¿Cuál puede ser en este caso, la causa de éste error "The truth value of a DataFrame is ambiguous"?

Iniciándome en Plotly. Importo en un dataframe cotizaciones de un valor de bolsa. Calculo varias medias móviles y construyo un único dataframe con las cotizaciones importadas y y las medias móviles calculadas. Utilizando plotly.express intento visualizar un gráfico que incluya todas que incluya la gráfica de las cotizaciones y las gráficas de las medias móviles. Este es el script.

# Importar cotizaciones
import pandas as pd
import pandas_datareader.data as pdr
start = "2016-1-4"
end = "2020-10-19"
iberdrola = pdr.DataReader("IBE.MC", "yahoo", start, end)
iberdrola = iberdrola[["Close"]]

# Calcular medias móviles
dfSMA =iberdrola[["Close"]].reset_index()

SMA2 = dfSMA["Close"].rolling(2).mean().dropna() 
SMA5 =  dfSMA["Close"].rolling(5).mean().dropna()
SMA10= dfSMA["Close"].rolling(10).mean().dropna() 
SMA20= dfSMA["Close"].rolling(20).mean().dropna() 
SMA50=  dfSMA["Close"].rolling(50).mean().dropna()
    
# Crear el DataFrame valor- medias móviles
dfs = [dfSMA, SMA2, SMA5, SMA10, SMA20, SMA50 ]
dfs = iter(dfs)
val_SMA = next(dfs)
for df_ in (dfs):
    val_SMA = val_SMA.merge(df_, left_index = True, right_index = True)

# Mostrar gráfica
import plotly.express as px
#df = px.data.stocks()

val_SMA.reset_index(inplace=True)
fig = px.line(val_SMA, x="Date", y=val_SMA.columns,
              hover_data={"Date": "|%B %d, %Y"},
              title='custom tick labels')
fig.update_xaxes(
    dtick="M1",
    tickformat="%bn%Y")
fig.show()

Me devuelve el error:

ValueError: The truth value of a DataFrame is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

Agradeceré ayuda para solucionar este problema.

Información completa del error:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-2-30075cd79f70> in <module>
     27 
     28 val_SMA.reset_index(inplace=True)
---> 29 fig = px.line(val_SMA, x="Date", y=val_SMA.columns,
     30               hover_data={"Date": "|%B %d, %Y"},
     31               title='custom tick labels')

~/anaconda3/envs/plotly/lib/python3.8/site-packages/plotly/express/_chart_types.py in line(data_frame, x, y, line_group, color, line_dash, hover_name, hover_data, custom_data, text, facet_row, facet_col, facet_col_wrap, facet_row_spacing, facet_col_spacing, error_x, error_x_minus, error_y, error_y_minus, animation_frame, animation_group, category_orders, labels, orientation, color_discrete_sequence, color_discrete_map, line_dash_sequence, line_dash_map, log_x, log_y, range_x, range_y, line_shape, render_mode, title, template, width, height)
    250     a polyline mark in 2D space.
    251     """
--> 252     return make_figure(args=locals(), constructor=go.Scatter)
    253 
    254 

~/anaconda3/envs/plotly/lib/python3.8/site-packages/plotly/express/_core.py in make_figure(args, constructor, trace_patch, layout_patch)
   1824     apply_default_cascade(args)
   1825 
-> 1826     args = build_dataframe(args, constructor)
   1827     if constructor in [go.Treemap, go.Sunburst] and args["path"] is not None:
   1828         args = process_dataframe_hierarchy(args)

~/anaconda3/envs/plotly/lib/python3.8/site-packages/plotly/express/_core.py in build_dataframe(args, constructor)
   1356     # now that things have been prepped, we do the systematic rewriting of `args`
   1357 
-> 1358     df_output, wide_id_vars = process_args_into_dataframe(
   1359         args, wide_mode, var_name, value_name
   1360     )

~/anaconda3/envs/plotly/lib/python3.8/site-packages/plotly/express/_core.py in process_args_into_dataframe(args, wide_mode, var_name, value_name)
   1177                 else:
   1178                     col_name = str(argument)
-> 1179                     df_output[col_name] = to_unindexed_series(df_input[argument])
   1180             # ----------------- argument is likely a column / array / list.... -------
   1181             else:

~/anaconda3/envs/plotly/lib/python3.8/site-packages/plotly/express/_core.py in to_unindexed_series(x)
   1028     required to get things to match up right in the new DataFrame we're building
   1029     """
-> 1030     return pd.Series(x).reset_index(drop=True)
   1031 
   1032 

~/anaconda3/envs/plotly/lib/python3.8/site-packages/pandas/core/series.py in __init__(self, data, index, dtype, name, copy, fastpath)
    200             name = ibase.maybe_extract_name(name, data, type(self))
    201 
--> 202             if is_empty_data(data) and dtype is None:
    203                 # gh-17261
    204                 warnings.warn(

~/anaconda3/envs/plotly/lib/python3.8/site-packages/pandas/core/construction.py in is_empty_data(data)
    584     is_none = data is None
    585     is_list_like_without_dtype = is_list_like(data) and not hasattr(data, "dtype")
--> 586     is_simple_empty = is_list_like_without_dtype and not data
    587     return is_none or is_simple_empty
    588 

~/anaconda3/envs/plotly/lib/python3.8/site-packages/pandas/core/generic.py in __nonzero__(self)
   1476 
   1477     def __nonzero__(self):
-> 1478         raise ValueError(
   1479             f"The truth value of a {type(self).__name__} is ambiguous. "
   1480             "Use a.empty, a.bool(), a.item(), a.any() or a.all()."

ValueError: The truth value of a DataFrame is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

    # Calcular medias móviles

dfSMA =iberdrola[["Close"]].reset_index()

dfSMA["SMA2"] = dfSMA["Close"].rolling(2).mean().dropna() 
dfSMA["SMA5"] =  dfSMA["Close"].rolling(5).mean().dropna()
dfSMA["SMA10"]= dfSMA["Close"].rolling(10).mean().dropna() 
dfSMA["SMA20"]= dfSMA["Close"].rolling(20).mean().dropna() 
dfSMA["SMA50"]=  dfSMA["Close"].rolling(50).mean().dropna()
    
import plotly.express as px

fig = px.line(dfSMA, x="Date", y=dfSMA.columns,
              hover_data={"Date": "|%B %d, %Y"},
              title='custom tick labels')
fig.update_xaxes(
    dtick="M1",
    tickformat="%bn%Y")
fig.show()