Manipulating Time Series Data in Python

A collection of observations (activity) for a single subject (entity) at various time intervals is known as time-series data. In the case of metrics, time series are equally spaced and in the case of events, time series are unequally spaced. We may add the date and time for each record in this Pandas module, as well as fetch dataframe records and discover data inside a specific date and time range. 

Generate a date range:

Pandas package is imported. pd.date_range() method is used to create a date range, the date range has a monthly frequency.

Output:

DatetimeIndex(['2020-01-31', '2020-02-29', '2020-03-31', '2020-04-30',
               '2020-05-31', '2020-06-30', '2020-07-31', '2020-08-31',
               '2020-09-30', '2020-10-31', '2020-11-30', '2020-12-31',
               '2021-01-31', '2021-02-28', '2021-03-31', '2021-04-30'],
              dtype='datetime64[ns]', freq='M')
<class 'pandas.core.indexes.datetimes.DatetimeIndex'>
<class 'pandas._libs.tslibs.timestamps.Timestamp'>

Operations on timestamp data:

The date range is converted into a dataframe with the help of pd.DataFrame() method. The column is converted to DateTime using to_datetime() method. info() method gives information about the dataframe if there are any null values and the datatype of the columns.

Output:

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 16 entries, 0 to 15
Data columns (total 1 columns):
 #   Column  Non-Null Count  Dtype         
---  ------  --------------  -----         
 0   Date    16 non-null     datetime64[ns]
dtypes: datetime64[ns](1)
memory usage: 256.0 bytes

Convert data from a string to a timestamp:

if we have a list of string data that resembles DateTime, we can first convert it to a dataframe using pd.DataFrame() method and convert it to DateTime column using pd.to_datetime() method. 

Output:

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 16 entries, 0 to 15
Data columns (total 1 columns):
 #   Column  Non-Null Count  Dtype         
---  ------  --------------  -----         
 0   Date    16 non-null     datetime64[ns]
dtypes: datetime64[ns](1)
memory usage: 256.0 bytes
None

According to the format of our string values, we can convert them to DateTime. datetime.strptime() function can be used in this scenario

Output:

[datetime.datetime(2021, 5, 20, 0, 0), datetime.datetime(2021, 5, 21, 0, 0), datetime.datetime(2021, 5, 22, 0, 0)]

<class ‘pandas.core.frame.DataFrame’>

RangeIndex: 3 entries, 0 to 2

Data columns (total 1 columns):

 #   Column  Non-Null Count  Dtype         

—  ——  ————–  —–         

 0   Date    3 non-null      datetime64[ns]

dtypes: datetime64[ns](1)

memory usage: 152.0 bytes

Slicing and indexing time series data:

CSV file is imported in this example and a column with string data is converted into DateTime using pd.to_timestamp() method. That particular column is set as an index which helps us slice and index data accordingly. data. loc[‘2020-01-22’][:10]  indexes data on the day ‘2020-01-22’ and the result is further sliced to return the first 10 observations on that day.

To view and download the CSV file click here.

Output:

                 Unnamed: 0 Province/State  ... Deaths Recovered
ObservationDate                             ...                 
2020-01-22                0          Anhui  ...    0.0       0.0
2020-01-22                1        Beijing  ...    0.0       0.0
2020-01-22                2      Chongqing  ...    0.0       0.0
2020-01-22                3         Fujian  ...    0.0       0.0
2020-01-22                4          Gansu  ...    0.0       0.0

[5 rows x 7 columns]
                 Unnamed: 0 Province/State  ... Deaths Recovered
ObservationDate                             ...                 
2020-01-22                0          Anhui  ...    0.0       0.0
2020-01-22                1        Beijing  ...    0.0       0.0
2020-01-22                2      Chongqing  ...    0.0       0.0
2020-01-22                3         Fujian  ...    0.0       0.0
2020-01-22                4          Gansu  ...    0.0       0.0
2020-01-22                5      Guangdong  ...    0.0       0.0
2020-01-22                6        Guangxi  ...    0.0       0.0
2020-01-22                7        Guizhou  ...    0.0       0.0
2020-01-22                8         Hainan  ...    0.0       0.0
2020-01-22                9          Hebei  ...    0.0       0.0

[10 rows x 7 columns]

In this example, we slice data from ‘2020-01-22’ to ‘2020-02-22’. 

