plt.plot(fpr, tpr, color='darkorange',label='ROC curve (area = %0.2f)' % roc_auc) plt.plot([0, 1], [0, 1], color='navy', linestyle='--') plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.0]) plt.xlabel('False Positive Rate') plt.ylabel('True Positive Rate') plt.title('ROC_c...
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1.2 导入数据 #加载包importnumpyasnpimportpandasaspdimportmatplotlib.pyplotaspltimportseabornassns%matplotlibinlinedata_train=pd.read_csv('cs-training.csv') 1.3 查看数据集信息 data_train.head() data_train.shape data_train.dtypes 了解各列名的含义: SeriousDlqin2yrs:超过90天或更糟的逾期拖欠 Revolving...
ax.bar(theta, values, align='center', alpha=0.7) # Customize the appearance of the bars for bar in bars: bar.set_facecolor(plt.cm.jet(np.random.rand())) bar.set_alpha(0.6) plt.title("Polar Bar Chart") plt.show() ### This code creates a simple Polar Bar Chart using Matplotlib....
When I plot magnitude of FFT result from other tools I get the clear 1kHz spike I expect (reflected, of course, in the negative frequency). When I plot the magnitude of the numpy result I get an unexpected 'oscillating' pattern.
df.plot(kind 'barh', stacked ,figsize (10,10)) plt.title(u"RevolvingUtilizationOfUnsecuredLines") plt.xlabel(u"log - RevolvingUtilizationOfUnsecuredLines") plt.ylabel(u"") 2020/6/28 Give Me Some Credit - Jupyter Notebook localhost:8888/notebooks/Kaggle/2_Give Me Some Credit/Give Me ...
import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec import seaborn as sns sns.set_style('whitegrid') %matplotlib inline # import sklearn from sklearn.linear_model import LogisticRegression from sklearn.ensemble import RandomForestClassifier ...
import matplotlib.pyplot as plt # 设置figure_size尺寸 plt.rcParams['figure.figsize'] = (8.0, 6.0) fig = plt.figure() # 设定图表颜色 fig.set(alpha=0.2) # 第一张小图 plt.subplot2grid((2,3),(0,0)) data_train['Survived'].value_counts().plot(kind='bar') plt.ylabel(u"人数") ...
import matplotlib.pyplot as plt # 设置figure_size尺寸 plt.rcParams['figure.figsize'] = (8.0, 6.0) fig = plt.figure() # 设定图表颜色 fig.set(alpha=0.2) # 第一张小图 plt.subplot2grid((2,3),(0,0)) data_train['Survived'].value_counts().plot(kind='bar') plt.ylabel(u"人数") ...