Resampling time series in Data Analysis Python - Step-by-Step Execution

Concept Flow - Resampling time series

Start with time series data

↓

Choose new frequency

↓

Apply resampling method

↓

Aggregate or interpolate values

↓

Get resampled time series

↓

End

Resampling changes the time intervals of data by grouping or interpolating values to a new frequency.

Execution Sample

Data Analysis Python

import pandas as pd

dates = pd.date_range('2024-01-01', periods=6, freq='D')
data = pd.Series([10, 20, 15, 30, 25, 40], index=dates)

resampled = data.resample('2D').mean()
print(resampled)

This code resamples daily data into 2-day intervals by averaging values.

Execution Table

Step	Action	Input Data	Resample Frequency	Aggregation	Output
1	Create daily time series	[10,20,15,30,25,40]	N/A	N/A	2024-01-01 to 2024-01-06 daily data
2	Choose frequency	Daily data	2D	N/A	Prepare to group every 2 days
3	Group data by 2-day bins	Daily data	2D	mean	Groups: [Jan 1-2], [Jan 3-4], [Jan 5-6]
4	Calculate mean per group	Groups	2D	mean	[ (10+20)/2=15, (15+30)/2=22.5, (25+40)/2=32.5 ]
5	Create resampled series	Means	2D	mean	2024-01-01:15, 2024-01-03:22.5, 2024-01-05:32.5
6	Print result	Resampled series	2D	mean	Output displayed
7	End	N/A	N/A	N/A	Resampling complete

💡 All original data grouped and averaged into 2-day intervals, resampling finished.

Variable Tracker

Variable	Start	After Step 1	After Step 3	After Step 4	Final
dates	empty	DatetimeIndex(['2024-01-01', '2024-01-02', '2024-01-03', '2024-01-04', '2024-01-05', '2024-01-06'])	same	same	same
data	empty	Series with 6 daily values	same	same	same
resampled	empty	empty	empty	Series with means per 2 days	Series with means per 2 days

Key Moments - 3 Insights

Why does the resampled series have fewer data points than the original?

Because resampling groups the original data into larger time bins (2 days here), so multiple original points combine into one aggregated value, as shown in execution_table step 4.

What happens if the original data has missing dates when resampling?

Resampling still creates bins for the new frequency. Missing dates result in NaN values in those bins unless an aggregation method handles them, as implied in step 3 grouping.

Why do we use mean() in resampling?

Mean averages all values in each time bin to summarize data. Other methods like sum() or max() can be used depending on the goal, as shown in step 4 aggregation.

Visual Quiz - 3 Questions

Look at the execution_table at step 4, what is the mean value for the group Jan 3-4?

A 15

B 25

C 22.5

D 32.5

💡 Hint

Check the 'Output' column at step 4 in execution_table for the calculated means.

At which step does the code group the original data into 2-day bins?

A Step 3

B Step 2

C Step 5

D Step 6

💡 Hint

Look at the 'Action' column in execution_table to find when grouping happens.

If we change the resample frequency to '3D', how many output points will the resampled series have?

A 4

B 3

C 2

D 6

💡 Hint

Refer to variable_tracker and execution_table to understand how grouping reduces points based on frequency.

Concept Snapshot

Resampling time series changes data frequency.
Use pandas.Series.resample(new_freq) to group data.
Apply aggregation like mean(), sum(), or interpolate.
New series has data at new intervals.
Useful for summarizing or filling missing data.

Full Transcript

Resampling time series means changing the time intervals of your data. You start with your original data indexed by time. Then you pick a new frequency, like every 2 days instead of daily. The data is grouped into these new time bins. Next, you apply an aggregation method such as mean to combine values in each bin. The result is a new time series with fewer or more points depending on the frequency. This process helps summarize data or fill gaps. The example code shows daily data resampled to 2-day averages. The execution table traces each step from creating data to printing the resampled output. Variables like dates, data, and resampled values change as the code runs. Key moments clarify why resampled data has fewer points and how missing dates affect results. The quiz tests understanding of grouping, aggregation, and frequency effects. The snapshot summarizes the main points for quick review.

Practice

1. What does resampling a time series data mean?

easy

A. Removing missing values

B. Sorting the data by values

C. Changing the time frequency of the data

D. Converting data to strings

Solution

1. Step 1: Understand the meaning of resampling

   Resampling means changing how often data points appear in time, like from daily to monthly.

