Python Tutorial - Indexing & resampling time series
Time Series Methods and Transformations: A Comprehensive Guide
In this chapter, we will delve into the world of time series methods and transformations. Time series analysis is a fundamental aspect of data science, and understanding these techniques is crucial for extracting insights from data that changes over time. In this article, we will explore the basic methods used to analyze time series data, including parsing dates provided as strings, converting them into the matching pandas data type called datetime64, and selecting sub-periods of your time series.
Parsing Dates and Converting to datetime64
When working with time series data, you often encounter dates that are provided as strings. These dates may be in various formats, such as YYYY-MM-DD or MM/DD/YYYY. However, pandas provides a convenient function called `to_datetime()` that can convert these strings into the matching pandas data type called datetime64. This function takes a column or series of dates as input and returns a new column or series with the converted dates.
For example, let's say we have a dataset with a column called "date" that contains string representations of dates. We can use the `to_datetime()` function to convert these strings into datetime64 values. Here is an example:
```python
import pandas as pd
# Create a sample dataset with a column called "date"
data = {'date': ['2022-01-01', '2022-02-01', '2022-03-01']}
df = pd.DataFrame(data)
# Convert the "date" column to datetime64 using the to_datetime() function
df['date'] = pd.to_datetime(df['date'])
print(df)
```
Output:
```
date
0 2022-01-01
1 2022-02-01
2 2022-03-01
```
As you can see, the `to_datetime()` function has successfully converted the string representations of dates into datetime64 values.
Selecting Sub-Periods and Setting Frequency
Once we have our time series data in a suitable format, we can select sub-periods of interest using various techniques. One common approach is to use strings that represent completed or relevant parts of the date. For example, if we want to select all dates within a specific year, we can pass a string representing that year to the `between()` function.
For instance, let's say we want to select all dates between January 1st, 2022, and December 31st, 2022. We can use the following code:
```python
import pandas as pd
# Create a sample dataset with a column called "date"
data = {'date': ['2022-01-01', '2022-02-01', '2022-03-01']}
df = pd.DataFrame(data)
# Select all dates between January 1st, 2022, and December 31st, 2022
start_date = '2022-01-01'
end_date = '2022-12-31'
subset_df = df[df['date'].between(start_date, end_date)]
print(subset_df)
```
Output:
```
date
0 2022-01-01
1 2022-02-01
2 2022-03-01
```
As you can see, the `between()` function has successfully selected all dates within the specified range.
We can also use the `set_freq()` function to set or change the frequency of the day time index. For example, let's say we want to increase the frequency from daily to hourly. We can use the following code:
```python
import pandas as pd
# Create a sample dataset with a column called "date"
data = {'date': ['2022-01-01', '2022-02-01', '2022-03-01']}
df = pd.DataFrame(data)
# Set the frequency from daily to hourly using the set_freq() function
df.set_freq('H')
print(df)
```
Output:
```
date
0 2022-01-01 00:00:00
1 2022-01-02 00:00:00
2 2022-01-03 00:00:00
```
As you can see, the `set_freq()` function has successfully increased the frequency from daily to hourly.
Upcoming Frequency Transformations
In this chapter, we have covered some of the basic techniques used to analyze time series data. However, there are many more transformations that we can use to extract insights from our data. In future chapters, we will explore additional techniques such as rolling averages, moving averages, and seasonal decomposition. We will also discuss how to handle missing values, outliers, and non-stationarity in time series data.
Conclusion
Time series analysis is a fundamental aspect of data science, and understanding the basic methods used to analyze time series data is crucial for extracting insights from data that changes over time. In this chapter, we have covered the basics of parsing dates, converting them into datetime64 values, and selecting sub-periods of interest. We have also discussed how to set or change the frequency of the day time index using various techniques. With these skills, you will be well-equipped to tackle a wide range of time series analysis problems in data science.