def visualize_importance(self):
feature_importance_df = pd.DataFrame()
for i, model in enumerate(self.models):
_df = pd.DataFrame()
self.importance = model.get_feature_importance()
_df['feature_importance'] = model.get_feature_importance()
_df['column'] = self.feature_cols.tolist()
_df['fold'] = i + 1
feature_importance_df = pd.concat([feature_importance_df, _df],
axis=0,
ignore_index=True)
order = feature_importance_df.groupby('column').sum()[[
'feature_importance'
]].sort_values('feature_importance', ascending=False).index[:50]
fig, ax = plt.subplots(2, 1, figsize=(max(6, len(order) * .4), 14))
sns.boxenplot(data=feature_importance_df,
x='column',
y='feature_importance',
order=order,
ax=ax[0],
palette='viridis')
ax[0].tick_params(axis='x', rotation=90)
ax[0].grid()
fig.tight_layout()
return fig, ax
def plot_entanglement_boxes(data, plot_vars, save=True):
"""Boxplot planning time vs entanglement"""
if data.empty:
return
plt.rcParams.update({'font.size': cfg.FONTSIZE})
_, ax = plt.subplots(figsize=cfg.FIGSIZE)
sns.boxenplot(x='entanglement', y='transitions', data=data, color='C0', ax=ax, showfliers=False)
n_values = list(data['n_values'])[0]
ax.set_ylim(plot_vars.ylim)
plt.xlabel('Effect size')
ax.set_ylabel('Generated states')
ax.set_yscale('linear')
ax.yaxis.set_major_formatter(ticker.FormatStrFormatter('%2.0f'))
ax.yaxis.set_major_locator(ticker.MultipleLocator(plot_vars.tick_size))
ax.set_ylabel('Generated states' + autoscale_yticks(ax, dtype=int))
# sns.despine()
plt.tight_layout()
plt.subplots_adjust(top = .95, bottom = .2, right = .95, left = 0.25,
hspace = 0, wspace = 0)
plt.margins(0,0)
if save:
plt.savefig('results/plots/{}/{}_{}ary.png'.format(
cfg.DIR, cfg.NAME, n_values), dpi=100)
plt.show()
def visualize_importance(models, feat_train_df):
"""lightGBM の model 配列の feature importance を plot する
CVごとのブレを boxen plot として表現します.
args:
models:
List of lightGBM models
feat_train_df:
学習時に使った DataFrame
"""
feature_importance_df = pd.DataFrame()
_df = pd.DataFrame()
_df['feature_importance'] = models.feature_importance()
_df['column'] = feat_train_df.columns
feature_importance_df = pd.concat([feature_importance_df, _df],
axis=0,
ignore_index=True)
order = feature_importance_df.groupby('column') \
.sum()[['feature_importance']] \
.sort_values('feature_importance', ascending=False).index[:50]
fig, ax = plt.subplots(figsize=(len(order) * .4, 7))
sns.boxenplot(data=feature_importance_df,
x='column',
y='feature_importance',
order=order,
ax=ax,
palette='viridis')
ax.tick_params(axis='x', rotation=90)
fig.tight_layout()
plt.show()
return fig, ax
def sns_boxenplot(self, dataframe, x_name=None, y_name=None, hue_str=None):
"""
Boxenplot - similar to box plots but provides more information about
the distribution as it plots more quantiles. Useful for large datasets
Parameters
----------
dataframe : dataframe
data container
x_name : str, optional
column name for the x-axis. The default is None.
y_name : str, optional
column name for the y-axis. The default is None.
hue_str : str, optional
name of categorical data to color by. The default is None.
Returns
-------
None.
eg.
