pd.read_csv header inference
#### Code Sample, a copy-pastable example if possible

With [data.csv.txt](https://github.com/pandas-dev/pandas/files/4410722/data.csv.txt):

```python
>>> pd.read_csv('/tmp/data.csv.txt')  # header='infer' default
   1540  125205  PRODAJA  04-JUN-18    -3  -.625  -.726
0  1540  125205  PRODAJA  29-JUN-18 -1.00  -0.62  -0.75
1  1540  125205  PRODAJA  09-JUL-18 -3.00  -0.62  -0.77
2  1540  125205  PRODAJA  25-JUL-18 -2.00  -0.41  -0.51
3  1540  125205  PRODAJA  01-NOV-18 -3.00  -0.62  -0.74
4  1540  125205  PRODAJA  24-AUG-18 -1.00  -0.21  -0.26

```
#### Problem description

Given above-like data, a simple RangeIndex header would be preferred.

As a heuristic, floats are rarely header representatives. As are values that are equal to next row's values. When six sevenths of values are rare header representatives, those are unlikely.

One can override it all by passing `header=None`, but the inference does seem to be lacking.

#### Expected Output

```py
>>> pd.read_csv('/tmp/data.csv')
      0       1        2          3     4     5     6
0  1540  125205  PRODAJA  04-JUN-18 -3.00 -0.62 -0.73
1  1540  125205  PRODAJA  29-JUN-18 -1.00 -0.62 -0.75
2  1540  125205  PRODAJA  09-JUL-18 -3.00 -0.62 -0.77
3  1540  125205  PRODAJA  25-JUL-18 -2.00 -0.41 -0.51
4  1540  125205  PRODAJA  01-NOV-18 -3.00 -0.62 -0.74
5  1540  125205  PRODAJA  24-AUG-18 -1.00 -0.21 -0.26
```

#### Output of ``pd.show_versions()``

<details>

1.1.0.dev0+786.gec7734169

</details>
