pynut 1.3.3

Function easing life :)

pynut - Laurent Tupin

It provides various functions to simplify the users life.

Installation

You can install the package from PyPI:

python -m pip install pynut

The package is supported on Python 3.7 and above.

How to use

You can call a function as this example:

$ ----------------------------------------------------
>>> from pyNut import nutDate
>>> nutDate.today()

This is the libraries I am using with the package

$ ----------------------------------------------------
>>> beautifulsoup4==4.7.1
>>> exchangelib==4.7.2
>>> openpyxl==3.0.5
>>> pandas==1.1.3
>>> paramiko==2.6.0
>>> psutil==5.9.0
>>> pyodbc==4.0.32
>>> pywin32==303
>>> selenium==3.141.0
>>> xlrd==1.2.0
>>> XlsxWriter==1.3.5
>>> xlwings==0.20.8

To use nutAPI / Selenium Functionnalities:

$ ----------------------------------------------------
# To use Chrome Driver
#  Go to chromedriver.chromium.org
#  download and UnZip the folder
#  Move it to Users/local/bin or C:\ProgramData\Anaconda3\Library\bin (Windows)

Documentation

Temporary documentation for nutDate :

from pyNut import nutDate as dat

dte_date = dat.fDte_formatToDate(dte_date, str_dateFormat = '%d/%m/%Y')
""" fDte_formatToDate makes sure you will have a varable with a date format
The first Argument is the Variable (date), and the format of the string if it is a sting
It allows you to avoid testing the type of the variable and get your get Date anyhow"""

int_dateDiff = dat.fInt_dateDifference(dte_date1, dte_date2)
""" fInt_dateDifference give you the difference in days between 2 dates"""

Date2 = dat.fDte_convertExcelInteger(Date)
""" fDte_convertExcelInteger takes an integer as input, 
This is the integer you can find in Excel when it is a date 
And return the associated date  """

Temporary documentation for nutDataframe :

import pyNut.nutDataframe as dframe

bl_isempty = dframe.fBl_isDataframeEmpty(df_simple)
""" Test if a Dataframe is empty"""

df_simple = dframe.fDf_createSimpleDataframe()
""" Create a simple dataframe to make test"""

bl_compare, df_compare = dframe.fBl_compareDfCol({'df': df_1, 'colJoin': 'colJoin','colToCompare':'data'},
                                                  {'df': df_2,'colJoin': 'colJoin','colToCompare':'data'})
""" compare 2 dataframe one a numeric column by joining the df and returning the difference """

df_1['DataRounded'] = df_1['DataToBeRounded'].apply(lambda x: dframe.round_down(x))
""" Use the Math Function floor() - Able to add a decimals like in Excel
floor() rounds down. int() truncates. 
The difference is clear when you use negative numbers
math.floor(-3.5)    -4
int(-3.5)           -3"""

df_2['DataRounded'] = df_2['DataToBeRounded'].apply(lambda x: dframe.round_up(x))
""" Use the Math Function ceil() - Able to add a decimals like in Excel"""

df_data = dframe.fDf_readCsv_enhanced(path, bl_header = None, str_sep = '|', l_names = range(33))
""" Use the pandas method read_csv
 but resolving Parse Error and will try again after displaying a message 
 Also resolving UnicodeDecodeError by detecting the encoding and trying again accordingly """
 
 df2 = dframe.fDf_removeDoublons(df1)
 """ Remove all rows that are exactly the same"""
 
 df2 = dframe.fDf_DropRowsIfNa_resetIndex(df1, l_colToDropNA = ['col1'])
 """ Drop the rows where all defined columns will be Nan
And reset the index"""

df2 = dframe.dDf_fillNaColumn(df1, 'col2', 'col1')
""" Replace Nan in a column by the value in another column or a Constant """

df2 = dframe.fDf_fillColUnderCondition(df1, 'NameColumnToApply', df1['data'], 'NameColumn Condition', 'YES', bl_except = False)
''' Transform DF with condition
ValueToApply can be a value or a lambda function'''

Temporary documentation for nutFiles :

from pyNut import nutFiles as fl

l_fileList_consti = fl.fL_listFile(str_path_consti)
"" fL_listFile is listing all files in a folder using the library glob """

o_file = fl.fO_readfile_parquet(str_path)
""" fO_readfile_parquet reads parquet - require the libraries : pyarrow / fastparquet"""

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