In [1]: importpandasaspd
In [2]: importmatplotlib.pyplotasplt

Data used for this tutorial:

Air quality data

For this tutorial, air quality data about $NO_2$ and Particulate matter less than 2.5 micrometers is used, made available by OpenAQ and downloaded using the py-openaq package. The air_quality_no2_long.csv" data set provides $NO_2$ values for the measurement stations FR04014, BETR801 and London Westminster in respectively Paris, Antwerp and London.

To raw data

In [3]: air_quality = pd.read_csv("data/air_quality_no2_long.csv")
In [4]: air_quality = air_quality.rename(columns={"date.utc": "datetime"})
In [5]: air_quality.head()
Out[5]: 
 city country datetime location parameter value unit
0 Paris FR 2019年06月21日 00:00:00+00:00 FR04014 no2 20.0 μg/m3
1 Paris FR 2019年06月20日 23:00:00+00:00 FR04014 no2 21.8 μg/m3
2 Paris FR 2019年06月20日 22:00:00+00:00 FR04014 no2 26.5 μg/m3
3 Paris FR 2019年06月20日 21:00:00+00:00 FR04014 no2 24.9 μg/m3
4 Paris FR 2019年06月20日 20:00:00+00:00 FR04014 no2 21.4 μg/m3

In [6]: air_quality.city.unique()
Out[6]: array(['Paris', 'Antwerpen', 'London'], dtype=object)

How to handle time series data with ease#

Using pandas datetime properties#

I want to work with the dates in the column datetime as datetime objects instead of plain text

In [7]: air_quality["datetime"] = pd.to_datetime(air_quality["datetime"])
In [8]: air_quality["datetime"]
Out[8]: 
0 2019年06月21日 00:00:00+00:00
1 2019年06月20日 23:00:00+00:00
2 2019年06月20日 22:00:00+00:00
3 2019年06月20日 21:00:00+00:00
4 2019年06月20日 20:00:00+00:00
 ... 
2063 2019年05月07日 06:00:00+00:00
2064 2019年05月07日 04:00:00+00:00
2065 2019年05月07日 03:00:00+00:00
2066 2019年05月07日 02:00:00+00:00
2067 2019年05月07日 01:00:00+00:00
Name: datetime, Length: 2068, dtype: datetime64[ns, UTC]

Initially, the values in datetime are character strings and do not provide any datetime operations (e.g. extract the year, day of the week,...). By applying the to_datetime function, pandas interprets the strings and convert these to datetime (i.e. datetime64[ns, UTC]) objects. In pandas we call these datetime objects similar to datetime.datetime from the standard library as pandas.Timestamp.

Note

As many data sets do contain datetime information in one of the columns, pandas input function like pandas.read_csv() and pandas.read_json() can do the transformation to dates when reading the data using the parse_dates parameter with a list of the columns to read as Timestamp:

pd.read_csv("../data/air_quality_no2_long.csv", parse_dates=["datetime"])

Why are these pandas.Timestamp objects useful? Let’s illustrate the added value with some example cases.

What is the start and end date of the time series data set we are working with?

In [9]: air_quality["datetime"].min(), air_quality["datetime"].max()
Out[9]: 
(Timestamp('2019年05月07日 01:00:00+0000', tz='UTC'),
 Timestamp('2019年06月21日 00:00:00+0000', tz='UTC'))

Using pandas.Timestamp for datetimes enables us to calculate with date information and make them comparable. Hence, we can use this to get the length of our time series:

In [10]: air_quality["datetime"].max() - air_quality["datetime"].min()
Out[10]: Timedelta('44 days 23:00:00')

The result is a pandas.Timedelta object, similar to datetime.timedelta from the standard Python library and defining a time duration.

To user guide

The various time concepts supported by pandas are explained in the user guide section on time related concepts.

I want to add a new column to the DataFrame containing only the month of the measurement

In [11]: air_quality["month"] = air_quality["datetime"].dt.month
In [12]: air_quality.head()
Out[12]: 
 city country datetime ... value unit month
0 Paris FR 2019年06月21日 00:00:00+00:00 ... 20.0 μg/m3 6
1 Paris FR 2019年06月20日 23:00:00+00:00 ... 21.8 μg/m3 6
2 Paris FR 2019年06月20日 22:00:00+00:00 ... 26.5 μg/m3 6
3 Paris FR 2019年06月20日 21:00:00+00:00 ... 24.9 μg/m3 6
4 Paris FR 2019年06月20日 20:00:00+00:00 ... 21.4 μg/m3 6
[5 rows x 8 columns]

By using Timestamp objects for dates, a lot of time-related properties are provided by pandas. For example the month, but also year, quarter,... All of these properties are accessible by the dt accessor.

To user guide

An overview of the existing date properties is given in the time and date components overview table. More details about the dt accessor to return datetime like properties are explained in a dedicated section on the dt accessor.

