import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import numpy as np from scipy.stats import norm from sklearn.preprocessing import StandardScaler from scipy import stats import warnings warnings.filterwarnings('ignore') %matplotlib inline #bring in the six packs df_train ...
import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import numpy as np from scipy.stats import norm from sklearn.preprocessing import StandardScaler from scipy import stats import warnings warnings.filterwarnings('ignore') %matplotlib inline 加载数据并显示数据的列 df_train =...
matplotlib绘图。(遇到复杂的制图需求时使用,最基础的库,所以每个函数的参数非常多并且复杂) pandas plot API。(日常绘图使用pandas足够了✅),优化matplotlib, 更方便绘图。 seaborn绘制统计图形。 基于matplotlib和pandas, 更高级,做了优化,可视化效果更好, 专业用于统计分析。 ⚠️:可视化课程的重点是:利用图形去...
import pandas as pd import matplotlib.pyplot as plt import seaborn as sns train = pd.read_csv('train.csv') #统计某一列中各个元素值出现的次数 train['MSSubClass'].value_counts() # #列出数据的偏斜度 train['MSSubClass'].skew() #列出数据的峰度 train['MSSubClass'].kurt() #计算两个列的...
#bivariate analysis saleprice/grlivareavar ='GrLivArea'data = pd.concat([df_train['SalePrice'], df_train[var]], axis=1) data.plot.scatter(x=var, y='SalePrice', ylim=(0,800000)); 删除偏离值: train.sort_values(by ='GrLivArea', ascending =False)[:2] ...
tutorials cover some foundational plotting techniques deemed essential for visualizing data distributions and relationships clearly and effectively. They combine the use of several well-known Python libraries for data visualization, such as seaborn and matplotlib, as well as pandas for handling data ...
In this step-by-step tutorial, you'll learn the fundamentals of descriptive statistics and how to calculate them in Python. You'll find out how to describe, summarize, and represent your data visually using NumPy, SciPy, pandas, Matplotlib, and the built
🔑 This repository contains the codes of important Python libraries for Data Science. The codes will be the part of my learning. The libraries are: - NumPy - Pandas - Scipy - Matplotlib - Seaborn - Scikit-Learn - Bokeh - Abhinandan57/Py_Libraries
(OX1,OX2, c='black',s=20, edgecolor='k') plt.axis('tight') # loc=2 is used for the top left corner plt.legend( [a.collections[0], b,c], ['learned decision function', 'inliers','outliers'], prop=matplotlib.font_manager.FontProperties(size=20), loc=2) plt.xlim((0, 1)) ...
This library is based on matplotlib. Seaborn offers various features such as built in themes, color palettes, functions and tools to visualize univariate, bivariate, linear regression, matrices of data, statistical time series etc which lets us to build complex visualizations. Its standard designs ...