kstreepy

def gs_group(df):
    gs_dict = {'GS-1' : 'GS 1-6',
                'GS-2' : 'GS 1-6',
                'GS-3' : 'GS 1-6',
                'GS-4' : 'GS 1-6',
                'GS-5' : 'GS 1-6',
                'GS-6' : 'GS 1-6',
                'GS-7' : 'GS 7-9',
                'GS-8' : 'GS 7-9',
                'GS-9' : 'GS 7-9',

kstreepy

import os, gzip, shutil

dir_name = 'x'

def gz_extract(directory):
    extension = ".gz"
    os.chdir(directory)
    for item in os.listdir(directory): # loop through items in dir
      if item.endswith(extension): # check for ".gz" extension
          gz_name = os.path.abspath(item) # get full path of files

kstreepy

import pandas as pd

def quick_melt(wide_df):
    '''
    Take wide dataframe and melt to long. Declare ID Columns (id_cols)
    and then establish all remaining columns as value columns
    '''

    id_cols = {'A', 'B', 'C'}
    value_cols = set(wide_df.columns) - id_cols

kstreepy

import pandas as pd
import os
import glob

def read_multi_csv(path):
    '''
    Given a file path with wildcard and extension, parse all files with that extension in directory 
    into a single dataframe.
    '''
    

kstreepy

import pandas as pd
import os
import glob

def read_multi_excel(path):
    '''
    Given a file path with wildcard and extension, parse all files with that extension in directory 
    into a single dataframe.
    '''
    

kstreepy

import pandas as pd
import glob

def read_multi_csv(path):
    '''
    Given a file path with wildcard and extension, parse all files with that extension in directory 
    into a single dataframe.
    '''
    

kstreepy

import pandas as pd
import glob

def read_multi_excel(path):
    '''
    Given a file path with wildcard and extension, parse all files with that extension in directory 
    into a single dataframe.
    '''
    
    all_files = glob.glob(path)