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pandas.DataFrame.values
返回DataFrame的Numpy表示。
只有DataFrame中的值会返回，ax标签会被删除。
返回:
numpy.ndarray
DataFrame的值。
pandas.DataFrame.index
检索索引标签
pandas.DataFrame.columns
检索列名
import pandas as pd
x="name,age,result\n'zhangsang',32,1\n'lisi',29,2\n'wangwu',30,1"
df=pd.read_csv(StringIO.StringIO(x))
print df
print df.index
print df.columns
name age result
0 'zhangsang' 32 1
1 'lisi' 29 2
2 'wangwu' 30 1
RangeIndex(start=0, stop=3, step=1)
Index([u'name', u'age', u'result'], dtype='object')
dtype将是一个较低的公共类型(隐式向上转换);也就是说，如果dtype(甚至是数字类型)是混合，就会选择容纳所有类型的dtype。如果您没有处理这些块，请小心使用。
例如，如果dtype是float16和float32，那么dtype将向上转换为float32。如果dtype为int32和uint8，则dtype将被向上转换为int32。通过numpy.find_common_type()转换，混合int64和uint64将产生float64 dtype。
df = pd.DataFrame({'age': [ 3, 29],
'height': [94, 170],
'weight': [31, 115]}
)
print df
print df.dtypes
print df.values
age height weight
0 3 94 31
1 29 170 115
age int64
height int64
weight int64
dtype: object
[[ 3 94 31]
[ 29 170 115]]
下面的代码提取特征列和结果列
import pandas as pd
x="name,age,result\n'zhangsang',32,1\n'lisi',29,2\n'wangwu',30,1"
df=pd.read_csv(StringIO.StringIO(x))
data=df.values
print df
print data
dataColName=df.columns
print dataColName[-1]
print dataColName[:len(dataColName)-1]
name age result
0 'zhangsang' 32 1
1 'lisi' 29 2
2 'wangwu' 30 1
[["'zhangsang'" 32 1]
["'lisi'" 29 2]
["'wangwu'" 30 1]]
result
Index([u'name', u'age'], dtype='object')
import pandas as pd
x="name,age,result\n'zhangsang',32,1\n'lisi',29,2\n'wangwu',30,1"
df=pd.read_csv(StringIO.StringIO(x))
print df
print
data=df.values
dataColName=df.columns
ftColName=dataColName[-1]
rsColName=list(dataColName[:len(dataColName)-1])
print df[ftColName].values
print df[rsColName].values
name age result
0 'zhangsang' 32 1
1 'lisi' 29 2
2 'wangwu' 30 1
[1 2 1]
[["'zhangsang'" 32]
["'lisi'" 29]
["'wangwu'" 30]]
读取葡萄酒质量样本数据
import pandas as pd
df=pd.read_csv("winequality-white-test.csv",sep=";")
print df
print
data=df.values
dataColName=df.columns
rsColName=dataColName[-1]
ftColName=list(dataColName[:len(dataColName)-1])
print df[rsColName].values
print '===='
print df[ftColName].values
fixedacidity volatileacidity citricacid residualsugar chlorides \
0 6.8 0.220 0.24 4.90 0.092
1 6.0 0.190 0.26 12.40 0.048
2 7.0 0.470 0.07 1.10 0.035
3 6.6 0.380 0.15 4.60 0.044
4 7.2 0.240 0.27 1.40 0.038
5 6.2 0.350 0.03 1.20 0.064
6 6.4 0.260 0.24 6.40 0.040
7 6.7 0.250 0.13 1.20 0.041
8 6.7 0.230 0.31 2.10 0.046
9 7.4 0.240 0.29 10.10 0.050
10 6.2 0.270 0.43 7.80 0.056
