Pandas DataFrames

The neo4j-viz library provides a convenience method for importing data from Pandas 数据框. These DataFrames can be created from many sources, such as CSV files. It requires and additional dependency to be installed, which you can do by running

pip install neo4j-viz[pandas]

Once you have installed the additional dependency, you can use the from_pandas method to import pandas DataFrames.

The from_dfs method takes two mandatory positional parameters

  • A Pandas DataFrame, or iterable (eg. list) of DataFrames representing the nodes of the graph. The rows of the DataFrame(s) should represent the individual nodes, and the columns should represent the node IDs and attributes. The node ID will be set on the 节点, Other columns will be a key in each node’s properties dictionary, that maps to the node’s corresponding value in the column. If the graph has no node properties, the nodes can be derived from the relationships DataFrame alone.

  • A Pandas DataFrame, or iterable (eg. list) of DataFrames representing the relationships of the graph. The rows of the DataFrame(s) should represent the individual relationships, and the columns should represent the relationship IDs and attributes. The relationship id, source and target node IDs will be set on the 关系. Other columns will be a key in each relationship’s properties dictionary, that maps to the relationship’s corresponding value in the column.

示例

In this small example, we import a tiny toy graph representing a social network from two Pandas DataFrames. As we can see the column names of the DataFrames map directly to the fields of 节点 and 关系.

from pandas import DataFrame
from neo4j_viz.pandas import from_dfs

nodes = DataFrame({
    "id": [1, 2, 3],
    "caption": ["Alice", "Bob", "Charlie"],
    "size": [20, 10, 10],
})

relationships = DataFrame({
    "source": [1, 2],
    "target": [2, 3],
    "caption": ["LIKES", "KNOWS"],
})

VG = from_dfs(nodes, relationships)