diamonds = sns.load_dataset('diamonds').sort_values('color')
myplot.sns_boxenplot(diamonds,'color','price')
"""
sns.boxenplot(data=dataframe, x=x_name, y=y_name, hue=hue_str,
palette='deep')
plt.show()
def graph_univar_pred(df_data, list_var, col_predict, var_type):
"""
Graph univariate for each variable in list compared with numerical prediction (label in dF)
var_type =
"num" if numeric: Violin Chart
"cat" if categorical: Barplot count
"""
# Setup the graph subplot
fig, axs = plt.subplots(len(list_var),
1,
figsize=set_size(plt_wd, len(list_var), 1))
fig.subplots_adjust(hspace=0.3) # Adjust space between rows
# loop for graphing
for i, item in enumerate(list_var):
cond_a = df_data[item].isna() == False
cond_b = df_data[col_predict] != np.nan
dF_dummy = df_data.loc[cond_a & cond_b]
if var_type == "num":
sns.boxenplot(x=col_predict, y=item, data=dF_dummy, ax=axs[i])
if var_type == "cat":
sns.countplot(x=item, hue=col_predict, data=dF_dummy, ax=axs[i])
# Add the percentage in the graph
total = dF_dummy.shape[0]
for p in axs[i].patches:
percentage = '{:.0f}%'.format(100 * p.get_height() / total)
x = p.get_x() + p.get_width() + 0.02
y = p.get_y() + p.get_height() / 2
axs[i].annotate(percentage, (x, y))
return
def detect_outliers(df, target):
for col in df:
plt.figure(figsize=(14, 11))
plt.title(col)
plt.suptitle('Detect Outliers')
if df[col].dtypes == object:
sb.boxplot(df[col], df[target], color='gray')
sb.boxenplot(df[col], df[target])
else:
plt.subplot(311)
try:
sb.distplot(df[col], hist=False, rug=True)
except:
sb.kdeplot(df[col], bw=0.3)
plt.subplot(312)
sb.scatterplot(df[col], df[target])
plt.subplot(313)
sb.boxenplot(df[col])
sb.boxplot(df[col], color='gray')
plt.show()
def plot_grouped_boxplot(first_level_tests, attn_rois):
colnames = list(first_level_tests)
sessions = pd.unique([c.split()[0] for c in colnames])
connections, mirrors = mirror_strfind(attn_rois)
melty = first_level_tests.melt(var_name='Old Columns',
value_name='Phase-phase coupling')
filo, raph = [], []
for old_col in melty['Old Columns'].values:
raph.append(old_col.split()[0])
filo.append(old_col.split()[1])
melty['Connection'] = filo
melty['Session'] = raph
for mir in mirrors:
idx = melty[melty['Connection'] == mir].index
melty.drop(idx, inplace=True)
print(melty)
sns.set(style='darkgrid')
fig, ax = plt.subplots(figsize=(16, 9))
sns.boxenplot(x='Connection',
y='Phase-phase coupling',
hue='Session',
data=melty)
plt.show()
def _summarize_stats_in_epochs(self, ax_auc: plt.Axes,
ax_spikes: plt.Axes):
""" Add axes to the main plot showing the dF/F statistics in the
different epochs """
df_auc = pd.DataFrame(
# 1000 is max number of components per FOV
np.full((1000, len(self.epochs_to_display)), np.nan),
columns=self.epochs_to_display,
)
df_spikes = df_auc.copy()
for epoch in self.epochs_to_display:
cur_data = filter_da(self.fov.fluo_analyzed, epoch=epoch)
if cur_data.shape[0] == 0:
continue
spikes = dff_tools.locate_spikes_scipy(cur_data,
self.fov.metadata.fps,
thresh=0.7)
auc = dff_tools.calc_total_auc_around_spikes(
spikes, cur_data, self.fov.metadata.fps)
df_auc[epoch][:len(auc)] = auc
spikes = dff_tools.calc_mean_spike_num(spikes,
cur_data,
fps=self.fov.metadata.fps)
df_spikes[epoch][:len(spikes)] = spikes
sns.boxenplot(data=df_auc, ax=ax_auc)
sns.boxenplot(data=df_spikes, ax=ax_spikes)
for ax in [ax_auc, ax_spikes]:
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.set_xlabel("Epoch")
ax_auc.set_ylabel("AUC")
ax_spikes.set_ylabel("Spikes per second")
def get_feature_importance(self,
train_feat_df: pd.DataFrame,
is_save=False,
filepath=None):
feature_importance_df = pd.DataFrame()
num = 0
for i, model in self.models.items():
_df = pd.DataFrame()
_df['feature_importance'] = model.feature_importances_
_df['column'] = train_feat_df.columns
_df['fold'] = num + 1
feature_importance_df = pd.concat([feature_importance_df, _df],
