Why time-based analysis reveals trends in Data Analysis Python - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to convert the 'date' column to datetime format.

Data Analysis Python

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

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

Atime

Btimestamp

Cdate

Dday

Attempts:

3 left

💡 Hint

Common Mistakes

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

The 'date' column must be converted to datetime to analyze trends over time.

2fill in blank

medium

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

Data Analysis Python

df = df.set_index('[1]')

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

Atime

Bdate

Cindex

Dtimestamp

Attempts:

3 left

💡 Hint

Common Mistakes

Setting the wrong column as index.

Setting 'date' as index helps in time-based analysis and plotting.

3fill in blank

hard

Fix the error in resampling the data by month and calculating the mean.

Data Analysis Python

monthly_avg = df.resample('[1]').mean()

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

AM

BW

CD

DY

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'D' or 'W' instead of 'M' for monthly data.

'M' stands for month in resampling frequency strings.

4fill in blank

hard

Fill both blanks to create a dictionary of average sales per month for products with sales greater than 100.

Data Analysis Python

monthly_sales = {product: df[df['product'] == product].resample([1]).[2]() for product in df['product'].unique() if df[df['product'] == product]['sales'].mean() > 100}

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

A'M'

Bmean

Csum

D'W'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'sum' instead of 'mean' for average.

Resample by month ('M') and calculate the mean sales.

5fill in blank

hard

Fill all three blanks to filter data for dates after 2020-01-01, resample weekly, and calculate total sales.

Data Analysis Python

filtered = df[df.index > [1]]
weekly_sales = filtered.resample([2])['sales'].[3]()

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

A'2020-01-01'

B'W'

Csum

D'2021-01-01'

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong date or resampling frequency.

Filter dates after '2020-01-01', resample weekly ('W'), and sum sales.