## Installation
`pip install s3namic`
---
## Import module
```python
from s3namic import s3namic
s3 = s3namic(
bucket="bucket_name",
access_key="access_key",
secret_key="secrey_key",
region="region",
)
```
---
## Check S3 structure in tree form
```python
s3_tree = s3.make_tree(
# with_file_name=True, # True if even the file name is included in the tree
)
import json
s3_tree = json.dumps(s3_tree, indent=4, ensure_ascii=False)
print(s3_tree)
```
output:
```python
{
"assets/": {
"assets/backup/": {},
"assets/batch_raw/": {
"assets/batch_raw/batchData": {}
},
...
}
```
---
## Check S3 structure in list form
```python
s3_list = s3.list_files()
print(s3_list[:5], "\n...\n", s3_list[-5:])
```
output:
```python
['first_file.json', 'second_file.json', ... ]
```
### Find a specific file in s3
- ### find_file
```python
test_path = s3.find_file(file_name="2021-12-31", str_contains=True)
print(f"2023-04-30 File path containing filename: '{test_path}'")
```
output:
```python
"2023-04-30 File path containing filename: 'assets/csv/2023-04-30.csv'"
```
- ### find_files
```python
prefix_path = test_path[:-len(test_path.split("/")[-1])]
test_files = s3.find_files(prefix=prefix_path)
print(f"Number of files under '{prefix_path}': {len(test_files)}")
```
output:
```python
"Number of files under 'assets/csv/': 112"
```
---
### Get from s3 to a specific path url
```python
print(s3.get_file_url(file_name=test_path, expires_in=3600)) # Expires in 3600 seconds (1 hour)
```
output:
```python
"https://bucket_name.s3.amazonaws.com/assets/csv/test.csv"
```
---
## CRUD from S3
### C, U(upload_file, \_write_file)
- Upload files to s3
- The upload_file method reads the file and uploads it to memory, and the `_write_file` method writes the file directly without reading it, so memory usage is small.
- Use `upload_file` to upload a file in your local to the s3 bucket, and use `_write_file` to directly save a variable to s3 in the code.
- The `write_csv`, `write_json`, `write_pkl`, `write_txt`, and `write_parquet` methods call the `_write_file` method to save the file according to the extension.
### R(\_read_file)
- read file from s3
- The `read_csv`, `read_json`, `read_pkl`, `read_txt`, and `read_parquet` methods call the `_read_file` method to read files according to the extension.
- The `read_auto` method calls the above methods according to the extension to read the file.
- The `read_thread` method speeds up the read_auto method by executing it multi-threaded.
### D(delete_file)
- Delete files from s3
### examples
- This example uses the csv extension, but json, pkl, txt, and parquet extensions can be used equally (refer to the above methods for usage).
```python
import pandas as pd
# Save variable to file (write_csv)
test_write_csv = pd.DataFrame({
"test": [
"νκΈ",
"English",
1234,
"!@#$%^&*()_+",
"ππππ»ππΌ"
]
})
# directly save the variable (dataframe)
s3.write_csv(file_name="assets/test/test_write.csv", file_content=test_write_csv, encoding="utf-8", index=False)
# Compress and save in gzip or bzip2 format
s3.write_csv(file_name="assets/test/test_write.csv.gz", file_content=test_write_csv, compression="gzip", encoding="utf-8", index=False)
s3.write_csv(file_name="assets/test/test_write.csv.bz2", file_content=test_write_csv, compression="bz2", encoding="utf-8", index=False)
```
```python
# Read the saved file (read_csv)
print(s3.read_csv.__doc__, end="\n====================\n")
pd.concat([
