numpy.loadtxt() 用法
文章目录
- 1.numpy.loadtxt 用法 读取txt文件
- 2.利用savetxt函数将数据存储到文件
- 3.利用 loadtxt函数读取csv文件
1.numpy.loadtxt 用法 读取txt文件
numpy.loadtxt(fname, dtype=, comments='#', delimiter=None, converters=None, skiprows=0, usecols=None, unpack=False, ndmin=0)
上面给出了loadtxt所有的关键字参数, 这里我们可以来一一解释并给出示例
import numpy as np
# 首先给出最简单的loadtxt的代码,实际上就是直接写文件名, 其他关键字参数都是默认的.
a = np.loadtxt('./data/test.txt')
print(a) # a为浮点数的原因为Python默认的数字的数据类型为双精度浮点数
[[ 1. 2. 3. 4.]
[ 2. 3. 4. 5.]
[ 3. 4. 5. 6.]
[ 4. 5. 6. 7.]]
# 这里的skiprows是指跳过前1行, 如果设置skiprows=2, 就会跳过前两行,数据类型设置为整形.
a = np.loadtxt('./data/test.txt', skiprows=1, dtype=int)
print(a)
[[2 3 4 5]
[3 4 5 6]
[4 5 6 7]]
# 这里的comment的是指, 如果行的开头为#就会跳过该行
a = np.loadtxt('./data/test1.txt', dtype=int, skiprows=1, comments='#')
print(a)
[[1 2 3]
[4 5 6]
[7 8 9]]
# 这里的usecols是指只使用0,2两列, unpack是指会把每一列当成一个向量输出, 而不是合并在一起。
(a, b) = np.loadtxt('./data/test2.txt', dtype=int, skiprows=1, comments='#', delimiter=',', usecols=(0, 2), unpack=True)
print(a, b)
[1 4 7] [3 6 9]
# 最后介绍converters参数, 这个是对数据进行预处理的参数, 我们可以先定义一个函数， 这里的converters是一个字典, 表示第零列使用函数add_one来进行预处理
def add_one(x):
return int(x)+1 # 注意到这里使用的字符的数据结构
(a, b) = np.loadtxt('./data/test2.txt', dtype=int, skiprows=1, converters={0:add_one}, comments='#', delimiter=',', usecols=(0, 2), unpack=True)
print(a, b)
[2 5 8] [3 6 9]
2.利用savetxt函数将数据存储到文件
savetxt(fileName,data)
参数：
fileName:保存文件路径和名称
data:需要保存的数据
3.利用 loadtxt函数读取csv文件
np.loadtxt(filepath,delimiter,usecols,unpack)
参数：
filepath:加载文件路径
delimiter:加载文件分隔符
usecols:加载数据文件中列索引
unpack:当加载多列数据时是否需要将数据列进行解耦赋值给不同的变量
data = np.loadtxt('./data/data.csv',delimiter=',',skiprows=1, usecols=(2,3))
print(data)
print(data.shape)
[[ 37.8 69.2]
[ 39.3 45.1]
[ 45.9 69.3]
[ 41.3 58.5]
[ 10.8 58.4]
[ 48.9 75. ]
[ 32.8 23.5]
[ 19.6 11.6]
[ 2.1 1. ]
[ 2.6 21.2]
[ 5.8 24.2]
[ 24. 4. ]
[ 35.1 65.9]
[ 7.6 7.2]
[ 32.9 46. ]
[ 47.7 52.9]
[ 36.6 114. ]
[ 39.6 55.8]
[ 20.5 18.3]
[ 23.9 19.1]
[ 27.7 53.4]
[ 5.1 23.5]
[ 15.9 49.6]
[ 16.9 26.2]
[ 12.6 18.3]
[ 3.5 19.5]
[ 29.3 12.6]
[ 16.7 22.9]
[ 27.1 22.9]
[ 16. 40.8]
[ 28.3 43.2]
[ 17.4 38.6]
[ 1.5 30. ]
[ 20. 0.3]
[ 1.4 7.4]
[ 4.1 8.5]
[ 43.8 5. ]
[ 49.4 45.7]
[ 26.7 35.1]
[ 37.7 32. ]
