本文整理汇总了Python中seaborn.stripplot方法的典型用法代码示例。如果您正苦于以下问题:Python seaborn.stripplot方法的具体用法?Python seaborn.stripplot怎么用?Python seaborn.stripplot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.stripplot方法的11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: violin_jitter
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import stripplot [as 别名]
def violin_jitter(X, genes, gene, labels, focus, background=None,
xlabels=None):
gidx = list(genes).index(gene)
focus_idx = focus == labels
if background is None:
background_idx = focus != labels
else:
background_idx = background == labels
if xlabels is None:
xlabels = [ 'Background', 'Focus' ]
x_gene = X[:, gidx].toarray().flatten()
x_focus = x_gene[focus_idx]
x_background = x_gene[background_idx]
plt.figure()
sns.violinplot(data=[ x_focus, x_background ], scale='width', cut=0)
sns.stripplot(data=[ x_focus, x_background ], jitter=True, color='black', size=1)
plt.xticks([0, 1], xlabels)
plt.savefig('{}_violin_{}.png'.format(NAMESPACE, gene))
示例2: plot_read_count_dists
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import stripplot [as 别名]
def plot_read_count_dists(counts, h=8, n=50):
"""Boxplots of read count distributions """
scols,ncols = base.get_column_names(counts)
df = counts.sort_values(by='mean_norm',ascending=False)[:n]
df = df.set_index('name')[ncols]
t = df.T
w = int(h*(len(df)/60.0))+4
fig, ax = plt.subplots(figsize=(w,h))
if len(scols) > 1:
sns.stripplot(data=t,linewidth=1.0,palette='coolwarm_r')
ax.xaxis.grid(True)
else:
df.plot(kind='bar',ax=ax)
sns.despine(offset=10,trim=True)
ax.set_yscale('log')
plt.setp(ax.xaxis.get_majorticklabels(), rotation=90)
plt.ylabel('read count')
#print (df.index)
#plt.tight_layout()
fig.subplots_adjust(bottom=0.2,top=0.9)
return fig
示例3: bar_box_violin_dot_plots
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import stripplot [as 别名]
def bar_box_violin_dot_plots(data, category_col, numeric_col, axes, file_name=None):
sns.barplot(category_col, numeric_col, data=data, ax=axes[0])
sns.boxplot(
category_col, numeric_col, data=data[data[numeric_col].notnull()], ax=axes[2]
)
sns.violinplot(
category_col,
numeric_col,
data=data,
kind="violin",
inner="quartile",
scale="count",
split=True,
ax=axes[3],
)
sns.stripplot(category_col, numeric_col, data=data, jitter=True, ax=axes[1])
sns.despine(left=True)
示例4: plot_similardishes
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import stripplot [as 别名]
def plot_similardishes(idx,xlim):
match = yum_ingr2.iloc[yum_cos[idx].argsort()[-21:-1]][::-1]
newidx = match.index.get_values()
match['cosine'] = yum_cos[idx][newidx]
match['rank'] = range(1,1+len(newidx))
label1, label2 =[],[]
for i in match.index:
label1.append(match.ix[i,'cuisine'])
label2.append(match.ix[i,'recipeName'])
fig = plt.figure(figsize=(10,10))
ax = sns.stripplot(y='rank', x='cosine', data=match, jitter=0.05,
hue='cuisine',size=15,orient="h")
ax.set_title(yum_ingr2.ix[idx,'recipeName']+'('+yum_ingr2.ix[idx,'cuisine']+')',fontsize=18)
ax.set_xlabel('Flavor cosine similarity',fontsize=18)
ax.set_ylabel('Rank',fontsize=18)
ax.yaxis.grid(color='white')
ax.xaxis.grid(color='white')
