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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()