What is the average $NO_2$ concentration for each day of the week for each of the measurement locations?
```
In [13]: air_quality.groupby(
 ....:  [air_quality["datetime"].dt.weekday, "location"])["value"].mean()
 ....: 
Out[13]: 
datetime location 
0 BETR801 27.875000
 FR04014 24.856250
 London Westminster 23.969697
1 BETR801 22.214286
 FR04014 30.999359
 ... 
5 FR04014 25.266154
 London Westminster 24.977612
6 BETR801 21.896552
 FR04014 23.274306
 London Westminster 24.859155
Name: value, Length: 21, dtype: float64
```
Remember the split-apply-combine pattern provided by groupby from the tutorial on statistics calculation? Here, we want to calculate a given statistic (e.g. mean $NO_2$) for each weekday and for each measurement location. To group on weekdays, we use the datetime property weekday (with Monday=0 and Sunday=6) of pandas Timestamp, which is also accessible by the dt accessor. The grouping on both locations and weekdays can be done to split the calculation of the mean on each of these combinations.

Danger

As we are working with a very short time series in these examples, the analysis does not provide a long-term representative result!

Plot the typical $NO_2$ pattern during the day of our time series of all stations together. In other words, what is the average value for each hour of the day?
```
In [14]: fig, axs = plt.subplots(figsize=(12, 4))
In [15]: air_quality.groupby(air_quality["datetime"].dt.hour)["value"].mean().plot(
 ....:  kind='bar', rot=0, ax=axs
 ....: )
 ....: 
Out[15]: <Axes: xlabel='datetime'>
In [16]: plt.xlabel("Hour of the day"); # custom x label using Matplotlib
In [17]: plt.ylabel("$NO_2 (μg/m^3)$");
```
../../_images/09_bar_chart.png
Similar to the previous case, we want to calculate a given statistic (e.g. mean $NO_2$) for each hour of the day and we can use the split-apply-combine approach again. For this case, we use the datetime property hour of pandas Timestamp, which is also accessible by the dt accessor.

Datetime as index#

In the tutorial on reshaping, pivot() was introduced to reshape the data table with each of the measurements locations as a separate column:

In [18]: no_2 = air_quality.pivot(index="datetime", columns="location", values="value")
In [19]: no_2.head()
Out[19]: 
location BETR801 FR04014 London Westminster
datetime 
2019年05月07日 01:00:00+00:00 50.5 25.0 23.0
2019年05月07日 02:00:00+00:00 45.0 27.7 19.0
2019年05月07日 03:00:00+00:00 NaN 50.4 19.0
2019年05月07日 04:00:00+00:00 NaN 61.9 16.0
2019年05月07日 05:00:00+00:00 NaN 72.4 NaN

Note

By pivoting the data, the datetime information became the index of the table. In general, setting a column as an index can be achieved by the set_index function.

Working with a datetime index (i.e. DatetimeIndex) provides powerful functionalities. For example, we do not need the dt accessor to get the time series properties, but have these properties available on the index directly:

In [20]: no_2.index.year, no_2.index.weekday
Out[20]: 
(Index([2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
 ...
 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019],
 dtype='int32', name='datetime', length=1033),
 Index([1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
 ...
 3, 3, 3, 3, 3, 3, 3, 3, 3, 4],
 dtype='int32', name='datetime', length=1033))

Some other advantages are the convenient subsetting of time period or the adapted time scale on plots. Let’s apply this on our data.

Create a plot of the $NO_2$ values in the different stations from the 20th of May till the end of 21st of May
```
In [21]: no_2["2019年05月20日":"2019年05月21日"].plot();
```
../../_images/09_time_section.png
By providing a string that parses to a datetime, a specific subset of the data can be selected on a DatetimeIndex.

To user guide

More information on the DatetimeIndex and the slicing by using strings is provided in the section on time series indexing.

Resample a time series to another frequency#

Aggregate the current hourly time series values to the monthly maximum value in each of the stations.
```
In [22]: monthly_max = no_2.resample("ME").max()
In [23]: monthly_max
Out[23]: 
location BETR801 FR04014 London Westminster
datetime 
2019年05月31日 00:00:00+00:00 74.5 97.0 97.0
2019年06月30日 00:00:00+00:00 52.5 84.7 52.0
```
A very powerful method on time series data with a datetime index, is the ability to resample() time series to another frequency (e.g., converting secondly data into 5-minutely data).

The resample() method is similar to a groupby operation:

it provides a time-based grouping, by using a string (e.g. M, 5H,...) that defines the target frequency
it requires an aggregation function such as mean, max,...

To user guide

An overview of the aliases used to define time series frequencies is given in the offset aliases overview table.

When defined, the frequency of the time series is provided by the freq attribute:

In [24]: monthly_max.index.freq
Out[24]: <MonthEnd>

Make a plot of the daily mean $NO_2$ value in each of the stations.
```
In [25]: no_2.resample("D").mean().plot(style="-o", figsize=(10, 5));
```
../../_images/09_resample_mean.png

To user guide

More details on the power of time series resampling is provided in the user guide section on resampling.

REMEMBER

Valid date strings can be converted to datetime objects using to_datetime function or as part of read functions.
Datetime objects in pandas support calculations, logical operations and convenient date-related properties using the dt accessor.
A DatetimeIndex contains these date-related properties and supports convenient slicing.
Resample is a powerful method to change the frequency of a time series.

To user guide

A full overview on time series is given on the pages on time series and date functionality.