Kaggle_distribution

0 setup

配置文件

import pandas as pd
pd.plotting.register_matplotlib_converters()
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
print("Setup Complete")

check

# Set up code checking
import os
if not os.path.exists("../input/cancer_b.csv"):
    os.symlink("../input/data-for-datavis/cancer_b.csv", "../input/cancer_b.csv")
    os.symlink("../input/data-for-datavis/cancer_m.csv", "../input/cancer_m.csv")
from learntools.core import binder
binder.bind(globals())
from learntools.data_viz_to_coder.ex5 import *
print("Setup Complete")

1 载入数据

# Paths of the files to read
cancer_b_filepath = "../input/cancer_b.csv"
cancer_m_filepath = "../input/cancer_m.csv"

# Fill in the line below to read the (benign) file into a variable cancer_b_data
cancer_b_data = pd.read_csv(cancer_b_filepath,index_col="Id")

# Fill in the line below to read the (malignant) file into a variable cancer_m_data
cancer_m_data = pd.read_csv(cancer_m_filepath,index_col="Id")

step2 review the data

打印出肿瘤数据的前五行 benign

# Print the first five rows of the (benign) data
cancer_b_data.head()

打印出肿瘤数据的前五行 malignant

cancer_m_data.head()

找到数据

# Fill in the line below: In the first five rows of the data for benign tumors, what is the
# largest value for 'Perimeter (mean)'?
max_perim = 87.46

# Fill in the line below: What is the value for 'Radius (mean)' for the tumor with Id 842517?
mean_radius = 20.57

良心肿瘤和恶性肿瘤的直方图

# Histograms for benign and maligant tumors
sns.distplot(a=cancer_b_data['Area (mean)'],label="Benign",kde=False)
sns.distplot(a=cancer_m_data['Area (mean)'],label="Malignant",kde=False)
step_3.a.check()

 

step4 a 曲线图下无阴影

sns.kdeplot(data=cancer_m_data['Radius (worst)'],label="Malignant") 
sns.kdeplot(data=cancer_b_data['Radius (worst)'],label="Benign") 

step4 a 曲线图下有阴影

sns.kdeplot(data=cancer_m_data['Radius (worst)'], shade=True, label="Malignant") 
sns.kdeplot(data=cancer_b_data['Radius (worst)'], shade=True, label="Benign") 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

	Diagnosis	Radius (mean)	Texture (mean)	Perimeter (mean)	Area (mean)	Smoothness (mean)	Compactness (mean)	Concavity (mean)	Concave points (mean)	Symmetry (mean)	...	Radius (worst)	Texture (worst)	Perimeter (worst)	Area (worst)	Smoothness (worst)	Compactness (worst)	Concavity (worst)	Concave points (worst)	Symmetry (worst)	Fractal dimension (worst)
Id
842302	M	17.99	10.38	122.80	1001.0	0.11840	0.27760	0.3001	0.14710	0.2419	...	25.38	17.33	184.60	2019.0	0.1622	0.6656	0.7119	0.2654	0.4601	0.11890
842517	M	20.57	17.77	132.90	1326.0	0.08474	0.07864	0.0869	0.07017	0.1812	...	24.99	23.41	158.80	1956.0	0.1238	0.1866	0.2416	0.1860	0.2750	0.08902
84300903	M	19.69	21.25	130.00	1203.0	0.10960	0.15990	0.1974	0.12790	0.2069	...	23.57	25.53	152.50	1709.0	0.1444	0.4245	0.4504	0.2430	0.3613	0.08758
84348301	M	11.42	20.38	77.58	386.1	0.14250	0.28390	0.2414	0.10520	0.2597	...	14.91	26.50	98.87	567.7	0.2098	0.8663	0.6869	0.2575	0.6638	0.17300
84358402	M	20.29	14.34	135.10	1297.0	0.10030	0.13280	0.1980	0.10430	0.1809	...	22.54	16.67	152.20	1575.0	0.1374	0.2050	0.4000	0.1625	0.2364	0.07678