tmp = pd.DataFrame(index = num_cols) for col in num_cols: tmp.loc[col, 'train_Skewness'] = data_train[col].skew() tmp.loc[col, 'test_Skewness'] = data_testA[col].skew() tmp.loc[col, 'train_Kurtosis'] = data_train[col].kurt() tmp.loc[col, 'test_Kurtosis'] = data_testA...
print("{}的特征有{}个不同的值".format(cat_fea,Train_data[cat_fea].nunique())) print(Train_data[cat_fea].value_counts()) 1. 2. 3. 4. 7. 数字特征分布 相关性分析(热力图) 查看几个特征的偏度和峰度 每个数字特征的分布可视化 数字特征相互之间的关系可视化 (pairs() in R?) 多变量互相...
used_data['grade'].value_counts() attr = ["C", "B","A","D","E","F","G"] pie = Pie("信用等级比例") pie.add("", attr, [float(i) for i in pd.value_counts(loans["grade"])] ,is_label_show=True) pie Lending Club平台对客户的信用等级分7类,A~G,信用等级为A的客户信用评...
# Plotting violin plots for selected features for feature in selected_features: plt.figure(figsize=(8, 6)) sns.violinplot(x='Survival_Status', y=feature, data=df, hue='Survival_Status', palette='Blues', inner='quartile', legend=False) plt.title(f'Violin Plot for {feature} by Survival ...
data.Age.fillna(data.Age.mean(),inplace=True) # 将age列缺失值填充均值。(偏正态分布,用均值填充,可以保持数据的均值) 中值插补 df['price'].fillna(df['price'].median()) # 偏长尾分布,使用中值填充,避免受异常值的影响。 最近数据插补 dataframe ['age'].fillna(method='pad') # 使用前一...
Data Wrangler includes built-in analyses that help you generate visualizations and data analyses in a few clicks. You can also create custom analyses using your own code. You add an analysis to a dataframe by selecting a step in your data flow, and then choosing Add analysis. To access an...
#数据集有的字段显示为数值型数据,但是实际类型为str,再将部分数值型数据转换成floatforcolinlist(data.columns):if('ft²'incolor'kBtu'incolor'Metric Tons CO2e'incolor'kWh'incolor'therms'incolor'gal'incolor'Score'incol): data[col]= data[col].astype(float) ...
importpandasaspd# 读取数据data=pd.read_csv('project_data.csv') 1. 2. 3. 4. 2. 数据清洗 清洗数据是EDA中至关重要的一步。我们需要处理缺失值和异常值,以确保数据的质量。 # 检查缺失值missing_values=data.isnull().sum()# 填充缺失值data['End Date'].fillna(data['End Date'].mean(),inplac...
Best in Test Finalists:EDA/DFx/Test data-analysis software
This AI-driven data analytics solution allows teams to unlock, connect, and analyze the vast amount of data collected across design, verification, manufacturing, test, and in-field operations. Its unique chip monitor technology enables optimization of power, performance, quality, yield, and ...