Python Time Series Data


Python Time Series Data is a significant practice of structured data. This is formed in several changed fields, for example, economics, finances, biology, neuroscience, or physics. Everything that is experimental or measured at many points in time forms a time series. A lot of time series are the static frequency that is to say that data points happen at fixed pauses. According to some rule it occur in intervals, for example, every 15 seconds, every 5 minutes, or once per month. We might have seconds and minute-wise time series as well, equal, number of clicks and user visits every minute etc. Python time series data may also be uneven deprived of a fixed unit or time or offset between units. How we mark and mention to time series data be determined by on the request and we can have one of the following:

  • Timestamps, precise split second in time
  • Stationary periods, for example, the month January 2020 or the full year 2021
  • Pauses of time, designated by a start and end timestamp. Periods may be believed of as distinctive cases of intervals.
  • Testing or passed time; each timestamp is an amount of time relative to a specific start time. For instance, the width of a cookie baking to each second in the meantime being retained in the oven

Python Time Series Data 1


We even study a time series because it is the introductory step before we develop a forecast of the series. Moreover, time series forecasting has huge commercial implications due to stuff that is essential to a business like demand and sales, number of visitors to a website, stock price etc are in essence time series data. Consequently what does examining a time series include?

Python time series data investigation includes considerate many features about the characteristic nature of the series so that we are well informed to create expressive and precise forecasts.

Pandas make available a standard set of time series tools and data algorithms. Using this, we can proficiently work with very big time series and without difficulty slice and dice, aggregate, and resample uneven and static frequency time series. For instance we might estimate, numerous of these tools are particularly beneficial for financial and economics applications. However we could surely use them to examine server log data, as well.

Python time series data plays an important role in time series analysis and forecasting. Plots of the raw sample data can provide valuable diagnostics to identify temporal structures like trends, cycles, and seasonality that can influence the choice of model. A problem is that many novices in the field of time series forecasting stop with line plots. In this tutorial, we will take a look at 6 different types of Python time series data that you can use on your own time series data. They are;

  • Line Plots.
  • Histograms and Density Plots.
  • Box and Whisker Plots.
  • Heat Maps.
  • Lag Plots or Scatter Plots.
  • Autocorrelation Plots

Python Time Series Data

Data Types and Tools for Date and Time

Data Types and Tools for Date and Time

The Python time series data library comprises data types for date and time data, along with calendar associated functionality. To start the datetime, time, and calendar modules are the main seats. The datetime.datetime type, or just datetime, is broadly used:

In [317]: from datetime import datetime

In [318]: now =

In [319]: now

Out[319]: datetime.datetime(2021, 8, 4, 17, 9, 21, 832092)

In [320]: now.year, now.month,

Out[320]: (2021, 8, 4)

datetime stores together the date and time down to the microsecond. datetime.time delta signifies the of time change between two datetime objects:

In [321]: delta = datetime(2011, 1, 7) - datetime(2008, 6, 24, 8, 15)

In [322]: delta

Out[322]: datetime.timedelta(926, 56700)

In [323]: delta.days In [324]: delta.seconds

Out[323]: 926 Out[324]: 56700

We may add or subtract a timedelta or several thereof to a datetime thing to return a new shifted object:

In [325]: from datetime import timedelta
In [326]: start = datetime(2011, 1, 7)
In [327]: start + timedelta(12)
Out[327]: datetime.datetime(2011, 1, 19, 0, 0)
In [328]: start - 2 * timedelta(12)
Out[328]: datetime.datetime(2010, 12, 14, 0, 0)
Fixed-frequency dates and time spans
In [4]: dti = pd.date_range("2018-01-01", periods=3, freq="H")
In [5]: dti
DatetimeIndex(['2018-01-01 00:00:00', '2018-01-01 01:00:00',
               '2018-01-01 02:00:00'],
              dtype='datetime64[ns]', freq='H')
Date times with timezone information conversion
In [6]: dti = dti.tz_localize("UTC")
In [7]: dti
DatetimeIndex(['2018-01-01 00:00:00+00:00', '2018-01-01 01:00:00+00:00',
               '2018-01-01 02:00:00+00:00'],
              dtype='datetime64[ns, UTC]', freq='H')
In [8]: dti.tz_convert("US/Pacific")
DatetimeIndex(['2017-12-31 16:00:00-08:00', '2017-12-31 17:00:00-08:00',
               '2017-12-31 18:00:00-08:00'],
              dtype='datetime64[ns, US/Pacific]', freq='H')
How to resample or convert a time series to a specific frequency
In [9]: idx = pd.date_range("2018-01-01", periods=5, freq="H")

