本文整理汇总了Python中seaborn.load_dataset方法的典型用法代码示例。如果您正苦于以下问题：Python seaborn.load_dataset方法的具体用法？Python seaborn.load_dataset怎么用？Python seaborn.load_dataset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.load_dataset方法的2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: test_probplot_with_FacetGrid_with_markers
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import load_dataset [as 别名]
def test_probplot_with_FacetGrid_with_markers(usemarkers):
iris = seaborn.load_dataset("iris")
hue_kws = None
species = sorted(iris['species'].unique())
markers = ['o', 'o', 'o']
if usemarkers:
markers = ['o', 's', '^']
hue_kws = {'marker': markers}
fg = (
seaborn.FacetGrid(data=iris, hue='species', hue_kws=hue_kws)
.map(viz.probplot, 'sepal_length')
.set_axis_labels(x_var='Probability', y_var='Sepal Length')
.add_legend()
)
_lines = filter(lambda x: isinstance(x, matplotlib.lines.Line2D), fg.ax.get_children())
result_markers = {
l.get_label(): l.get_marker()
for l in _lines
}
expected_markers = dict(zip(species, markers))
assert expected_markers == result_markers 示例2: heatmap_pData
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import load_dataset [as 别名]
def heatmap_pData(df):
import pandas as pd
import seaborn as sns
sns.set()
# Load the brain networks example dataset
# df = sns.load_dataset("brain_networks", header=[0, 1, 2], index_col=0)
# Select a subset of the networks
used_networks = [1, 5, 6, 7, 8, 12, 13, 17]
# used_columns = [True,]*len(df.columns)
# print(len(used_columns))
# print(used_columns)
# df = df.loc[:, used_columns]
columnsList=['shapelyArea', 'shapelyLength','shapeIdx', 'FRAC',
'popu_mean', 'popu_std','SVFW_mean', 'SVFW_std',
'SVFep_std', 'SVFep_median','SVFep_majority', 'SVFep_minority',
'facilityFre',
'HVege_mean','HVege_count','MVege_mean', 'MVege_count','LVege_mean', 'LVege_count',
'cla_treeCanopy', 'cla_grassShrub', 'cla_bareSoil','cla_buildings', 'cla_roadsRailraods', 'cla_otherPavedSurfaces','cla_water',
]
df=df[columnsList]
# Create a categorical palette to identify the networks
network_pal = sns.husl_palette(8, s=.45)
network_lut = dict(zip(map(str, used_networks), network_pal))
# Convert the palette to vectors that will be drawn on the side of the matrix
networks = df.columns
network_colors = pd.Series(networks, index=df.columns).map(network_lut)
# Draw the full plot
sns.clustermap(df.corr(), center=0, cmap="vlag",
row_colors=network_colors, col_colors=network_colors,
linewidths=.75, figsize=(13, 13))