| Name | py-excel-rs JSON |
| Version |
0.5.3
JSON |
| download |
| home_page | None |
| Summary | Some performant utility functions to convert common data structures to XLSX |
| upload_time | 2024-09-29 12:59:07 |
| maintainer | None |
| docs_url | None |
| author | Carl Voller |
| requires_python | >=3.7 |
| license | MIT |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# excel-rs
An *extremely* fast set of Rust and Python utilities to efficiently convert CSVs to Excel XLSX files.
This library is available as a CLI tool and Python PIP package.
This library was created with the goal of being simple, lightweight, and *extremely* performant. As such, many features such as Excel formatting is not currently supported. This library gives you the quickest possible way to convert a `.csv` file to `.xlsx`.
The Python utilities also gives you the quickest possible way to export a Pandas DataFrame, Numpy 2D array or Postgres database as a `.xlsx` file.
## Python
### Installing
```bash
$ pip install py-excel-rs
```
### Convert a pandas DataFrame to Excel:
```python
import pandas as pd
from py_excel_rs import df_to_xlsx
df = pd.read_csv("file.csv")
xlsx = df_to_xlsx(df)
with open('report.xlsx', 'wb') as f:
f.write(xlsx)
```
### Convert a `csv` file to Excel:
```python
from py_excel_rs import csv_to_xlsx
f = open('file.csv', 'rb')
file_bytes = f.read()
xlsx = csv_to_xlsx(file_bytes)
with open('report.xlsx', 'wb') as f:
f.write(xlsx)
```
### Convert Postgres response to Excel:
```python
import py_excel_rs
conn_string = "dbname=* user=* password=* host=*"
query = "SELECT * FROM table_name"
xlsx = py_excel_rs.pg_to_xlsx(query, conn_string, disable_strict_ssl=False)
with open('report.xlsx', 'wb') as f:
f.write(xlsx)
```
### Build Postgres Query to Excel:
```python
from py_excel_rs import ExcelPostgresBuilder, OrderBy
conn_string = "dbname=* user=* password=* host=*"
builder = ExcelPostgresBuilder(conn_str=conn_string, table_name="my_schema.my_table")
xlsx = builder.select_all()
.exclude(["Unwanted_Column1", "Unwanted_Column2"])
.orderBy("Usernames", OrderBy.ASCENDING)
.execute()
with open('report.xlsx', 'wb') as f:
f.write(xlsx)
```
## Command Line Tool
To install, download the latest release of `cli-excel-rs` for your platform from Github Releases [here](https://github.com/carlvoller/excel-rs/releases?q=cli-excel-rs&expanded=true).
```bash
$ wget https://github.com/carlvoller/excel-rs/releases/download/cli-0.2.0/excel-rs-linux-aarch64.zip
$ unzip excel-rs-linux-aarch64.zip
$ chmod +x ./cli-excel-rs
```
Then simply run the binary:
```bash
$ ./cli-excel-rs csv --in my_csv.csv --out my_excel.xlsx
```
If you would like the build the binary yourself, you can do so using these commands:
```bash
$ git clone https://github.com/carlvoller/excel-rs
$ cargo build --release
$ ./target/release/cli-excel-rs csv --in my_csv.csv --out my_excel.xlsx
```
## Rust
TODO: Add rust documentation
## Benchmarks
With a focus on squeezing out as much performance as possible, **py-excel-rs** is up to **45.5x** faster than `pandas` and **12.5x** faster than the fastest `xlsx` writer on pip.
**cli-excel-rs** also managed to out perform [csv2xlsx](https://github.com/mentax/csv2xlsx?tab=readme-ov-file), the most poopular csv to xlsx tool. It is up to **12x** faster given the same dataset.
These tests used a sample dataset from [DataBlist](https://www.datablist.com/learn/csv/download-sample-csv-files) that contained 1,000,000 rows and 9 columns.
