Question 1

Question 1Assertion. A quantile refers to equally distributed portion of a data set.Reason. A median divides a distribution in 2 quantiles while a quartile divides a distribution in 4 quantiles.Both A and R are true and R is the correct explanation of A.Both A and R are true but R is not the correct explanation of A.A is true but R is false.A is false but R is true.

Accepted Answer

Both A and R are true and R is the correct explanation of A.ExplanationQuantile is a statistical term that refers to dividing the total distribution of given data into a given number of equal proportions. Each portion is called a quantile, and it represents a specific percentage of the data set. The median divides a distribution into two equal parts, representing the middle value of a data set when the data is sorted in ascending order. A quartile divides a distribution into four equal parts, with each quartile representing 25% of the data set.

Question 2

Question 2Assertion. Data aggregation produces a summary statistics of a dataset.Reason. Data aggregation summarizes data using statistical aggregation functions.Both A and R are true and R is the correct explanation of A.Both A and R are true but R is not the correct explanation of A.A is true but R is false.A is false but R is true.

Accepted Answer

Both A and R are true and R is the correct explanation of A.ExplanationData aggregation produces a summary statistic of a dataset because it involves combining and summarizing data from multiple sources or data points, and then calculating a single summary statistic or a set of summary statistics. This process utilizes statistical aggregation functions, such as sum, average, count, minimum, and maximum.

Question 3

Question 3Assertion. In a dataset, there can be missing values that cannot contribute to any computation.Reason. In a dataset, NULL, NaN or None are considered the missing values.Both A and R are true and R is the correct explanation of A.Both A and R are true but R is not the correct explanation of A.A is true but R is false.A is false but R is true.

Accepted Answer

Both A and R are true and R is the correct explanation of A.ExplanationMissing values are the values that cannot contribute to any computation or we can say that missing values are the values that carry no computational significance. In a dataset, NULL, NaN or None are considered the missing values.

Question 4

Question 4Assertion (A). The output of addition of two series will be NaN, if one of the elements or both the elements have no value(s).Reason (R). While performing mathematical operations on a series, by default all missing values are filled in with 0.Both A and R are true and R is the correct explanation of A.Both A and R are true but R is not the correct explanation of A.A is true but R is false.A is false but R is true.

Accepted Answer

A is true but R is false.ExplanationWhen adding two pandas Series and encountering NaN values in either or both Series, the result of the addition operation will be NaN for those corresponding elements. Additionally, pandas does not automatically replace missing values with 0 during mathematical operations but instead with NaN.

Question 5

Question 1Name some descriptive statistic functions used with DataFrames.

Accepted Answer

Some descriptive statistic functions used with DataFrames are min(), max(), idxmax(), idxmin(), mode(), mean(), median(), count(), sum(), quantile(), std(), var().

Question 6

Question 2To calculate statistical values for each row, the axis argument should be.

Accepted Answer

The axis argument should be equal to 1 to calculate statistical values for each row.

Question 7

Question 3What are quantile and quartiles ?

Accepted Answer

Quantile is a process of dividing the total distribution of given data into a given number of equal proportions.Quartile refers to the division of the total distribution of given data into a four equal proportions with each containing one fourth of the total population.

Question 8

Question 4What does quantile() do ?

Accepted Answer

The quantile() function is used to calculate any type of quantiles in Pandas.

Question 9

Question 5Name pivoting functions available for DataFrames.

Accepted Answer

The pivoting functions available for DataFrames are pivot(), pivot_table(), melt(), stack(), unstack(), crosstab().

Question 10

Question 6What does hist() do ?

Accepted Answer

The hist() function of PyPlot module is used to create and plot histogram from a given sequence(s) of numbers.

Question 11

Question 7What are missing values in a dataset ?

Accepted Answer

Missing values are the values that cannot contribute to any computation or we can say that missing values are the values that carry no computational significance.

Question 12

Question 8Write two methods generally used for handling missing values.

Accepted Answer

The dropna() and fillna() are the two methods used for handling missing values in a DataFrame.

Question 13

Question 9What is the difference between dropna() and fillna() ?

Accepted Answer

The dropna() method is used to drop all rows that contain NaN values from a DataFrame. On the other hand, the fillna() method is used to replace all NaN values in a Pandas object with the specified value.

Question 14

Question 1iterrows() and iteritems() functions help you to iterate over a DataFrame.

Accepted Answer