Category: Buiness Analysis

Install Python and Jupyter Notebook to Windows 10 (64 bit)

This blog post is a step-by-step tutorial to install Python and Jupyter Notebook to Windows 10 (64 bit). Python 3.3 or greater, or Python 2.7 is required to install the Jupyter Notebook. Download Python 3.7.4 from “https://www.python.org/downloads/release/python-374/” url 2. Choose and select “x86–64 executable installer” for Windows 10–64 bit computer 3. Select location to save the executable… Continue reading Install Python and Jupyter Notebook to Windows 10 (64 bit)

محاسبه حداکثر میزان تخفیف قابل پرداخت به مشتریان جهت بازگشت

فرمول محاسبه (از نظر ما) اینه:‌
حداکثر تخفیف قابل تخصیص = میانگین درآمد از هر سفارش + احتمال بازگشت‌های بیشتر * متوسط تعداد خریدهای بعدی * میانگین درآمد از هر سفارش

رفع خطای اتصال به سرور MDS توسط افزونه Excel

مطمئن بشید ویندوزتون آپدیته
ببینید سرور روی نام دامنه یا نام سیستم شما فیلتر شده یا نه.
Control Panel\All Control Panel Items\Credential Manager\Add a windows credential \
یادتون نره با دامنه مربوطه یوزرنیم رو وارد کنید. چون در اصل دارید توی سرور مربوطه این یوزر رو وارد می‌کنید.
تمام!!

Selecting Subsets of Data in Pandas

from What Code single column  df[‘food’] multiple columns df[[‘color’, ‘food’, ‘score’]] single row df.loc[‘Niko’] multiple rows df.loc[[‘Niko’, ‘Penelope’]] slice notation to select a range of rows df.loc[‘Niko’:’Dean’]   df.loc[:’Aaron’] stepping by 2 df.loc[‘Niko’:’Christina’:2] rows and columns df.loc[row_selection, column_selection]   df.loc[‘Jane’:’Penelope’, [‘state’, ‘color’]] single row df.iloc[3] multiple rows df.iloc[[5, 2, 4]]   df.iloc[3:5]   df.iloc[[2,3], [0,… Continue reading Selecting Subsets of Data in Pandas

python, Pandas Categorize the range

df[‘PriceBin’] = pd.cut(df[‘PriceAvg’], bins = 3)df[‘PriceBin’].value_counts() (54060.0, 2040000.0] 209 (2040000.0, 4020000.0] 4 (4020000.0, 6000000.0] 1 Name: PriceBin, dtype: int64 df[‘PriceBin’] = pd.qcut(df[‘PriceAvg’], q=3) df[‘PriceBin’].value_counts().sort_index() (59999.999, 210000.0] 77 (210000.0, 315000.0] 66 (315000.0, 6000000.0] 71 Name: PriceBin, dtype: int64 h = df.groupby(‘PriceBin’, as_index=False).median()[‘SalesAvg’] h = pd.DataFrame(h) h.reset_index(inplace=True) h PriceBin SalesAvg0(59999.999, 210000.0] 42.0000001(210000.0, 315000.0] 145.1666672(315000.0, 6000000.0] 114.200000

Useful Python pandas codes

– To Rename the data framedf.rename(columns={“contract_id”:”deal_id”},inplace=True) – Where statement tips[tips[‘time’] == ‘Dinner’].head(۵) – vlookupmg = pd.merge(df,AgReg,on=”deal_id”,how=”left”) – choose the first column of an array or first part of a string with a delimitter df[“cat”] = df[“CategoryID”].str.split(‘,’,1).str[0] – filling na or nan or Null values df[“CategoryID”].fillna(“”,inplace=True) – Convert To date time pd.to_datetime(df[“start_date”],errors=’ignore’) combination of where and select some.… Continue reading Useful Python pandas codes

Process mining – Introduction 2

Case ID Activity Name Time Stamp Play out: A possible scenario Play in: simple process allowing for 4 traces Replay Process mining: Discovery Conformance Enhancement Machine learning: Supervised learning: response variable that labels each instance (we labeled each data and the machine will learn from that) Classification: classify to predict (i.e. decision tree) Regression: final… Continue reading Process mining – Introduction 2