> For the complete documentation index, see [llms.txt](https://vecco-insight.gitbook.io/crash-visualization/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://vecco-insight.gitbook.io/crash-visualization/bokeh/5.2-data-sources.md).

# 5.2 Data Sources

In this section, we will explain several ways of providing data for plots.

### 1. Providing  directly

In Bokeh, it is possible to pass lists of values directly into plotting functions. In the example below, the data, `x_values` and `y_values`, are passed directly to the `circle` plotting method.

```
from bokeh.plotting import figure

x_values = [1, 2, 3, 4, 5]
y_values = [6, 7, 2, 3, 6]

p = figure()
p.circle(x=x_values, y=y_values)
```

When you pass in data like this, Bokeh works behind the scenes to make a `ColumnDataSource` for you. It is the core of most Bokeh plots, providing the data that is visualized by the glyphs of the plot. With the`ColumnDataSource` , it is easy to share data between multiple plots and widgets.

### 2. ColumnDataSource

At the most basic level, a `ColumnDataSource` is simply a mapping between column names and lists of data. Once the`ColumnDataSource`has been created, it can be passed into the`source`parameter of plotting methods which allows you to pass a column’s name as a stand-in for the data values.

```
from bokeh.plotting import figure
from bokeh.models import ColumnDataSource

data = {'x_value': [1, 2, 3, 4, 5],
        'y_values': [6, 7, 2, 3, 6]}

source = ColumnDataSource(data=data)

p = figure()
p.circle(x='x_values', y='y_values', source=source)
```

### 3. Pandas

The `data`parameter can also be a Pandas `DataFrame` or `GroupBy` object.

```
source = ColumnDataSource(df)
```

Pandas  `GroupBy`.

```
group = df.groupby(('colA', 'ColB'))
source = ColumnDataSource(group)
```

### 4. Streaming

`ColumnDataSource` streaming is an efficient way to append new data to a CDS. By using the`stream`method, Bokeh only sends new data to the browser instead of the entire dataset. 

The `stream` method takes a `new_data` parameter containing a `dict` mapping column names to sequences of data to be appended to the respective columns.

```
source = ColumnDataSource(data=dict(foo=[], bar=[]))

# has new, identical-length updates for all columns in source
new_data = {
    'foo' : [10, 20],
    'bar' : [100, 200],
}

source.stream(new_data)
```


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