Feature Request: "Skiprows" by a condition or set of conditions
#### Code Sample, a copy-pastable example if possible

```python
# data file path: foo/bar/data.csv
  col_1 col_2  ...  col_n
0   940    45  ...  0.023
1  1040    52  ...  0.430
2  1530    45  ...  0.302
3   753    43  ...  0.450
4   890    32  ...  0.023

schema={
    "col_1": int,
    ...,
    "col_n": float
}
mask = (col_1 >= 1000)
df = pd.read_csv('foo/bar/data.csv', skiprows=mask, dtype=schema)

```

#### Problem description

Pandas read methods currently support skipping rows by index with the parameter `skiprows`. It'd be a good feature if usage of `skiprows` is extended in way that it can take conditional statements just like many pandas objects. I anticipate that this feature may not be useful to all, it is certain that it will ease the people's pain who are dealing with many (large) files. Memory usage would drastically be downed in some situations I think. 

I am opening this issue with hoping a welcome on dev side, not opening this because it first glanced as a fancy request, but because I think it may affect many people's work in a good way. It may be later used as a schema reference as well. Similar to schema validation of a data set.

**NOTE:** I am uncertain how can this be achieved or even if it can be done at all. It may not apply to pandas module. Consider this also as a brain storming.

#### Expected Output

This requires passing column names & dtype (schema) before reading the file, but I am not sure how to convey the mask for skiprows it should be similar to `pandas.DataFrame` condition & masks, but we may only pass column names since there is no `pandas.DataFrame` object.

```
  col_1 col_2  ...  col_n
0  1040    52  ...  0.430
1  1530    45  ...  0.302

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

<details>

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.1.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 18.7.0
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 0.25.3
numpy            : 1.18.0
pytz             : 2019.2
dateutil         : 2.8.1
pip              : 20.0.2
setuptools       : 45.2.0
Cython           : 0.29.12
pytest           : 5.3.4
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : 1.2.0
lxml.etree       : 4.3.4
html5lib         : None
pymysql          : 0.9.3
psycopg2         : None
jinja2           : 2.10.1
IPython          : 7.6.1
pandas_datareader: None
bs4              : 4.8.0
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : 4.3.4
matplotlib       : 3.1.1
numexpr          : 2.6.9
odfpy            : None
openpyxl         : 2.6.2
pandas_gbq       : None
pyarrow          : None
pytables         : None
s3fs             : 0.4.0
scipy            : 1.3.1
sqlalchemy       : None
tables           : 3.5.2
xarray           : None
xlrd             : 1.2.0
xlwt             : None
xlsxwriter       : 1.2.0

</details>
