Transforming a pre-formatted excel report into a tabular format without macros — Reporting system part 2

# Import libraries
import pandas as pd
import datetime
from os import listdir
from os.path import isfile, join
import re
import time
from airflow.models import VariableRAW_DATA_LOCATION = Variable.get('raw_data_location')
STAGING_DATA_LOCATION = Variable.get('staging_data_location')
# Set Pandas to display 500 rows
pd.set_3('display.max_rows', 500)
# Set Folder to use
folder = RAW_DATA_LOCATION
def cleanfiles(folder):
#List files in folder
folderfiles = [f for f in listdir(folder) if isfile(join(folder, f))]
files = []
#Remove hidden files
for file in folderfiles:
if not file.startswith('~$'):
files.append(file)
#Return only excel files
files = [file for file in files if file.endswith('xlsx')]
return files
def cleandata(folder):
df_list = []
for file in cleanfiles(folder): #Read in excel files
df = pd.read_excel(folder+'/'+file, skiprows=9)
#Drop empty column
df = df.drop('Unnamed: 0',axis=1)
# Drop all empty rows
df = df.dropna(how='all',axis=0)
# Fill NAN cells with 0
df['18'].fillna(0, inplace=True)
df['19'].fillna(0, inplace=True)
# Drop any remaining rows with NAN values in any cell
df = df.dropna(how='any', axis=0)
# Drop duplicate rows ( This eliminates the duplicate headers from the separate files)
df = df.drop_duplicates(keep=False)
#Add date in new column
date_time_str = file.split('.')[0]
filedate = datetime.datetime.strptime(date_time_str, '%Y-%m-%d')
df['date'] = filedate
# Rename and change types
df['2'] = df['2'].astype('int64')
df['3'] = df['3'].astype('int64')
df['4'] = df['4'].astype('int64')
df['7'] = df['7'].astype('float')
df['8'] = df['8'].astype('float')
df['9'] = df['9'].astype('int64')
df['10'] = df['10'].astype('float')
df['11'] = df['11'].astype('int64')
df['12'] = df['12'].astype('int64')
df['13'] = df['13'].astype('int64')
df['14'] = df['14'].astype('int64')
df['15'] = df['15'].astype('int64')
df['16'] = df['16'].astype('int64')
df['17'] = df['17'].astype('int64')
df['18'] = df['18'].astype('int64')
df['19'] = df['19'].astype('int64')
df.rename(columns =
{'12':'9-1'
,'13':'9-2'
,'14':'9-3'
},inplace=True)
#Append to list of data frames
df_list.append(df)
#Concatenate newly clean dataframes into one large dataframe
df = pd.concat(df_list)
return df
def category_df(folder):
category_df_list = []
def hasNumbers(inputString):
return bool(re.search(r'\d', inputString))
def isDept(x):
# 1 refers to column 1 which is the sub-category
if x =='1':
return True
def category_df(folder):
category_df_list = []
def hasNumbers(inputString):
return bool(re.search(r'\d', inputString))
def isDept(x):
if x =='1':
return True
for file in cleanfiles(folder):
#Read in excel files
categorydf = pd.read_excel(folder+'/'+file, skiprows=6)
categorydf = categorydf['Unnamed: 1'].reset_index()
categorydf = categorydf.fillna(method='ffill')
categorydf.drop('index',axis=1, inplace=True)
#Add column to determine if column has integer in string
categorydf['bool'] = categorydf['Unnamed: 1'].map(lambda x: hasNumbers(x))
#If the column contains an integer use this value as the column value for the category column
categorydf['Category'] = categorydf.apply(lambda x: x['Unnamed: 1'] if x['bool'] ==True else None, axis=1 )
#forward fill categories to associate the sub category with the category
categorydf.fillna(method='ffill', inplace=True)
#Create boolean column to decide if the column is the column header
categorydf['isDept?'] = categorydf['Unnamed: 1'].map(lambda x: isDept(x))
categorydf.drop_duplicates(inplace=True)
#Filter out any rows with the sub-category header
mask = (categorydf['bool'] ==False) & (categorydf['isDept?'] != True)
categorydf = categorydf[mask]
categorydf = categorydf.copy()
categorydf.rename(columns={'Unnamed: 1': '1'}, inplace=True)
categorydf['Category'] = categorydf['Category'].map(lambda x: re.sub(r'\d+', '', x))
categorydf['Category'] = categorydf['Category'].map(lambda x: x[1:])
categorydf.drop(['bool','isDept?'], axis=1,inplace=True)
category_df_list.append(categorydf)