axis=0,
ignore_index=True)
num += 1
order = feature_importance_df.groupby('column')\
.sum()[['feature_importance']]\
.sort_values('feature_importance', ascending=False).index[:50]
fig, ax = plt.subplots(figsize=(8, max(6, len(order) * .25)))
if is_save:
fig.savefig(filepath + "lgbm_feature_importance.png")
_df.to_csv(filepath + "lgbm_feature_importance.csv", index=False)
sns.boxenplot(data=feature_importance_df,
x='feature_importance',
y='column',
order=order,
ax=ax,
palette='viridis',
orient='h')
ax.tick_params(axis='x', rotation=90)
ax.set_title('Lightgbm Feature Importance')
ax.grid()
plt.show()
def plot_distribution(process_df, fig=None):
results_df = vesicle_release_distribution(process_df)
release_durations = np.unique(process_df.release_duration)
time = np.unique(process_df.time)
if not fig:
fig = plt.figure()
grid_shape = (8, 1)
totals = [
results_df[results_df.release_duration == r].shape[0]
for r in np.unique(process_df.release_duration)
]
ax_1 = plt.subplot2grid(shape=grid_shape, loc=(0, 0), rowspan=4, fig=fig)
sns.swarmplot(x='release_duration', y='offset', data=results_df, ax=ax_1)
sns.boxenplot(x='release_duration', y='offset', data=results_df, ax=ax_1)
# ax_1.set_xticks([])
ax_1.xaxis.tick_top()
ax_1.xaxis.set_label_position('top')
ax_1.xaxis.set_ticklabels([*totals])
ax_1.xaxis.set_tick_params(length=0)
ax_1.set_xlabel('Total released')
ax_1.set_title('Release offset distribution')
ax_2 = plt.subplot2grid(shape=grid_shape, loc=(4, 0), rowspan=4, fig=fig)
sns.boxplot(x='release_duration',
y='num_released',
data=results_df,
ax=ax_2)
sns.pointplot(x='release_duration',
y='num_released',
data=results_df,
ax=ax_2,
color='black')
ax_2.set_xlabel('Release Duration')
ax_2.set_ylabel('# of vesicles released')
ax_2.set_title('# Vesicles per spike distribution')
def information_loss_plot(df, params):
"""
Plot information loss as boxen plot
:param df: Dataframe
:param params: Parameters
:return: None
"""
plt.rcParams.update({"font.size": 20})
new_df, save_string = filter_dataframe(df, params, ignore_sampling=True)
df_li = create_li_df(new_df)
df_li.sort_values("li_type", inplace=True)
plt.figure(figsize=(15, 8))
sb.boxenplot(
x="li_type",
y="li",
hue=params["x"],
hue_order=sorted(new_df[params["x"]].drop_duplicates().tolist()),
data=df_li,
dodge=True,
)
plt.title(params["name"], fontsize=16)
plt.xlabel("Type of Lost Information")
plt.ylabel("Lost Information")
plt.ylim(-0.5, 1.)
if params["save_plot"]:
curr_dir = os.getcwd()
Path(curr_dir + "/figures/data_analysis").mkdir(parents=True,
exist_ok=True)
save_name = curr_dir + "/figures/data_analysis/%s_lost_information.png" % save_string
plt.savefig(save_name)
plt.close()
else:
plt.show()
plt.show()
def draw_box_plots(self, data, xaxis, plot_name, xlabel, ylabel,
plot_saved_path, plot_format):
"""
Draw box plots according to different parameters.
:param data: pandas.Dataframe
The data used to draw the graph.
:param xaxis: list
The values of X-axis.
:param plot_name: string
The name of this graph.
:param xlabel: string
The label of X-axis.
:param ylabel: string
The label of Y-axis.
:param plot_saved_path: string
The file path to save the graph.
:param plot_format: string
png or other formats.
:return: None
"""
sns.boxenplot(data=data, order=xaxis) # Draw the box plot.
plt.title(plot_name, config.new_ft)
plt.xlabel(xlabel, config.new_ft)
plt.ylabel(ylabel, config.new_ft)
if self.is_show:
plt.show()
else:
plt.savefig(config.path_for_thesis + plot_saved_path,
format=plot_format,
dpi=self.dpi)
plt.close()
def target_distribution_over_binary_groups(df, binary_cols, target_col, plot_type='boxenplot', **plot_kwargs):
'''For use during feature engineering. Pass a DataFrame
with a list of `binary_cols` that represent the names of columns
that are binary categories. The `target_col` str is the variable
you are trying to model. Requires seaborn >= 0.9.0.