Output:

                 Unnamed: 0   Province/State  ... Deaths Recovered
ObservationDate                               ...                 
2020-01-22                0            Anhui  ...    0.0       0.0
2020-01-22                1          Beijing  ...    0.0       0.0
2020-01-22                2        Chongqing  ...    0.0       0.0
2020-01-22                3           Fujian  ...    0.0       0.0
2020-01-22                4            Gansu  ...    0.0       0.0
...                     ...              ...  ...    ...       ...
2020-02-22             2169  San Antonio, TX  ...    0.0       0.0
2020-02-22             2170      Seattle, WA  ...    0.0       1.0
2020-02-22             2171        Tempe, AZ  ...    0.0       0.0
2020-02-22             2172          Unknown  ...    0.0       0.0
2020-02-22             2173              NaN  ...    0.0       0.0

[2174 rows x 7 columns]

Resampling time series data for various aggregates/summary statistics for different time periods:

To resample time-series data, use the pandas resample() function. It is a time series frequency conversion and resampling convenience technique. The caller must give the label of a DateTime-like series/index to the on/level keyword argument if the object has a DateTime-like index.

Output:

                 Unnamed: 0      Confirmed       Deaths     Recovered
ObservationDate                                                      
2020-12-31          96232.5   39696.116550  1160.959453  24659.893368
2021-12-31         249447.0  163315.277678  3514.893386  93925.632661

Calculate a rolling statistic like a rolling average:

Dataframe created with Pandas. The rolling() method allows you to calculate rolling windows. The idea of calculating a rolling window is most commonly employed in signal processing and time-series data. To put it another way, we take a window of size k at a time and apply some mathematical operation to it. A window of size k signifies that k successive values are displayed at the same time. All of the ‘k’ values are equally weighted in the simplest instance. In the below example window size is 5.

Output:

                         Last Update  Confirmed  rolling_sum
ObservationDate                                            
2020-01-22      2020-01-22 17:00:00        1.0          NaN
2020-01-22      2020-01-22 17:00:00       14.0          NaN
2020-01-22      2020-01-22 17:00:00        6.0          NaN
2020-01-22      2020-01-22 17:00:00        1.0          NaN
2020-01-22      2020-01-22 17:00:00        0.0         22.0

Dealing with missing data:

In the previous example, the rolling_sum column has Nan values, so we can use that data to demonstrate how to deal with missing data.  

Null values appear as NaN in Data Frame when a CSV file contains null values. Fillna() handles and lets the user replace NaN values with their own values, similar to how the pandas dropna() function maintains and removes Null values from a data frame. Filling the missing values in the dataframe in a backward manner is accomplished by passing backfill as the method argument value in fillna(). Fillna() fills the missing values in the dataframe in a forward direction by passing ffill as the method parameter value.

Output:

                        Last Update  Confirmed  rolling_sum
ObservationDate                                            
2020-01-22      2020-01-22 17:00:00        1.0          NaN
2020-01-22      2020-01-22 17:00:00       14.0          NaN
2020-01-22      2020-01-22 17:00:00        6.0          NaN
2020-01-22      2020-01-22 17:00:00        1.0          NaN
2020-01-22      2020-01-22 17:00:00        0.0         22.0
                        Last Update  Confirmed  rolling_sum  rolling_backfilled
ObservationDate                                                                
2020-01-22      2020-01-22 17:00:00        1.0          NaN                22.0
2020-01-22      2020-01-22 17:00:00       14.0          NaN                22.0
2020-01-22      2020-01-22 17:00:00        6.0          NaN                22.0
2020-01-22      2020-01-22 17:00:00        1.0          NaN                22.0
2020-01-22      2020-01-22 17:00:00        0.0         22.0                22.0

Fundamentals of Unix/epoch time:

One may come across time values in Unix time while working with time-series data. The amount of seconds since 00:00:00 Coordinated Universal Time (UTC), Thursday, January 1, 1970, is known as Unix time, sometimes known as Epoch time. Unix time helps us decipher time stamps so we don’t get confused by time zones, daylight savings time, and other factors. 

In the below example we convert epoch time to timestamp using pd.to_timestamp()  method. If we want time in UTC to a particular time zone, tz_localize() and tz. convert() methods are used. In the below example we convert it to the ‘Europe/Berlin’ timezone.

Output:

2020-08-30 08:43:09
2020-08-30 10:43:09+02:00