Question: I try to put this HTML into my page content using dashboard:

```
<div>
     <p class="text-center">
         <a class="btn btn-large btn-light" title="Kontakt" href="/?page_id=20">Kontakt <i class="fa fa-phone"></i></a>

         <a class="btn btn-large btn-light" title="Standorte &amp; Informationen" href="/?page_id=20">Standorte &amp;
             Informationen <i class="fa fa-globe"></i></a>
     </p>
</div>

```

But here is the HTMl what I getting on page:

```
<div>
    <p class="text-center">
        <a class="btn btn-large btn-light" title="Kontakt" href="/?page_id=20">Kontakt <i class="fa fa-phone"></i></a>
    </p>
    <p>
        <a class="btn btn-large btn-light" title="Standorte &amp; Informationen" href="/?page_id=20">Standorte &amp;<br />
        Informationen <i class="fa fa-globe"></i></a>
    </p>
</div>

```

See the difference? So why it genereates two `<p>`, and first has the class what I assing? Maybe it my mistake and I didnt get something with WordPress?

Answer: 
You could make use of the [`numpy.genfromtxt()`](http://docs.scipy.org/doc/numpy/reference/generated/numpy.genfromtxt.html#numpy-genfromtxt) function, if you specify the number of columns:

```
import numpy as np

a = np.genfromtxt('bigfile.csv',dtype=str,usecols=(0)) 
b = np.genfromtxt('bigfile.csv',dtype=float,delimiter='\t',usecols=range(1,4)) 
                                                                             #^enter # of cols here

d = dict(zip(a,b.tolist()))    #if you want a numpy array, just remove .tolist()

print d

```

**Output:**

```
{'abc': [-0.123, 0.6524, 0.325], 'bar': [0.23123, -0.123124, -0.1232], 'foo': [-0.9808, 0.874, -0.2341]}

```

---

*Note: To programatically find the number of `cols` you could do:*

```
with open('bigfile.csv', 'r') as f:
    num_cols = len(f.readline().split())

```

*And then use `num_cols` for the `usecols` parameter.*
One way to use `Pandas`. Assuming you do `df = pd.read_csv(file)` and the `df` is like

```
In [220]: df
Out[220]:
     k       a1        a2      a3
0  abc -0.12300  0.652400  0.3250
1  foo -0.98080  0.874000 -0.2341
2  bar  0.23123 -0.123124 -0.1232

```

*I've added dummy column names, you have flexibility to change that while reading the csv file* 

Then you could do the following.

```
In [221]: df.set_index('k').T.to_dict('list')
Out[221]:
{'abc': [-0.12300000000000001, 0.65239999999999998, 0.32500000000000001],
 'bar': [0.23123000000000002, -0.123124, -0.1232],
 'foo': [-0.98080000000000001, 0.87400000000000011, -0.2341]}

```