Question 1

Which of the following is False about main modules?

Accepted Answer

Other main modules can import main modules — [{"id": "f1865ed0-2e41-82c2-55cb-6ebab8e3fc36", "type": "html", "value": " Main modules are not meant to be imported into other modules. "}]

Question 2

What is the primary purpose of the Pandas library in Python when working with data?

Accepted Answer

To perform efficient data manipulation and analysis on structured data — Pandas is a powerful Python library designed specifically for working efficiently with structured data such as tables and time series, enabling data manipulation and analysis.

Question 3

Which of the following methods in Pandas is used to remove rows or columns containing missing values represented as NaN?

Accepted Answer

dropna() — The dropna() method removes rows or columns that contain missing values (NaN). By default, it removes any row with at least one NaN value.

Question 4

Which Pandas method replaces missing values in a DataFrame with a specified value such as zero, mean, or median?

Accepted Answer

fillna() — The fillna() method is used to replace missing values (NaN) with a specified value or method like zero, mean, median, forward fill, or backward fill.

Question 5

What does the isnull() method in Pandas return when applied to a DataFrame?

Accepted Answer

Boolean mask indicating positions of missing values — The isnull() method returns a Boolean DataFrame of the same shape indicating True where values are missing (NaN) and False otherwise.

Question 6

Which of the following is NOT a valid aggregation function in Pandas when using groupby()?

Accepted Answer

fillna() — fillna() is used for handling missing data, not for aggregation. Aggregation functions include mean(), sum(), count(), min(), max(), etc.

Question 7

What type of join in Pandas merge() returns all rows from both DataFrames, filling missing matches with NaN?

Accepted Answer

Outer join — An outer join returns all rows from both DataFrames, placing NaN where there is no matching data.

Question 8

Which Pandas method concatenates DataFrames vertically or horizontally, stacking rows or adding columns respectively?

Accepted Answer

concat() — The concat() method concatenates multiple DataFrames either vertically (axis=0) stacking rows, or horizontally (axis=1) adding columns.

Question 9

Which function converts string dates into datetime objects in Pandas, essential for time series operations?

Accepted Answer

to_datetime() — to_datetime() converts strings or other formats into datetime objects, enabling time-based indexing and operations.

Question 10

What does the resample() method in Pandas do when applied to time series data?

Accepted Answer

Changes the frequency of time series data, e.g., daily to monthly — resample() allows changing the frequency of time series data by aggregating values over new time intervals like monthly averages from daily data.

Question 11

Which method would you use to calculate a moving average over a rolling window in time series data using Pandas?

Accepted Answer

rolling() — The rolling() method creates a rolling window over data, allowing calculations like moving averages to smooth time series.

Question 12

Which Pandas method sorts a DataFrame by one or more columns in ascending or descending order?

Accepted Answer

sort_values() — sort_values() sorts the DataFrame based on specified column(s) in ascending or descending order.

Question 13

What is the difference between sort_values() and sort_index() methods in Pandas?

Accepted Answer

sort_values() sorts by column values; sort_index() sorts by index — sort_values() sorts the DataFrame by specified column values, while sort_index() sorts the DataFrame by its index labels.

Question 14

Which Pandas method assigns ranks to data points in a column, handling ties by assigning average ranks?

Accepted Answer

rank() — The rank() method assigns ranks to data points, handling ties by default with average ranks, useful for ordering data relative to others.

Question 15

Which of the following is NOT a valid way to filter data in Pandas?

Accepted Answer

fillna() method — fillna() is used to fill missing data, not for filtering. Boolean indexing, query(), and loc[] are valid filtering methods.

Data Handling using Pandas - II

Data Handling using Pandas - II — Study Notes

3.1 INTRODUCTION

3.2 DESCRIPTIVE STATISTICS

3.3 DATA AGGREGATIONS

Practice Questions — Data Handling using Pandas - II

All 7 Chapters in Informatics Practices