s3.read_csv(file_name="assets/test/test_write.csv", encoding="utf-8").rename(columns={"test": "Basic format"}),
# Read compressed files in gzip or bzip2 format
s3.read_csv(file_name="assets/test/test_write.csv.gz", encoding="utf-8").rename(columns={"test": "gzip format"}),
s3.read_csv(file_name="assets/test/test_write.csv.bz2", encoding="utf-8").rename(columns={"test": "bzip2 format"})
], axis=1)
```
output:
<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>Basic format</th>
<th>gzip format</th>
<th>bzip2 format</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>νκΈ</td>
<td>νκΈ</td>
<td>νκΈ</td>
</tr>
<tr>
<th>1</th>
<td>English</td>
<td>English</td>
<td>English</td>
</tr>
<tr>
<th>2</th>
<td>1234</td>
<td>1234</td>
<td>1234</td>
</tr>
<tr>
<th>3</th>
<td>!@#$%^&*()_+</td>
<td>!@#$%^&*()_+</td>
<td>!@#$%^&*()_+</td>
</tr>
<tr>
<th>4</th>
<td>ππππ»ππΌ</td>
<td>ππππ»ππΌ</td>
<td>ππππ»ππΌ</td>
</tr>
</tbody>
</table>
</div>
</br>
```python
# Download the saved file locally (download_file)
print(s3.download_file.__doc__, end="\n====================\n")
load_path = os.getcwd()
s3.download_file(file_name="assets/test/test_write.csv", load_path=load_path+"/test_write.csv")
s3.download_file(file_name="assets/test/test_write.csv.gz", load_path=load_path+"/test_write.csv.gz")
s3.download_file(file_name="assets/test/test_write.csv.bz2", load_path=load_path+"/test_write.csv.bz2")
```
```python
# Delete a file on s3 (delete_file)
print(s3.delete_file.__doc__, end="\n====================\n")
print(f"List of files before deletion: {s3.find_files(prefix='assets/test/')}")
s3.delete_file(file_name="assets/test/test_write.csv")
s3.delete_file(file_name="assets/test/test_write.csv.gz")
s3.delete_file(file_name="assets/test/test_write.csv.bz2")
print(f"List of files after deletion: {s3.find_files(prefix='assets/test/')}")
```
output:
```python
"List of files before deletion: ['assets/test/', 'assets/test/test.csv', 'assets/test/test.json', 'assets/test/test.parquet', 'assets/test/test.pickle', 'assets/test/test.pkl', 'assets/test/test.txt', 'assets/test/test_write.csv', 'assets/test/test_write.csv.bz2', 'assets/test/test_write.csv.gz']"
"List of files after deletion: ['assets/test/', 'assets/test/test.csv', 'assets/test/test.json', 'assets/test/test.parquet', 'assets/test/test.pickle', 'assets/test/test.pkl', 'assets/test/test.txt']"
```
</br>
```python
# Upload a file stored locally (upload_file)
print(s3.upload_file.__doc__, end="\n====================\n")
print(f"List of files before upload: {s3.find_files(prefix='assets/test/')}")
s3.upload_file(file_name="assets/test/test_write.csv", file_path=load_path+"/test_write.csv")
s3.upload_file(file_name="assets/test/test_write.csv.gz", file_path=load_path+"/test_write.csv.gz")
s3.upload_file(file_name="assets/test/test_write.csv.bz2", file_path=load_path+"/test_write.csv.bz2")
print(f"List of files after upload: {s3.find_files(prefix='assets/test/')}")
```
output:
```python
"List of files before upload: ['assets/test/', 'assets/test/test.csv', 'assets/test/test.json', 'assets/test/test.parquet', 'assets/test/test.pickle', 'assets/test/test.pkl', 'assets/test/test.txt']"
"List of files after upload: ['assets/test/', 'assets/test/test.csv', 'assets/test/test.json', 'assets/test/test.parquet', 'assets/test/test.pickle', 'assets/test/test.pkl', 'assets/test/test.txt', 'assets/test/test_write.csv', 'assets/test/test_write.csv.bz2', 'assets/test/test_write.csv.gz']"