for label, y,x, in zip(label2, match['rank'],match['cosine']):
ax.text(x+0.001,y-1,label, ha = 'left')
ax.legend(loc = 'lower right',prop={'size':14})
ax.set_ylim([20,-1])
ax.set_xlim(xlim)
示例5: geoValueWeightedVisulization
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import stripplot [as 别名]
def geoValueWeightedVisulization(valueDes):
valueDes["ID"]=valueDes.index
sns.set(style="whitegrid")
# Make the PairGrid
extractedColumns=["count","mean","std","max"]
g=sns.PairGrid(valueDes.sort_values("count", ascending=False),x_vars=extractedColumns, y_vars=["ID"],height=10, aspect=.25)
# Draw a dot plot using the stripplot function
g.map(sns.stripplot, size=10, orient="h",palette="ch:s=1,r=-.1,h=1_r", linewidth=1, edgecolor="w")
# Use the same x axis limits on all columns and add better labels
g.set(xlabel="value", ylabel="") #g.set(xlim=(0, 25), xlabel="Crashes", ylabel="")
# Use semantically meaningful titles for the columns
titles=valueDes.columns.tolist()
for ax, title in zip(g.axes.flat, titles):
# Set a different title for each axes
ax.set(title=title)
# Make the grid horizontal instead of vertical
ax.xaxis.grid(False)
ax.yaxis.grid(True)
sns.despine(left=True, bottom=True)
示例6: astro_oligo_violin
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import stripplot [as 别名]
def astro_oligo_violin(X, genes, gene, labels, name):
X = X.toarray()
gidx = list(genes).index(gene)
astro = X[labels == 'astro', gidx]
oligo = X[labels == 'oligo', gidx]
both = X[labels == 'both', gidx]
plt.figure()
sns.violinplot(data=[ astro, oligo, both ], scale='width', cut=0)
sns.stripplot(data=[ astro, oligo, both ], jitter=True, color='black', size=1)
plt.xticks([0, 1, 2], ['Astrocytes', 'Oligodendrocytes', 'Both'])
plt.savefig('{}_violin_{}.svg'.format(name, gene))
示例7: plot_imp_strip
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import stripplot [as 别名]
def plot_imp_strip(d, mi, imp_col, palette=None,
title="Imputation Strip", **plot_kwgs):
"""Create the strip plot for multiply imputed data.
Args:
d (list): dataset returned from multiple imputation.
mi (MultipleImputer): multiple imputer used to generate d.
imp_col (str): column to plot. Should be a column with imputations.
title (str, Optional): title of plot. Default is "Imputation Strip".
palette (list, tuple, Optional): colors for the imps and observed.
Default is None. if None, colors default to ["r","c"].
**plot_kwgs: keyword arguments used by sns.set.
Returns:
sns.distplot: stripplot for imputed data
Raises:
ValueError: see _validate_data method.
"""
# set plot type, validate, and define names necessary
_default_plot_args(**plot_kwgs)
_validate_data(d, mi, imp_col)
datasets_merged = _melt_df(d, mi, imp_col)
if palette is None:
palette = ["r", "c"]
# stripplot example
sns.stripplot(
x="imp_num", y=imp_col, hue="imputed", palette=palette,
data=datasets_merged, jitter=True, hue_order=["yes", "no"], dodge=True
).set(xlabel="Imputation Number", title=title)
示例8: run_strip_plot
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import stripplot [as 别名]
def run_strip_plot(panc_df, gtex_df, panc_labels, gtex_labels):
assert panc_df.columns.equals(gtex_df.columns)