[ 22.3 31.6]
[ 33.4 38.7]
[ 27.7 1.8]
[ 8.4 26.4]
[ 25.7 43.3]
[ 22.5 31.5]
[ 9.9 35.7]
[ 41.5 18.5]
[ 15.8 49.9]
[ 11.7 36.8]
[ 3.1 34.6]
[ 9.6 3.6]
[ 41.7 39.6]
[ 46.2 58.7]
[ 28.8 15.9]
[ 49.4 60. ]
[ 28.1 41.4]
[ 19.2 16.6]
[ 49.6 37.7]
[ 29.5 9.3]
[ 2. 21.4]
[ 42.7 54.7]
[ 15.5 27.3]
[ 29.6 8.4]
[ 42.8 28.9]
[ 9.3 0.9]
[ 24.6 2.2]
[ 14.5 10.2]
[ 27.5 11. ]
[ 43.9 27.2]
[ 30.6 38.7]
[ 14.3 31.7]
[ 33. 19.3]
[ 5.7 31.3]
[ 24.6 13.1]
[ 43.7 89.4]
[ 1.6 20.7]
[ 28.5 14.2]
[ 29.9 9.4]
[ 7.7 23.1]
[ 26.7 22.3]
[ 4.1 36.9]
[ 20.3 32.5]
[ 44.5 35.6]
[ 43. 33.8]
[ 18.4 65.7]
[ 27.5 16. ]
[ 40.6 63.2]
[ 25.5 73.4]
[ 47.8 51.4]
[ 4.9 9.3]
[ 1.5 33. ]
[ 33.5 59. ]
[ 36.5 72.3]
[ 14. 10.9]
[ 31.6 52.9]
[ 3.5 5.9]
[ 21. 22. ]
[ 42.3 51.2]
[ 41.7 45.9]
[ 4.3 49.8]
[ 36.3 100.9]
[ 10.1 21.4]
[ 17.2 17.9]
[ 34.3 5.3]
[ 46.4 59. ]
[ 11. 29.7]
[ 0.3 23.2]
[ 0.4 25.6]
[ 26.9 5.5]
[ 8.2 56.5]
[ 38. 23.2]
[ 15.4 2.4]
[ 20.6 10.7]
[ 46.8 34.5]
[ 35. 52.7]
[ 14.3 25.6]
[ 0.8 14.8]
[ 36.9 79.2]
[ 16. 22.3]
[ 26.8 46.2]
[ 21.7 50.4]
[ 2.4 15.6]
[ 34.6 12.4]
[ 32.3 74.2]
[ 11.8 25.9]
[ 38.9 50.6]
[ 0. 9.2]
[ 49. 3.2]
[ 12. 43.1]
[ 39.6 8.7]
[ 2.9 43. ]
[ 27.2 2.1]
[ 33.5 45.1]
[ 38.6 65.6]
[ 47. 8.5]
[ 39. 9.3]
[ 28.9 59.7]
[ 25.9 20.5]
[ 43.9 1.7]
[ 17. 12.9]
[ 35.4 75.6]
[ 33.2 37.9]
[ 5.7 34.4]
[ 14.8 38.9]
[ 1.9 9. ]
[ 7.3 8.7]
[ 49. 44.3]
[ 40.3 11.9]
[ 25.8 20.6]
[ 13.9 37. ]
[ 8.4 48.7]
[ 23.3 14.2]
[ 39.7 37.7]
[ 21.1 9.5]
[ 11.6 5.7]
[ 43.5 50.5]
[ 1.3 24.3]
[ 36.9 45.2]
[ 18.4 34.6]
[ 18.1 30.7]
[ 35.8 49.3]
[ 18.1 25.6]
[ 36.8 7.4]
[ 14.7 5.4]
[ 3.4 84.8]
[ 37.6 21.6]
[ 5.2 19.4]
[ 23.6 57.6]
[ 10.6 6.4]
[ 11.6 18.4]
[ 20.9 47.4]
[ 20.1 17. ]
[ 7.1 12.8]
[ 3.4 13.1]
[ 48.9 41.8]
[ 30.2 20.3]
[ 7.8 35.2]
[ 2.3 23.7]
[ 10. 17.6]
[ 2.6 8.3]
[ 5.4 27.4]
[ 5.7 29.7]
[ 43. 71.8]
[ 21.3 30. ]
[ 45.1 19.6]
[ 2.1 26.6]
[ 28.7 18.2]
[ 13.9 3.7]
[ 12.1 23.4]
[ 41.1 5.8]
[ 10.8 6. ]
[ 4.1 31.6]
[ 42. 3.6]
[ 35.6 6. ]
[ 3.7 13.8]
[ 4.9 8.1]
[ 9.3 6.4]
[ 42. 66.2]
[ 8.6 8.7]]
(200, 2)
#当加载csv文件的多列数据时可以使用unpack将加载的数据列进场解耦到不同数组中
data_seven,data_eight = np.loadtxt("./data/data.csv",delimiter=",",skiprows=1, usecols=(2,3),unpack=True)
