python如何读取csv文件某几行某几列,Python：使用Excel CSV文件只读某些列和行

While I can read csv file instead of reading to whole file how can I print only certain rows and columns?

Imagine as if this is Excel:

A B C D E

State |Heart Disease Rate| Stroke Death Rate | HIV Diagnosis Rate |Teen Birth Rate

Alabama 235.5 54.5 16.7 18.01

Alaska 147.9 44.3 3.2 N/A

Arizona 152.5 32.7 11.9 N/A

Arkansas 221.8 57.4 10.2 N/A

California 177.9 42.2 N/A N/A

Colorado 145.3 39 8.4 9.25

Heres what I have:

import csv

try:

risk = open('riskfactors.csv', 'r', encoding="windows-1252").read() #find the file

except:

while risk != "riskfactors.csv": # if the file cant be found if there is an error

print("Could not open", risk, "file")

risk = input("\nPlease try to open file again: ")

else:

with open("riskfactors.csv") as f:

reader = csv.reader(f, delimiter=' ', quotechar='|')

data = []

for row in reader:# Number of rows including the death rates

for col in (2,4): # The columns I want read B and D

data.append(row)

data.append(col)

for item in data:

print(item) #print the rows and columns

I need to only read column B and D with all statistics to read like this:

A B D

State |Heart Disease Rate| HIV Diagnosis Rate |

Alabama 235.5 16.7

Alaska 147.9 3.2

Arizona 152.5 11.9

Arkansas 221.8 10.2

California 177.9 N/A

Colorado 145.3 8.4

Edited

no errors

Any ideas on how to tackle this? Everything I try isn't working. Any help or advice is much appreciated.

解决方案

If you're still stuck, there's really no reason you have to read the file with the CSV module as all CSV files are just comma separated strings. So, for something simple you could try this, which would give you a list of tuples of the form (state,heart disease rate,HIV diagnosis rate)

output = []

f = open( 'riskfactors.csv', 'rU' ) #open the file in read universal mode

for line in f:

cells = line.split( "," )

output.append( ( cells[ 0 ], cells[ 1 ], cells[ 3 ] ) ) #since we want the first, second and third column

f.close()

print output

Just note that you would then have to go through and ignore the header rows if you wanted to do any sort of data analysis.