Welcome to DP Learn Python’s documentation!¶

Quickstart¶

Installation¶

$ pip install dplearn

Or, you can download the source and

$ python setup.py install

Add sudo in the beginning if you met problem.

User Guide¶

Overview of dplearn¶

Why Should I Use This?¶

This is a Python package for data analysis which contains some very useful tools and functions.

The package is contiually updated.

Modules¶

tools

Useful functions helping you to speed up your programming.
math

Functions that realated to math calculation.
extra

Some other functions that are very useful in specific situations.

Usage¶

Use As A Python Module¶

from dplearn.tools import clean
data = clean(data)
print(data)

Documentation¶

Check out the latest dplearn documentation at Read the Docs

Contributing¶

Please send pull requests, very much appriciated.

Fork the repository on GitHub.
Make a branch off of master and commit your changes to it.
Create a Pull Request with your contribution

Tutorial¶

Tutorial on module dplearn.quant¶

wrapKLine¶

This is a function of wrapping K-Line dataframe into longer-duration one.

import dplearn.data_sample as ds
from dplearn.quant import wrapKLine

data = ds.kLine()
open_c = "open"
close_c = "close"
high_c = "high"
low_c = "low"
vol_c = "vol"
ts_c = "time"
ts_format = "%Y-%m-%d %H:%M:%S"
wrap = "1h"

df_new = wrapKLine(data, open_c, close_c, high_c, low_c, vol_c, ts_c, ts_format, wrap)

print(df_new.head())

Tutorial on module dplearn.tools¶

continue_check¶

from dplearn.tools import continue_check
check_value = continue_check("Do you want to continue this procedure? [Y/n]: ")

if check_value in ['n', 'N']:
   break
else:
   pass