吴裕雄--天生自然 python数据分析:健康指标聚集分析(健康分析)
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# This Python 3 environment comes with many helpful analytics libraries installed # It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python # For example, here's several helpful packages to load in import numpy as np # linear algebra import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) # Input data files are available in the "../input/" directory. # For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory
df=pd.read_csv('F:\\kaggleDataSet\\Key_indicator_districtwise\\Key_indicator_districtwise.csv') df.head()
x=df['AA_Sample_Units_Total'] y=df['AA_Sample_Units_Rural'] z=df['AA_Population_Urban'] import matplotlib.pyplot as plt import seaborn as sns plt.title('State_District_Name vs AA_Sample_Units_Total ') plt.xlabel('State_District_Name') plt.ylabel('AA_Sample_Units_Total') plt.scatter(x,y)
plt.hist(x) plt.title('AA_Sample_Units_Total vs Frequency') plt.xlabel('AA_Sample_Units_Total') plt.ylabel('Frequency')
plt.hist(y) plt.title('AA_Sample_Units_Rural vs frequency') plt.xlabel('AA_Sample_Units_Rural') plt.ylabel('Frequency')
plt.hist(z) plt.title('AA_Population_Urban vs Frequency') plt.xlabel('AA_Population_Urban') plt.ylabel('Frequency')
q=df['AA_Ever_Married_Women_Aged_15_49_Years_Total'] q w=q.sort_values() w
plt.boxplot(w)
plt.boxplot(y)
import matplotlib.pyplot as plt import numpy as np from sklearn import datasets, linear_model, metrics # load the boston dataset boston = datasets.load_boston(return_X_y=False) # defining feature matrix(X) and response vector(y) X = boston.data y = boston.target # splitting X and y into training and testing sets from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=1) # create linear regression object reg = linear_model.LinearRegression() # train the model using the training sets reg.fit(X_train, y_train) # regression coefficients print('Coefficients: \n', reg.coef_) # variance score: 1 means perfect prediction print('Variance score: {}'.format(reg.score(X_test, y_test))) # plot for residual error ## setting plot style plt.style.use('fivethirtyeight') ## plotting residual errors in training data plt.scatter(reg.predict(X_train), reg.predict(X_train) - y_train, color = "green", s = 10, label = 'Train data') ## plotting residual errors in test data plt.scatter(reg.predict(X_test), reg.predict(X_test) - y_test, color = "blue", s = 10, label = 'Test data') ## plotting line for zero residual error plt.hlines(y = 0, xmin = 0, xmax = 50, linewidth = 2) ## plotting legend plt.legend(loc = 'upper right') ## plot title plt.title("Residual errors") ## function to show plot plt.show()
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