2. Step 2: Compare options to definition

   Only Changing the time frequency of the data correctly describes changing the time frequency of data.

3. Final Answer:

   Changing the time frequency of the data -> Option C

4. Quick Check:

   Resampling = Changing frequency [OK]

Hint: Resampling changes how often data points appear [OK]

Common Mistakes:

• Thinking resampling sorts data
• Confusing resampling with cleaning data
• Believing resampling changes data type

2. Which of the following is the correct syntax to resample a pandas DataFrame df to monthly frequency and take the mean?

easy

A. df.resample('month').mean()

B. df.resample('monthly').mean()

C. df.resample('M').average()

D. df.resample('M').mean()

Solution

1. Step 1: Identify the correct frequency string

   Pandas uses 'M' for monthly frequency, not 'monthly' or 'month'.

2. Step 2: Check aggregation function name

   The correct function is mean(), not average().

3. Final Answer:

   df.resample('M').mean() -> Option D

4. Quick Check:

   Use 'M' and mean() for monthly average [OK]

Hint: Use 'M' for month and mean() to aggregate [OK]

Common Mistakes:

• Using 'monthly' or 'month' instead of 'M'
• Using average() instead of mean()
• Missing parentheses after mean

3. Given the following code, what will be the output?

import pandas as pd
idx = pd.date_range('2023-01-01', periods=4, freq='D')
data = [10, 20, 30, 40]
df = pd.DataFrame({'value': data}, index=idx)
resampled = df.resample('2D').sum()
print(resampled)

medium

A. 2023-01-01 30
2023-01-03 70

B. 2023-01-01 10
2023-01-03 30
2023-01-05 40

C. 2023-01-01 30
2023-01-03 40

D. 2023-01-01 10
2023-01-02 20
2023-01-03 30
2023-01-04 40

Solution

1. Step 1: Understand the original data and frequency

   Data has values 10,20,30,40 on consecutive days from 2023-01-01 to 2023-01-04.

2. Step 2: Resample with '2D' and sum

   Resampling groups every 2 days: (Jan 1+2) and (Jan 3+4). Sums are 10+20=30 and 30+40=70.

3. Final Answer:

   2023-01-01 30 2023-01-03 70 -> Option A

4. Quick Check:

   Sum every 2 days = 30, 70 [OK]

Hint: Sum values in 2-day groups starting from first date [OK]

Common Mistakes:

• Summing only one day instead of two
• Confusing sum with mean
• Misreading the date index

4. What is wrong with this code snippet?

df = pd.DataFrame({'value': [1, 2, 3]}, index=pd.date_range('2023-01-01', periods=3))
resampled = df.resample('W').mean

medium

A. Missing parentheses after 'mean' function call

B. Wrong frequency string 'W' for weekly resampling

C. DataFrame index is not a datetime index

D. Aggregation function 'mean' does not exist

Solution

1. Step 1: Check the resample syntax

   The code uses mean without parentheses, so it returns a method, not the result.

2. Step 2: Verify frequency and index

   'W' is correct for weekly, and the index is a datetime index, so no issues there.

3. Final Answer:

   Missing parentheses after 'mean' function call -> Option A

4. Quick Check:

   Call mean() with () to get result [OK]

Hint: Add () after mean to execute function [OK]

Common Mistakes:

• Forgetting parentheses on aggregation functions
• Thinking 'W' is invalid frequency
• Not setting datetime index before resampling

5. You have minute-level temperature data in a DataFrame df indexed by datetime. You want to resample it to hourly data but only keep hours where the average temperature is above 20°C. Which code snippet correctly does this?

hard

A. df.resample('H').mean()[lambda x: x['temperature'] > 20]

B. df.resample('H').mean().query('temperature > 20')

C. df.resample('H').mean()[df.resample('H').mean() > 20]

D. df.resample('H').mean().filter(lambda x: x > 20)

Solution

1. Step 1: Resample to hourly and calculate mean

   Use df.resample('H').mean() to get hourly averages.

2. Step 2: Filter rows where temperature > 20

   Using .query('temperature > 20') filters rows correctly by the 'temperature' column.

3. Final Answer:

   df.resample('H').mean().query('temperature > 20') -> Option B

4. Quick Check:

   Use query() to filter by column condition [OK]

Hint: Use query() after resample mean to filter rows [OK]

Common Mistakes:

• Applying boolean filter on DataFrame without column name
• Using filter() incorrectly for row filtering
• Repeating resample twice causing inefficiency