psi_df = pd.concat((panc_df, gtex_df), axis=0)
assert psi_df.shape[0] == panc_df.shape[0] + gtex_df.shape[0]
assert psi_df.columns.unique().size == psi_df.shape[1]
assert panc_df.index.equals(panc_labels.index)
assert gtex_df.index.equals(gtex_labels.index)
event_list = psi_df.columns.tolist()
psi_df_aug = psi_df.copy()
psi_df_aug['cnc'] = None
psi_df_aug.loc[panc_df.index, ['cnc']] = panc_labels.loc[panc_df.index]
psi_df_aug.loc[gtex_df.index, ['cnc']] = gtex_labels.loc[gtex_df.index]
unq_panc_labels = sorted(panc_labels.unique().tolist())
unq_gtex_labels = sorted(gtex_labels.unique().tolist())
assert np.intersect1d(unq_panc_labels, unq_gtex_labels).size == 0
plt.close()
label_list = unq_panc_labels + unq_gtex_labels
color_lut = _get_stripplot_color_lut(unq_panc_labels, unq_gtex_labels)
for event in event_list:
outpath = os.path.join(PLOT_DIR, 'stripplots', '%s_stripplot.png'%event)
if not os.path.exists(os.path.dirname(outpath)): os.makedirs(os.path.dirname(outpath))
fig, ax = plt.subplots(figsize=(20,3))
sns.stripplot(x='cnc', y=event, data=psi_df_aug,
palette=color_lut, s=3,
order=label_list,
jitter=True, ax=ax)
ax.axvline(len(unq_panc_labels) - .5, color='black', ls='--')
ax.set_xticklabels(ax.get_xticklabels(), rotation = 90)
ax.set_ylim(-.05, 1.05)
ax.title.set_text('Gene: %s Event type: %s Event ID: %d'%(_decode_event_name(event)))
ax.set_ylabel('psi')
ax.set_xlabel('')
axs.clean_axis(ax)
print "Writing: %s" %outpath
plt.savefig(outpath, bbox_inches='tight')
pdf_outpath = re.sub('.png$', '.pdf', outpath)
print "Writing: %s" %pdf_outpath
plt.savefig(pdf_outpath, bbox_inches='tight')
plt.close()
return
示例9: strip
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import stripplot [as 别名]
def strip(self, x, y):
"""
Stripplot plot ``x`` across ``y`` feature values.
"""
plt.figure(figsize=(8,4))
sns.stripplot(x, y, hue=Base.target, data=Base.train, jitter=True)
plt.xlabel(x, fontsize=12)
plt.ylabel(y, fontsize=12)
plt.show();
示例10: geoValVisulization_a
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import stripplot [as 别名]
def geoValVisulization_a(geoPd):
geoPd["ID"]=geoPd.index.astype(str)
print(geoPd.columns)
'''
Index(['park_no', 'label', 'park_class', 'location', 'acres', 'shape_area',
'shape_leng', 'perimeter', 'geometry', 'shapelyArea', 'shapelyLength',
'shapeIdx', 'FRAC', 'popu_count', 'popu_mean', 'popu_std', 'popu_min',
'popu_25%', 'popu_50%', 'popu_75%', 'popu_max', 'SVFW_count',
'SVFW_mean', 'SVFW_std', 'SVFW_min', 'SVFW_25%', 'SVFW_50%', 'SVFW_75%',
'SVFW_max', 'polyID', 'SVFep_min', 'SVFep_max', 'SVFep_mean',
'SVFep_count', 'SVFep_sum', 'SVFep_std', 'SVFep_median',
'SVFep_majority', 'SVFep_minority', 'SVFep_unique', 'SVFep_range',
'SVFep_nodata', 'HVege_min', 'HVege_max', 'HVege_mean', 'HVege_count',
'HVege_sum', 'HVege_std', 'HVege_median', 'HVege_majority',
'HVege_minority', 'HVege_range', 'HVege_nodata', 'MVege_min',
'MVege_max', 'MVege_mean', 'MVege_count', 'MVege_sum', 'MVege_std',
'MVege_median', 'MVege_majority', 'MVege_minority', 'MVege_range',
'MVege_nodata', 'LVege_min', 'LVege_max', 'LVege_mean', 'LVege_count',
'LVege_sum', 'LVege_std', 'LVege_median', 'LVege_majority',
