获取多维数据帧中的最小列数-Python
我对熊猫不熟悉。我正在尝试按列获取最小数。以下是我遵循的步骤:获取多维数据帧中的最小列数-Python,python,pandas,Python,Pandas,我对熊猫不熟悉。我正在尝试按列获取最小数。以下是我遵循的步骤: 我使用CSV文件读取文件 数据=[f在temp中的pd.read\u csv(f,index\u col=None,header=None)] 然后将其添加到另一个数据框flow=pd.DataFrame(data),使其成为“3d”数据框 因此,数据有[128行x 14列]*60个样本没有索引列和标题 其中一个样本是: [ 0 1 2 3 4 5
flow=pd.DataFrame(data)
,使其成为“3d”数据框数据
有[128行x 14列]*60个样本
没有索引列
和标题
其中一个样本是:
[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13
0 3985.1 4393.3 4439.5 3662.1 5061.0 3990.8 4573.8 4036.9 4717.9 4225.6 4638.5 4157.9 4496.4 4007.7
1 3998.5 4398.5 4447.2 3660.0 5062.6 3986.7 4573.3 4045.1 4733.8 4238.5 4650.3 4167.2 4509.2 4022.6
2 3995.4 4397.9 4442.1 3659.5 5058.5 3987.2 4569.7 4039.5 4724.1 4234.9 4645.6 4161.5 4506.2 4014.9
3 3985.1 4396.9 4432.3 3660.0 5054.9 3988.2 4568.2 4037.9 4719.0 4230.3 4632.3 4150.8 4500.5 4004.1
4 3985.1 4391.3 4428.2 3661.5 5057.9 3987.2 4570.8 4044.6 4731.3 4236.9 4631.8 4151.8 4503.1 4005.6
5 3991.3 4391.8 4430.8 3662.6 5059.5 3987.7 4572.8 4044.6 4730.8 4237.4 4639.5 4157.4 4507.2 4009.7
6 3989.7 4396.9 4436.9 3661.5 5057.4 3987.7 4571.3 4035.4 4716.9 4230.3 4641.0 4156.9 4505.1 4010.8
7 3983.6 4392.8 4435.4 3660.0 5056.9 3987.2 4570.8 4032.8 4719.5 4227.7 4634.4 4153.8 4497.4 4008.2
8 3983.1 4388.7 4428.7 3661.5 5056.9 3987.7 4571.8 4041.0 4728.2 4231.8 4631.3 4154.4 4499.0 4004.6
9 3988.2 4395.9 4433.3 3662.1 5057.9 3987.7 4572.3 4040.5 4720.5 4231.3 4636.9 4154.9 4503.1 4005.1
10 3988.7 4398.5 4439.0 3660.0 5060.0 3986.7 4572.3 4032.3 4710.3 4225.1 4640.5 4154.9 4497.4 4008.2
11 3983.6 4391.3 4434.4 3661.0 5059.0 3988.7 4570.3 4041.0 4724.6 4235.4 4642.6 4163.1 4499.5 4010.8
12 3984.1 4388.7 4432.8 3664.1 5058.5 3991.8 4574.4 4051.8 4740.5 4245.1 4645.1 4170.8 4507.7 4014.4
13 3986.7 4390.8 4432.8 3664.1 5057.9 3991.3 4583.1 4043.1 4724.6 4231.8 4642.1 4161.5 4505.6 4012.8
14 3984.6 4395.4 4433.8 3661.5 5059.0 3991.3 4583.1 4036.9 4713.8 4222.1 4641.0 4157.4 4503.1 4010.8
15 3989.2 4400.5 4440.0 3661.0 5066.7 3994.4 4579.5 4045.1 4732.8 4233.8 4648.2 4170.3 4509.2 4016.4
16 3990.8 4394.4 4437.4 3661.5 5071.8 3996.4 4580.5 4045.1 4738.5 4239.5 4650.3 4171.3 4509.7 4016.4
17 3979.0 4383.6 4426.7 3660.0 5065.6 3995.4 4577.4 4034.4 4715.4 4228.2 4643.6 4158.5 4504.6 4005.1
18 3972.8 4383.1 4426.2 3660.0 5057.9 3991.8 4569.7 4034.4 4712.3 4228.2 4639.5 4157.9 4502.6 3999.0
19 3982.6 4386.7 4430.3 3661.5 5055.9 3987.2 4568.7 4045.6 4737.4 4243.1 4641.0 4166.7 4504.1 4007.7
20 3990.3 4389.7 4432.3 3661.5 5059.5 3989.7 4571.8 4047.2 4740.5 4245.1 4647.2 4169.2 4506.2 4014.9
