我是熊猫新手。我正在尝试按列获取最小数量。所以这些是我遵循的步骤：
我使用CSV文件读取了文件
数据= [pd.read_csv（f，index_col = None，header = None）临时为f]
然后将其添加到另一个数据帧flow = pd.DataFrame(data)，使其成为“ 3d”数据帧。
因此，data的[128 rows x 14 columns] * 60 samples不含index_col和header
示例之一是：
[ 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]]
我正在尝试为每个样本逐列获取最小数量。我该怎么办？
我尝试通过执行
min()来使用data[0][0].min()，但输出如下：[[ 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]]
与样本相同。我不知道这是什么问题。
最佳答案
我认为您需要：
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
...
...
也许更好的是省略
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])
关于python - 在多维Pandas DataFrame中明智地获取最小数量列-Python，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/39739432/