```
```python
# Delete local files
os.remove(load_path+"/test_write.csv")
os.remove(load_path+"/test_write.csv.gz")
os.remove(load_path+"/test_write.csv.bz2")
```
---
### Methods that use CRUD in various ways
- **`read_auto`**
- A method that executes one of `read_csv`, `read_excel`, `read_json`, `read_parquet`, and `read_pkl` depending on the file extension
- You can automatically find the extension in the file name or specify the extension with the extension argument.<br><br>
- **`read_thread`**
- Execute the `read_auto` method with multi-threading<br><br>
- **`compress`**, **`decompress`**
- Compress and decompress files in s3 bucket and save as files
- Using `_read_file()` method, `_write_file()` method<br><br>
<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>test</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>νκΈ</td>
</tr>
<tr>
<th>1</th>
<td>English</td>
</tr>
<tr>
<th>2</th>
<td>1234</td>
</tr>
<tr>
<th>3</th>
<td>!@#$%^&*()_+</td>
</tr>
<tr>
<th>4</th>
<td>ππππ»ππΌ</td>
</tr>
</tbody>
</table>
</div>
```python
auto_path = s3.find_file(file_name="2022-12", str_contains=True) # File path with filename containing 2022-12
print(f"File path with filename containing 2023-04-30: {auto_path}")
# Just put the folder path as prefix
folder_path = auto_path[:auto_path.rfind('/')] + '/'
print(f"Folder path of the file path: {folder_path}")
print(f"Number of files in the folder: {len(s3.find_files(prefix=folder_path))}")
auto_file = s3.read_thread(prefix=folder_path, encoding="cp949", workers=os.cpu_count(), extension="csv")
print(f"Number of data frames of files in the folder (list type): {len(auto_file)}")
```
output:
```python
"File path with filename containing 2023-04-30: assets/csv/2023-04-30.csv"
"Folder path of the file path: assets/csv/"
"Number of files in the folder: 112"
"Number of data frames of files in the folder (list type): 112"
```
</br>
```python
s3.compress(file_name="assets/test/test_write.csv", compression="gzip")
s3.compress(file_name="assets/test/test_write.csv", compression="bz2")
s3.decompress(file_name="assets/test/test_write.csv.gz")
s3.decompress(file_name="assets/test/test_write.csv.bz2")
```
output:
```python
"The file assets/test/test_write.csv was compressed using gzip and saved as assets/test/test_write.csv.gz."
"The file assets/test/test_write.csv was compressed using bz2 and saved as assets/test/test_write.csv.bz2."
"The file assets/test/test_write.csv.gz was unzipped and saved as assets/test/test_write.csv."
"The file assets/test/test_write.csv.bz2 was unzipped and saved as assets/test/test_write.csv"
```
Raw data
{
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"home_page": "https://github.com/hyoj0942/s3namic",
"name": "s3namic",
"maintainer": "",
"docs_url": null,
"requires_python": "",
"maintainer_email": "",
"keywords": "aws,s3,bucket,management",
"author": "Joey Kim",
"author_email": "hyoj0942@gmail.com",
"download_url": "https://files.pythonhosted.org/packages/6c/ed/83a84a12bdad89d03de6f38d2d25eac7bbcc0e9a2355fe098b36ce7986d8/s3namic-0.0.5.tar.gz",
"platform": null,
