How to use the missingno.heatmap function in missingno

def test_simple_heatmap(self):
        msno.heatmap(self.simple_df)
        return plt.gcf()

def test_alternative_colormap_heatmap(self):
        msno.heatmap(self.simple_df, cmap='viridis')
        return plt.gcf()

def test_unlabelled_heatmap(self):
        msno.heatmap(self.simple_df, labels=False)
        return plt.gcf()

Returns:
      The resulting missing values heatmap plot encoded as a string.
    """

    height = 4
    if len(data.columns) > 10:
        height += int((len(data.columns) - 10) / 5)
    height = min(height, 10)

    font_size = get_font_size(data)
    if len(data.columns) > 40:
        font_size /= 1.4

    labels = config["plot"]["missing"]["force_labels"].get(bool)
    missingno.heatmap(
        data,
        figsize=(10, height),
        fontsize=font_size,
        cmap=config["plot"]["missing"]["cmap"].get(str),
        labels=labels,
    )

    if len(data.columns) > 40:
        plt.subplots_adjust(left=0.1, right=0.9, top=0.9, bottom=0.3)
    else:
        plt.subplots_adjust(left=0.2, right=0.9, top=0.8, bottom=0.3)

    return plot_360_n0sc0pe(plt)

"""Plot the nullility correlation of missing data within a DataFrame.

    Args:
        data (pd.DataFrame): DataFrame to plot.
        **kwargs: Keyword arguments for plot. Passed to missingno.heatmap.

    Returns:
        matplotlib.axes._subplots.AxesSubplot: nullility correlation plot.

    Raises:
        TypeError: if data is not a DataFrame. Error raised through decorator.
        ValueError: dataset fully observed. Raised through helper method.
    """
    _fully_complete(data)
    _default_plot_args(**kwargs)
    msno.heatmap(data, **kwargs)

sleep1.head()

sleep = sleep1.copy()

sns.heatmap(sleep.isnull(), cbar=False)
#NonD, Dream, Sleep, Span, Gest have missing values
sleep.isna().sum()


#
#  pip install missingno
import missingno as msno

msno.matrix(sleep)
#In addition to the heatmap, there is a bar on the right side of this diagram. This is a line plot for each row's data completeness.
msno.heatmap(sleep)
#missingno.heatmap visualizes the correlation matrix about the locations of missing values in columns.

#%%
dataset = sleep.copy()
total = dataset.isnull().sum().sort_values(ascending=False)
percent = (dataset.isnull().sum()/dataset.isnull().count()).sort_values( ascending=False)
missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])
f, ax = plt.subplots(figsize=(15, 6))
plt.xticks(rotation='90')
sns.barplot(x=missing_data.index, y=missing_data['Percent'])
plt.xlabel('Features', fontsize=15)
plt.ylabel('Percent of missing values', fontsize=15)
plt.title('Percent missing data by feature', fontsize=15)
missing_data.head()