Unverified Commit 03ac5b22 authored by Simon Bowly's avatar Simon Bowly
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Add alternative command for centered rolling mean.

parent da62bcaf
%% Cell type:markdown id: tags:
# Handling Timeseries Data in Pandas
* Topic: Data manipulation
* Unit: ADS1002
* Level: Beginner
* Authors: Simon Bowly
* Version: 3
* Version: 3.1
Required files (download these from the Gitlab site [here](https://gitlab.erc.monash.edu.au/bads/data-challenges-resources/-/tree/main/Pandas-DataFrames/Time-Series) into the same directory as the notebook on your computer):
* [traffic-data.csv](https://gitlab.erc.monash.edu.au/bads/data-challenges-resources/-/tree/main/Pandas-DataFrames/Time-Series/traffic-data.csv)
The objective of this notebook is to give some background to time series data and to introduce you to the Pandas commands needed to manipulate time series. Most of the projects this semester involve working with some form of time series, and you will most likely need to filter, plot, and aggregate various parts of the data along the way. If your data fits this description, spend some time trying the commands introduced here with your dataset.
%% Cell type:markdown id: tags:
## Time Series Data
A time series is any dataset which varies over time. This usually means one or more measurements taken at some repeating interval. Some datasets you will work with will be measured at low frequencies, e.g. daily temperature forecasts, monthly sales numbers, and annual revenue, while others may be measured at high frequencies, for example hourly power usage, or millisecond frequency data from medical imaging equipment.
Pandas will handle time series data well if it is in the right format. It expects that rows in a dataframe are time-based observations, and columns are different measurements taken at the same time. As an example, each data `Series` (single column) should be structured like this:
%% Cell type:code id: tags:
``` python
import pandas as pd
import matplotlib.pyplot as plt
# Create a datetime index, with 6 hourly timestamps.
index = pd.date_range(start="2020-03-02", periods=6, freq='H')
# Some data (just some numbers increasing over time).
data = [1, 2, 3, 4, 5, 6]
# Construct a series using the index and data.
series = pd.Series(data, index=index)
# Show the index and the series.
print(index)
series
```
%%%% Output: stream
DatetimeIndex(['2020-03-02 00:00:00', '2020-03-02 01:00:00',
'2020-03-02 02:00:00', '2020-03-02 03:00:00',
'2020-03-02 04:00:00', '2020-03-02 05:00:00'],
dtype='datetime64[ns]', freq='H')
%%%% Output: execute_result
2020-03-02 00:00:00 1
2020-03-02 01:00:00 2
2020-03-02 02:00:00 3
2020-03-02 03:00:00 4
2020-03-02 04:00:00 5
2020-03-02 05:00:00 6
Freq: H, dtype: int64
%% Cell type:markdown id: tags:
and a multi-column `DataFrame` should be structured like this:
%% Cell type:code id: tags:
``` python
# A datetime index, with 6 hourly timestamps.
index = pd.date_range(start="2020-03-02", periods=6, freq='H')
# Construct a dataframe (two columns) using the index and data.
dataframe = pd.DataFrame(
data={
"field1": [1, 2, 3, 4, 5, 6],
"field2": ["a", "b", "c", "d", "e", "f"],
},
index=index,
)
dataframe
```
%%%% Output: execute_result
field1 field2
2020-03-02 00:00:00 1 a
2020-03-02 01:00:00 2 b
2020-03-02 02:00:00 3 c
2020-03-02 03:00:00 4 d
2020-03-02 04:00:00 5 e
2020-03-02 05:00:00 6 f
%% Cell type:markdown id: tags:
Of course, you'll mostly be loading prepared datasets from another format, rather than constructing them this way, but it is useful to know how these datasets are assembled.
The examples below use road traffic data from several intersections around Melbourne. Values in the dataset represent the number of cars counted at a specific intersection in a given 15 minute period. We'll look at:
* converting types to ensure we have the correct time series layout;
* filtering subsets of the data;
* plotting the data against time; and
* grouping & aggregating data using time series functions.