"description": "## Installation\n\n`pip install s3namic`\n\n---\n\n## Import module\n\n```python\nfrom s3namic import s3namic\ns3 = s3namic(\n bucket=\"bucket_name\",\n access_key=\"access_key\",\n secret_key=\"secrey_key\",\n region=\"region\",\n)\n```\n\n---\n\n## Check S3 structure in tree form\n\n```python\ns3_tree = s3.make_tree(\n # with_file_name=True, # True if even the file name is included in the tree\n )\n\nimport json\ns3_tree = json.dumps(s3_tree, indent=4, ensure_ascii=False)\nprint(s3_tree)\n```\n\noutput:\n\n```python\n{\n \"assets/\": {\n \"assets/backup/\": {},\n \"assets/batch_raw/\": {\n \"assets/batch_raw/batchData\": {}\n },\n ...\n}\n```\n\n---\n\n## Check S3 structure in list form\n\n```python\ns3_list = s3.list_files()\nprint(s3_list[:5], \"\\n...\\n\", s3_list[-5:])\n```\n\noutput:\n\n```python\n['first_file.json', 'second_file.json', ... ]\n```\n\n### Find a specific file in s3\n\n- ### find_file\n\n```python\ntest_path = s3.find_file(file_name=\"2021-12-31\", str_contains=True)\nprint(f\"2023-04-30 File path containing filename: '{test_path}'\")\n```\n\noutput:\n\n```python\n\"2023-04-30 File path containing filename: 'assets/csv/2023-04-30.csv'\"\n```\n\n- ### find_files\n\n```python\nprefix_path = test_path[:-len(test_path.split(\"/\")[-1])]\ntest_files = s3.find_files(prefix=prefix_path)\nprint(f\"Number of files under '{prefix_path}': {len(test_files)}\")\n```\n\noutput:\n\n```python\n\"Number of files under 'assets/csv/': 112\"\n```\n\n---\n\n### Get from s3 to a specific path url\n\n```python\nprint(s3.get_file_url(file_name=test_path, expires_in=3600)) # Expires in 3600 seconds (1 hour)\n```\n\noutput:\n\n```python\n\"https://bucket_name.s3.amazonaws.com/assets/csv/test.csv\"\n```\n\n---\n\n## CRUD from S3\n\n### C, U(upload_file, \\_write_file)\n\n- Upload files to s3\n- The upload_file method reads the file and uploads it to memory, and the `_write_file` method writes the file directly without reading it, so memory usage is small.\n- Use `upload_file` to upload a file in your local to the s3 bucket, and use `_write_file` to directly save a variable to s3 in the code.\n- The `write_csv`, `write_json`, `write_pkl`, `write_txt`, and `write_parquet` methods call the `_write_file` method to save the file according to the extension.\n\n### R(\\_read_file)\n\n- read file from s3\n- The `read_csv`, `read_json`, `read_pkl`, `read_txt`, and `read_parquet` methods call the `_read_file` method to read files according to the extension.\n- The `read_auto` method calls the above methods according to the extension to read the file.\n- The `read_thread` method speeds up the read_auto method by executing it multi-threaded.\n\n### D(delete_file)\n\n- Delete files from s3\n\n### examples\n\n- This example uses the csv extension, but json, pkl, txt, and parquet extensions can be used equally (refer to the above methods for usage).\n\n```python\nimport pandas as pd\n\n# Save variable to file (write_csv)\ntest_write_csv = pd.DataFrame({\n \"test\": [\n \"\ud55c\uae00\",\n \"English\",\n 1234,\n \"!@#$%^&*()_+\",\n \"\ud83d\ude00\ud83d\udc4d\ud83d\udc4d\ud83c\udffb\ud83d\udc4d\ud83c\udffc\"\n ]\n})\n# directly save the variable (dataframe)\ns3.write_csv(file_name=\"assets/test/test_write.csv\", file_content=test_write_csv, encoding=\"utf-8\", index=False)\n# Compress and save in gzip or bzip2 format\ns3.write_csv(file_name=\"assets/test/test_write.csv.gz\", file_content=test_write_csv, compression=\"gzip\", encoding=\"utf-8\", index=False)\ns3.write_csv(file_name=\"assets/test/test_write.csv.bz2\", file_content=test_write_csv, compression=\"bz2\", encoding=\"utf-8\", index=False)\n```\n\n```python\n# Read the saved file (read_csv)\nprint(s3.read_csv.