11 6.8 0.300 0.23 4.60 0.061
12 6.0 0.270 0.28 4.80 0.063
13 8.6 0.230 0.46 1.00 0.054
14 6.7 0.230 0.31 2.10 0.046
15 7.4 0.240 0.29 10.10 0.050
16 7.1 0.180 0.36 1.40 0.043
17 7.0 0.320 0.34 1.30 0.042
18 7.4 0.180 0.30 8.80 0.064
19 6.7 0.540 0.28 5.40 0.060
20 6.8 0.220 0.31 1.40 0.053
21 7.1 0.200 0.34 16.00 0.050
22 7.1 0.340 0.20 6.10 0.063
23 7.3 0.220 0.30 8.20 0.047
24 7.1 0.430 0.61 11.80 0.045
25 7.1 0.440 0.62 11.80 0.044
26 7.2 0.390 0.63 11.00 0.044
27 6.8 0.250 0.31 13.30 0.050
28 7.1 0.430 0.61 11.80 0.045
29 7.1 0.440 0.62 11.80 0.044
30 7.2 0.390 0.63 11.00 0.044
31 6.1 0.270 0.43 7.50 0.049
32 6.9 0.240 0.33 1.70 0.035
33 6.9 0.210 0.33 1.80 0.034
34 7.5 0.170 0.32 1.70 0.040
35 7.1 0.260 0.29 12.40 0.044
36 6.0 0.340 0.66 15.90 0.046
37 8.6 0.265 0.36 1.20 0.034
38 9.8 0.360 0.46 10.50 0.038
39 6.0 0.340 0.66 15.90 0.046
40 7.4 0.250 0.37 13.50 0.060
41 7.1 0.120 0.32 9.60 0.054
42 6.0 0.210 0.24 12.10 0.050
43 7.5 0.305 0.40 18.90 0.059
44 7.4 0.250 0.37 13.50 0.060
45 7.3 0.130 0.32 14.40 0.051
46 7.1 0.120 0.32 9.60 0.054
47 7.1 0.230 0.35 16.50 0.040
48 7.1 0.230 0.35 16.50 0.040
49 6.9 0.330 0.28 1.30 0.051
50 6.5 0.170 0.54 8.50 0.082
51 7.2 0.270 0.46 18.75 0.052
52 7.2 0.310 0.50 13.30 0.056
53 6.7 0.410 0.34 9.20 0.049
54 6.7 0.410 0.34 9.20 0.049
55 5.5 0.485 0.00 1.50 0.065
freesulfurdioxide totalsulfurdioxide density pH sulphates alcohol \
0 30.0 123.0 0.9951 3.03 0.46 8.6
1 50.0 147.0 0.9972 3.30 0.36 8.9
2 17.0 151.0 0.9910 3.02 0.34 10.5
3 25.0 78.0 0.9931 3.11 0.38 10.2
4 31.0 122.0 0.9927 3.15 0.46 10.3
5 29.0 120.0 0.9934 3.22 0.54 9.1
6 27.0 124.0 0.9903 3.22 0.49 12.6
7 81.0 174.0 0.9920 3.14 0.42 9.8
8 30.0 96.0 0.9926 3.33 0.64 10.7
9 21.0 105.0 0.9962 3.13 0.35 9.5
10 48.0 244.0 0.9956 3.10 0.51 9.0
11 50.5 238.5 0.9958 3.32 0.60 9.5
12 31.0 201.0 0.9964 3.69 0.71 10.0
13 9.0 72.0 0.9941 2.95 0.49 9.1
14 30.0 96.0 0.9926 3.33 0.64 10.7
15 21.0 105.0 0.9962 3.13 0.35 9.5
16 31.0 87.0 0.9898 3.26 0.37 12.7
17 20.0 69.0 0.9912 3.31 0.65 12.0
18 26.0 103.0 0.9961 2.94 0.56 9.3
19 21.0 105.0 0.9949 3.27 0.37 9.0
20 34.0 114.0 0.9929 3.39 0.77 10.6
21 51.0 166.0 0.9985 3.21 0.60 9.2
22 47.0 164.0 0.9946 3.17 0.42 10.0
23 42.0 207.0 0.9966 3.33 0.46 9.5
24 54.0 155.0 0.9974 3.11 0.45 8.7
25 52.0 152.0 0.9975 3.12 0.46 8.7
26 55.0 156.0 0.9974 3.09 0.44 8.7
27 69.0 202.0 0.9972 3.22 0.48 9.7
28 54.0 155.0 0.9974 3.11 0.45 8.7
29 52.0 152.0 0.9975 3.12 0.46 8.7
30 55.0 156.0 0.9974 3.09 0.44 8.7
31 65.0 243.0 0.9957 3.12 0.47 9.0