%% Cell type:markdown id: tags:
## Loading Data
First, we load the dataset from csv. To use pandas to its fullest potential here, we need to ensure two things:
1. the ata is in a format where each row is a single time step, and each column is a different observation at that time; and
2. data types are set appropriately (in particular, the `datetime` data type is used to index the data).
**Please note** that it may take more work to get your particular dataset into this format, as some time series data is stored in a different layout. I haven't included specific examples here (since each case may be different) so ask the teaching staff how to approach your particular case.
%% Cell type:code id: tags:
``` python
raw_traffic_data = pd.read_csv("traffic-data.csv")
raw_traffic_data
```
%%%% Output: execute_result
Timestamp Site 100 Site 101 Site 102 Site 103 Site 105 \
0 2021-01-01 00:00:00 66.0 18.0 46.0 19.0 97.0
1 2021-01-01 00:15:00 124.0 83.0 88.0 57.0 231.0
2 2021-01-01 00:30:00 121.0 102.0 83.0 91.0 252.0
3 2021-01-01 00:45:00 130.0 115.0 112.0 59.0 279.0
4 2021-01-01 01:00:00 160.0 115.0 107.0 49.0 226.0
... ... ... ... ... ... ...
2971 2021-01-31 22:45:00 79.0 51.0 70.0 0.0 153.0
2972 2021-01-31 23:00:00 94.0 44.0 40.0 0.0 148.0
2973 2021-01-31 23:15:00 61.0 36.0 42.0 0.0 120.0
2974 2021-01-31 23:30:00 59.0 45.0 42.0 0.0 95.0
2975 2021-01-31 23:45:00 61.0 30.0 41.0 18.0 72.0
Site 106 Site 107 Site 108 Site 109
0 39.0 66.0 31.0 51.0
1 79.0 207.0 112.0 109.0
2 96.0 272.0 106.0 143.0
3 89.0 278.0 87.0 126.0
4 86.0 235.0 98.0 111.0
... ... ... ... ...
2971 45.0 145.0 55.0 56.0
2972 31.0 134.0 54.0 43.0
2973 34.0 118.0 38.0 42.0
2974 23.0 96.0 40.0 25.0
2975 11.0 70.0 26.0 17.0
[2976 rows x 10 columns]
%% Cell type:markdown id: tags:
This dataset is already in the correct layout: each row corresponds to a single timestamp, and there are multiple measurements (columns). All that remains is to check the data types.
%% Cell type:code id: tags:
``` python
# Show data types for all columns in the dataframe.
raw_traffic_data.dtypes
```
%%%% Output: execute_result
Timestamp object
Site 100 float64
Site 101 float64
Site 102 float64
Site 103 float64
Site 105 float64
Site 106 float64
Site 107 float64
Site 108 float64
Site 109 float64
dtype: object
%% Cell type:markdown id: tags:
We can see above that our count values are in `float` format, which is fine, but the 'Timestamp' column is in object format. Looking a little more closely we will see that these are actually stored as strings.
%% Cell type:code id: tags:
``` python
# Get the 'Timestamp' column from the first row.
single_timestamp = raw_traffic_data["Timestamp"].iloc[0]
# Print the value and it's type.
print("The first timestamp has value '{}' and data type {}".format(single_timestamp, type(single_timestamp)))
```
%%%% Output: stream
The first timestamp has value '2021-01-01 00:00:00' and data type <class 'str'>
%% Cell type:markdown id: tags:
This is a typical outcome when loading data from csv. In most cases, this will be easy enough to convert using `pd.to_datetime`. We also want the timestamps to be an index in the data, rather than a regular column. So we create a new dataframe as follows:
%% Cell type:code id: tags:
``` python
# For reference; this is the result of a datetime conversion.
# Note that the 'dtype' for this converted data is now 'datetime64'.
pd.to_datetime(raw_traffic_data["Timestamp"])
```
%%%% Output: execute_result
0 2021-01-01 00:00:00
1 2021-01-01 00:15:00
2 2021-01-01 00:30:00
3 2021-01-01 00:45:00
4 2021-01-01 01:00:00
...
