Python Pandas - 2 Type B Sumita Arora Class 12 Information Practices Solution

Python Pandas - 2 || Type B || Sumita Arora || Class 12 || Information Practices || Solution

Note :-  Please Click on Question to Answer of that Question !!!

Q1. How do you iterate over a DataFrame?

Q2. What are binary operations? Name the functions that let you perform binary operations on a DataFrame.

Q3. What is descriptive statistics? Name Pandas descriptive statistics functions.

Q4. The info() and describe() are said to be inspection functions. What do they do?

Q5. What is the difference between sum and cumulative sum? How do you perform the two on DataFrame?

Q6. Name and explain some cumulative functions provided by Pandas.

Q7. The head() and tail() extract rows or columns from a dataframe. Explain.

Q8. Why does Python change the datatype of a column as soon as it stores an empty value (NaN) even though it has all other values stored as integer?

Q9. What do quantile and var() functions do?

Q10. What is a quartile? How is it different from quantile?

Q11. How do you create quantiles and quartiles in Python Pandas?

Q12. What is Pivoting? How is it useful?

Q13. How is pivot_table() different from pivot() when both perform pivoting ?

Q14. Assume that required libraries (Pandas and Numpy) are imported and dataframe ndf has been created as shown in photo.

Predict the output produced by following code fragment:

print (ndf[ndf["age"] > 30])

print(ndf.head(2))

print(ndf.tail(3))

Q15. Given the two dataframes as:
In [126]: dfc1

Out[126] :

    0  1

0  2  a

1  3  b

2  4  c
In [130]: dfc2

Out[130] :

    0  1  2

0  2  a  4

2  p  q  r

Why are following statements giving errors?
(a) print(dfc1 + dfc2)

(b) print(dfc1. sub(dfc2))

(c) print(dfc1 * dfc2 )

Q16. Consider the following code that creates two dataframes:
ore1 = pd.DataFrame(np.array([[20,35,25,20], [11,28,32, 29]]), columns = ['iron', 'magnesium', 'copper', 'silver'])
ore2 = pd. Dataframe(np.array([[14, 34, 26, 26], [33, 19, 25, 23]]), columns = ['iron', 'magnesium', 'gold', 'silver'])
What will be the output produced by the following code fragments:
(a)

print(ore1 + ore2)

ore3 = ore1.radd(ore2)

print(ore)
(b)

print (ore1 - ore2)

ore3 = ore1.rsub(ore2)

print(ore3)
(c)

print (ore1 * ore2)

ore3 = ore1.mul(ore2)

print (ore3)
(d)

print (ore1 / ore2)

ore3 = ore1.rdiv(ore2)

print(ore3)

Q17. Consider the dataframe wdf as shown below.
(a) Write statement(s) to calculate minimum value for each of the columns.

(b) Write statement(s) to calculate maximum value for each of the rows.

(c) Write statement(s) to calculate variance for column Rainfall.

(d) Write statement(s) to compute mean, mode median for last 10 rows.

Q18. Explain what the following statement is doing?
df.pivot_table(df, index = 'rows', columns = 'cols', values = 'values', aggfunc = np.mean).

Q19. Write code snippet to create histogram only for column "Agg" from DataFrame mdf.

Q20. Consider following dataframes, namely Sprices1 and Sprices2 storing prices closing 1 year apart.

            Sprices1

    closingPrice

05-01    531.35

05-02    527.93

05-03    NaN

05-04    NaN

05-05    527.81

05-06    515.14

05-07    509.96

        Sprices2

    closingPrice

05-01    631.35

05-02    427.93

05-03    805

05-04    NaN

05-05    267.81

05-06    455.14

05-07    NaN

What will be the output produced by the following code fragments?

(a)

print(Sprices1.isnull())

print(Sprices1.notnull()
(b)

pd.merge (Sprices1, Sprices2)

pd.merge(Sprices2, Sprices1)
(c)

pandas.concat(Sprices1, Sprices2)

Q21. Consider the dataframes of previous question and figure out why the following statement is producing an error?

Sprices2.join(Sprices1)

Q22. How are merge() and join() similar or different ? Support your answer with example.