category_df = pd.concat(category_df_list)
category_df.drop_duplicates(keep='first',inplace=True)
#Final result with department names and categories side by side as a look up table category_df = category_df[['Category','1']].sort_values(by='Category', ascending=True).reset_index().drop('index',axis=1)
return category_df
df = cleandata(folder)df.to_csv(STAGING_DATA_LOCATION+'sales_stage.csv')categorydf = category_df(folder)
categorydf.to_csv(STAGING_DATA_LOCATION+'category_stage.csv')
# Import libraries
import pandas as pd
import datetime
from os import listdir
from os.path import isfile, join
from airflow.models import Variable
def dim_status(df):
current_status = df['6'].drop_duplicates().reset_index().drop('index',axis=1)
current_status['Status_SK'] = range(1, len(current_status.index)+1)
current_status = current_status[['Status_SK','6']]
return current_status
def to_integer(dt_time):
return 10000*dt_time.year + 100*dt_time.month + dt_time.day
def create_date_table(df):
start = df['date'].min()
end = df['date'].max()
df = pd.DataFrame({"Date": pd.date_range(start, end)})
df['Date_SK'] = df['Date'].map(lambda x: to_integer(x))
df["Day"] = df.Date.dt.day_name
df["Week"] = df.Date.dt.weekofyear
df["Quarter"] = df.Date.dt.quarter
df["Year"] = df.Date.dt.year
df["Year_half"] = (df.Quarter + 1) // 2
date_table = df
cols = ['Date_SK','Date','Day','Week','Quarter','Year','Year_half']
date_table = date_table[cols]
return date_table
def dim_item(df):
mask = ['5','1','4','2','3']
df2 = df[mask].drop_duplicates().sort_values(by='5')
df2['Item_SK'] = range(1, len(df2.index)+1)
Item = df2[['Item_SK','5','1','4','2','3']]
Item = Item.merge(category_df,left_on='1', right_on='1', how='left')
Item_codes = Item['3']
Item_urls = []
for code in Item_codes:
URL = 'https://www.company.com/uk/p/{}'.format(code)
Item_urls.append(URL)
Image_urls = []
for code in Item_codes:
URL = 'https://media2.companyassets.com/i/company/{}/'.format(code)
Image_urls.append(URL)
Item['ItemURL'] = Item_urls
Item['ImageURL'] = Image_urls
return Itemdef sales(df):
df = df.merge(Status, left_on='6',right_on='6', how='left').drop('6', axis=1)
cols_to_use = Item.columns.difference(df.columns)
Item[cols_to_use]
columns = ['2','3','5','4','1']
sales = df.merge(Item, left_on=columns, right_on=columns, how='left').drop(columns,axis=1)
sales.rename(columns={'date':'Date'}, inplace=True)
sales['Date']= pd.to_datetime(sales['Date'])
Date_table = Date[['Date_SK','Date']]
sales = sales.merge(Date_table,left_on='Date',right_on='Date')
cols = ['Item_SK','Status_SK','Date_SK','7', '8', '9',
'10', '11', '9-1', '9-2',
'9-3', '',
'16', '17', '18',
'19' ]
sales = sales[cols]
return salesSTAGING_DATA_LOCATION = Variable.get('staging_data_location')
STAR_SCHEMA_LOCATION = Variable.get('star_schema_location')
staging_df = pd.read_csv(STAGING_DATA_LOCATION+'sales_stage.csv')
category_df = pd.read_csv(STAGING_DATA_LOCATION+'category_stage.csv')
Status = dim_status(staging_df)
Item = dim_item(staging_df)
Date = create_date_table(staging_df)
Sales = sales(staging_df)
table_dict = {'Status':Status,'Item':Item,'Date':Date,'Sales':Sales}for name,table in table_dict.items():
table.to_csv(STAR_SCHEMA_LOCATION+name+'.csv')

Love podcasts or audiobooks? Learn on the go with our new app.

Recommended from Medium

6 Tips for a Successful Developer Career

Image of female at computer.

Unit testing OpenAPI in Laravel

Dell Hiring Freshers For Software Test Engineer Position | Apply Now

HackTheBox-Armageddon

Mutable, Immutable… Everything is an Object!

Google Syntax

Creating a New User, an S3 Bucket and granting the user access to the S3.

Enhance your SEO with Drupal Semantic Web

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Alexander

Alexander

More from Medium

How to setup Streamlit in minutes, your question answered here.

Meet The Team: Nick Wong

Innovation of Data Integration Technology in the Intelligent Era

How to Use Python & SQL to Append New Rows to a BigQuery Table Without Overwriting Your Data

A python, coiled.