2971 2021-01-31 22:45:00
2972 2021-01-31 23:00:00
2973 2021-01-31 23:15:00
2974 2021-01-31 23:30:00
2975 2021-01-31 23:45:00
Name: Timestamp, Length: 2976, dtype: datetime64[ns]
%% Cell type:code id: tags:
``` python
# Create a new index from the timestamp column, with the proper type.
traffic_data = raw_traffic_data.set_index(pd.to_datetime(raw_traffic_data["Timestamp"]))
# Delete the column with our old string representation of times.
traffic_data = traffic_data.drop(columns=["Timestamp"])
# Show the index and the dataframe.
print(traffic_data.index)
traffic_data
```
%%%% Output: stream
DatetimeIndex(['2021-01-01 00:00:00', '2021-01-01 00:15:00',
'2021-01-01 00:30:00', '2021-01-01 00:45:00',
'2021-01-01 01:00:00', '2021-01-01 01:15:00',
'2021-01-01 01:30:00', '2021-01-01 01:45:00',
'2021-01-01 02:00:00', '2021-01-01 02:15:00',
...
'2021-01-31 21:30:00', '2021-01-31 21:45:00',
'2021-01-31 22:00:00', '2021-01-31 22:15:00',
'2021-01-31 22:30:00', '2021-01-31 22:45:00',
'2021-01-31 23:00:00', '2021-01-31 23:15:00',
'2021-01-31 23:30:00', '2021-01-31 23:45:00'],
dtype='datetime64[ns]', name='Timestamp', length=2976, freq=None)
%%%% Output: execute_result
Site 100 Site 101 Site 102 Site 103 Site 105 \
Timestamp
2021-01-01 00:00:00 66.0 18.0 46.0 19.0 97.0
2021-01-01 00:15:00 124.0 83.0 88.0 57.0 231.0
2021-01-01 00:30:00 121.0 102.0 83.0 91.0 252.0
2021-01-01 00:45:00 130.0 115.0 112.0 59.0 279.0
2021-01-01 01:00:00 160.0 115.0 107.0 49.0 226.0
... ... ... ... ... ...
2021-01-31 22:45:00 79.0 51.0 70.0 0.0 153.0
2021-01-31 23:00:00 94.0 44.0 40.0 0.0 148.0
2021-01-31 23:15:00 61.0 36.0 42.0 0.0 120.0
2021-01-31 23:30:00 59.0 45.0 42.0 0.0 95.0
2021-01-31 23:45:00 61.0 30.0 41.0 18.0 72.0
Site 106 Site 107 Site 108 Site 109
Timestamp
2021-01-01 00:00:00 39.0 66.0 31.0 51.0
2021-01-01 00:15:00 79.0 207.0 112.0 109.0
2021-01-01 00:30:00 96.0 272.0 106.0 143.0
2021-01-01 00:45:00 89.0 278.0 87.0 126.0
2021-01-01 01:00:00 86.0 235.0 98.0 111.0
... ... ... ... ...
2021-01-31 22:45:00 45.0 145.0 55.0 56.0
2021-01-31 23:00:00 31.0 134.0 54.0 43.0
2021-01-31 23:15:00 34.0 118.0 38.0 42.0
2021-01-31 23:30:00 23.0 96.0 40.0 25.0
2021-01-31 23:45:00 11.0 70.0 26.0 17.0
[2976 rows x 9 columns]
%% Cell type:markdown id: tags:
Success! The data is now in native pandas time series format: the index is a `DatetimeIndex` and the remaining columns contain only our observed data, not the timestamp values.
%% Cell type:markdown id: tags:
## Selecting Data
Generally you will want to work with a subset of the data, particularly when it comes to visualisation. The following commands show how to select subsets of the data by specifying two dates as endpoints.
