explore_trend_plot

source

explore_trend_plot(
    df,
    var=None,
    *,
    time=None,
    entity=None,
    spaghetti=False,
    title=None,
    subtitle=None,
)

Line-plot the mean (with standard-error bars) of variables over time.

Parameters

Name	Type	Description	Default
df	pd.DataFrame	Data frame containing `time` and the numeric variables to plot.	required
var	Sequence[str] \| None	Variables to plot. Defaults to all numeric columns other than `time`/`entity`.	`None`
time	str \| None	Column name of the time identifier. Defaults to the panel `time` declared via :func:`expdpy.set_panel`.	`None`
entity	str \| None	Column name of the cross-sectional (unit) identifier. Only used when `spaghetti=True`. Defaults to the panel `entity` declared via :func:`expdpy.set_panel`.	`None`
spaghetti	bool	If `True` (and a single `var` and an `entity` are available), draw a faint per-unit trajectory backdrop behind the mean line. For richer per-unit views see :func:`expdpy.explore_spaghetti_plot`.	`False`

Returns

Name	Type	Description
	TrendGraphResult	`df` (columns `variable`, `time`, `mean`, `se`) and the Plotly `fig`.

Examples

Basic — mean of a single variable over time, with standard-error bars:

import expdpy as ex
from expdpy.data import load_kuznets, load_kuznets_data_def

df = ex.set_labels(load_kuznets(), load_kuznets_data_def(), set_panel=True)
ex.explore_trend_plot(df, var=["log_gdp_pc"]).fig

Advanced — several variables on one chart, with the aggregated frame from .df:

import expdpy as ex
from expdpy.data import load_kuznets, load_kuznets_data_def

df = ex.set_labels(load_kuznets(), load_kuznets_data_def(), set_panel=True)
result = ex.explore_trend_plot(df, var=["log_gdp_pc", "trade_share"])
result.fig
result.df.head()

	variable	year	mean	se
0	log_gdp_pc	2015	9.077922	0.167032
1	log_gdp_pc	2016	9.105304	0.165961
2	log_gdp_pc	2017	9.132692	0.165664
3	log_gdp_pc	2018	9.148910	0.165501
4	log_gdp_pc	2019	9.180302	0.165741