low_memory=True in read_csv leads to non documented, silent errors
#### Problem description

Say I have a csv file with a "considerable" number of rows, and a column that matches some value, i.e.

> Note: I've marked the matched values between many `*` 

```bash
$ grep -a --text -e "11815057" somefile.csv
9999;40043;2;DUMMY1;1;1101;DUMMY2;3;0;110;1;B;****11815057*****;1;20051105;0;1101;DUMMY3;0;0;6,8;97;P;P
9999;40043;2;DUMMY1;1;1101;DUMMY2;3;0;110;1;F;*****11815057******;1;20051105;0;1101;DUMMY3;0;0;0;0;Y;T
```
Number of rows (including header)
```bash
$ wc -l somefile.csv 
3308478 somefile.csv
```
Number of columns
```bash
$ head -1 somefile.csv | column -t -s ";" | wc -w
24
```
Using pandas, I could read this file, using `read_csv()`

```python
In [81]: df = pd.read_csv("somefile.csv", sep=";", encoding="latin1", low_memory=True)

In [82]: df[df.col_12 == "11815057"]
Out[82]: 
         col_0  col_1  col_2   col_3  col_4  col_5    col_6  col_7  col_8  col_9  col_10 col_11    col_12  col_13    col_14  col_15  col_16   col_17  col_18  col_19 col_20  col_21 col_22 col_23
3276678   9999  40043      2  DUMMY1      1   1101   DUMMY2      3      0    110       1      B  11815057       1  20051105       0    1101   DUMMY3       0       0    6,8      97      P      P


[1 rows x 24 columns]

In [83]: df[df.col_12 == 11815057]
Out[83]: 
         col_0  col_1  col_2   col_3  col_4  col_5    col_6  col_7  col_8  col_9  col_10 col_11    col_12  col_13    col_14  col_15  col_16   col_17  col_18  col_19 col_20  col_21 col_22 col_23
3276879   9999  40043      2  DUMMY1      1   1101   DUMMY2      3      0    110       1      F  11815057       1  20051105       0    1101   DUMMY3       0       0      0       0      Y      T

In [84]: df.shape 
Out[84]: (3308477, 24)   
```
Note that the comparison check is not returning both rows. In other words, `low_memory=True` breaks silently any kind of further operations that rely on comparison checks, like slicing a dataframe, for instance. In my case, it was silently not dropping the second row using `drop_duplicates(subset="col_12")`

#### Expected Output

This can be solved using `low_memory=False`, which consumes more memory, but enforce consistent datatypes along the column.

```
In [85]: df = pd.read_csv("somefile.csv", sep=";", encoding="latin1", low_memory=False)

In [87]: df[df.col_12 == 11815057]
Out[87]: 
Empty DataFrame
Columns: [col_0, col_1, col_2, col_3, col_4, col_5, col_6, col_7, col_8, col_9, col_10, col_11, col_12, col_13, col_14, col_15, col_16, col_17, col_18, col_19, col_20, col_21, col_22, col_23]
Index: []


n [97]: df[df.col_12 == "11815057" ]
Out[97]: 
         col_0  col_1  col_2   col_3  col_4  col_5    col_6  col_7  col_8  col_9  col_10 col_11    col_12  col_13    col_14  col_15  col_16   col_17  col_18  col_19 col_20  col_21 col_22 col_23
3276678   9999  40043      2  DUMMY1      1   1101   DUMMY2      3      0    110       1      B  11815057       1  20051105       0    1101   DUMMY3       0       0    6,8      97      P      P
3276879   9999  40043      2  DUMMY1      1   1101   DUMMY2      3      0    110       1      F  11815057       1  20051105       0    1101   DUMMY3       0       0      0       0      Y      T
```

I believe this should be properly documented both in the docs and in the current warning displayed after the method is called, considering that `low_memory` is a parameter that is mentioned as "deprecated but working" in [other issues](https://github.com/pandas-dev/pandas/issues/5888). I don't know the current status of the parameter as of now, but the comments in [this commit](https://github.com/pandas-dev/pandas/pull/13293/files) mention that this feature is meant to work on chunks of bytes, which makes the process a lot harder to debug.

Also, what is the behavior in the same case but for `read_excel()`?

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

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.0.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-29-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.23.3
pytest: None
pip: 18.0
setuptools: 39.0.1
Cython: None
numpy: 1.14.5
scipy: None
pyarrow: None
xarray: None
IPython: 6.4.0
sphinx: None
patsy: None
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: 2.5.4
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
sqlalchemy: 1.2.9
pymysql: None
psycopg2: 2.7.5 (dt dec pq3 ext lo64)
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