%% Cell type:code id: tags:
``` python
# Select a 2 hour period between two dates.
# The syntax is similar to other pandas commands - start and end of the interval,
# in square brackets, separated by a colon.
morning_data = traffic_data[pd.Timestamp("2021-01-04 06:00:00"):pd.Timestamp("2021-01-04 08:00:00")]
morning_data
```
%%%% Output: execute_result
Site 100 Site 101 Site 102 Site 103 Site 105 \
Timestamp
2021-01-04 06:00:00 70.0 84.0 141.0 64.0 224.0
2021-01-04 06:15:00 108.0 94.0 184.0 85.0 269.0
2021-01-04 06:30:00 157.0 114.0 198.0 107.0 331.0
2021-01-04 06:45:00 132.0 127.0 227.0 101.0 354.0
2021-01-04 07:00:00 143.0 142.0 256.0 110.0 355.0
2021-01-04 07:15:00 167.0 165.0 243.0 119.0 372.0
2021-01-04 07:30:00 217.0 156.0 298.0 127.0 512.0
2021-01-04 07:45:00 240.0 204.0 346.0 150.0 606.0
2021-01-04 08:00:00 192.0 174.0 322.0 143.0 479.0
Site 106 Site 107 Site 108 Site 109
Timestamp
2021-01-04 06:00:00 87.0 241.0 72.0 82.0
2021-01-04 06:15:00 149.0 324.0 72.0 135.0
2021-01-04 06:30:00 152.0 378.0 109.0 180.0
2021-01-04 06:45:00 138.0 355.0 107.0 171.0
2021-01-04 07:00:00 142.0 415.0 123.0 181.0
2021-01-04 07:15:00 166.0 478.0 139.0 203.0
2021-01-04 07:30:00 188.0 484.0 158.0 239.0
2021-01-04 07:45:00 184.0 570.0 181.0 239.0
2021-01-04 08:00:00 176.0 603.0 190.0 239.0
%% Cell type:markdown id: tags:
An alternative way to write this is to use 'timedelta' to specify a start time and duration of the interval.
%% Cell type:code id: tags:
``` python
# Store a fixed start time in a variable.
start = pd.Timestamp("2021-01-04 06:00:00")
# Add an offset to use as the end time.note, you can use any time unit here; try e.g. days=2)
# and check the range of values returned.
traffic_data[start:start + pd.Timedelta(hours=1)]
```
%%%% Output: execute_result
Site 100 Site 101 Site 102 Site 103 Site 105 \
Timestamp
2021-01-04 06:00:00 70.0 84.0 141.0 64.0 224.0
2021-01-04 06:15:00 108.0 94.0 184.0 85.0 269.0
2021-01-04 06:30:00 157.0 114.0 198.0 107.0 331.0
2021-01-04 06:45:00 132.0 127.0 227.0 101.0 354.0
2021-01-04 07:00:00 143.0 142.0 256.0 110.0 355.0
Site 106 Site 107 Site 108 Site 109
Timestamp
2021-01-04 06:00:00 87.0 241.0 72.0 82.0
2021-01-04 06:15:00 149.0 324.0 72.0 135.0
2021-01-04 06:30:00 152.0 378.0 109.0 180.0
2021-01-04 06:45:00 138.0 355.0 107.0 171.0
2021-01-04 07:00:00 142.0 415.0 123.0 181.0
%% Cell type:markdown id: tags:
Of course we can also select columns as normal, along with selecting a time range.
%% Cell type:code id: tags:
``` python
start = pd.Timestamp("2021-01-04 06:00:00")
end = start + pd.Timedelta(hours=1)
# Separate the row and column filters by a comma. Also note that
# we must use .loc[] syntax when specifying both rows and columns.
traffic_data.loc[start:end, ["Site 100", "Site 101"]]
```
%%%% Output: execute_result
Site 100 Site 101
Timestamp
2021-01-04 06:00:00 70.0 84.0
2021-01-04 06:15:00 108.0 94.0
2021-01-04 06:30:00 157.0 114.0
2021-01-04 06:45:00 132.0 127.0
2021-01-04 07:00:00 143.0 142.0
%% Cell type:markdown id: tags:
# Plotting
Pandas has an in-built set of plotting methods (some of which you've seen before). While `seaborn` is most useful for plotting relationships between data, `pandas` own plotting commands are easier to work with when visualising time series. We'll use a subset of the data in these plots as plotting the entire dataset becomes very cluttered.
