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iris.csv

 

Histogram

import matplotlib.pyplot as pltimport pandas as pdiris_df = pd.read_csv("iris.csv")iris_df["sepallength"].hist(by=iris_df["class"]) #Group data by class labelplt.show()

 

Stacked histogram

堆叠直方图

import matplotlib.pyplot as pltimport pandas as pdiris_df = pd.read_csv("iris.csv")labels = iris_df["class"].unique() #get all class labels#Extract attribute values with the specific labeldef filterData(df, attribute, label):    values = iris_df[iris_df["class"] == label][[attribute]].values #Convert to NumPu array    return pd.DataFrame(data=values,  columns=[label])#Return as DataFrame with a single column, using class label column namedf = pd.concat([filterData(iris_df, "sepallength", label)                for label in labels], axis="columns")   #Combine data of different class labels into a tabledf.plot.hist(stacked=True, bins=5)plt.show()

 

Stacked histogram using Seaborn

import matplotlib.pyplot as pltimport pandas as pdimport seaborn as snsiris_df = pd.read_csv("iris.csv")sns.histplot(iris_df, x="sepallength", hue="class", multiple="stack")plt.show()

 

All histograms

import matplotlib.pyplot as pltimport pandas as pdimport seaborn as snsiris_df = pd.read_csv("iris.csv")labels = iris_df['class'].unique()columns = [column           for column in iris_df.columns           if column not in ['id', 'class']]  #Get all attributes except id and class labels_, axes = plt.subplots(1, len(columns)) #Prepare plot areasfor c in range(len(columns)):    sns.histplot(iris_df, x=columns[c], hue="class",                 multiple="stack", ax=axes[c])  #Plot each histogram at the correct areaplt.show()

 

 

 

 

 

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