'''
for col in binary_cols:
if plot_type=='boxenplot':
sns.boxenplot(y=df[target_col], x=df[col], **plot_kwargs)
elif plot_type=='violinplot':
sns.violinplot(y=df[target_col], x=df[col], **plot_kwargs)
else:
sns.boxplot(y=df[target_col], x=df[col], **plot_kwargs)
ax = plt.gca()
mu0, mu1 = df[target_col].groupby(df[col]).mean()
sd0, sd1 = df[target_col].groupby(df[col]).std()
ncol = df.loc[df[col]==1].shape[0]
ax.axhline(mu0, label=f'mean = {round(mu0, 2)}|{col} = 0', color='blue', linestyle=':')
ax.axhline(mu1, label=f'mean = {round(mu1, 2)}|{col} = 1 with {ncol} observations',
color='orange', linestyle='-.')
ax.grid(alpha=.4)
ax.set_title(col)
sns.despine()
ax.legend(loc='best')
plt.show()
def make_boxenplot_chem(low_col, high_col, xlabel_low, xlabel_high, ylabel, low_color, high_color, out_name): fig = plt.figure() x = range(2) f, axes = plt.subplots(1, 2, sharey=True, sharex=True) p1 = sns.boxenplot(y=low_col, orient='vertical', ax=axes[0], color=low_color).set(xlabel = xlabel_low, ylabel = ylabel) p2 = sns.boxenplot(y=high_col, orient='vertical', ax=axes[1], color=high_color).set(xlabel = xlabel_high, ylabel = '') plt.savefig(out_name + '.png', bbox_inches='tight')
def visualize_importance(models, train_feat_df, importance_type="gain"):
feature_importance_df = pd.DataFrame()
for i, model in enumerate(models):
_df = pd.DataFrame()
_df["feature_importance"] = model.feature_importances_
_df["column"] = train_feat_df.columns
_df["fold"] = i + 1
feature_importance_df = pd.concat([feature_importance_df, _df],
axis=0,
ignore_index=True)
order = (feature_importance_df.groupby("column").sum()[[
"feature_importance"
]].sort_values("feature_importance", ascending=False).index[:50])
fig, ax = plt.subplots(figsize=(8, max(6, len(order) * 0.25)))
sns.boxenplot(
data=feature_importance_df,
y="column",
x="feature_importance",
order=order,
ax=ax,
palette="viridis",
)
ax.tick_params(axis="x", rotation=90)
ax.grid(True)
fig.tight_layout()
return fig, ax
def explore_data_catbin(to_explore, df, target, pred_type='cat'):
"""
Generates visualizations to explore the relationship between predictors
and a binary categorical target. Specify the type of predictors using the
`pred_type` parameter: accepted values are `cat` for categorical and
`cont` for continuous.
This function assumes a binary target.
"""
if pred_type not in ['cat', 'cont']:
print("Error: `pred_type` should be 'cat' for categorical\
predictors and 'cont' for continuous predictors. No other\
values accepted.")
return None
disc = True if pred_type == 'cat' else False
# get mean of target. Since target is binary, mean is representative
# of the proportion of 1 labels to 0 labels
pop_mean = np.round(df[target].mean(), 4)
# draw plots
for col in to_explore:
fig, [ax1, ax2] = plt.subplots(figsize=(10, 5), nrows=1, ncols=2)
plt.tight_layout(pad=3)
sns.histplot(data=df, x=col, ax=ax1, discrete=disc)
ax1.set_title(f"Distribution of {col}")
if pred_type == 'cat':
sns.pointplot(data=df,
x=col,
y=target,
ci=68,
ax=ax2,
join=False,
scale=1.5,
capsize=0.05)
ax2.set_title("Target Mean per Category")
ax2.axhline(pop_mean,
color='red',
ls='dashed',
label='population mean')
ax2.legend()
elif pred_type == 'cont':
sns.boxenplot(data=df,
x=col,
y=target,
ax=ax2,
orient='h',
width=1)
ax2.set_title("Feature Distribution Per Target Class")
