Tag: python

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)

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

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

Pandas V.S SQL

If you knew SQL before and want to migrate to Python, you can use this article. TiTle SQL Pandas Desc Simple SELECT total_bill, tip, smoker, time FROM tips LIMIT ۵; tips[[‘total_bill’, ‘tip’, ‘smoker’, ‘time’]].head(۵)   Where SELECT * FROM tips WHERE time = ‘Dinner’ LIMIT ۵; tips[tips[‘time’] == ‘Dinner’].head(۵)   Multiple conditions SELECT * FROM tips WHERE… Continue reading Pandas V.S SQL