32 47.0 136.0 0.9900 3.26 0.40 12.6
33 48.0 136.0 0.9899 3.25 0.41 12.6
34 51.0 148.0 0.9916 3.21 0.44 11.5
35 62.0 240.0 0.9969 3.04 0.42 9.2
36 26.0 164.0 0.9979 3.14 0.50 8.8
37 15.0 80.0 0.9913 2.95 0.36 11.4
38 4.0 83.0 0.9956 2.89 0.30 10.1
39 26.0 164.0 0.9979 3.14 0.50 8.8
40 52.0 192.0 0.9975 3.00 0.44 9.1
41 64.0 162.0 0.9962 3.40 0.41 9.4
42 55.0 164.0 0.9970 3.34 0.39 9.4
43 44.0 170.0 1.0000 2.99 0.46 9.0
44 52.0 192.0 0.9975 3.00 0.44 9.1
45 34.0 109.0 0.9974 3.20 0.35 9.2
46 64.0 162.0 0.9962 3.40 0.41 9.4
47 60.0 171.0 0.9990 3.16 0.59 9.1
48 60.0 171.0 0.9990 3.16 0.59 9.1
49 37.0 187.0 0.9927 3.27 0.60 10.3
50 64.0 163.0 0.9959 2.89 0.39 8.8
51 45.0 255.0 1.0000 3.04 0.52 8.9
52 68.0 195.0 0.9982 3.01 0.47 9.2
53 29.0 150.0 0.9968 3.22 0.51 9.1
54 29.0 150.0 0.9968 3.22 0.51 9.1
55 8.0 103.0 0.9940 3.63 0.40 9.7
quality
0 6
1 6
2 5
3 6
4 6
5 5
6 7
7 5
8 8
9 5
10 6
11 5
12 5
13 6
14 8
15 5
16 7
17 7
18 5
19 5
20 6
21 6
22 5
23 6
24 5
25 6
26 6
27 6
28 5
29 6
30 6
31 5
32 7
33 7
34 7
35 6
36 6
37 7
38 4
39 6
40 5
41 5
42 5
43 5
44 5
45 6
46 5
47 6
48 6
49 5
50 6
51 5
52 5
53 5
54 5
55 4
[6 6 5 6 6 5 7 5 8 5 6 5 5 6 8 5 7 7 5 5 6 6 5 6 5 6 6 6 5 6 6 5 7 7 7 6 6
7 4 6 5 5 5 5 5 6 5 6 6 5 6 5 5 5 5 4]
====
[[6.800e+00 2.200e-01 2.400e-01 4.900e+00 9.200e-02 3.000e+01 1.230e+02
9.951e-01 3.030e+00 4.600e-01 8.600e+00]
[6.000e+00 1.900e-01 2.600e-01 1.240e+01 4.800e-02 5.000e+01 1.470e+02
9.972e-01 3.300e+00 3.600e-01 8.900e+00]
[7.000e+00 4.700e-01 7.000e-02 1.100e+00 3.500e-02 1.700e+01 1.510e+02
9.910e-01 3.020e+00 3.400e-01 1.050e+01]
[6.600e+00 3.800e-01 1.500e-01 4.600e+00 4.400e-02 2.500e+01 7.800e+01
9.931e-01 3.110e+00 3.800e-01 1.020e+01]
[7.200e+00 2.400e-01 2.700e-01 1.400e+00 3.800e-02 3.100e+01 1.220e+02
9.927e-01 3.150e+00 4.600e-01 1.030e+01]
[6.200e+00 3.500e-01 3.000e-02 1.200e+00 6.400e-02 2.900e+01 1.200e+02
9.934e-01 3.220e+00 5.400e-01 9.100e+00]
[6.400e+00 2.600e-01 2.400e-01 6.400e+00 4.000e-02 2.700e+01 1.240e+02
9.903e-01 3.220e+00 4.900e-01 1.260e+01]
[6.700e+00 2.500e-01 1.300e-01 1.200e+00 4.100e-02 8.100e+01 1.740e+02
9.920e-01 3.140e+00 4.200e-01 9.800e+00]
[6.700e+00 2.300e-01 3.100e-01 2.100e+00 4.600e-02 3.000e+01 9.600e+01
9.926e-01 3.330e+00 6.400e-01 1.070e+01]
[7.400e+00 2.400e-01 2.900e-01 1.010e+01 5.000e-02 2.100e+01 1.050e+02
9.962e-01 3.130e+00 3.500e-01 9.500e+00]
[6.200e+00 2.700e-01 4.300e-01 7.800e+00 5.600e-02 4.800e+01 2.440e+02
9.956e-01 3.100e+00 5.100e-01 9.000e+00]
