所以我一般都是Python的新手，我一直在阅读很多文章，但我仍然不确定如何忽略带有'＃'的行。
我需要：
将此tsv文件中的四列（col2-col5）分成不同的列表。 （我如何选择忽略夏威夷的这条线，因为它有不完整的数据，因此使用49个数据点。）
然后定义一个函数Pearson（X，Y），它将两个列表作为参数并返回Pearson相关系数。设X = [x1，x2，...，xn]，Y = [y1，y2，...，yn]。 X和Y之间的Pearson相关系数由下式给出：
r =（nΣxiyi-ΣxiΣyi）/（（√（nΣxi^ 2-（Σxi^ 2）^ 2（nΣyi^ 2-（Σyi）^ 2）
listT = [26, 24, 23, 14, 15, 19, 21, 22, 18, 17, 16, 23, 24, 21, 20]
listH = [75, 69, 77, 51, 48, 68, 83, 68, 71, 51, 54, 71, 77, 67, 68]
def Pearson(X,Y):
# Do something
return
# Should print: var T and var H: 0.8139
print("var T and var H: %.4f"%(Pearson(listT, listH)))
在定义函数时，我该如何写出Σ符号？
#------------------------------------------------------------------------
# Data from the CDC -- http://www.cdc.gov/ -- reports on prevalence of
# smoking, incidence of lung cancer, and deaths attributed to smoking.
#
# Col1: state
# Col2: cases of lung cancer (per 100,000 inhabitants)
# Col3: smoking among adults (%)
# Col4: attempts to quit (%)
# Col5: smoking-related deaths (per 100,000 inhabitants)
#------------------------------------------------------------------------
Alabama 107.1 24.9 47.1 321.1
Alaska 89 24.9 54.2 296.2
Arizona 63.4 18.6 49.4 248.9
Arkansas 105 25.7 45.6 334.1
California 64.4 14.8 51.4 261
Colorado 56.9 20.1 42.4 252.7
Connecticut 81.1 18.1 49 253.8/
Delaware 98.8 24.5 48.7 296
District of Columbia 80.2 21 54.2 257.3
Florida 85.5 20.4 44.2 275.5
Georgia 98.3 20.1 54.8 312.3
Hawaii 68.2 NA NA 185.1
Idaho 62.7 17.5 47.2 254.1
Illinois 92 22.2 49.3 278.4
Indiana 102.8 25 47.5 322.2
Iowa 91.8 20.8 42.9 256.7
Kansas 84.8 19.8 43.7 270.8
Kentucky 132.6 27.6 47.6 378.1
Louisiana 108 23.6 51.8 309.1
Maine 99.3 21 55.3 303.8
Maryland 80.1 19.7 51.1 279.5
Massachusetts 83.3 18.5 52.5 258.6
Michigan 90 23.4 55.6 296.3
Minnesota 65 20.7 43.6 225.3
Mississippi 115.4 24.6 48.9 343.2
Missouri 103.9 24.1 43 325
Montana 73.1 20.4 45.4 292.6
Nebraska 82.8 20.3 46.7 251.9
Nevada 82.7 23.2 41.4 370.4
New Hampshire 80.6 21.8 53.2 294.8
New Jersey 78.8 18.9 49.6 253.1
New Mexico 57.6 20.3 45.6 250.8
New York 76.7 20 51.5 259.6
North Carolina 104.1 23.2 49.2 307
North Dakota 71.5 19.9 43.9 233
Ohio 97.4 25.9 41.3 310.6
Oklahoma 102.6 26.1 45.1 321.7
Oregon 77.6 20 46 277.5
Pennsylvania 89.4 22.7 47.1 269.1
Rhode Island 84.6 21.3 53.1 283
South Carolina 99.4 24.5 49.1 303.3
South Dakota 78.8 20.3 46.4 253.8
Tennessee 111.1 26.1 46.6 333.6
Texas 83.2 20.6 46.4 287.4
Utah 33.1 10.5 53.7 144.9
Vermont 90.2 20 55.4 272.2
Virginia 86.7 20.9 44.8 288.7
Washington 76.2 19.2 51.6 279.1
West Virginia 120 26.9 46.2 361.6
Wisconsin 75 22 47.7 258.2
Wyoming 57.8 21.7 48.5 294.2
这是我到目前为止所做的：
import csv
import operator
import math
import sys
cases_lung_cancer = [] #blank list for 1st column
smoking_adults = [] #blank list for 2nd column
attempts_quit = [] #blank list for 3rd column
smoking_deaths = [] #blank list for 4th column
def Pearson(X, Y)
with open('cdc_data.tsv', newline= ' ') as csv_f:
for row in csv.DictReader(csv_f, delimiter='\t'):
答案 0 :(得分：2)
import pandas as pd
data = pd.read_tsv('cdc_data.tsv', header=None)
correlation = data.corr(method='pearson')
答案 1 :(得分：1)
将此tsv文件中的四列（col2-col5）分成单独的列表， 而且我选择忽略夏威夷的这条线，因为它不完整 数据，因此使用49个数据点。
col0 = []
col1 = []
col2 = []
col3 = []
col4 = []
f = open('cdc_data.tsv', 'r')
contents = f.read()
lines = contents.split('\n') # split file into seperate lines
for line in lines:
if (line[0:1] == '#'): # filter out comments
continue
split_line = line.split('\t') # split line into seperate words seperated by TAB
if (len(split_line) < 5): # drop any line that isn't 5 columns
continue
# assign each column into a separate list
col0.append(split_line[0])
col1.append(split_line[1])
col2.append(split_line[2])
col3.append(split_line[3])
col4.append(split_line[4])
我将问题留给夏威夷和你的＃2问题作为练习让你完成。
答案 2 :(得分：0)
import math
col0 = []
col1 = []
col2 = []
col3 = []
col4 = []
f = open('cdc_data.tsv', 'r')
def Pearson(X,Y):
n=50
a=0
b=0
c=0
d=0
e=0
f=0
g=0
for i in range(n):
a+=X[i]*Y[i]
b+=X[i]
c+=Y[i]
d+=X[i]**2
e+=X[i] #remember to square this
f+=Y[i]**2
g+=Y[i] #remember to square this
return (n*a-b*c)/(math.sqrt((n*d-e**2)*(n*f-g**2)))
contents = f.read()
lines = contents.split('\n') # split file into seperate lines
for line in lines:
if (line[0:1] == '#'): # filter out comments
continue
if (line[0:1] == 'H'): #filter out Hawaii
continue
split_line = line.split('\t') # split line into seperate words seperated by TAB
if (len(split_line) < 5): # drop any line that isn't 5 columns
continue
# assign each column into a separate list
col0.append(split_line[0])
col1.append(float(split_line[1]))
col2.append(float(split_line[2]))
col3.append(float(split_line[3]))
col4.append(float(split_line[4]))
print("Correlation for col1 and col2: %.4f" %(Pearson(col1,col2)))
print("Correlation for col1 and col3: %.4f" %(Pearson(col1,col3)))
print("Correlation for col1 and col4: %.4f" %(Pearson(col1,col4)))
print("Correlation for col2 and col3: %.4f" %(Pearson(col2,col3)))
print("Correlation for col2 and col4: %.4f" %(Pearson(col2,col4)))
print("Correlation for col3 and col4: %.4f" %(Pearson(col3,col4)))