print("第7列的数据：\n",data_seven.tolist())
print("第8列的数据：\n",data_eight)
第7列的数据：
[37.8, 39.3, 45.9, 41.3, 10.8, 48.9, 32.8, 19.6, 2.1, 2.6, 5.8, 24.0, 35.1, 7.6, 32.9, 47.7, 36.6, 39.6, 20.5, 23.9, 27.7, 5.1, 15.9, 16.9, 12.6, 3.5, 29.3, 16.7, 27.1, 16.0, 28.3, 17.4, 1.5, 20.0, 1.4, 4.1, 43.8, 49.4, 26.7, 37.7, 22.3, 33.4, 27.7, 8.4, 25.7, 22.5, 9.9, 41.5, 15.8, 11.7, 3.1, 9.6, 41.7, 46.2, 28.8, 49.4, 28.1, 19.2, 49.6, 29.5, 2.0, 42.7, 15.5, 29.6, 42.8, 9.3, 24.6, 14.5, 27.5, 43.9, 30.6, 14.3, 33.0, 5.7, 24.6, 43.7, 1.6, 28.5, 29.9, 7.7, 26.7, 4.1, 20.3, 44.5, 43.0, 18.4, 27.5, 40.6, 25.5, 47.8, 4.9, 1.5, 33.5, 36.5, 14.0, 31.6, 3.5, 21.0, 42.3, 41.7, 4.3, 36.3, 10.1, 17.2, 34.3, 46.4, 11.0, 0.3, 0.4, 26.9, 8.2, 38.0, 15.4, 20.6, 46.8, 35.0, 14.3, 0.8, 36.9, 16.0, 26.8, 21.7, 2.4, 34.6, 32.3, 11.8, 38.9, 0.0, 49.0, 12.0, 39.6, 2.9, 27.2, 33.5, 38.6, 47.0, 39.0, 28.9, 25.9, 43.9, 17.0, 35.4, 33.2, 5.7, 14.8, 1.9, 7.3, 49.0, 40.3, 25.8, 13.9, 8.4, 23.3, 39.7, 21.1, 11.6, 43.5, 1.3, 36.9, 18.4, 18.1, 35.8, 18.1, 36.8, 14.7, 3.4, 37.6, 5.2, 23.6, 10.6, 11.6, 20.9, 20.1, 7.1, 3.4, 48.9, 30.2, 7.8, 2.3, 10.0, 2.6, 5.4, 5.7, 43.0, 21.3, 45.1, 2.1, 28.7, 13.9, 12.1, 41.1, 10.8, 4.1, 42.0, 35.6, 3.7, 4.9, 9.3, 42.0, 8.6]
第8列的数据：
[ 69.2 45.1 69.3 58.5 58.4 75. 23.5 11.6 1. 21.2
24.2 4. 65.9 7.2 46. 52.9 114. 55.8 18.3 19.1
53.4 23.5 49.6 26.2 18.3 19.5 12.6 22.9 22.9 40.8
43.2 38.6 30. 0.3 7.4 8.5 5. 45.7 35.1 32.
31.6 38.7 1.8 26.4 43.3 31.5 35.7 18.5 49.9 36.8
34.6 3.6 39.6 58.7 15.9 60. 41.4 16.6 37.7 9.3
21.4 54.7 27.3 8.4 28.9 0.9 2.2 10.2 11. 27.2
38.7 31.7 19.3 31.3 13.1 89.4 20.7 14.2 9.4 23.1
22.3 36.9 32.5 35.6 33.8 65.7 16. 63.2 73.4 51.4
9.3 33. 59. 72.3 10.9 52.9 5.9 22. 51.2 45.9
49.8 100.9 21.4 17.9 5.3 59. 29.7 23.2 25.6 5.5
56.5 23.2 2.4 10.7 34.5 52.7 25.6 14.8 79.2 22.3
46.2 50.4 15.6 12.4 74.2 25.9 50.6 9.2 3.2 43.1
8.7 43. 2.1 45.1 65.6 8.5 9.3 59.7 20.5 1.7
12.9 75.6 37.9 34.4 38.9 9. 8.7 44.3 11.9 20.6
37. 48.7 14.2 37.7 9.5 5.7 50.5 24.3 45.2 34.6
30.7 49.3 25.6 7.4 5.4 84.8 21.6 19.4 57.6 6.4
18.4 47.4 17. 12.8 13.1 41.8 20.3 35.2 23.7 17.6
8.3 27.4 29.7 71.8 30. 19.6 26.6 18.2 3.7 23.4
5.8 6. 31.6 3.6 6. 13.8 8.1 6.4 66.2 8.7]