[6.800e+00 3.000e-01 2.300e-01 4.600e+00 6.100e-02 5.050e+01 2.385e+02
9.958e-01 3.320e+00 6.000e-01 9.500e+00]
[6.000e+00 2.700e-01 2.800e-01 4.800e+00 6.300e-02 3.100e+01 2.010e+02
9.964e-01 3.690e+00 7.100e-01 1.000e+01]
[8.600e+00 2.300e-01 4.600e-01 1.000e+00 5.400e-02 9.000e+00 7.200e+01
9.941e-01 2.950e+00 4.900e-01 9.100e+00]
[6.700e+00 2.300e-01 3.100e-01 2.100e+00 4.600e-02 3.000e+01 9.600e+01
9.926e-01 3.330e+00 6.400e-01 1.070e+01]
[7.400e+00 2.400e-01 2.900e-01 1.010e+01 5.000e-02 2.100e+01 1.050e+02
9.962e-01 3.130e+00 3.500e-01 9.500e+00]
[7.100e+00 1.800e-01 3.600e-01 1.400e+00 4.300e-02 3.100e+01 8.700e+01
9.898e-01 3.260e+00 3.700e-01 1.270e+01]
[7.000e+00 3.200e-01 3.400e-01 1.300e+00 4.200e-02 2.000e+01 6.900e+01
9.912e-01 3.310e+00 6.500e-01 1.200e+01]
[7.400e+00 1.800e-01 3.000e-01 8.800e+00 6.400e-02 2.600e+01 1.030e+02
9.961e-01 2.940e+00 5.600e-01 9.300e+00]
[6.700e+00 5.400e-01 2.800e-01 5.400e+00 6.000e-02 2.100e+01 1.050e+02
9.949e-01 3.270e+00 3.700e-01 9.000e+00]
[6.800e+00 2.200e-01 3.100e-01 1.400e+00 5.300e-02 3.400e+01 1.140e+02
9.929e-01 3.390e+00 7.700e-01 1.060e+01]
[7.100e+00 2.000e-01 3.400e-01 1.600e+01 5.000e-02 5.100e+01 1.660e+02
9.985e-01 3.210e+00 6.000e-01 9.200e+00]
[7.100e+00 3.400e-01 2.000e-01 6.100e+00 6.300e-02 4.700e+01 1.640e+02
9.946e-01 3.170e+00 4.200e-01 1.000e+01]
[7.300e+00 2.200e-01 3.000e-01 8.200e+00 4.700e-02 4.200e+01 2.070e+02
9.966e-01 3.330e+00 4.600e-01 9.500e+00]
[7.100e+00 4.300e-01 6.100e-01 1.180e+01 4.500e-02 5.400e+01 1.550e+02
9.974e-01 3.110e+00 4.500e-01 8.700e+00]
[7.100e+00 4.400e-01 6.200e-01 1.180e+01 4.400e-02 5.200e+01 1.520e+02
9.975e-01 3.120e+00 4.600e-01 8.700e+00]
[7.200e+00 3.900e-01 6.300e-01 1.100e+01 4.400e-02 5.500e+01 1.560e+02
9.974e-01 3.090e+00 4.400e-01 8.700e+00]
[6.800e+00 2.500e-01 3.100e-01 1.330e+01 5.000e-02 6.900e+01 2.020e+02
9.972e-01 3.220e+00 4.800e-01 9.700e+00]
[7.100e+00 4.300e-01 6.100e-01 1.180e+01 4.500e-02 5.400e+01 1.550e+02
9.974e-01 3.110e+00 4.500e-01 8.700e+00]
[7.100e+00 4.400e-01 6.200e-01 1.180e+01 4.400e-02 5.200e+01 1.520e+02
9.975e-01 3.120e+00 4.600e-01 8.700e+00]
[7.200e+00 3.900e-01 6.300e-01 1.100e+01 4.400e-02 5.500e+01 1.560e+02
9.974e-01 3.090e+00 4.400e-01 8.700e+00]
[6.100e+00 2.700e-01 4.300e-01 7.500e+00 4.900e-02 6.500e+01 2.430e+02
9.957e-01 3.120e+00 4.700e-01 9.000e+00]
[6.900e+00 2.400e-01 3.300e-01 1.700e+00 3.500e-02 4.700e+01 1.360e+02
9.900e-01 3.260e+00 4.000e-01 1.260e+01]
[6.900e+00 2.100e-01 3.300e-01 1.800e+00 3.400e-02 4.800e+01 1.360e+02