__doc__, end=\"\\n====================\\n\")\npd.concat([\n s3.read_csv(file_name=\"assets/test/test_write.csv\", encoding=\"utf-8\").rename(columns={\"test\": \"Basic format\"}),\n # Read compressed files in gzip or bzip2 format\n s3.read_csv(file_name=\"assets/test/test_write.csv.gz\", encoding=\"utf-8\").rename(columns={\"test\": \"gzip format\"}),\n s3.read_csv(file_name=\"assets/test/test_write.csv.bz2\", encoding=\"utf-8\").rename(columns={\"test\": \"bzip2 format\"})\n], axis=1)\n```\n\noutput:\n\n<div>\n\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Basic format</th>\n <th>gzip format</th>\n <th>bzip2 format</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>\ud55c\uae00</td>\n <td>\ud55c\uae00</td>\n <td>\ud55c\uae00</td>\n </tr>\n <tr>\n <th>1</th>\n <td>English</td>\n <td>English</td>\n <td>English</td>\n </tr>\n <tr>\n <th>2</th>\n <td>1234</td>\n <td>1234</td>\n <td>1234</td>\n </tr>\n <tr>\n <th>3</th>\n <td>!@#$%^&*()_+</td>\n <td>!@#$%^&*()_+</td>\n <td>!@#$%^&*()_+</td>\n </tr>\n <tr>\n <th>4</th>\n <td>\ud83d\ude00\ud83d\udc4d\ud83d\udc4d\ud83c\udffb\ud83d\udc4d\ud83c\udffc</td>\n <td>\ud83d\ude00\ud83d\udc4d\ud83d\udc4d\ud83c\udffb\ud83d\udc4d\ud83c\udffc</td>\n <td>\ud83d\ude00\ud83d\udc4d\ud83d\udc4d\ud83c\udffb\ud83d\udc4d\ud83c\udffc</td>\n </tr>\n </tbody>\n</table>\n</div>\n\n</br>\n\n```python\n# Download the saved file locally (download_file)\nprint(s3.download_file.__doc__, end=\"\\n====================\\n\")\nload_path = os.getcwd()\ns3.download_file(file_name=\"assets/test/test_write.csv\", load_path=load_path+\"/test_write.csv\")\ns3.download_file(file_name=\"assets/test/test_write.csv.gz\", load_path=load_path+\"/test_write.csv.gz\")\ns3.download_file(file_name=\"assets/test/test_write.csv.bz2\", load_path=load_path+\"/test_write.csv.bz2\")\n```\n\n```python\n# Delete a file on s3 (delete_file)\nprint(s3.delete_file.__doc__, end=\"\\n====================\\n\")\nprint(f\"List of files before deletion: {s3.find_files(prefix='assets/test/')}\")\ns3.delete_file(file_name=\"assets/test/test_write.csv\")\ns3.delete_file(file_name=\"assets/test/test_write.csv.gz\")\ns3.delete_file(file_name=\"assets/test/test_write.csv.bz2\")\nprint(f\"List of files after deletion: {s3.find_files(prefix='assets/test/')}\")\n```\n\noutput:\n\n```python\n\"List of files before deletion: ['assets/test/', 'assets/test/test.csv', 'assets/test/test.json', 'assets/test/test.parquet', 'assets/test/test.pickle', 'assets/test/test.pkl', 'assets/test/test.txt', 'assets/test/test_write.csv', 'assets/test/test_write.csv.bz2', 'assets/test/test_write.csv.gz']\"\n\"List of files after deletion: ['assets/test/', 'assets/test/test.csv', 'assets/test/test.json', 'assets/test/test.parquet', 'assets/test/test.pickle', 'assets/test/test.pkl', 'assets/test/test.txt']\"\n```\n\n</br>\n\n```python\n# Upload a file stored locally (upload_file)\nprint(s3.upload_file.__doc__, end=\"\\n====================\\n\")\nprint(f\"List of files before upload: {s3.find_files(prefix='assets/test/')}\")\ns3.upload_file(file_name=\"assets/test/test_write.csv\", file_path=load_path+\"/test_write.csv\")\ns3.upload_file(file_name=\"assets/test/test_write.csv.gz\", file_path=load_path+\"/test_write.csv.gz\")\ns3.upload_file(file_name=\"assets/test/test_write.csv.bz2\", file_path=load_path+\"/test_write.csv.bz2\")\nprint(f\"List of files after upload: {s3.find_files(prefix='assets/test/')}\")\n```\n\noutput:\n\n```python\n\"List