1. WAP
in Python to plot a quadratic equation using dashed Line Chart.
2. WAP
in Python to plot frequency of marks using Line Chart
Marks=[50,40,50,65,65,75,75,80,80,90,90,90]
3. WAP
in Python to compare the sugar levels among men and women in a city using
histogram.
Men:[113,85,90,150,149,88,93,115,135,80,77,82,129]
Women:[67,98,120,133,150,84,69,89,79,120,112,100]
5. WAPin Python to create a bar chart by using multiple x values on the same chartfor men and women.
men:
(22,30,35,35,26)
women:
(25,32,30,35,29)
6. First
10 terms of Fibonacci series are stored in a list namely fib:
fib = [0, 1, 1, 2, 3, 5, 8, 13, 21,
34]
Write a program to plot Fibonacci
terms and their square-roots with two separate lines on the same plot.
(a) Series should be plotted as a cyan line with ‘o’ markers
having size as 5 and edge-color as red.
(b) The square-root series should be plotted as a black line
with ‘+’ markers having size as 7 and edge-color as red.
[0,
-1, -1, -2, -3, -5, -8, -13, -21, -34, 0, 1, 1, 2, 3, 5, 8, 13, 21, 34]
Write a program to plot nfib with following specifications:
(a) The line color should be magenta
(b) The marker edge color should be black with size 5
(c) Grid should be displayed
8. Write
a program to plot a bar chart from the medals won by the four countries. Make
sure that bars are separately visible.
Country |
Gold |
Silver |
Bronze |
Total |
India |
26 |
20 |
20 |
66 |
Australia |
80 |
59 |
59 |
198 |
England |
45 |
45 |
46 |
136 |
Canada |
10 |
12 |
14 |
36 |
9.
Create a Series using
dictionary.
10. Create a data frame using dictionary.
11. WAP in
Python to sort the pandas DataFrame on the basis of a single column in
ascending order.
12. Create a
Series and access the top 2 and last 2 elements using head() and tail()
methods.
13. WAP to
perform mathematical operation addition, subtraction and multiplication on two
series.
14. WAP to show indexes and axes of a DataFrame.
15. WAP to represent size and shape of the DataFrame.
16. WAP to transpose the values of index and columns in a DataFrame.
18. Consider the saleDf shown below.
|
Target |
Sales |
ZoneA |
56000 |
58000 |
ZoneB |
70000 |
68000 |
ZoneC |
75000 |
78000 |
ZoneD |
60000 |
61000 |
Write a program to rename indexes of
ZoneC and ZoneD as Central and Dakshin respectively and the column names Target
and Sales as Targeted and Achieved respectively.
19. WAP in Python to create a the following DataFrame –
|
Population |
Hospitals |
Schools |
Delhi |
10927986.0 |
189.0 |
7916.0 |
Mumbai |
12691836.0 |
208.0 |
8508.0 |
Kolkata |
46192.0 |
149 |
7226.0 |
Chennai |
4328063.0 |
157 |
7617.0 |
Banglore |
5678097.0 |
1200.0 |
1200.0 |
Create another DataFrame from the
above DataFrame which not contains column ‘Population’ and raw Banglore.
20. Consider the following DataFrame saleDf
|
Target |
Sales |
ZoneA |
56000 |
58000 |
ZoneB |
70000 |
68000 |
ZoneC |
75000 |
78000 |
ZoneD |
60000 |
61000 |
WAP a program to add a column namely
Orders having values 6000, 6700, 6200 and 6000 respectively for the zones A, B,
C and D. The program should also add a new row for a new zone ZoneE. Add some
dummy values in this row.
22. Consider the DataFrame (dfmks)given below.
|
A |
B |
C |
D |
Acct |
99 |
94.0 |
92 |
97.0 |
Eco |
90 |
94.0 |
92 |
97.0 |
Eng |
95 |
89.0 |
91 |
89.0 |
IP |
94 |
NaN |
99 |
95.0 |
Math |
97 |
100.0 |
99 |
NaN |
WAP to print the maximum marks
scored in each subject across all sections.
23. Consider the DataFrame (dfmks)given below.
|
A |
B |
C |
D |
Acct |
99 |
94.0 |
92 |
97.0 |
Eco |
90 |
94.0 |
92 |
97.0 |
Eng |
95 |
89.0 |
91 |
89.0 |
IP |
94 |
NaN |
99 |
95.0 |
Math |
97 |
100.0 |
99 |
NaN |
WAP to print the maximum marks
scored in a section, across all subjects.
24. WAP to calculate mode for each subject and each section in DataFrame dfmks.
|
A |
B |
C |
D |
Acct |
99 |
97 |
92 |
97 |
Eco |
94 |
94 |
92 |
97 |
Eng |
95 |
89 |
91 |
89 |
IP |
94 |
87 |
99 |
94 |
Math |
97 |
87 |
99 |
99 |
25. WAP to calculate median and mean for each subject in DataFrame dfmks.
|
A |
B |
C |
D |
Acct |
99 |
97 |
92 |
97 |
Eco |
94 |
94 |
92 |
97 |
Eng |
95 |
89 |
91 |
89 |
IP |
94 |
87 |
99 |
94 |
Math |
97 |
87 |
99 |
99 |
Part – 2 MYSQL
SQL Practical
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