Tests were conducted on an Macbook Pro M1 Max with 64GB of RAM
### Python
#### py-excel-rs (2.89s)
```bash
$ time python test-py-excel-rs.py
python3 test-py-excel-rs.py 2.00s user 0.18s system 99% cpu 2.892 total
```
#### openpyxl (97.38s)
```bash
$ time python test-openpyxl.py
python3 test-openpyxl.py 94.48s user 2.39s system 99% cpu 1:37.38 total
```
#### pandas `to_excel()` (131.24s)
```bash
$ time python test-pandas.py
python3 test-pandas.py 127.99s user 2.75s system 99% cpu 2:11.24 total
```
#### pandas `to_excel(engine="xlsxwriter")` (82.29s)
```bash
$ time python test-pandas-xlsxwriter.py
python3 test-pandas-xlsxwriter.py 76.86s user 1.95s system 95% cpu 1:22.29 total
```
#### xlsxwriter (42.543s)
```bash
$ time python test-xlsxwriter.py
python3 test-xlsxwriter.py 41.58s user 0.81s system 99% cpu 42.543 total
```
#### pyexcelerate (35.821s)
```bash
$ time python test-pyexcelerate.py
python3 test-pyexcelerate.py 35.27s user 0.33s system 99% cpu 35.821 total
```
### Command Line Tools
#### cli-excel-rs (2.756s)
```bash
$ time ./cli-excel-rs csv --in organizations-1000000.csv --out results.xlsx
./cli-excel-rs csv --in organizations-1000000.csv --out 2.69s user 0.07s system 99% cpu 2.756 total
```
#### [csv2xlsx](https://github.com/mentax/csv2xlsx?tab=readme-ov-file) (33.74s)
```bash
$ time ./csv2xlsx --output results.xlsx organizations-1000000.csv
./csv2xlsx --output results.xlsx organizations-1000000.csv 57.63s user 1.62s system 175% cpu 33.740 total
```
### Rust
TODO: Add Rust Benchmark comparisons to rust_xlsxwriter, etc.
Raw data
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"description": "# excel-rs\n\nAn *extremely* fast set of Rust and Python utilities to efficiently convert CSVs to Excel XLSX files.\n\nThis library is available as a CLI tool and Python PIP package.\n\nThis library was created with the goal of being simple, lightweight, and *extremely* performant. As such, many features such as Excel formatting is not currently supported. This library gives you the quickest possible way to convert a `.csv` file to `.xlsx`.\n\nThe Python utilities also gives you the quickest possible way to export a Pandas DataFrame, Numpy 2D array or Postgres database as a `.xlsx` file.\n\n## Python\n\n### Installing\n```bash\n$ pip install py-excel-rs \n```\n\n### Convert a pandas DataFrame to Excel:\n```python\nimport pandas as pd\nfrom py_excel_rs import df_to_xlsx\n\ndf = pd.read_csv(\"file.csv\")\nxlsx = df_to_xlsx(df)\n\nwith open('report.xlsx', 'wb') as f:\n f.write(xlsx)\n```\n\n### Convert a `csv` file to Excel:\n```python\nfrom py_excel_rs import csv_to_xlsx\n\nf = open('file.csv', 'rb')\n\nfile_bytes = f.read()\nxlsx = csv_to_xlsx(file_bytes)\n\nwith open('report.xlsx', 'wb') as f:\n f.write(xlsx)\n```\n\n### Convert Postgres response to Excel:\n```python\nimport py_excel_rs\n\nconn_string = \"dbname=* user=* password=* host=*\"\nquery = \"SELECT * FROM table_name\"\nxlsx = py_excel_rs.pg_to_xlsx(query, conn_string, disable_strict_ssl=False)\n\nwith open('report.xlsx', 'wb') as f:\n f.write(xlsx)\n```\n\n### Build Postgres Query to Excel:\n```python\nfrom py_excel_rs import ExcelPostgresBuilder, OrderBy\n\nconn_string = \"dbname=* user=* password=* host=*\"\nbuilder = ExcelPostgresBuilder(conn_str=conn_string, table_name=\"my_schema.my_table\")\n\nxlsx = builder.select_all()\n .exclude([\"Unwanted_Column1\", \"Unwanted_Column2\"])\n .orderBy(\"Usernames\", OrderBy.ASCENDING)\n .execute()\n\nwith open('report.xlsx', 'wb') as f:\n f.write(xlsx)\n```\n\n## Command Line Tool\nTo install, download the latest release of `cli-excel-rs` for your platform from Github Releases [here](https://github.com/carlvoller/excel-rs/releases?q=cli-excel-rs&expanded=true).