本文整理汇总了Python中seaborn.countplot方法的典型用法代码示例。如果您正苦于以下问题:Python seaborn.countplot方法的具体用法?Python seaborn.countplot怎么用?Python seaborn.countplot使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类seaborn
的用法示例。
在下文中一共展示了seaborn.countplot方法的14个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: plot_model_comparison
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import countplot [as 别名]
def plot_model_comparison(df, save_cfg=cfg.saving_config):
"""Plot bar graph showing the types of baseline models used.
"""
fig, ax = plt.subplots(figsize=(save_cfg['text_width'] / 4 * 2,
save_cfg['text_height'] / 5))
sns.countplot(y=df['Baseline model type'].dropna(axis=0), ax=ax)
ax.set_xlabel('Number of papers')
ax.set_ylabel('')
plt.tight_layout()
model_prcts = df['Baseline model type'].value_counts() / df.shape[0] * 100
logger.info('% of studies that used at least one traditional baseline: {}'.format(
model_prcts['Traditional pipeline'] + model_prcts['DL & Trad.']))
logger.info('% of studies that used at least one deep learning baseline: {}'.format(
model_prcts['DL'] + model_prcts['DL & Trad.']))
logger.info('% of studies that did not report baseline comparisons: {}'.format(
model_prcts['None']))
if save_cfg is not None:
fname = os.path.join(save_cfg['savepath'], 'model_comparison')
fig.savefig(fname + '.' + save_cfg['format'], **save_cfg)
return ax
示例2: plot_country
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import countplot [as 别名]
def plot_country(df, save_cfg=cfg.saving_config):
"""Plot bar graph showing the country of the first author's affiliation.
"""
fig, ax = plt.subplots(figsize=(save_cfg['text_width'] / 4 * 3,
save_cfg['text_height'] / 5))
sns.countplot(x=df['Country'], ax=ax,
order=df['Country'].value_counts().index)
ax.set_ylabel('Number of papers')
ax.set_xlabel('')
ax.set_xticklabels(ax.get_xticklabels(), rotation=90)
plt.tight_layout()
top3 = df['Country'].value_counts().index[:3]
logger.info('Top 3 countries of first author affiliation: {}'.format(top3.values))
if save_cfg is not None:
fname = os.path.join(save_cfg['savepath'], 'country')
fig.savefig(fname + '.' + save_cfg['format'], **save_cfg)
return ax
示例3: plot_cross_validation
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import countplot [as 别名]
def plot_cross_validation(df, save_cfg=cfg.saving_config):
"""Plot bar graph of cross validation approaches.
"""
col = 'Cross validation (clean)'
df[col] = df[col].fillna('N/M')
cv_df = ut.split_column_with_multiple_entries(
df, col, ref_col='Citation', sep=';\n', lower=False)
fig, ax = plt.subplots(
figsize=(save_cfg['text_width'] / 2, save_cfg['text_height'] / 5))
sns.countplot(y=cv_df[col], order=cv_df[col].value_counts().index, ax=ax)
ax.set_xlabel('Number of papers')
ax.set_ylabel('')
plt.tight_layout()
if save_cfg is not None:
fname = os.path.join(save_cfg['savepath'], 'cross_validation')
fig.savefig(fname + '.' + save_cfg['format'], **save_cfg)
return ax
示例4: plot_classification_frequency
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import countplot [as 别名]
def plot_classification_frequency(df, category, file_name, convert_labels = False):
'''
Plots the frequency at which labels occur
INPUT
df: Pandas DataFrame of the image name and labels
category: category of labels, from 0 to 4
file_name: file name of the image
convert_labels: argument specified for converting to binary classification
OUTPUT
Image of plot, showing label frequency
'''
if convert_labels == True:
labels['level'] = change_labels(labels, 'level')
sns.set(style="whitegrid", color_codes=True)
sns.countplot(x=category, data=labels)
plt.title('Retinopathy vs Frequency')
plt.savefig(file_name)
示例5: update_plot
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import countplot [as 别名]
def update_plot(self):
plt.ioff()
col = self.picker.currentText()
plt.figure()
arr = self.df[col].dropna()
if self.df[col].dtype.name in ['object', 'bool', 'category']:
ax = sns.countplot(y=arr, color='grey', order=arr.value_counts().iloc[:10].index)
else:
ax = sns.distplot(arr, color='black', hist_kws=dict(color='grey', alpha=1))
self.figure_viewer.setFigure(ax.figure)
# Examples
示例6: plot_chemical_trajectory
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import countplot [as 别名]
def plot_chemical_trajectory(self, environment, filename):
"""
Plot the trajectory through chemical space.
