A Practical Approach to Timeseries Forecasting Using Python - Shape and NULL Check

The video tutorial explains how to analyze a dataset by examining its columns, data types, shape, and null values. It demonstrates using commands like DF.columns, DF.shape, and DF.isnull().any() to gather insights. The tutorial also covers preparing the dataset for time series analysis by setting the date column as the index, ensuring no null values are present. The next steps involve converting the dataset into a time series format.

What command is used to display the column names of a data set?

• DF.headers

• DF.columns

• DF.names

• DF.titles

How many columns and entries does the data set have?

• 5 columns and 1000 entries

• 4 columns and 741 entries

• 3 columns and 500 entries

• 2 columns and 300 entries

What data type is used to indicate the presence of null values in the data set?

• Float

• String

• Integer

• Boolean

What is the next step after checking the data set for null values?

• Performing data visualization

• Running a regression analysis

• Exporting the data set

• Checking the head of the data set

Why is it important to set the date column as the index for time series analysis?

• To ensure each entry has a unique identifier

• To improve the speed of data processing

• To make the date column part of the data set columns

• To reduce the size of the data set