1.
#Add New Column in DataFrame using df['columnname']
import pandas as pd
D={"Name":["Ram","Raj","Sam","John","Amit"],"Roll No.":[10,20,30,40,50]}
df=pd.DataFrame(D)
print(df)
df["IP"]=[25,30,32,21,22]
df["Maths"]=[22,20,22,31,32]
df["Total"]=df["IP"]+df["Maths"]
df["Pecentage"]=df["Total"]*100/70
print(df)
Output:
Name Roll No.
0 Ram 10
1 Raj 20
2 Sam 30
3 John 40
4 Amit 50
Name Roll No. IP Maths Total Pecentage
0 Ram 10 25 22 47 67.142857
1 Raj 20 30 20 50 71.428571
2 Sam 30 32 22 54 77.142857
3 John 40 21 31 52 74.285714
4 Amit 50 22 32 54 77.142857
2.
#Add New Column in DataFrame using insert() function
import pandas as pd
D={"Name":["Ram","Raj","Sam","John","Amit"],"Roll No.":[10,20,30,40,50]}
df=pd.DataFrame(D)
print(df)
df.insert(2,"IP",[25,30,32,21,22])
df.insert(3,"Maths",[22,20,22,31,32])
df["Total"]=df["IP"]+df["Maths"]
df["Pecentage"]=df["Total"]*100/70
print(df)
Output:
Name Roll No.
0 Ram 10
1 Raj 20
2 Sam 30
3 John 40
4 Amit 50
Name Roll No. IP Maths Total Pecentage
0 Ram 10 25 22 47 67.142857
1 Raj 20 30 20 50 71.428571
2 Sam 30 32 22 54 77.142857
3 John 40 21 31 52 74.285714
4 Amit 50 22 32 54 77.142857
3.
Access a column from DataFrame using df['columnname'] and df.columnname
import pandas as pd
D={"Name":["Ram","Raj","Sam","John","Amit"],"Roll No.":[10,20,30,40,50], \
"IP":[25,30,32,21,22]}
df=pd.DataFrame(D)
print(df)
print(df['IP'])
print(df.IP)
Output:
Name Roll No. IP
0 Ram 10 25
1 Raj 20 30
2 Sam 30 32
3 John 40 21
4 Amit 50 22
0 25
1 30
2 32
3 21
4 22
Name: IP, dtype: int64
0 25
1 30
2 32
3 21
4 22
Name: IP, dtype: int64
4.
#selecting a column from a DataFrame using iloc
import pandas as pd
D={"Name":["Ram","Raj","Sam","John","Amit"],"Roll No.":[10,20,30,40,50], \
"IP":[25,30,32,21,22]}
df=pd.DataFrame(D)
print(df)
print(df['IP'])
print(df['Roll No.'])
print (df.iloc[:,[1,2]])
Output
==========
Name Roll No. IP
0 Ram 10 25
1 Raj 20 30
2 Sam 30 32
3 John 40 21
4 Amit 50 22
0 25
1 30
2 32
3 21
4 22
Name: IP, dtype: int64
0 10
1 20
2 30
3 40
4 50
Name: Roll No., dtype: int64
Roll No. IP
0 10 25
1 20 30
2 30 32
3 40 21
4 50 22
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