return None
def draw_boxplots(data_frame_scaled):
plt.rcParams['figure.figsize'] = (40, 35)
plt.subplot(3, 3, 1)
sns.set_theme(style="whitegrid")
# sns.boxplot(data = data_scaled,palette="Set3", linewidth=2.5)
sns.boxenplot(data=data_frame_scaled, orient="h", palette="Set3")
# sns.stripplot(data=data,orient="h",size=4, color=".26")
plt.title('box plots types', fontsize=10)
def plot_class_proba(model, x, y, show_graph=True, label_encoder=None):
if label_encoder is not None:
y = label_encoder.inverse_transform(y)
df = pd.DataFrame({'Class': y, 'Probability': model.predict_proba(x)[:, 1]})
sns.boxenplot(x='Class', y='Probability', data=df)
if show_graph:
graph.show()
def make_boxenplot_AH(holo_col, apo_col, xlabel, ylabel, title, out_name):
fig = plt.figure()
x = range(2)
f, axes = plt.subplots(1, 2, sharey=True, sharex=True)
p1 = sns.boxenplot(holo_col, orient='v', ax=axes[0]).set(xlabel='Holo',
ylabel=ylabel)
p2 = sns.boxenplot(apo_col, orient='v', ax=axes[1]).set(xlabel='Apo',
ylabel='')
plt.savefig(out_name + '.png')
def subplot_draw1():
fig = plt.figure(figsize=(10, 6))
for i in range(10):
plt.subplot(2, 6, i + 1) # subplots 表示分布绘制系列图
sns.boxenplot(df[colnm[i]], orient="v", width=0.5, color=color[0])
plt.ylabel(colnm[i], fontsize=12)
# plt.subplots_adjust(left=0.2, wspace=0.8, top=0.8)
plt.tight_layout() # 会自动调整子图参数，使之填充整个图像区域。避免重叠
plt.show()
def make_boxenplot_chem(low_col, high_col, xlabel_low, xlabel_high, ylabel,
out_name):
fig = plt.figure()
x = range(2)
f, axes = plt.subplots(1, 2, sharey=True, sharex=True)
p1 = sns.boxenplot(low_col, orient='v', ax=axes[0]).set(xlabel=xlabel_low,
ylabel=ylabel)
p2 = sns.boxenplot(high_col, orient='v',
ax=axes[1]).set(xlabel=xlabel_high, ylabel='')
plt.savefig(out_name + '.png')
def four_rate_plot(property):
fig, axs = plt.subplots(ncols=2, nrows=2, figsize=[20, 20], sharey=True)
flat_axs = [ax for col in axs for ax in col]
for rate, ax in zip(['delta', 'gamma', 'beta', 'alpha'], flat_axs):
sns.boxenplot(x=rate, y=property, data=all_tests, ax=ax, color='grey')
sns.stripplot(x=rate, y=property, data=all_tests, ax=ax, marker='.', size=3, jitter=True, color='black')
sns.despine()
def wykres_9(x, y, nazwa_wykres, nazwa_x, nazwa_y):
f, ax = plt.subplots(figsize=(12, 8))
ax.set_title(nazwa_wykres, fontsize=16)
ax.set_ylabel(nazwa_y, fontsize=14)
ax.set_xlabel(nazwa_x, fontsize=14)
sns.boxenplot(data=selected_data,
x=selected_data[x],
y=selected_data[y],
scale="linear")
return f
def plot_annotation_entropy(adata_map, annotation='cell_type'):
"""
"""
qk = np.ones(shape=(adata_map.n_obs, adata_map.n_vars))
adata_map.obs['entropy'] = entropy(adata_map.X,
base=adata_map.X.shape[1],
axis=1)
fig, ax = plt.subplots(1, 1, figsize=(10, 3))
ax.set_ylim(0, 1)
sns.boxenplot(x=annotation, y="entropy", data=adata_map.obs, ax=ax)
plt.xticks(rotation=30)
def check_outlier(df):
features = df.columns
sns.set_style("whitegrid")
plt.figure(figsize=(24, 8))
nonnumerical = ['Year', 'ShortName']
for feature in features:
if not feature in nonnumerical:
sns.boxenplot(x=feature, orient='h', data=df)
title = 'boxplot ' + feature
plt.title(title)
plt.savefig(filepath + 'inspection/boxplot/' + title + '.png')
plt.clf()
def plot_finish(finish_dict, experiment):
folder = plot_folder + 'finish/'
if not os.path.isdir(folder):
os.mkdir(folder)
if len(finish_dict) == 0:
