Get minimum number column wise in a multidimensional Pandas DataFrame - Python
<p>I am new to Pandas. I am trying to get the minimum number column-wise. So these are the steps I have followed:</p>

<ol>
<li><p>I read the files from CSV files using </p>

<p>data = [pd.read_csv(f, index_col=None, header=None) for f in temp]</p></li>
<li><p>Then added it to another data frame <code>flow = pd.DataFrame(data)</code>, making it "3d" data-frame.</p></li>
</ol>

<p>So, <code>data</code> has <code>[128 rows x 14 columns] * 60 samples</code> with out <code>index_col</code> and <code>header</code></p>

<p>One of the samples is:</p>

<pre><code>[          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]]
</code></pre>

<p>I am trying to get the minimum number column-wise for every sample. How should I do it?</p>

<p>I tried using <code>min()</code>, by doing <code>data[0][0].min()</code> but I get the following as the output:</p>

<pre><code>[[ 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]]
</code></pre>

<p>It's same as the sample. I don't know what's the problem here.</p>

<p>I think you need:</p>

<pre><code>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
...
...
</code></pre>

<p>Maybe beter is omit <code>flow = pd.DataFrame(data)</code> and use:</p>

<pre><code>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])
</code></pre>

python pandas