21 3989.2 4392.8 4435.4 3661.0 5066.7 3996.9 4573.8 4035.9 4713.8 4232.3 4650.3 4166.7 4505.6 4014.4
22 3989.7 4391.8 4435.4 3661.5 5069.7 3997.4 4571.8 4035.4 4711.8 4231.3 4647.2 4167.7 4507.7 4017.4
23 3990.8 4389.7 4432.8 3660.0 5069.2 3996.9 4569.2 4044.6 4734.9 4237.9 4646.2 4168.7 4509.7 4020.0
24 3988.7 4393.3 4434.9 3659.0 5070.3 4000.5 4570.8 4041.0 4725.6 4232.8 4648.2 4166.7 4504.6 4016.4
25 3990.3 4397.9 4440.0 3661.0 5065.6 3997.9 4571.8 4039.0 4713.8 4230.8 4650.3 4169.7 4506.7 4019.0
26 3990.8 4396.4 4437.4 3662.1 5057.9 3988.7 4572.3 4045.1 4729.2 4236.4 4648.2 4169.7 4509.2 4022.6
27 3984.6 4385.1 4425.6 3661.5 5056.4 3990.8 4577.4 4041.5 4727.2 4231.8 4641.5 4158.5 4495.4 4010.3
28 3983.6 4381.0 4424.6 3662.1 5057.4 3999.5 4585.1 4037.4 4716.9 4229.7 4641.5 4157.4 4491.8 4006.2
29 3991.8 4391.3 4434.9 3662.1 5056.9 4000.0 4588.7 4040.5 4723.1 4234.4 4647.7 4167.7 4503.1 4017.4
.. ... ... ... ... ... ... ... ... ... ... ... ... ... ...
98 3988.2 4372.3 4424.1 3662.1 5040.5 3989.2 4585.6 4033.3 4719.0 4233.3 4647.2 4163.6 4502.1 4011.8
99 3993.8 4382.1 4429.2 3660.5 5042.1 3988.2 4590.3 4045.1 4737.4 4255.9 4659.0 4176.9 4514.4 4021.5
100 3992.8 4384.1 4430.3 3661.0 5041.0 3989.7 4601.5 4039.5 4733.3 4264.1 4663.1 4186.2 4512.3 4023.6
101 3988.2 4374.9 4424.6 3663.6 5040.0 3991.3 4601.0 4028.7 4719.0 4247.7 4654.9 4171.8 4505.1 4017.4
102 3989.7 4374.9 4427.2 3662.1 5040.5 3990.8 4590.3 4033.3 4716.9 4234.4 4654.4 4168.7 4508.7 4015.9
103 3987.2 4372.3 4428.7 3660.5 5036.4 3988.2 4585.1 4035.9 4719.5 4231.8 4651.3 4171.3 4504.6 4012.8
104 3979.5 4365.6 4421.5 3662.1 5030.3 3984.1 4586.2 4030.3 4717.4 4229.7 4641.0 4158.5 4491.8 4005.6
105 3982.1 4372.8 4420.5 3662.1 5032.3 3974.9 4586.2 4034.4 4719.0 4233.8 4640.0 4155.4 4495.4 4006.2
106 3987.7 4380.0 4427.7 3659.5 5037.9 3973.8 4584.1 4039.0 4720.5 4241.0 4644.1 4165.1 4509.2 4010.8
107 3987.2 4374.4 4428.7 3662.6 5039.5 3982.6 4585.1 4034.4 4719.0 4233.3 4641.5 4158.5 4506.7 4007.7
108 3982.6 4370.8 4420.0 3664.1 5036.9 3982.6 4587.7 4034.9 4724.1 4228.7 4639.0 4150.8 4495.4 4000.5
109 3979.0 4372.3 4414.4 3658.5 5029.2 3971.8 4580.0 4037.4 4723.6 4233.8 4639.5 4154.9 4492.8 3997.4
110 3979.0 4374.4 4418.5 3658.5 5027.7 3970.3 4571.3 4029.7 4712.3 4225.6 4640.0 4155.4 4496.9 3998.5
111 3986.2 4381.0 4428.2 3663.1 5037.4 3980.5 4580.0 4025.6 4705.1 4217.9 4643.6 4157.9 4504.1 4003.1
112 3991.3 4383.6 4430.3 3661.5 5042.6 3985.6 4585.6 4027.2 4708.7 4225.6 4644.6 4166.7 4508.2 4007.2
