The video tutorial covers data visualization using Pandas. It begins with importing and cleansing data, followed by plotting data as bar graphs. Advanced techniques such as creating stacked and horizontal bar graphs are demonstrated. The tutorial also introduces area plots and histograms, and concludes with data analytics, comparing sales data with total and average sales. The use of Matplotlib for customization is mentioned for future lessons.

What function is used to read a CSV file in pandas?

• read_csv()

• read_excel()

• read_json()

• read_table()

What is the purpose of data cleansing?

• To add more data

• To create new data

• To remove errors and inconsistencies

• To visualize data

Which method is used to visualize data as a bar graph in pandas?

• plot_bar()

• bar()

• bar_plot()

• plot()

How can you change the orientation of a bar graph to horizontal in pandas?

• Use the 'horizontal' parameter

• Use 'plot_horizontal()'

• Set 'orientation' to 'horizontal'

• Add 'barh' to the plot function

What is the purpose of using stacked bar graphs?

• To compare parts of a whole

• To display data in 3D

• To highlight outliers

• To show data trends over time

What is the significance of data visualization?

• To hide data errors

• To simplify data analysis

• To increase data size

• To make data look pretty

Which plot type is useful for understanding the distribution of data?

• Histogram

• Scatter plot

• Pie chart

• Line plot

What is the main advantage of using area plots?

• To show individual data points

• To highlight data trends

• To compare quantities over time

• To display categorical data

In pandas, which method is used to calculate the mean of a column?

• sum()

• mean()

• median()

• average()

Which library does pandas use for plotting?

• Matplotlib

• Seaborn

• Plotly

• Bokeh