Steam Review Scraper
Installation
The package can be installed by:
>>> pip install steam-review-scaper
Usage
search_game_id(search_term, all_results=False)
Return Dataframe of game ids of the search term from Steam’s search result page.
Args:
search_term (str): Game name to search. all_results (bool, optional): Whether to return all games results of the search term or the top one result. Defaults to False.
Returns:
Dataframe: Dataframe with two columns
gameandid.
Example:
>>> from steam_review_scraper import search_game_id
>>> search_game_id("Counter-Strike: Global Offensive")
game id
0 Counter-Strike: Global Offensive 730
get_game_ids(n, filter='topsellers')
Return Dataframe of n games’ ids from Steam’s search result page.
Args:
n (int): number of games to collect. filter (str, optional): filter for search results. Defaults to ‘topsellers’.
Returns:
Dataframe: Dataframe with two columns
gameandid.
Example:
>>> from steam_review_scraper import get_game_ids
>>> get_game_ids(5)
game id
0 BIOMUTANT 597820
1 Mass Effect™ Legendary Edition 1328670
2 Destiny 2 1085660
3 Counter-Strike: Global Offensive 730
4 Apex Legends™ 1172470
get_review_count(id)
Return total number of reviews of default language.
Args:
id (int or str): Game id.
Returns:
int: Number of reviews.
Example:
>>> from steam_review_scraper import get_review_count
>>> get_review_count(730)
1646275
get_game_review(id, language='default')
Collect all review for a given game.
Args:
id (int or str): Game id language (str, optional): The language in which to get the reviews. Defaults to ‘default’, which is the default language of your Steam account.
Returns:
Dataframe: Dataframe for reviews with the following columns:
| name | description | dtype |
|---|---|---|
| user | user name of the review | object |
| playtime | total playtime (in hours) the user spent on this game | float64 |
| user_link | user's profile page url | object |
| post_date | review's post date | object |
| helpfulness | number of people found this review helpful | int64 |
| review | review content | object |
| recommend | whether the user recommend the game. | object |
| early_access_review | whether this is an early access review. | object |
Example:
English reviews for Counter-Strike: Global Offensive:
- Game id 730 can be found using
search_game_id(‘Counter-Strike: Global Offensive’)or from game’s Steam page url https://store.steampowered.com/app/730/CounterStrike_Global_Offensive.
>>> from steam_review_scraper import get_game_review
>>> reviews = get_game_review(730, language=’english’)
Metadata
Release files for steam-review-scraper 0.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| steam-review-scraper-0.1.0.tar.gz | 5.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| steam_review_scraper-0.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 12.3 kB
Release files / steam-review-scraper-0.1.0.tar.gz
| Download URL | steam-review-scraper-0.1.0.tar.gz |
|---|---|
| Size | 5.7 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.5.0.1 requests/2.24.0 requests-toolbelt/0.9.1 tqdm/4.47.0 CPython/3.8.3
|
Release files / steam_review_scraper-0.1.0-py3-none-any.whl
| Download URL | steam_review_scraper-0.1.0-py3-none-any.whl |
|---|---|
| Size | 6.6 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.5.0.1 requests/2.24.0 requests-toolbelt/0.9.1 tqdm/4.47.0 CPython/3.8.3
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