### Install Vaex using Conda or Pip

Source: https://vaex.io/docs/index

Install the Vaex library and its dependencies using either the conda package manager from the conda-forge channel or the pip package manager. These commands will set up the necessary environment to start working with Vaex.

```bash
conda install -c conda-forge vaex
```

```bash
pip install --upgrade vaex
```

--------------------------------

### Import Vaex and Load an Example Dataset

Source: https://vaex.io/docs/index

This snippet demonstrates how to import the vaex library and load the built-in example dataset using the `vaex.example()` function. This is the initial step for working with vaex.

```python
import vaex
df = vaex.example()  # open the example dataset provided with vaex
```

--------------------------------

### Calculate 1D Binned Statistics with Vaex

Source: https://vaex.io/docs/index

Vaex can efficiently calculate statistics over a regular grid. This example computes the mean of a column, binned by another column 'x' into 32 bins within the range of -10 to 10.

```python
df.mean(df.r, binby=df.x, shape=32, limits=[-10, 10]) # create statistics on a regular grid (1d)
```

--------------------------------

### Create a Vaex Expression

Source: https://vaex.io/docs/index

Vaex expressions are computed lazily and do not consume memory until they are needed. This example creates a new expression by adding two DataFrame columns together. The output shows a preview of the expression's values.

```python
some_expression = df.x + df.z
some_expression
```

--------------------------------

### Filter and Select Data from a Vaex DataFrame

Source: https://vaex.io/docs/index

This example shows how to filter a vaex DataFrame to create a new view without copying data. It first creates a filtered DataFrame `df_negative` for rows where `x < 0`, and then selects the first five rows and only the 'x' and 'y' columns from this filtered view.

```python
df_negative = df[df.x < 0]  # easily filter your DataFrame, without making a copy
df_negative[:5][['x', 'y']]  # take the first five rows, and only the 'x' and 'y' column (no memory copy!)
```

--------------------------------

### Calculate 2D Binned Statistics with Vaex

Source: https://vaex.io/docs/index

Statistics can also be calculated on a 2D grid by passing a list of columns to the `binby` argument. This example demonstrates calculating the mean and count (histogram) for a 2D grid defined by columns 'x' and 'y'.

```python
df.mean(df.r, binby=[df.x, df.y], shape=32, limits=[-10, 10]) # or 2d
df.count(df.r, binby=[df.x, df.y], shape=32, limits=[-10, 10]) # or 2d counts/histogram
```

--------------------------------

### Generate a Heatmap with Vaex

Source: https://vaex.io/docs/index

Vaex includes convenient visualization functions for quick plotting. This snippet uses the `.viz.heatmap` accessor to generate and display a heatmap of columns 'x' and 'y'.

```python
df.viz.heatmap(df.x, df.y, show=True);  # make a plot quickly
```

--------------------------------

### Display a Vaex DataFrame

Source: https://vaex.io/docs/index

To inspect the contents of a vaex DataFrame in an interactive environment like a Jupyter notebook, simply reference the DataFrame variable. This will render a formatted table showing a preview of the data.

```python
df  # will pretty print the DataFrame
```

--------------------------------

### Import NumPy for Vaex Computations

Source: https://vaex.io/docs/index

Import the NumPy library to be used with vaex. Many NumPy functions can be applied directly to vaex DataFrames and columns, enabling lazy, on-the-fly computations without significant memory overhead.

```python
import numpy as np
```

--------------------------------

### Add a Virtual Column to a Vaex DataFrame

Source: https://vaex.io/docs/index

An expression can be added to a DataFrame as a virtual column, which is computed on-the-fly and does not use additional memory. This snippet adds the previously created expression as a new column 'r' and then calculates the mean for both a normal and a virtual column.

```python
df['r'] = some_expression  # add a (virtual) column that will be computed on the fly
df.mean(df.x), df.mean(df.r)  # calculate statistics on normal and virtual columns
```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.