Parameters
----------
environment : str
the name of the environment for which the chemical space trajectory is desired
"""
chemical_state_trajectory = self.extract_state_trajectory(environment)
visited_states = list(set(chemical_state_trajectory))
state_trajectory = np.zeros(len(chemical_state_trajectory))
for idx, chemical_state in enumerate(chemical_state_trajectory):
state_trajectory[idx] = visited_states.index(chemical_state)
with PdfPages(filename) as pdf:
sns.set(font_scale=2)
fig = plt.figure(figsize=(28, 12))
plt.subplot2grid((1,2), (0,0))
ax = sns.scatterplot(np.arange(len(state_trajectory)), state_trajectory)
plt.yticks(np.arange(len(visited_states)), visited_states)
plt.title("Trajectory through chemical space in {}".format(environment))
plt.xlabel("iteration")
plt.ylabel("chemical state")
plt.tight_layout()
plt.subplot2grid((1,2), (0,1))
ax = sns.countplot(y=state_trajectory)
pdf.savefig(fig)
plt.close()
示例7: plot_type_of_paper
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import countplot [as 别名]
def plot_type_of_paper(df, save_cfg=cfg.saving_config):
"""Plot bar graph showing the type of each paper (journal, conference, etc.).
"""
# Move supplements to journal paper category for the plot (a value of one is
# not visible on a bar graph).
df_plot = df.copy()
df_plot.loc[df['Type of paper'] == 'Supplement', :] = 'Journal'
fig, ax = plt.subplots(figsize=(save_cfg['text_width'] / 4,
save_cfg['text_height'] / 5))
sns.countplot(x=df_plot['Type of paper'], ax=ax)
ax.set_xlabel('')
ax.set_ylabel('Number of papers')
ax.set_xticklabels(ax.get_xticklabels(), rotation=90)
plt.tight_layout()
counts = df['Type of paper'].value_counts()
logger.info('Number of journal papers: {}'.format(counts['Journal']))
logger.info('Number of conference papers: {}'.format(counts['Conference']))
logger.info('Number of preprints: {}'.format(counts['Preprint']))
logger.info('Number of papers that were initially published as preprints: '
'{}'.format(df[df['Type of paper'] != 'Preprint'][
'Preprint first'].value_counts()['Yes']))
if save_cfg is not None:
fname = os.path.join(save_cfg['savepath'], 'type_of_paper')
fig.savefig(fname + '.' + save_cfg['format'], **save_cfg)
return ax
示例8: plot_hardware
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import countplot [as 别名]
def plot_hardware(df, save_cfg=cfg.saving_config):
"""Plot bar graph showing the hardware used in the study.
"""
col = 'EEG Hardware'
hardware_df = ut.split_column_with_multiple_entries(
df, col, ref_col='Citation', sep=',', lower=False)
# Remove N/Ms because they make it hard to see anything
hardware_df = hardware_df[hardware_df[col] != 'N/M']
# Add low cost column
hardware_df['Low-cost'] = False
low_cost_devices = ['EPOC (Emotiv)', 'OpenBCI (OpenBCI)', 'Muse (InteraXon)',
'Mindwave Mobile (Neurosky)', 'Mindset (NeuroSky)']
hardware_df.loc[hardware_df[col].isin(low_cost_devices),
'Low-cost'] = True
fig, ax = plt.subplots(figsize=(save_cfg['text_width'] / 4 * 2,
save_cfg['text_height'] / 5 * 2))
sns.countplot(hue=hardware_df['Low-cost'], y=hardware_df[col], ax=ax,
order=hardware_df[col].value_counts().index,
dodge=False)
# sns.catplot(row=hardware_df['low_cost'], y=hardware_df['hardware'])
ax.set_xlabel('Number of papers')
ax.set_ylabel('')
plt.tight_layout()
if save_cfg is not None:
fname = os.path.join(save_cfg['savepath'], 'hardware')
fig.savefig(fname + '.' + save_cfg['format'], **save_cfg)
return ax
示例9: create_distribution
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import countplot [as 别名]
def create_distribution(dataFile):
return sb.countplot(x='Label', data=dataFile, palette='hls')
#by calling below we can see that training, test and valid data seems to be failry evenly distributed between the classes
示例10: weather_distribution