of files before upload: ['assets/test/', 'assets/test/test.csv', 'assets/test/test.json', 'assets/test/test.parquet', 'assets/test/test.pickle', 'assets/test/test.pkl', 'assets/test/test.txt']\"\n\"List of files after upload: ['assets/test/', 'assets/test/test.csv', 'assets/test/test.json', 'assets/test/test.parquet', 'assets/test/test.pickle', 'assets/test/test.pkl', 'assets/test/test.txt', 'assets/test/test_write.csv', 'assets/test/test_write.csv.bz2', 'assets/test/test_write.csv.gz']\"\n```\n\n```python\n# Delete local files\nos.remove(load_path+\"/test_write.csv\")\nos.remove(load_path+\"/test_write.csv.gz\")\nos.remove(load_path+\"/test_write.csv.bz2\")\n```\n\n---\n\n### Methods that use CRUD in various ways\n\n- **`read_auto`**\n - A method that executes one of `read_csv`, `read_excel`, `read_json`, `read_parquet`, and `read_pkl` depending on the file extension\n - You can automatically find the extension in the file name or specify the extension with the extension argument.<br><br>\n- **`read_thread`**\n - Execute the `read_auto` method with multi-threading<br><br>\n- **`compress`**, **`decompress`**\n - Compress and decompress files in s3 bucket and save as files\n - Using `_read_file()` method, `_write_file()` method<br><br>\n\n<div>\n\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>test</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>\ud55c\uae00</td>\n </tr>\n <tr>\n <th>1</th>\n <td>English</td>\n </tr>\n <tr>\n <th>2</th>\n <td>1234</td>\n </tr>\n <tr>\n <th>3</th>\n <td>!@#$%^&*()_+</td>\n </tr>\n <tr>\n <th>4</th>\n <td>\ud83d\ude00\ud83d\udc4d\ud83d\udc4d\ud83c\udffb\ud83d\udc4d\ud83c\udffc</td>\n </tr>\n </tbody>\n</table>\n</div>\n\n```python\nauto_path = s3.find_file(file_name=\"2022-12\", str_contains=True) # File path with filename containing 2022-12\nprint(f\"File path with filename containing 2023-04-30: {auto_path}\")\n# Just put the folder path as prefix\nfolder_path = auto_path[:auto_path.rfind('/')] + '/'\nprint(f\"Folder path of the file path: {folder_path}\")\nprint(f\"Number of files in the folder: {len(s3.find_files(prefix=folder_path))}\")\nauto_file = s3.read_thread(prefix=folder_path, encoding=\"cp949\", workers=os.cpu_count(), extension=\"csv\")\nprint(f\"Number of data frames of files in the folder (list type): {len(auto_file)}\")\n```\n\noutput:\n\n```python\n\"File path with filename containing 2023-04-30: assets/csv/2023-04-30.csv\"\n\"Folder path of the file path: assets/csv/\"\n\"Number of files in the folder: 112\"\n\"Number of data frames of files in the folder (list type): 112\"\n```\n\n</br>\n\n```python\ns3.compress(file_name=\"assets/test/test_write.csv\", compression=\"gzip\")\ns3.compress(file_name=\"assets/test/test_write.csv\", compression=\"bz2\")\ns3.decompress(file_name=\"assets/test/test_write.csv.gz\")\ns3.decompress(file_name=\"assets/test/test_write.csv.bz2\")\n```\n\noutput:\n\n```python\n\"The file assets/test/test_write.csv was compressed using gzip and saved as assets/test/test_write.csv.gz.\"\n\"The file assets/test/test_write.csv was compressed using bz2 and saved as assets/test/test_write.csv.bz2.\"\n\"The file assets/test/test_write.csv.gz was unzipped and saved as assets/test/test_write.csv.\"\n\"The file assets/test/test_write.csv.bz2 was unzipped and saved as assets/test/test_write.csv\"\n```\n",
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