## Resampling Time Series Data

The practice of resampling time series data involves converting a time series from one frequency or time period to another. This is often used when analyzing time series data collected at a higher frequency or resolution than necessary, to improve the data’s readability and manageability.

## Syntax for Resampling

The primary keyword for resampling time series data is “resample”. The syntax for resampling eliminates the need for extensive code.

### Example Code Syntax

`data.resample('OPTIONAL_FREQUENCY').apply(OPTIONAL_FUNCTION)`

In the code, “data” refers to the time series that you intend to resample. “OPTIONAL_FREQUENCY” represents the time period you want to resample the data to, and it can be a month, week, day, or any other time frame.

Finally, “OPTIONAL_FUNCTION” refers to the aggregate function used to calculate the summary statistics.

## Time Periods for Resampling

When resampling time series data, you can select different time periods to resample to. The most common time periods are weeks and months.

You can also opt to resample the data to an arbitrary frequency by specifying any offset that is a multiple of existing observations. Here are a few other example frequencies:

- ‘D’ for daily frequency
- ‘M’ for month-end frequency
- ‘H’ for hourly frequency

## Example of Resampling on Sales Data

To understand how resampling is applied, let’s consider an example of resampling a sales data set. Suppose we have collected daily sales data for a retail store over a year.

We can use resampling to summarize the sales data by week. Here is a basic outline of the steps involved:

```
data = pd.read_csv("sales_data.csv", index_col="Date", parse_dates=True)
by_weeks = data.resample("W").sum()
by_weeks.plot()
```

In this example, we first import the sales data using pandas.

Using the `resample` function, we then summarize the sales data by week and compute the total sales amount for that week. Finally, we visualize the time series data in a line plot format, showing how sales vary by week.

## Creating a Reproducible Example

When working with time series data, it is essential to ensure that your analysis is reproducible. This means that whoever looks at your code and data should be able to reproduce your analysis and obtain the same results.

### Creating a DataFrame with Hourly Index

To create hourly sales data, we can use pandas to create a DataFrame with an hourly index. This can be achieved using the `date_range` function.

```
index = pd.date_range("01/01/2022", "01/31/2022 23:00", freq="H")
data = pd.DataFrame(index=index, columns=["Sales"])
```

In this code, we create an hourly index for a month of data from January 1, 2022, to January 31, 2022. We then create an empty DataFrame with a single column “Sales”.

### Adding Sales Data to the DataFrame

To add sales data to the DataFrame, we can use a simple random integer generator.

```
import numpy as np
data["Sales"] = np.random.randint(1, 100, size=len(data))
```

In this code, we use numpy’s random integer generator to create a column of random numbers between 1 and 100. This column represents the sales data for each hour in January for our imaginary retailer.

### Visualizing Sales Data with a Line Plot

Finally, we can visualize the hourly sales data using a line plot.

```
import matplotlib.pyplot as plt
plt.plot(data.index, data["Sales"])
plt.show()
```

In this code snippet, we use matplotlib to create a simple line plot of the hourly sales data.

The `plot` function takes the index (which represents time) as the horizontal axis and the `Sales` column as the vertical axis. We then use the `show` method to display the plot.

## Conclusion

Resampling time series data is a crucial step in summarizing and analyzing large time series data sets. With just a few lines of code, we can convert high-frequency data into lower-frequency data, making it more manageable and readable.

By creating reproducible examples, we can share our code and insights with others, ensuring that our analysis remains transparent and trustworthy.

## Summarizing Sales Data by Week

Sales data is often tracked at a fine-grained level, such as hourly or daily, which can be difficult to analyze and understand. One way to make the data more manageable is to summarize it at a coarser level, such as weekly.

In this section, we will explore how to create a weekly summary of sales data.