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import countplot [as 别名]
def weather_distribution(self):
data_dir = g_singletonDataFilePath.getTrainDir()
self.gapdf = self.load_weatherdf(data_dir)
print self.gapdf['weather'].describe()
# sns.distplot(self.gapdf['gap'],kde=False, bins=100);
sns.countplot(x="weather", data=self.gapdf, palette="Greens_d");
plt.title('Countplot of Weather')
# self.gapdf['weather'].plot(kind='bar')
# plt.xlabel('Weather')
# plt.title('Histogram of Weather')
return
示例11: plot_data
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import countplot [as 别名]
def plot_data(data):
# barplot for the depencent variable
sns.countplot(x='y', data=data, palette='hls')
plt.show()
# check the missing values
print(data.isnull().sum())
# customer distribution plot
sns.countplot(y='job', data=data)
plt.show()
# customer marital status distribution
sns.countplot(x='marital', data=data)
plt.show()
# barplot for credit in default
sns.countplot(x='default', data=data)
plt.show()
# barptot for housing loan
sns.countplot(x='housing', data=data)
plt.show()
# barplot for personal loan
sns.countplot(x='loan', data=data)
plt.show()
# barplot for previous marketing campaign outcome
sns.countplot(x='poutcome', data=data)
plt.show()
示例12: bar_plot
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import countplot [as 别名]
def bar_plot(data, col, hue=None, file_name=None):
sns.countplot(col, hue=hue, data=data.sort_values(col))
sns.despine(left=True)
subplots = [
x for x in plt.gcf().get_children() if isinstance(x, matplotlib.axes.Subplot)
]
for plot in subplots:
rectangles = [
x
for x in plot.get_children()
if isinstance(x, matplotlib.patches.Rectangle)
]
autolabel(rectangles)
示例13: distribution
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import countplot [as 别名]
def distribution(self, **kwargs):
"""Plots the label distribution."""
self._label_times._assert_single_target()
target_column = self._label_times.target_columns[0]
dist = self._label_times[target_column]
is_discrete = self._label_times.is_discrete[target_column]
if is_discrete:
ax = sns.countplot(dist, palette=COLOR, **kwargs)
else:
ax = sns.distplot(dist, kde=True, color=COLOR[1], **kwargs)
ax.set_title('Label Distribution')
ax.set_ylabel('Count')
return ax
示例14: cat_count
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import countplot [as 别名]
def cat_count(anns, names, show_count=False, save=False):
fig, axes = plt.subplots(1, len(anns), sharey=False)
# Making axes iterable if only single annotation is present
if len(anns) == 1:
axes = [axes]
# Prepare annotations dataframe
# This should be done at the start
for ann, name, ax in zip(anns, names, axes):
ann_df = pd.DataFrame(ann.anns).transpose()
if 'category_name' in ann_df.columns:
chart = sns.countplot(data=ann_df,
x='category_name',
order=ann_df['category_name'].value_counts().index,
palette='Set1',
ax=ax)
else:
# Add a new column -> category name
ann_df['category_name'] = ann_df.apply(lambda row: ann.cats[row.category_id]['name'],axis=1)
chart = sns.countplot(data=ann_df,
x='category_name',
order=ann_df['category_name'].value_counts().index,
palette='Set1',
ax=ax)
chart.set_title(name)
chart.set_xticklabels(chart.get_xticklabels(), rotation=90)
if show_count is True:
for p in chart.patches:
height = p.get_height()
chart.text(p.get_x() + p.get_width() / 2.,
height + 0.9,
height,
ha="center")
plt.suptitle('Instances per category', fontsize=14, fontweight='bold')
plt.tight_layout()
fig = plt.gcf()
fig.set_size_inches(11, 11)
out_dir = os.path.join(os.getcwd(), 'results', 'plots')
if save is True:
if os.path.exists(out_dir) is False:
os.mkdir(out_dir)
plt.savefig(os.path.join(out_dir, "cat_dist" + ".png"),
bbox_inches='tight',
pad_inches=0,
dpi=plt.gcf().dpi)
plt.show()