113 3983.6 4378.5 4432.8 3659.0 5034.4 3976.9 4573.8 4032.8 4725.6 4236.9 4643.6 4165.6 4504.1 4005.1
114 3976.4 4380.0 4443.6 3661.0 5028.2 3968.7 4572.8 4037.4 4735.4 4247.2 4649.7 4168.2 4507.7 4008.2
115 3973.8 4378.5 4441.5 3661.5 5033.3 3974.4 4585.6 4028.2 4713.3 4236.9 4650.8 4170.8 4508.2 4004.1
116 3971.8 4370.3 4431.8 3661.0 5036.4 3983.6 4588.7 4019.0 4696.4 4212.3 4639.0 4159.0 4496.9 3991.8
117 3972.3 4371.8 4437.4 3661.0 5031.3 3982.1 4585.1 4032.3 4720.5 4218.5 4637.4 4155.9 4496.9 3994.9
118 3973.8 4379.0 4444.1 3660.5 5032.3 3980.0 4587.2 4041.0 4730.8 4236.9 4646.7 4166.7 4506.2 4006.7
119 3982.1 4385.1 4447.2 3661.5 5040.5 3984.1 4586.7 4024.6 4708.2 4230.3 4648.2 4168.7 4506.7 4010.3
120 3991.3 4390.8 4452.8 3663.1 5043.1 3985.1 4576.4 4019.0 4710.8 4228.2 4650.3 4168.7 4505.6 4011.8
121 3989.2 4386.7 4451.3 3660.5 5041.0 3981.5 4568.2 4032.3 4733.3 4237.9 4657.4 4172.8 4508.2 4011.3
122 3983.6 4384.1 4448.7 3658.5 5040.0 3982.6 4574.4 4036.9 4730.8 4237.4 4656.4 4172.3 4505.6 4008.7
123 3987.7 4391.3 4455.4 3661.0 5038.5 3984.6 4585.6 4029.7 4716.4 4231.3 4655.4 4171.3 4504.1 4012.8
124 3990.8 4392.8 4460.0 3660.0 5038.5 3983.6 4583.1 4026.2 4714.4 4231.3 4656.9 4172.3 4506.2 4013.8
125 3988.7 4390.8 4456.4 3657.9 5040.0 3984.6 4576.4 4025.1 4715.9 4231.3 4651.8 4167.2 4505.1 4012.8
126 3990.3 4393.8 4455.9 3659.0 5040.0 3983.1 4577.4 4026.7 4720.5 4231.8 4647.2 4167.2 4505.6 4018.5
127 3988.2 4392.8 4453.3 3660.0 5040.5 3976.9 4581.5 4033.8 4732.8 4235.4 4649.2 4170.8 4506.2 4015.9
[128 rows x 14 columns]]
我试图得到每个样本的最小列数。我该怎么做
我尝试使用min()
,方法是执行data[0][0].min()
,但我得到以下输出:
[[ 3985.1 4393.3 4439.5 ..., 4157.9 4496.4 4007.7]
[ 3998.5 4398.5 4447.2 ..., 4167.2 4509.2 4022.6]
[ 3995.4 4397.9 4442.1 ..., 4161.5 4506.2 4014.9]
...,
[ 3988.7 4390.8 4456.4 ..., 4167.2 4505.1 4012.8]
[ 3990.3 4393.8 4455.9 ..., 4167.2 4505.6 4018.5]
[ 3988.2 4392.8 4453.3 ..., 4170.8 4506.2 4015.9]]
和样品一样。我不知道这里有什么问题。我想您需要:
print (data[0].min(axis=1))
0 3662.1
1 3660.0
2 3659.5
3 3660.0
4 3661.5
5 3662.6
6 3661.5
7 3660.0
8 3661.5
9 3662.1
10 3660.0
11 3661.0
12 3664.1
13 3664.1
14 3661.5
15 3661.0
...
...
可能beter省略了flow=pd.DataFrame(data)
并使用:
data = [pd.read_csv(f, index_col=None, header=None) for f in temp]
mins = [df.min(axis=1) for df in data[0]]
print (mins[0])
print (mins[1])
您是否认为
data[0][0].min(axis=1)
?@jezrael我收到一个错误ValueError:没有为对象类型命名为1的轴
Nope。我得到ValueError:没有为对象类型命名为1的轴
error我认为当我再次将列表添加到数据帧时,我正在创建一个额外的维度。如果我对数据[0]中的df执行mins=[df.min(axis=1)]
则它工作正常。好的,超级。一个问题-您需要使用“3d”数据框吗?或者更好的方法是创建一个包含所有样本的大数据帧?我不必使用3d
,我该怎么做呢?抱歉,数据帧非常新!没问题,我们可以找到解决办法。