EDA(Exploratory Data Analysis),全名数据探索性分析,是通过了解数据集,了解变量间的相互关系以及变量与预测值之间的关系,从而帮助我们后期更好地进行特征工程和建立模型,是数据挖掘中十分重要的一步。 所需工具:数据科学库(pandas、numpy、scipy)、可视化库(matplotlib、seabon) 大致包含步骤: 数据总览 查看数据缺失和异...
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...
#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) 通过describe 和 matplotlib 可视化查看数据...
EDA(Exploratory Data Analysis)数据探索性分析 EDA目的:通过了解数据集的分布情况,数据之间的关系,来帮我们更好的后期进行特征工程和建立模型。本文主要是一个根据coco数据集格式的json文件,来分析数据集中图片尺寸,宽高比,bbox尺寸,宽高比,以及每张图片中bbox数量的分布情况。分析...
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...
In statistics, exploratory data analysis(EDA) is an approach to analyzing data sets to summarize their maincharacteristics, often with visual methods. A statistical model can be used ornot, but primarily EDA is for seeing what the data can tell us beyond theformal modeling or hypothesis testing...
探索性数据分析 Exploratory Data Analysis 查看数据整体情况dataset.head(), dataset.tail() #首先查看头尾五行,看一下数据是怎样的 dataset.shape #看一下数据规模,几个样本,几个特征 dataset.columns #看一…
It consists of a set of interactive programs which allow to carry out all steps of the EXAFS data analysis procedure. There are two main differences from known packages. First, a significantly improved algorithm is used for atomic-like background removal in the EXAFS extraction procedure. Second...
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 Status') ...
By employing descriptive statistics, data visualization, and pattern recognition, this study delves into viewing patterns, content popularity, user demographics, and regional variations in user engagement. The findings of this analysis shed light on the factors driving Netflix's success and offer ...