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pandas tutorial

Insert, Delete and Update Command in Pandas 0

Pandas Tutorials

Insert, Delete and Update Command in Pandas

Program 1 Pandas Dataset import pandas as pd emp=pd.read_excel(“D://mypandas/employee.xlsx”) print(emp) # Delete # emp=emp.drop(emp[emp.gender==’male’].index) # # emp=emp.drop(emp[emp.totalsalary==10000].index) # print(emp) # Update # #emp.loc[emp[‘totalsalary’]==7000]=7600 # emp.loc[emp[‘totalsalary’]==15000,’totalsalary’]=25000 # print(“——————————————————–“) # print(emp) # Largest #print(emp.nlargest(5,columns=’totalsalary’).tail(5)) #print(emp.nlargest(10,columns=’HRA’)) #print(emp.columns)...

How to Use SQL in Pandas 0

Pandas Tutorials

How to Use SQL in Pandas

Program 1 Pandas Dataset import pandas as pd emp=pd.read_excel(“D://mypandas/employee.xlsx”) #print(emp.nlargest(5,columns=’totalsalary’)) #print(emp.nlargest(5,columns=’totalsalary’).tail(4)) #Insert # Update # emp.loc[emp[‘totalsalary’]==10000]=15000 # emp.loc[emp[‘totalsalary’]==15000,’totalsalary’]=25000 # print(emp) #Delete #emp=emp.drop(emp[emp.totalsalary==10000].index) print(emp) #print(emp.head(6)) #print(emp.tail(6)) #select * from employee where salary>10000; #print(emp.columns) #select *...

How to Use Join without a Common Column Name in Pandas 0

Pandas Tutorials

How to Use Join without a Common Column Name in Pandas

Program 1 # Advance Data Analysis #Join in DataFrame import pandas as pd df1=pd.DataFrame({’empname’:[‘rahul’,’vijay’,’dipesh’],’Age’:[45,43,54]}) df2=pd.DataFrame({‘citizen’:[‘rahul’,’vijay’,’amit’],’Salary in lakhs ‘:[23,33,28]}) # Outer Join df3=pd.merge(df2,df1,left_on=’citizen’,right_on=’empname’,how=’inner’) print(df1) print(“—————————————-“) print(df2) print(“——————————————-“) print(df3) # Outer Join df3=pd.merge(df1,df2,left_on=’empname’,right_on=’citizen’,how=’outer’) print(df1) print(“—————————————-“) print(df2)...

Concat Function in Pandas 0

Pandas Tutorials

Concat Function in Pandas

Program 1 # Advance Data Analysis #Concat DataFrame import pandas as pd df1=pd.DataFrame({’empname’:[‘Rahul’,’Vijay’,’Dipesh’],’Age’:[45,43,54]}) df2=pd.DataFrame({‘dept’:[‘CSE’,’IT’,’HR’],’Salary in lakhs ‘:[23,33,28]}) df3=pd.DataFrame({‘HRA’:[5000,6000,4000],’TA’:[3000,4000,3400]}) print(“EmpName Age Dept Salary HRA TA”) print(“————————————–“) df4=pd.concat([df1,df2,df3],ignore_index=True,axis=1) print(df4) # df1=pd.DataFrame({’empname’:[‘rahul’,’vijay’,’dipesh’],’Age’:[45,43,54]}) # df2=pd.DataFrame({‘citizen’:[‘rahul’,’vijay’,’amit’],’Salary in lakhs ‘:[23,33,28]})...

Pandas DataFrame where() Method 0

Pandas Tutorials

Pandas DataFrame where() Method

Program 1 Pandas Dataset 1 Pandas Dataset 2 Pandas Dataset 3 import pandas as pd df=pd.read_excel(“D://mypandas/studentinfo.xlsx”) print(df) df.set_index(‘Name’,inplace=True) print(df) print(“——————————————“) df1=df.where(lambda x:x<80,’A+’) print(df1) df1.to_excel(“D://resultdata/agrade.xlsx”) print(“——————————————“) df2=df.where(lambda x:x>80,’B+’) print(df2) df2.to_excel(“D://resultdata/bgrade.xlsx”) print(“——–Success————–“) # print(df) # print(“————————————“)...