'LVege_minority', 'LVege_range', 'LVege_nodata', 'facilityFre',
'facilityID', 'cla_treeCanopy', 'cla_grassShrub', 'cla_bareSoil',
'cla_buildings', 'cla_roadsRailraods', 'cla_otherPavedSurfaces',
'cla_water', 'classi_count', 'ID'],
dtype='object')
'''
sns.set(style="whitegrid")
# Make the PairGrid
extractedColumns=['shapelyArea','shapelyLength',
'shapeIdx','FRAC',
'SVFW_mean','SVFW_std',
'SVFW_mean','SVFW_std',
'popu_std','popu_mean',
'facilityFre',
'classi_count','cla_treeCanopy', 'cla_grassShrub','cla_bareSoil', 'cla_buildings', 'cla_roadsRailraods', 'cla_otherPavedSurfaces','cla_water',
'HVege_count','HVege_mean',
'LVege_count','LVege_mean',
]
# geoPdSort=geoPd.sort_values('shapelyArea', ascending=False)
g=sns.PairGrid(geoPd.sort_values('shapelyArea', ascending=False),x_vars=extractedColumns, y_vars=["label"],height=20, aspect=.25)
# g=sns.PairGrid(geoPd,x_vars=extractedColumns, y_vars=["ID"],height=20, aspect=.25)
# Draw a dot plot using the stripplot function
g.map(sns.stripplot, size=5, orient="h",palette="ch:s=1,r=-.1,h=1_r", linewidth=1, edgecolor="w")
# Use the same x axis limits on all columns and add better labels
g.set(xlabel="value", ylabel="") #g.set(xlim=(0, 25), xlabel="Crashes", ylabel="")
# Use semantically meaningful titles for the columns
g.fig.set_figwidth(30)
g.fig.set_figheight(80)
titles=extractedColumns
for ax, title in zip(g.axes.flat, titles):
# Set a different title for each axes
ax.set(title=title)
# Make the grid horizontal instead of vertical
ax.xaxis.grid(False)
ax.yaxis.grid(True)
sns.despine(left=True, bottom=True)
return geoPd
示例11: visualisationDF
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import stripplot [as 别名]
def visualisationDF(df):
dataFrameInfoPrint(df)
#graph-01
# df['shapelyArea'].plot.hist(alpha=0.5)
#graph-02
# df['shapelyArea'].plot.kde()
#graph-03
# df[['shapelyLength','shapeIdx']].plot.scatter('shapelyLength','shapeIdx')
#normalize data in a range of columns
cols_to_norm=['shapeIdx', 'FRAC']
df[cols_to_norm]=df[cols_to_norm].apply(lambda x: (x - x.min()) / (x.max() - x.min()))
a='shapeIdx'
b='FRAC'
c='park_class'
#graph-04
# sns.jointplot(a,b,df,kind='hex')
#graph-05
# sns.jointplot(a, b, df, kind='kde')
#graph-06
# sns.catplot(x='park_class',y=a,data=df)
#graph-07
'''
# Initialize the figure
f, ax = plt.subplots()
sns.despine(bottom=True, left=True)
# Show each observation with a scatterplot
sns.stripplot(x=a, y=c, hue=c,data=df, dodge=True, alpha=.25, zorder=1)
# Show the conditional means
sns.pointplot(x=a, y=c, hue=c,data=df, dodge=.532, join=False, palette="dark",markers="d", scale=.75, ci=None)
# Improve the legend
handles, labels = ax.get_legend_handles_labels()
ax.legend(handles[3:], labels[3:], title=b,handletextpad=0, columnspacing=1,loc="lower right", ncol=3, frameon=True)
'''
#graph-08
# sns.catplot(x=c,y=a,data=df,kind='box')
#graph-09
# sns.catplot(x=c,y=a,data=df,kind='violin')
#graph-10
'''
f, axs = plt.subplots(1, 2, figsize=(12, 6))
# First axis
df[b].plot.hist(ax=axs[0])
# Second axis
df[b].plot.kde(ax=axs[1])
# Title
f.suptitle(b)
# Display
plt.show()
'''
#从新定义栅格投影,参考投影为vector .shp文件