## Quick CSV
Read and write small or large CSV/TXT files in a simple manner
### Installation
```pip
pip install quick-csv
```
### Examples for small files
Example 1: read and write csv or txt files
```python
from quickcsv.file import *
# read a csv file
list_model=read_csv('data/test.csv')
for idx,model in enumerate(list_model):
print(model)
list_model[idx]['id']=idx
# save a csv file
write_csv('data/test1.csv',list_model)
# write a text file
write_text('data/text1.txt',"Hello World!")
# read a text file
print(read_text('data/text1.txt'))
```
Example 2: create dataframe from a list of models
```python
from quickcsv.file import *
# read a csv file
list_model=read_csv('data/test.csv')
# create a dataframe from list_model
df=create_df(list_model)
# print
print(df)
```
### Examples for large files
Example 1: read large csv file
```python
from quickcsv.largefile import *
if __name__=="__main__":
csv_path=r"umls_atui_rels.csv" # a large file (>500 MB)
total_count=0
def process_partition(part_df,i):
print(f"Part {i}")
def process_row(row,i):
global total_count
print(i)
total_count+=1
list_results=read_large_csv(csv_file=csv_path,row_func=process_row,partition_func=process_partition)
print("Return: ")
print(list_results)
print("Total Record Num: ",total_count)
```
Example 2: query from a large csv file
```python
from quickcsv.largefile import *
if __name__=="__main__":
csv_path=r"umls_sui_nodes.csv" # a large file (>500 MB)
total_count=0
# process each partition in the large file
def process_partition(part_df,i):
print(f"Part {i}")
print()
# process each row in a partition while reading
def process_row(row,i):
global total_count
print(row)
total_count+=1
# field is a field in the csv file, and value is the value you need to find within the csv file
list_results=read_large_csv(csv_file=csv_path, field="SUI",value="S0000004", append_row=True, row_func=process_row,partition_func=process_partition)
print("Return: ")
print(list_results)
print("Total Record Num: ",total_count)
```
Example 3: read top N records from the large csv file
```python
from quickcsv.largefile import *
if __name__=="__main__":
csv_path=r"umls_atui_rels.csv"
total_count=0
# return top 10 rows in the csv file
list_results=read_large_csv(csv_file=csv_path,head_num=10)
print("Return: ")
print(list_results)
print("Total Record Num: ",total_count)
```
### License
The `quick-csv` project is provided by [Donghua Chen](https://github.com/dhchenx).
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"description": "## Quick CSV\n\nRead and write small or large CSV/TXT files in a simple manner\n\n### Installation\n```pip\npip install quick-csv\n```\n\n### Examples for small files\nExample 1: read and write csv or txt files\n```python\nfrom quickcsv.file import *\n# read a csv file\nlist_model=read_csv('data/test.csv')\nfor idx,model in enumerate(list_model):\n print(model)\n list_model[idx]['id']=idx\n# save a csv file\nwrite_csv('data/test1.csv',list_model)\n\n# write a text file\nwrite_text('data/text1.txt',\"Hello World!\")\n# read a text file\nprint(read_text('data/text1.txt'))\n```\nExample 2: create dataframe from a list of models\n```python\nfrom quickcsv.file import *\n# read a csv file\nlist_model=read_csv('data/test.csv')\n# create a dataframe from list_model\ndf=create_df(list_model)\n# print\nprint(df)\n```\n\n### Examples for large files\nExample 1: read large csv file\n```python\nfrom quickcsv.largefile import *\nif __name__==\"__main__\":\n csv_path=r\"umls_atui_rels.csv\" # a large file (>500 MB)\n total_count=0\n\n def process_partition(part_df,i):\n print(f\"Part {i}\")\n\n def process_row(row,i):\n global total_count\n print(i)\n total_count+=1\n\n list_results=read_large_csv(csv_file=csv_path,row_func=process_row,partition_func=process_partition)\n\n print(\"Return: \")\n print(list_results)\n\n print(\"Total Record Num: \",total_count)\n\n```\n\nExample 2: query from a large csv file\n```python\nfrom quickcsv.largefile import *\n\nif __name__==\"__main__\":\n csv_path=r\"umls_sui_nodes.csv\" # a large file (>500 MB)\n total_count=0\n # process each partition in the large file\n def process_partition(part_df,i):\n print(f\"Part {i}\")\n print()\n # process each row in a partition while reading\n def process_row(row,i):\n global total_count\n print(row)\n total_count+=1\n # field is a field in the csv file, and value is the value you need to find within the csv file\n list_results=read_large_csv(csv_file=csv_path, field=\"SUI\",value=\"S0000004\", append_row=True, row_func=process_row,partition_func=process_partition)\n\n print(\"Return: \")\n print(list_results)\n\n print(\"Total Record Num: \",total_count)\n```\n\nExample 3: read top N records from the large csv file\n```python\nfrom quickcsv.largefile import *\n\nif __name__==\"__main__\":\n csv_path=r\"umls_atui_rels.csv\"\n total_count=0\n # return top 10 rows in the csv file\n list_results=read_large_csv(csv_file=csv_path,head_num=10)\n\n print(\"Return: \")\n print(list_results)\n\n print(\"Total Record Num: \",total_count)\n```\n\n### License\n\nThe `quick-csv` project is provided by [Donghua Chen](https://github.com/dhchenx). \n\n\n\n",
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