Pandas DataFrame aggregate() Method 0

Pandas Tutorials

Pandas DataFrame aggregate() Method

Program 1 # Advance Data Analysis #Aggrigate Functions # max(),min(),avg(),count(),sum(),mod(),mean() import pandas as pd product=[(‘Limca’,20,’Sanchi’),(‘Frooti’,25,’Amul’),(‘Milk’,10,’BK’),(‘Water’,20,’BK’), (‘Limca’,25,’AK’),(‘Frooti’,20,’SK’),(‘Milk’,28,’BK’),(‘Water’,30,’Sanchi’), (‘Limca’,27,’Sanchi’),(‘Frooti’,29,’AK’),(‘Milk’,25,’Amul’),(‘Water’,45,’Sanchi’), (‘Water’,29,’Amul’),(‘Limca’,40,’DK’),(‘Frooti’,12,’Amul’),(‘Water’,40,’Sanchi’) ] #print(product) df=pd.DataFrame(product,columns=[‘Product Name’,’Price’,’Distributor’]) #print(df) df1=df.groupby(‘Product Name’) # for name,rows in df1: # print(name) # print(rows)...

Pandas DataFrame groupby() Method 0

Pandas Tutorials

Pandas DataFrame groupby() Method

Program 1 # Advance Data Analysis #Group by method import pandas as pd product=[(‘Limca’,20,’Sanchi’),(‘Frooti’,25,’Amul’),(‘Milk’,20,’BK’),(‘Water’,20,’BK’), (‘Limca’,25,’AK’),(‘Frooti’,20,’SK’),(‘Milk’,28,’BK’),(‘Water’,30,’Sanchi’), (‘Limca’,27,’Sanchi’),(‘Frooti’,29,’AK’),(‘Milk’,25,’Amul’),(‘Water’,45,’Sanchi’), (‘Water’,29,’Amul’),(‘Limca’,40,’DK’),(‘Frooti’,32,’Amul’),(‘Water’,40,’Sanchi’) ] #print(product) df=pd.DataFrame(product,columns=[‘Product Name’,’Price’,’Distributor’]) df1=df.groupby(‘Product Name’) print(df1.agg([max])) #print(df1[‘Price’].agg([max,min])) #print(df1[‘Product Name’].agg([‘count’])) #print(df1[‘Price’].agg([sum])) # print(df1.get_group(‘Limca’).max()) # print(df1.get_group(‘Frooti’).min()) # df1=df.groupby(‘Distributor’)...

Difference Between loc() and iloc() in Pandas 0

Pandas Tutorials

Difference Between loc() and iloc() in Pandas

Program 1 Pandas Dataset # loc and iloc methods import pandas as pd myfile=”D://mypandas/employee1.xlsx” df=pd.read_excel(myfile) print(df.loc[5,’empname’]) # m=int(input(“Enter starting index value: “)) # n=int(input(“Enter ending index value: “)) #print(df.iloc[m:n,[1,4]]) #print(df.iloc[m:n,[1,2,3]]) # m=int(input(“Enter index value:...

How to Apply Filter in Pandas 0

Pandas Tutorials

How to Apply Filter in Pandas

Program 1 Pandas Dataset # Filter in Python import pandas as pd myfile=”D://mypandas/employee2.xlsx” df=pd.read_excel(myfile) #print(df.loc[(df[‘totalsalary’]>10000)]) # and & or | not ~ #print(df.loc[(df[‘gender’]==’male’) & (df[‘totalsalary’]>10000)]) #print(df.loc[(df[’empdept’]==’CS’) & (df[‘gender’]==’female’)]) #print(df.loc[(df[’empname’].str.contains(‘A’)) | (df[’empname’].str.contains(‘a’))]) #print(df.loc[(df[’empname’].str.startswith(‘Raj’))]) #print(df.loc[~(df[‘totalsalary’]>10000)])  

How to Apply Joins in Pandas 0

Pandas Tutorials

How to Apply Joins in Pandas

Program 1 # Advance Data Analysis #Join in DataFrame import pandas as pd df1=pd.DataFrame({’empname’:[‘rahul’,’vijay’,’dipesh’],’Age’:[45,43,54]}) df2=pd.DataFrame({’empname’:[‘rahul’,’vijay’,’amit’],’Salary in lakhs ‘:[23,33,28]}) print(df1) print(“—————————————-“) print(df2) print(“——————————————-“) # outer join (Full Join) df3=pd.merge(df1,df2,on=’empname’,how=’outer’) print(df3) # right join #df3=pd.merge(df1,df2,on=’empname’,how=’right’) #left...