> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/speedyapply/JobSpy/llms.txt
> Use this file to discover all available pages before exploring further.

# Filtering results

> Narrow down job results before and after scraping using built-in parameters and pandas operations.

You can filter jobs at two stages: **before scraping** by passing parameters to `scrape_jobs()`, and **after scraping** by querying the returned DataFrame.

## Filtering before scraping

These parameters are sent to the job boards and reduce the number of results fetched.

### Job type

Use the `job_type` parameter to restrict results to a specific employment type.

```python theme={null}
from jobspy import scrape_jobs

jobs = scrape_jobs(
    site_name=["indeed", "linkedin"],
    search_term="software engineer",
    location="New York, NY",
    job_type="fulltime",
)
```

Accepted values: `"fulltime"`, `"parttime"`, `"internship"`, `"contract"`.

### Remote jobs

Set `is_remote=True` to filter for remote positions.

```python theme={null}
jobs = scrape_jobs(
    site_name=["indeed", "linkedin"],
    search_term="frontend developer",
    location="United States",
    is_remote=True,
)
```

### Location and distance

The `location` parameter accepts a city, state, or country string. The `distance` parameter sets the search radius in miles (default `50`).

```python theme={null}
jobs = scrape_jobs(
    site_name="zip_recruiter",
    search_term="nurse practitioner",
    location="Boston, MA",
    distance=25,  # within 25 miles
)
```

### Recency

Use `hours_old` to return only jobs posted within the last N hours.

```python theme={null}
jobs = scrape_jobs(
    site_name="linkedin",
    search_term="product designer",
    location="Los Angeles, CA",
    hours_old=48,
)
```

### Easy apply

Set `easy_apply=True` to filter for jobs hosted directly on the job board (i.e. you apply without leaving the site).

```python theme={null}
jobs = scrape_jobs(
    site_name="indeed",
    search_term="marketing manager",
    location="Chicago, IL",
    easy_apply=True,
)
```

<Note>
  The LinkedIn Easy Apply filter no longer works on LinkedIn's end. Setting `easy_apply=True` with `site_name="linkedin"` has no effect.
</Note>

### Offset

Use `offset` to start results from the Nth position. This is useful for paginating through results across multiple calls.

```python theme={null}
# First batch
jobs_1 = scrape_jobs(
    site_name="indeed",
    search_term="data analyst",
    location="Denver, CO",
    results_wanted=25,
    offset=0,
)

# Second batch
jobs_2 = scrape_jobs(
    site_name="indeed",
    search_term="data analyst",
    location="Denver, CO",
    results_wanted=25,
    offset=25,
)
```

## Platform-specific limitations

<Warning>
  **Indeed** only supports one of the following per search:

  * `hours_old`
  * `job_type` and/or `is_remote`
  * `easy_apply`

  Combining them will not raise an error, but only one filter will take effect.
</Warning>

<Warning>
  **LinkedIn** only supports one of the following per search:

  * `hours_old`
  * `easy_apply`
</Warning>

## Filtering after scraping

After `scrape_jobs()` returns a DataFrame, you can use standard pandas operations to further narrow results.

### Filter by column value

```python theme={null}
import pandas as pd
from jobspy import scrape_jobs

jobs = scrape_jobs(
    site_name=["indeed", "linkedin"],
    search_term="software engineer",
    location="Austin, TX",
    results_wanted=50,
)

# Remote jobs only
remote_jobs = jobs[jobs["is_remote"] == True]

# Full-time jobs only
fulltime_jobs = jobs[jobs["job_type"] == "fulltime"]
```

### Filter by salary range

```python theme={null}
# Jobs with a minimum salary above $100,000/year
high_paying = jobs[
    (jobs["min_amount"] >= 100_000) & (jobs["interval"] == "yearly")
]

# Jobs within a salary band
banded = jobs[
    (jobs["min_amount"] >= 80_000) &
    (jobs["max_amount"] <= 150_000) &
    (jobs["interval"] == "yearly")
]
```

### Drop duplicates

The same posting can appear on multiple boards. Use `job_url` or `title` + `company` to deduplicate.

```python theme={null}
# Deduplicate by exact job URL
jobs = jobs.drop_duplicates(subset=["job_url"])

# Deduplicate by title and company (catches cross-board duplicates)
jobs = jobs.drop_duplicates(subset=["title", "company"])
```

### Filter by keyword in title

```python theme={null}
# Only jobs with "senior" in the title (case-insensitive)
senior_jobs = jobs[jobs["title"].str.contains("senior", case=False, na=False)]
```

### Filter by site

```python theme={null}
# Only LinkedIn results
linkedin_jobs = jobs[jobs["site"] == "linkedin"]
```