如何在 Pandas 中一起使用 groupby、select、count(*) 和 SQL 的命令

Question

I am new to writing SQL queries in python.

I have an SQL query like this.

 select Category, Date, count(*) as Uniq, sum(FCnt) as Total,
 sum(FCnt)/count(*) as RepRatio, Mod,Act,Exp, Sel,
 Bias,Sel_B,Bias_B,Bias_P,Con_Num, Sel_Str,CG_D,CGM,TC,P_Value from
 FCntt_Table where Sel_B=Bias_B group by
 Mod,Act,Exp,Bias_P,Con_Num,CG_D order by RepRatio desc, Uniq desc;

I am trying to convert this query into a python code so that i can perform the operation done by this query using python. I came across options using Pandas.

I have the SQL table as.csv format.

The code I have written is

import pandas as pd
import numpy as np

tips=pd.read_csv("fc.csv")


tips["Total"]=tips.groupby(['Mod','Act','Exp','Bias_P','Con_Num','CG_D'])["FCnt"].transform("sum")

tips[tips['Sel_B'] == tips['Bias_B']]
print tips.groupby(['Mod','Act','Exp','Bias_P','Con_Num','CG_D']).agg({'Uniq':np.size})

print tips.head(5)

But this gives me error for Uniq. Kindly help me with this code.

Sample Data: (Provided by OP in comments)

Date,Category,FCnt,TC,Mod,Con_Num,SC,Sel_P,Bias_P,Sel_B,Bias_B,Act,Exp,CG_D,CGM,P_val
20200622,T1,5,RE,649,SB3,01,0,0,0,1,0,GP2,cg1,0,Pattern1
20200622,T1,1,RE,649,SB3,10,1,0,0,1,0,GP2,cg2,0,pattern2
20200622,T1,4,RE,649,SB3,11,0,0,0,1,0,GP2,cg1,0,pattern1
20200622,T1,4,RE,649,SB3,11,1,0,0,1,0,GP2,cg1,0,pattern1

Answer 1

As you want to take the sum and count of the group, I have used aggregation function in different way ie, .agg({'FCnt':(np.sum, np.size)}) .

Code:

tips=pd.read_clipboard(sep=',')
# filtered_tips = tips[tips['Sel_B'] == tips['Bias_B']] # In given sample data, there is zero records after filter.

# So, considering original df
tips.groupby(['Mod','Act','Exp','Bias_P','Con_Num','CG_D']).agg({'FCnt':(np.sum,np.size)})
tips.columns = ['Total', 'Count']

---

Output:

print(group_df.reset_index())

   Mod  Act  Exp  Bias_P Con_Num CG_D  Total  Count
0  649    0  GP2       0     SB3  cg1     13      3
1  649    0  GP2       0     SB3  cg2      1      1