Why time series has unique challenges in ML Python - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the library used for time series data handling.

ML Python

import [1] as pd

Drag options to blanks, or click blank then click option'

Amatplotlib

Bpandas

Cnumpy

Dsklearn

Attempts:

3 left

💡 Hint

Common Mistakes

Using numpy which is for numerical arrays but lacks time series specific features.

We use pandas to handle time series data because it provides easy-to-use data structures like DataFrame and Series with time-based indexing.

2fill in blank

medium

Complete the code to convert a column to datetime format for time series analysis.

ML Python

df['date'] = pd.to_datetime(df['[1]'])

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Adate

Bdate_string

Cvalue

Dtimestamp

Attempts:

3 left

💡 Hint

Common Mistakes

Using a wrong column name that does not exist in the data.

The column named date is converted to datetime format to allow time-based operations.

3fill in blank

hard

Fix the error in the code to set the datetime column as the index for time series data.

ML Python

df.set_index('[1]', inplace=True)

Drag options to blanks, or click blank then click option'

Aindex

Btime

Cdate

Dtimestamp

Attempts:

3 left

💡 Hint

Common Mistakes

Using a non-datetime column as index causing errors in time series operations.

Setting the date column as the index allows pandas to recognize the data as time series.

4fill in blank

hard

Fill both blanks to resample the time series data to monthly frequency and calculate the mean.

ML Python

monthly_data = df.[1]('M').[2]()

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Aresample

Bmean

Csum

Dgroupby

Attempts:

3 left

💡 Hint

Common Mistakes

Using groupby instead of resample which does not handle time frequency.

We use resample('M') to group data by month and mean() to calculate the average for each month.

5fill in blank

hard

Fill all three blanks to create a lag feature for the time series data.

ML Python

df['lag_[1]'] = df['value'].[2]([3])

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A1

Bshift

C2

Ddiff

Attempts:

3 left

💡 Hint

Common Mistakes

Using diff which calculates difference instead of lagging values.

Creating a lag feature means shifting the 'value' column by 1 time step using shift(1). This helps the model learn from past values.