Numpy读取csv文件

一.利用savetxt函数将数据存储到文件:

savetxt(fileName,data)

---

fileName:保存文件路径和名称
data:需要保存的数据

np.savetxt("exaple.txt",example)
print("保存完成")
文件----->0.000000000000000000e+00 1.000000000000000000e+00 2.000000000000000000e+00    3.000000000000000000e+00 4.000000000000000000e+00 5.000000000000000000e+00 6.000000000000000000e+00 7.000000000000000000e+00
8.000000000000000000e+00 9.000000000000000000e+00 1.000000000000000000e+01 1.100000000000000000e+01 1.200000000000000000e+01 1.300000000000000000e+01 1.400000000000000000e+01 1.500000000000000000e+01
1.600000000000000000e+01 1.700000000000000000e+01 1.800000000000000000e+01 1.900000000000000000e+01 2.000000000000000000e+01 2.100000000000000000e+01 2.200000000000000000e+01 2.300000000000000000e+01

二.利用 loadtxt函数读取csv文件

np.loadtxt(filepath,delimiter,usecols,unpack)

---

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
——–>上图是 data.csv

close=np.loadtxt("data.csv",delimiter=",",usecols=(6,7))
print(close)------------------->[[3.36100e+02 2.11448e+07]
                                 [3.39320e+02 1.34730e+07]
                                 [3.45030e+02 1.52368e+07]
                                 [3.44320e+02 9.24260e+06]
                                 [3.43440e+02 1.40641e+07]
                                 [3.46500e+02 1.14942e+07]
                                 [3.51880e+02 1.73221e+07]
                                 [3.55200e+02 1.36085e+07]
                                 [3.58160e+02 1.72408e+07]
                                 [3.54540e+02 3.31624e+07]
                                 [3.56850e+02 1.31275e+07]
                                 [3.59180e+02 1.10862e+07]
                                 [3.59900e+02 1.01490e+07]
                                 [3.63130e+02 1.71841e+07]
                                 [3.58300e+02 1.89490e+07]
                                 [3.50560e+02 2.91445e+07]
                                 [3.38610e+02 3.11622e+07]
                                 [3.42620e+02 2.39947e+07]
                                 [3.42880e+02 1.78535e+07]
                                 [3.48160e+02 1.35720e+07]
                                 [3.53210e+02 1.43954e+07]
                                 [3.49310e+02 1.62903e+07]
                                 [3.52120e+02 2.15210e+07]
                                 [3.59560e+02 1.78852e+07]
                                 [3.60000e+02 1.61880e+07]
                                 [3.55360e+02 1.95043e+07]
                                 [3.55760e+02 1.27180e+07]
                                 [3.52470e+02 1.61927e+07]
                                 [3.46670e+02 1.81388e+07]
                                 [3.51990e+02 1.68242e+07]]
print(close.shape)-------------->(30, 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
close,amount=np.loadtxt("data.csv",delimiter=",",usecols=(6,7),unpack=True)
print("收盘价：\n",close)-------->
收盘价：
[336.1  339.32 345.03 344.32 343.44 346.5  351.88 355.2  358.16 354.54
 356.85 359.18 359.9  363.13 358.3  350.56 338.61 342.62 342.88 348.16
 353.21 349.31 352.12 359.56 360.   355.36 355.76 352.47 346.67 351.99]

print("成交量：\n",amount)------->
成交量：
 [21144800. 13473000. 15236800.  9242600. 14064100. 11494200. 17322100.
 13608500. 17240800. 33162400. 13127500. 11086200. 10149000. 17184100.
 18949000. 29144500. 31162200. 23994700. 17853500. 13572000. 14395400.
 16290300. 21521000. 17885200. 16188000. 19504300. 12718000. 16192700.
 18138800. 16824200.]

三.