In this article Important QnA iterrows iteritems class 12 IP, you will get some important questions based on the topic iterrows(), iteritems(), insert index, row and column for class 12 IP. 3
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Important QnA iterrows iteritems class 12 IP
Let us begin this article QnA iterrows iteritems class 12 with short answer questions.
Short Answer questions / Conceptual Questions
Q 1. Explain iterrows() function with example.
Ans.: The iterrows() function is used to iterate values by rows indexes. For example, Our school subject wise highest marks of the past 3 years are considered in the following data frame and accessed row-wise with the help of iterrows() function.
import pandas as pd
hm_past3={2018:{'English':98,'Physics':99,'Chemistry':100,
'Biology':100,'Maths':100,'CS':100},
2019:{'English':99,'Physics':97,'Chemistry':99,
'Biology':100,'Maths':100,'CS':99},
2020:{'English':97,'Physics':98,'Chemistry':98,
'Biology':98,'Maths':99,'CS':96},
}
df1=pd.DataFrame(hm_past3)
for i, row in df1.iterrows():
print("-----------------------")
print(row)
Output
Q – 2 Print the text in front of Rows like ‘highest marks in [Year]” and column heading “Subject:[Subject]”
Ans.:
import pandas as pd
hm_past3={2018:{'English':98,'Physics':99,'Chemistry':100,
'Biology':100,'Maths':100,'CS':100},
2019:{'English':99,'Physics':97,'Chemistry':99,
'Biology':100,'Maths':100,'CS':99},
2020:{'English':97,'Physics':98,'Chemistry':98,
'Biology':98,'Maths':99,'CS':96},
}
df1=pd.DataFrame(hm_past3)
for i, row in df1.iterrows():
print("-----------------------")
print("Subject:",i)
y=2018
for vl in row:
print("Highest marks of ",y,":",vl)
y += 1
Output
Q -3 Write a program to print each data value along with its row index and column name using iterrows(). [Consider the same data given in the above example]
import pandas as pd
hm_past3={2018:{'English':98,'Physics':99,'Chemistry':100,
'Biology':100,'Maths':100,'CS':100},
2019:{'English':99,'Physics':97,'Chemistry':99,
'Biology':100,'Maths':100,'CS':99},
2020:{'English':97,'Physics':98,'Chemistry':98,
'Biology':98,'Maths':99,'CS':96},
}
df1=pd.DataFrame(hm_past3)
for i, row in df1.iterrows():
print("-----------------------")
ix=row.index
a = 0
for vl in row:
print("[",i,"][",ix[a],"]:",vl)
a += 1
Output:
Q – 4 Write a program to iterate over a dataframe and print data using iteritems(). [Consider data given above]
import pandas as pd
hm_past3={2018:{'English':98,'Physics':99,'Chemistry':100,
'Biology':100,'Maths':100,'CS':100},
2019:{'English':99,'Physics':97,'Chemistry':99,
'Biology':100,'Maths':100,'CS':99},
2020:{'English':97,'Physics':98,'Chemistry':98,
'Biology':98,'Maths':99,'CS':96},
}
df1=pd.DataFrame(hm_past3)
for i, col in df1.iteritems():
print("-----------------------")
print(col)
Output
Q – 5 In the given dataframe customer’s records stored. Write a program to calculate the total purchase done by them and assigned a category like “Gold”, “Silver” and “Platinum”. Consider the following criteria to assign a category.
bill_amt 0 to 1000 👉 Platinum
bill_amt 1001 to 5000 👉 Silver
bill_amt >5001 👉 Gold
import pandas as pd
def dframe():
dt = {'cname':['Aman','Chaman','Baman'],
'Jan':[1100,3500,2500],
'Feb':[100,500,200],
'March':[700,250,600]
}
cust_df=pd.DataFrame(dt)
for (i, r) in cust_df.iterrows():
t_amt=r['Jan']+r['Feb']+r['March']
if(t_amt>0 and t_amt<=1000):
cat='Platinum'
elif(t_amt>1000 and t_amt<=3000):
cat='Silver'
elif(t_amt>3000 and t_amt<=5000):
cat='Gold'
print(r['cname'],":",t_amt,"-",cat)
Output:
Q – 6 Consider the above data and do the following:
(a) Add new row at the last: Jaman, 2300, 500, 600
(b) Add these three rows: [Gaman, 1100, 300, 1200] – [Kaman,2200,700,400] – [Laman,1200,1800,750]
(c) Add this data at to row number 5: [Naman,900,1300,700]
(d) Add column with title “total” at right side with total amount
(e) Add column with title “City” after name
Ans:
(a) cust_df=cust_df.append({‘Cname’:’Jaman’,’Jan’:2300,’Feb’:500,’March’:600})
(b) To add multiple rows, first generate list of series with data and set dataframe columns as index of series. Then use append method. Look at the following code:
new_cols=[pd.Series([‘Gaman’,1100,300,1200],index=cust_df.columns),pd.series[‘Kaman’,2200,700,400],index=cust_df.columns),[‘Laman’,1200,1800,750],index=cust_df.columns)]
cust_df=df.append(new_cols,ignore_index=True)
(c) cust_df.loc[5]=[‘Naman’,900,1300,700]
(d) cust_df[‘Total’]=t_amt
Note: Considered previous code for total
(e) cust_df.insert(2,”City”,[“Ahmedabad”,”Baroda”,”Surat”,”Bharuch”],True)
Note: Add the value as much as the data you have in the dataframe.
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