%% Cell type:code id: tags:
``` python
start = pd.Timestamp("2021-01-04 06:00:00")
end = start + pd.Timedelta(hours=6)
morning_data_three_sites = traffic_data.loc[start:end, ["Site 100", "Site 101", "Site 103"]]
# Plots each column as a different line, over the time range in the filtered dataframe.
morning_data_three_sites.plot.line();
```
%%%% Output: display_data
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)
%% Cell type:markdown id: tags:
Other types than the standard line plot can also be produced using the same style. An area plot gives a 'stacked' view of the data (each series is added on top of one another). This helps to show both the individual series' relative sizes as well as the total of all series at a given time.
Note you can use the `figsize` argument in any of these plot commands to expand the area of the plot in the notebook. The argument to figsize is a (width, height) tuple.
%% Cell type:code id: tags:
``` python
morning_data_three_sites.plot.area(figsize=(10, 5));
```
%%%% Output: display_data
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)
%% Cell type:markdown id: tags:
## Resampling
Resampling helps to aggregate data based on time. It is used when we have relatively high frequency data and need to 'downsample' to more useful statistics at a lower frequency. A useful result for this dataset might be to get daily traffic numbers by adding up all the 15 minute measurement windows.
Note that any of the usual statistics (e.g. min/max/mean/median...) can be used in place of `.sum()` here, to calculate different statistics.
%% Cell type:code id: tags:
``` python
three_sites = traffic_data[["Site 103", "Site 105", "Site 106"]]
# Compute the sum of all 15 minute counts over each day.
# 1D = group by one day periods
daily = three_sites.resample("1D").sum()
daily.head()
```
%%%% Output: execute_result
Site 103 Site 105 Site 106
Timestamp
2021-01-01 10107.0 25724.0 11707.0
2021-01-02 12073.0 33606.0 14556.0
2021-01-03 11007.0 31182.0 12367.0
2021-01-04 12816.0 40829.0 15377.0
2021-01-05 13312.0 42254.0 16466.0
%% Cell type:markdown id: tags:
Note that the resulting dataframe now has an index with only daily entries, no specific times. Plotting the result as normal allows us to show a visual comparison of overall daily traffic at each site.
%% Cell type:code id: tags:
``` python
daily.plot.line();
```
%%%% Output: display_data
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)
%% Cell type:markdown id: tags:
## Rolling Mean
Rolling statistics (especially rolling averages) can be useful to smooth out time series data, correcting for minor fluctuations or measurement issues in order to observe general trends. They can also be used to compute "anomaly" measures, which compare observed values at a single time period to long- or short-term averages. A rolling mean takes a 'window' around each timestamp, of a given duration, and computes the average of all values in the window.
Computing rolling means is straightforward since our data is in the correct format. The code below plots the data against a rolling mean. Note that the 'centered' rolling mean is perhaps more informative here, since it will use 2 hours either side. The default rolling mean is computed based on 4 hours of data before the current time.
%% Cell type:code id: tags:
``` python
start = pd.Timestamp("2021-01-04 06:00:00")
end = start + pd.Timedelta(days=2)
# This gives us just a single series (for one site).
site_105 = traffic_data.loc[start:end, "Site 105"]
# Compute rolling means (4H = 4 hour rolling window).
rolling_mean = site_105.rolling('4H').mean()
centered_rolling_mean = site_105.rolling('4H', center=True).mean()
# These two commands (the first is commented out since it only
# works in newer pandas versions) give a centered rolling mean. It
# is more convenient to use '4H' here, but 16 time periods (since
# the data is at 15 minute frequency is equivalent in this case.
# centered_rolling_mean = site_105.rolling('4H', center=True).mean()
centered_rolling_mean = site_105.rolling(16, center=True).mean()
# Plot each series. We can use the 'label' argument to label them
# individually since they are separate series.
site_105.plot.line(label='Data', figsize=(10, 4))
rolling_mean.plot.line(label='Rolling Mean')
centered_rolling_mean.plot.line(label='Centered Rolling Mean')
# Add a legend to show the labels.
plt.legend();
```
%%%% Output: display_data
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)
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