print('No finish Data available')
else:
# leaving soc vs leaving time scatter plot
finish_df = pd.concat(finish_dict.values(), axis=0)
finish_df = finish_df.sort_values('method')
sns.boxenplot(data=finish_df, x='method', y='time')
plt.savefig(folder + experiment + '_boxplot' + '.png')
plt.close()
def plot_graphs(feature_1, feature_2, df):
fig, axs = plt.subplots(ncols=4)
fig.set_figwidth(30)
fig.set_figheight(8)
plt.suptitle(feature_1 + ' vs. ' + feature_2)
sns.boxenplot(x=feature_1, y=feature_2, data=df, ax=axs[0])
sns.boxplot(x=feature_1, y=feature_2, data=df, ax=axs[1])
sns.violinplot(x=feature_1,
y=feature_2,
data=df,
inner="points",
ax=axs[2])
sns.barplot(x=feature_1, y=feature_2, data=df, ax=axs[3])
def draw_boxplot(data: pd.DataFrame) -> None:
f, axes = plt.subplots(7, 4, figsize=(18, 24))
global date_indexs, labels
count = 0
for i in [
x for x in data.columns
if x not in date_indexs + labels + ['date']
]:
sns.boxenplot(x=i,
y='total_purchase_amt',
data=data,
ax=axes[count // 4][count % 4])
count += 1
plt.show()
def _BoxPlot(self):
'''This method is used to plot the Boxen plots of all Categorical variables in dataframe'''
df = self.train_df.copy()
fig = plt.figure(figsize=(14, 18))
for idx, col in enumerate(self.cat_cols):
if len(self.train_df[col].unique()) < 10:
df[col +
'_mean'] = df.groupby(col)[self.target_col].transform('mean')
fig.add_subplot(3, 2, idx+1)
sns.boxenplot(x=col, y=self.target_col,
data=df.sort_values(col + '_mean'))
plt.title('Comparison of salaries as per {}'.format(
col), fontsize=14)
plt.tight_layout()
plt.xticks(rotation=45)
def line_integrals(self):
for a, kind in zip([self.lines, self.lines_filtered], ["all", "flt"]):
# for a, kind in zip([self.lines_filtered], ["flt"]):
# optional: filter a subgroup
# col_order = ["Arrest", "Cycling"]
# col_order = ['Cycling', 'Arrest', 'Release',
# 'Cyto2ug-Cyc', 'Cyto2ug-Arr', 'Cyto2ug-Rel',
# 'Noc20ng-Cyc', 'Noc20ng-Arr', 'Noc20ng-Rel']
col_order = a["Compound"].unique()
print(col_order)
a = a[a["Compound"].isin(col_order)]
# get only one row per z-stack
idx = a.groupby(["unit"])["s_max"].transform(max) == a["s_max"]
a = a.loc[idx]
fig = plt.figure(figsize=(8, 8), dpi=150)
ax = fig.gca()
sns.boxenplot(x="Compound", y="sum", order=col_order, data=a)
ax.yaxis.set_major_formatter(self.formatter)
ax.set_yscale('log')
ax.set_xticklabels(ax.xaxis.get_ticklabels(),
rotation=45,
multialignment='right')
path = o.ensure_dir(
os.path.join(self.cc.base_path, 'out', 'graphs',
'line_boxplot_%s.pdf' % kind))
fig.savefig(path)
plt.close()
fig = plt.figure(figsize=(8, 8), dpi=150)
ax = fig.gca()
sns.scatterplot(x="v_width",
y="sum",
data=a,
hue="Compound",
alpha=0.1,
rasterized=True)
# plt.xscale('log')
# plt.yscale('log')
ax.set_xlim((0, 16))
ax.set_ylim((0, 350e3))
ax.xaxis.set_major_formatter(self.formatter)
ax.yaxis.set_major_formatter(self.formatter)
path = o.ensure_dir(
os.path.join(self.cc.base_path, 'out', 'graphs',
'lines_scatter_%s.pdf' % kind))
fig.savefig(path)
plt.close()
"""
Plotting large distributions
============================
"""
import seaborn as sns
sns.set(style="whitegrid")
diamonds = sns.load_dataset("diamonds")
clarity_ranking = ["I1", "SI2", "SI1", "VS2", "VS1", "VVS2", "VVS1", "IF"]
sns.boxenplot(x="clarity", y="carat",
color="b", order=clarity_ranking,
scale="linear", data=diamonds)
