Time series analysis patterns in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to convert the 'date' column to a datetime type.

Pandas

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

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

Ato_string

Bto_datetime

Cto_numeric

Dto_list

Attempts:

3 left

💡 Hint

Common Mistakes

Using to_string instead of to_datetime.

Trying to convert with to_numeric which is for numbers.

Use pd.to_datetime to convert a column to datetime format in pandas.

2fill in blank

medium

Complete the code to set the 'date' column as the DataFrame index.

Pandas

df = df.set_index([1])

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

A'index'

B'time'

C'date'

D'timestamp'

Attempts:

3 left

💡 Hint

Common Mistakes

Using a wrong column name like 'time' or 'timestamp'.

Not using quotes around the column name.

Setting the 'date' column as index helps with time series operations.

3fill in blank

hard

Fix the error in the code to resample the data by month and calculate the mean.

Pandas

monthly = df.resample([1]).mean()

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A'M'

B'W'

C'D'

D'Y'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'D' for day or 'W' for week instead of month.

Using lowercase 'm' which is not valid.

Use 'M' to resample data by month in pandas.

4fill in blank

hard

Fill both blanks to create a rolling average with a window of 3 days.

Pandas

rolling_avg = df['value'].[1](window=[2]).mean()

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Arolling

B3

C5

Dshift

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'shift' instead of 'rolling'.

Using window size 5 instead of 3.

Use rolling(window=3) to calculate a rolling average over 3 days.

5fill in blank

hard

Fill all three blanks to create a dictionary of max values for each year.

Pandas

yearly_max = {year: group['value'].[1]() for year, group in df.groupby(df.index.[2]) if group['value'].[3]() > 50}

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

Amax

Byear

Dmin

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'min' instead of 'max' for maximum values.

Using wrong attribute instead of 'year'.

This code groups data by year, then finds the max value for each year where the max is greater than 50.