我们从Python开源项目中，提取了以下6个代码示例，用于说明如何使用seaborn.load_dataset()。
def munros(edge_length=10): """Number of munros in different rectangular areas of Scotland. Parameters ---------- edge_length : float Size of the rectangular grid in minutes. Returns ------- h : physt.histogram_nd.Histogram2D Histogram in latitude and longitude. """ data = load_dataset("munros") return h2(data["lat"], data["long"], "fixed_width", edge_length / 60, name="munros", title="Munros of Scotland")
def load_dataset(name): """Load example dataset. If seaborn is present, its datasets can be loaded. Physt also includes some datasets in CSV format. Parameters ---------- name : str Returns ------- dataset : pandas.DataFrame """ # Our custom datasets: try: try: import pandas as pd except ImportError: raise RuntimeError("Pandas not installed.") import pkgutil import io binary_data = pkgutil.get_data('physt', 'examples/{0}.csv'.format(name)) return pd.read_csv(io.BytesIO(binary_data)) except FileNotFoundError: pass # Seaborn datasets? try: import seaborn as sns import warnings with warnings.catch_warnings(): warnings.simplefilter("ignore") if name in sns.get_dataset_names(): return sns.load_dataset(name) except ImportError: pass # Fall through raise RuntimeError("Dataset {0} not available.".format(name))
def iris_h1(x="sepal_length"): """One-dimensional histogram of classical iris data. Parameters ---------- x : str Name of the property to be histogrammed (sepal_length, sepal_width, petal_length, petal_width) """ iris = load_dataset("iris") return h1(iris[x], "human", 20, name="iris")
def iris_h2(x="sepal_length", y="sepal_width"): """Two-dimensional histogram of classical iris data. Parameters ---------- x, y : str Names of the properties to be histogrammed (sepal_length, sepal_width, petal_length, petal_width) """ iris = load_dataset("iris") return h2(iris[x], iris[y], "human", 20, name="iris")
def testcase1(): tips=sns.load_dataset('tips') sns.jointplot('total_bill','tips',tips,kind='reg')
def titanic_1(): titanic = sns.load_dataset('titanic') print titanic.head() # survived pclass sex age ...... # 0 0 male 22 # 1 1 1 female 38.0 # 2 1 3 female 26.0 # 3 1 1 female 35.0 # 4 0 3 male 35.0 print titanic.groupby('sex')[['survived']].mean() # survived # sex # female 0.742038 # male 0.188908 print titanic.groupby(['sex', 'class'])['survived'].aggregate('mean').unstack() # class First Second Third # sex # female 0.968085 0.921053 0.500000 # male 0.368852 0.157407 0.135447 print titanic.pivot_table('survived', index='sex', columns='class') # class First Second Third # sex # female 0.968085 0.921053 0.500000 # male 0.368852 0.157407 0.135447 age = pd.cut(titanic['age'], [0, 18, 80]) print titanic.pivot_table('survived', ['sex', age], 'class') # class First Second Third # sex age # female (0, 18] 0.909091 1.000000 0.511628 # (18, 80] 0.972973 0.900000 0.423729 # male (0, 18] 0.800000 0.600000 0.215686 # (18, 80] 0.375000 0.071429 0.133663 print titanic.pivot_table(index='sex', columns='class', aggfunc={'survived': sum, 'fare': 'mean'}) print titanic.pivot_table('survived', index='sex', columns='class', margins=True) # class First Second Third All # sex # female 0.968085 0.921053 0.500000 0.742038 # male 0.368852 0.157407 0.135447 0.188908 # All 0.629630 0.472826 0.242363 0.383838
