سال ماه شمسی از میلادی ماه میلادی 1398 فروردین 01/01/1398 2019-03-21 March 1398 اردیبهشت 01/02/1398 2019-04-21 April 1398 خرداد 01/03/1398 2019-05-22 May 1398 تیر 01/04/1398 2019-06-22 June 1398 مرداد 01/05/1398 2019-07-23 July 1398 شهریور 01/06/1398 2019-08-23 August 1398 مهر 01/07/1398 2019-09-23 September 1398 آبان 01/08/1398 2019-10-23 October 1398 آذر 01/09/1398 2019-11-22 November 1398 دی 01/10/1398… Continue reading time
Category: Programming
pandas create new column based on values from other columns
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
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