Next, import the Plotly Express library and create your plot of interest.
In the example below, we created a scatter plot for the iris dataset, and save the Plotly object as a Python variable called `fig`.
```python theme={null}
import plotly.express as px
df = px.data.iris()
fig = px.scatter(
df,
x="sepal_length",
y="sepal_width",
template="simple_white"
)
```
Select the `fig` variable to display the interactive Plotly figure in the output of the cell.
