在多维 Pandas DataFrame 中获取最小列数 - Python答案

【问题标题】：Get minimum number column wise in a multidimensional Pandas DataFrame - Python在多维 Pandas DataFrame 中获取最小列数 - Python
【发布时间】：2016-09-28 06:21:28
【问题描述】：

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
从 CSV 文件中读取文件
data = [pd.read_csv(f, index_col=None, header=None) for f in temp]
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。

所以，data 有 [128 rows x 14 columns] * 60 samples 没有 index_col 和 header

其中一个示例是：

[          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]]

我正在尝试获取每个样本的最小列数。我该怎么做？

我尝试使用min()，通过使用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]]

它与示例相同。我不知道这里有什么问题。

【问题讨论】：

你觉得data[0][0].min(axis=1)？
@jezrael 我收到一个错误ValueError: No axis named 1 for object type <class 'pandas.core.series.Series'>

标签： python pandas

【解决方案1】：

我认为你需要：

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])

【讨论】：

不。我收到ValueError: No axis named 1 for object type <class 'pandas.core.series.Series'> 错误
我认为当我再次将列表添加到数据框时，我正在创建一个额外的维度。如果我这样做mins = [df.min(axis=1) for df in data[0]]，那么它就可以工作了。
好的，超级。一个问题 - 您需要与 "3d" data-frame 合作吗？或者更好的是创建一个包含所有样本的大数据框？
我不必与3d 合作，我该怎么做？抱歉，对数据框很陌生！
没问题，我们可以找到解决办法。