9.899e-01 3.250e+00 4.100e-01 1.260e+01]
[7.500e+00 1.700e-01 3.200e-01 1.700e+00 4.000e-02 5.100e+01 1.480e+02
9.916e-01 3.210e+00 4.400e-01 1.150e+01]
[7.100e+00 2.600e-01 2.900e-01 1.240e+01 4.400e-02 6.200e+01 2.400e+02
9.969e-01 3.040e+00 4.200e-01 9.200e+00]
[6.000e+00 3.400e-01 6.600e-01 1.590e+01 4.600e-02 2.600e+01 1.640e+02
9.979e-01 3.140e+00 5.000e-01 8.800e+00]
[8.600e+00 2.650e-01 3.600e-01 1.200e+00 3.400e-02 1.500e+01 8.000e+01
9.913e-01 2.950e+00 3.600e-01 1.140e+01]
[9.800e+00 3.600e-01 4.600e-01 1.050e+01 3.800e-02 4.000e+00 8.300e+01
9.956e-01 2.890e+00 3.000e-01 1.010e+01]
[6.000e+00 3.400e-01 6.600e-01 1.590e+01 4.600e-02 2.600e+01 1.640e+02
9.979e-01 3.140e+00 5.000e-01 8.800e+00]
[7.400e+00 2.500e-01 3.700e-01 1.350e+01 6.000e-02 5.200e+01 1.920e+02
9.975e-01 3.000e+00 4.400e-01 9.100e+00]
[7.100e+00 1.200e-01 3.200e-01 9.600e+00 5.400e-02 6.400e+01 1.620e+02
9.962e-01 3.400e+00 4.100e-01 9.400e+00]
[6.000e+00 2.100e-01 2.400e-01 1.210e+01 5.000e-02 5.500e+01 1.640e+02
9.970e-01 3.340e+00 3.900e-01 9.400e+00]
[7.500e+00 3.050e-01 4.000e-01 1.890e+01 5.900e-02 4.400e+01 1.700e+02
1.000e+00 2.990e+00 4.600e-01 9.000e+00]
[7.400e+00 2.500e-01 3.700e-01 1.350e+01 6.000e-02 5.200e+01 1.920e+02
9.975e-01 3.000e+00 4.400e-01 9.100e+00]
[7.300e+00 1.300e-01 3.200e-01 1.440e+01 5.100e-02 3.400e+01 1.090e+02
9.974e-01 3.200e+00 3.500e-01 9.200e+00]
[7.100e+00 1.200e-01 3.200e-01 9.600e+00 5.400e-02 6.400e+01 1.620e+02
9.962e-01 3.400e+00 4.100e-01 9.400e+00]
[7.100e+00 2.300e-01 3.500e-01 1.650e+01 4.000e-02 6.000e+01 1.710e+02
9.990e-01 3.160e+00 5.900e-01 9.100e+00]
[7.100e+00 2.300e-01 3.500e-01 1.650e+01 4.000e-02 6.000e+01 1.710e+02
9.990e-01 3.160e+00 5.900e-01 9.100e+00]
[6.900e+00 3.300e-01 2.800e-01 1.300e+00 5.100e-02 3.700e+01 1.870e+02
9.927e-01 3.270e+00 6.000e-01 1.030e+01]
[6.500e+00 1.700e-01 5.400e-01 8.500e+00 8.200e-02 6.400e+01 1.630e+02
9.959e-01 2.890e+00 3.900e-01 8.800e+00]
[7.200e+00 2.700e-01 4.600e-01 1.875e+01 5.200e-02 4.500e+01 2.550e+02
1.000e+00 3.040e+00 5.200e-01 8.900e+00]
[7.200e+00 3.100e-01 5.000e-01 1.330e+01 5.600e-02 6.800e+01 1.950e+02
9.982e-01 3.010e+00 4.700e-01 9.200e+00]
[6.700e+00 4.100e-01 3.400e-01 9.200e+00 4.900e-02 2.900e+01 1.500e+02
9.968e-01 3.220e+00 5.100e-01 9.100e+00]
[6.700e+00 4.100e-01 3.400e-01 9.200e+00 4.900e-02 2.900e+01 1.500e+02
9.968e-01 3.220e+00 5.100e-01 9.100e+00]
[5.500e+00 4.850e-01 0.000e+00 1.500e+00 6.500e-02 8.000e+00 1.030e+02
9.940e-01 3.630e+00 4.000e-01 9.700e+00]]