\n```bash\n$ wget https://github.com/carlvoller/excel-rs/releases/download/cli-0.2.0/excel-rs-linux-aarch64.zip\n$ unzip excel-rs-linux-aarch64.zip\n$ chmod +x ./cli-excel-rs\n```\nThen simply run the binary:\n```bash\n$ ./cli-excel-rs csv --in my_csv.csv --out my_excel.xlsx\n```\n\nIf you would like the build the binary yourself, you can do so using these commands:\n```bash\n$ git clone https://github.com/carlvoller/excel-rs\n$ cargo build --release\n$ ./target/release/cli-excel-rs csv --in my_csv.csv --out my_excel.xlsx\n```\n\n## Rust\nTODO: Add rust documentation\n\n## Benchmarks\nWith a focus on squeezing out as much performance as possible, **py-excel-rs** is up to **45.5x** faster than `pandas` and **12.5x** faster than the fastest `xlsx` writer on pip.\n\n**cli-excel-rs** also managed to out perform [csv2xlsx](https://github.com/mentax/csv2xlsx?tab=readme-ov-file), the most poopular csv to xlsx tool. It is up to **12x** faster given the same dataset.\n\nThese tests used a sample dataset from [DataBlist](https://www.datablist.com/learn/csv/download-sample-csv-files) that contained 1,000,000 rows and 9 columns.\n\nTests were conducted on an Macbook Pro M1 Max with 64GB of RAM\n\n### Python \n\n#### py-excel-rs (2.89s)\n```bash\n$ time python test-py-excel-rs.py\npython3 test-py-excel-rs.py 2.00s user 0.18s system 99% cpu 2.892 total\n```\n\n#### openpyxl (97.38s)\n```bash\n$ time python test-openpyxl.py\npython3 test-openpyxl.py 94.48s user 2.39s system 99% cpu 1:37.38 total\n```\n\n#### pandas `to_excel()` (131.24s)\n```bash\n$ time python test-pandas.py\npython3 test-pandas.py 127.99s user 2.75s system 99% cpu 2:11.24 total\n```\n\n#### pandas `to_excel(engine=\"xlsxwriter\")` (82.29s)\n```bash\n$ time python test-pandas-xlsxwriter.py\npython3 test-pandas-xlsxwriter.py 76.86s user 1.95s system 95% cpu 1:22.29 total\n```\n\n#### xlsxwriter (42.543s)\n```bash\n$ time python test-xlsxwriter.py\npython3 test-xlsxwriter.py 41.58s user 0.81s system 99% cpu 42.543 total\n```\n\n#### pyexcelerate (35.821s)\n```bash\n$ time python test-pyexcelerate.py\npython3 test-pyexcelerate.py 35.27s user 0.33s system 99% cpu 35.821 total\n```\n\n### Command Line Tools\n\n#### cli-excel-rs (2.756s)\n```bash\n$ time ./cli-excel-rs csv --in organizations-1000000.csv --out results.xlsx\n./cli-excel-rs csv --in organizations-1000000.csv --out 2.69s user 0.07s system 99% cpu 2.756 total\n```\n\n#### [csv2xlsx](https://github.com/mentax/csv2xlsx?tab=readme-ov-file) (33.74s)\n```bash\n$ time ./csv2xlsx --output results.xlsx organizations-1000000.csv\n./csv2xlsx --output results.xlsx organizations-1000000.csv 57.63s user 1.62s system 175% cpu 33.740 total\n```\n\n### Rust\n\nTODO: Add Rust Benchmark comparisons to rust_xlsxwriter, etc.\n",
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