### Creating a New DataFrame for Weekly Sales Data

To create a weekly summary of sales data, we start by creating a new DataFrame that aggregates sales data by week.

```
import pandas as pd
data = pd.read_csv('sales_data.csv', parse_dates=['date'])
data_weekly = pd.DataFrame({'total_sales': data.groupby(pd.Grouper(key='date',freq='W'))['sales'].sum()}).reset_index()
```

In this code, we read in the original sales data from a CSV file and convert the ‘date’ column to a pandas datetime object using the ‘parse_dates’ parameter. We then group the sales data by week using the ‘groupby’ function applied to a ‘pd.Grouper’ object with ‘freq’ set to ‘W’ for week.

Next, we apply the ‘sum’ function to the ‘sales’ column to compute the total sales for each week. Finally, we create a new DataFrame with two columns: a ‘date’ column containing the start date of each week, and a ‘total_sales’ column containing the total sales for that week.

### Summarizing Sales Data by Week

Now that we have created a new DataFrame with weekly sales data, we can further summarize the data by computing aggregate statistics for each week. To do this, we resample the data, grouping by week and applying a summary function such as ‘sum.’

`weekly_sales = data.resample('W', on='date').sum()`

In this code, the ‘resample’ function is used to group the sales data by week.

The ‘sum’ function is then applied to compute the total sales for each week. Note that the ‘on’ parameter is used to specify the ‘date’ column as the column to resample on.

### Visualizing Sales Data with a Time Series Plot

To visualize the weekly sales data, we can create a time series plot that shows how the sales have evolved over time.

```
import matplotlib.pyplot as plt
plt.plot(weekly_sales.index, weekly_sales['sales'])
plt.xlabel('Week')
plt.ylabel('Total Sales')
plt.title('Weekly Sales')
plt.show()
```

This code uses matplotlib to create a line plot of the weekly sales data.

The plot shows the total sales for each week on the y-axis and the week number on the x-axis. By visualizing the data in this way, we can quickly see how the sales have changed over time.

## Time Periods for Resampling

While weekly summaries of sales data are useful in some contexts, other time periods may be more appropriate for summarizing sales data depending on the nature and purpose of the analysis. In this section, we briefly discuss how to summarize sales data by month or quarter.

### Summarizing Sales Data by Month or Quarter

To summarize sales data by month or quarter, we can modify the ‘freq’ parameter when creating the weekly DataFrame.

```
data_monthly = pd.DataFrame({'total_sales': data.groupby(pd.Grouper(key='date',freq='M'))['sales'].sum()}).reset_index()
data_quarterly = pd.DataFrame({'total_sales': data.groupby(pd.Grouper(key='date',freq='Q'))['sales'].sum()}).reset_index()
```

In this code, we create a new DataFrame containing the total sales data summarized by month or quarter, respectively.

We apply the ‘sum’ function to the ‘sales’ column, as before. Note that we specify ‘M’ or ‘Q’ as the value for the ‘freq’ parameter when creating the ‘pd.Grouper’ object.

### Fewer Data Points with Summarized Sales Data

Summarizing sales data by month or quarter results in fewer data points than summarizing by week, which can be beneficial for situations where we want to reduce the complexity of the data. This can be useful for modeling or forecasting tasks where the goal is to identify patterns in the data rather than detail-level analysis.

In summary, we have discussed how to summarize sales data by week, month, or quarter using pandas’ resampling method. By summarizing sales data at different levels of granularity, we can gain insights into the data that may not be apparent at the detailed level.

Additionally, summarizing the data to a lower frequency can help to reduce the complexity of the data and facilitate modeling and forecasting tasks. In conclusion, summarizing time series data is a crucial step towards understanding and interpreting large data sets.

By resampling data to different frequencies including weeks, months, and quarters, we can gain valuable insights into the data, identify patterns, and reduce complexity. Creating reproducible examples is also essential to ensure that our analysis is transparent and trustworthy.

The main takeaway is that summarizing time series data should be a regular practice when analyzing large data sets to extract meaningful information. With the right approach and tools such as pandas and matplotlib, analyzing time series data can be less daunting and yield valuable insights that drive informed decisions.