[英]Read the second row from CSV file to Python
我有一個csv文件:
Index,X1,X2,X3,X4,X5,Y
1,-1.608052,-0.377992,1.204209,1.313808,1.218265,1
2,0.393766,0.630685,-1.222062,0.090558,0.015893,0
3,-0.466243,0.276972,2.519047,0.673745,0.16729,1
4,1.47121,-0.046791,-0.303291,-0.365437,1.989287,0
5,-1.672906,1.25588,-0.355706,0.123143,-2.241941,1
我想創建一個分類系統程序,數據在第二行。 我正在嘗試從第二行獲取數據。 我試着這樣的next(list):
def load_DataTrain(filename):
try:
with open(filename, newline='') as iFile:
return list(reader(iFile, delimiter=','))
next(list)
except FileNotFoundError as e:
raise e
但這不起作用,並且由於程序從第一行讀取而出現錯誤。 我沒有使用pandas或csv.reader來讀取我的csv。 這是我從Divyesh GitHub獲得的代碼:
from csv import reader
from sys import exit
from math import sqrt
from operator import itemgetter
def load_DataTrain(filename):
try:
with open(filename) as iFile:
return list(reader(iFile, delimiter=','))
next(list)
except FileNotFoundError as e:
raise e
def convert_to_float(DataTrain, mode):
new_set = []
try:
if mode == 'training':
for data in DataTrain:
new_set.append([float(x) for x in data[:len(data)-1]] + [data[len(data)-1]])
elif mode == 'test':
for data in DataTrain:
new_set.append([float(x) for x in data])
else:
print('Invalid mode, program will exit.')
exit()
return new_set
except ValueError as v:
print(v)
print('Invalid data set format, program will exit.')
exit()
def get_classes(training_set):
return list(set([c[-1] for c in training_set]))
def find_neighbors(distances, k):
return distances[0:k]
def find_response(neighbors, classes):
votes = [0] * len(classes)
for instance in neighbors:
for ctr, c in enumerate(classes):
if instance[-2] == c:
votes[ctr] += 1
return max(enumerate(votes), key=itemgetter(1))
def knn(training_set, test_set, k):
distances = []
dist = 0
limit = len(training_set[0]) - 1
# generate response classes from training data
classes = get_classes(training_set)
try:
for test_instance in test_set:
for row in training_set:
for x, y in zip(row[:limit], test_instance):
dist += (x-y) * (x-y)
distances.append(row + [sqrt(dist)])
dist = 0
distances.sort(key=itemgetter(len(distances[0])-1))
# find k nearest neighbors
neighbors = find_neighbors(distances, k)
# get the class with maximum votes
index, value = find_response(neighbors, classes)
# Display prediction
print('The predicted class for sample ' + str(test_instance) + ' is : ' + classes[index])
print('Number of votes : ' + str(value) + ' out of ' + str(k))
# empty the distance list
distances.clear()
except Exception as e:
print(e)
def main():
try:
# get value of k
k = int(input('Enter the value of k : '))
# load the training and test data set
training_file = input('Enter name of training data file : ')
test_file = input('Enter name of test data file : ')
training_set = convert_to_float(load_DataTrain(training_file), 'training')
test_set = convert_to_float(load_DataTrain(test_file), 'test')
if not training_set:
print('Empty training set')
elif not test_set:
print('Empty test set')
elif k > len(training_set):
print('Expected number of neighbors is higher than number of training data instances')
else:
knn(training_set, test_set, k)
except ValueError as v:
print(v)
except FileNotFoundError:
print('File not found')
if __name__ == '__main__':
main()
結果是:
could not convert string to float: 'Index'
我應該怎么做才能讀取csv文件中的第二行?
def returnSecondRow(delimit):
i=0
with open(filename) as fh:
reader = csv.reader(fh, delimiter=delimit)
for row in reader:
if i==1:
# do nothing
return row
i += 1
由於您在示例中使用的是虹膜數據集,所以我想您正在嘗試機器學習嗎? 如果是這樣,我認為使用熊貓讀取和處理您的.csv文件會更明智。
import pandas as pd
df = pd.read_csv(filename)
row_two = df.iloc[1]
功能上的微小變化。
如果只想返回2nd
行,則可以在下面的代碼中將[1:]
替換為[1]
。
from csv import reader
def load_DataTrain(filename):
try:
with open(filename, newline='') as iris:
# returning from 2nd row
return list(reader(iris, delimiter=','))[1:]
except FileNotFoundError as e:
raise e
load_DataTrain("file.csv")
輸出:
[['1', '-1.608052', '-0.377992', '1.204209', '1.313808', '1.218265', '1'],
['2', '0.393766', '0.630685', '-1.222062', '0.090558', '0.015893', '0'],
['3', '-0.466243', '0.276972', '2.519047', '0.673745', '0.16729', '1'],
['4', '1.47121', '-0.046791', '-0.303291', '-0.365437', '1.989287', '0'],
['5', '-1.672906', '1.25588', '-0.355706', '0.123143', '-2.241941', '1']]
另一種使用pandas
將df.values.tolist()
更改為df.iloc[0].values.tolist()
,僅返回2nd
行。
import pandas as pd
df = pd.read_csv("dummy.csv")
pprint(df.values.tolist())
輸出:
[[1.0, -1.608052, -0.377992, 1.204209, 1.313808, 1.218265, 1.0],
[2.0, 0.393766, 0.630685, -1.222062, 0.090558, 0.015893, 0.0],
[3.0,
-0.466243,
0.276972,
2.519047,
0.6737449999999999,
0.16729000000000002,
1.0],
[4.0,
1.4712100000000001,
-0.046791,
-0.303291,
-0.365437,
1.9892869999999998,
0.0],
[5.0,
-1.6729060000000002,
1.2558799999999999,
-0.355706,
0.123143,
-2.241941,
1.0]]
使用基本的Python(不導入)
f = open("Data.csv","r")
head = f.readline()
print(head)
for line in f:
line = line.strip('\n')
line = line.split(",")
line =[float(i) for i in line]
line[0] = int(line[0])
line[-1] = int(line[-1])
print(line, "and do something")
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