In [10]: ts = pd.Series(range(len(idx)), index=idx)

In [11]: ts


2018-01-01 00:00:00    0

2018-01-01 01:00:00    1

2018-01-01 02:00:00    2

2018-01-01 03:00:00    3

2018-01-01 04:00:00    4

Freq: H, dtype: int64

In [12]: ts.resample("2H").mean()


2018-01-01 00:00:00    0.5

2018-01-01 02:00:00    2.5

2018-01-01 04:00:00    4.0

Freq: 2H, dtype: float64
How to perform date and time arithmetic with total or relative time increments.
In [13]: friday = pd.Timestamp("2018-01-05")

In [14]: friday.day_name()

Out[14]: 'Friday'

# Add 1 day

In [15]: saturday = friday + pd.Timedelta("1 day")

In [16]: saturday.day_name()

Out[16]: 'Saturday'

# Add 1 business day (Friday --> Monday)

In [17]: monday = friday + pd.offsets.BDay()

In [18]: monday.day_name()

Out[18]: 'Monday'
Timestamps versus time spans

Timestamped data is the greatest simple type of Python time series data that links values with points in time. For pandas objects it worth using the points in time.

In [28]: pd.Timestamp(datetime.datetime(2012, 5, 1))

Out[28]: Timestamp('2012-05-01 00:00:00')

In [29]: pd.Timestamp("2012-05-01")

Out[29]: Timestamp('2012-05-01 00:00:00')

In [30]: pd.Timestamp(2012, 5, 1)

Out[30]: Timestamp('2012-05-01 00:00:00')
How to convert to timestamps

We may use the to_datetime function to convert a Series or list-like object of date-like objects for example strings, epochs, or a mixture. It returns a series by the same index while a list-like is converted to a DatetimeIndex when passed a series:

In [43]: pd.to_datetime(pd.Series(["Jul 31, 2009", "2010-01-10", None]))


0   2009-07-31

1   2010-01-10

2          NaT

dtype: datetime64[ns]

In [44]: pd.to_datetime(["2005/11/23", "2010.12.31"])

Out[44]: DatetimeIndex(['2005-11-23', '2010-12-31'], dtype='datetime64[ns]', freq=None)

We can pass the dayfirst flag if we use dates which start with the day first for instance European style:

In [45]: pd.to_datetime(["04-01-2012 10:00"], dayfirst=True)

Out[45]: DatetimeIndex(['2012-01-04 10:00:00'], dtype='datetime64[ns]', freq=None)

In [46]: pd.to_datetime(["14-01-2012", "01-14-2012"], dayfirst=True)

Out[46]: DatetimeIndex(['2012-01-14', '2012-01-14'], dtype='datetime64[ns]', freq=None)
How to provide format argument

Furthermore to the necessary datetime string, a format argument may be passed to make sure exact parsing. This could likewise potentially speed up the conversion significantly.

In [51]: pd.to_datetime(“2010/11/12”, format=”%Y/%m/%d“) Out[51]: Timestamp(‘2010-11-12 00:00:00’) In [52]: pd.to_datetime(“12-11-2010 00:00″, format=”%d-%m-%Y %H:%M”) Out[52]: Timestamp(‘2010-11-12 00:00:00’)

Python time Series String format() Method

With Python 3.0, the format() method has been introduced for handling complex string formatting more efficiently. This method of the built-in string class provides functionality for complex variable substitutions and value formatting. This new formatting technique is regarded as more elegant. The general syntax of format() method is string.format(var1, var2,…)

Using a Single Formatter in Python time series data :

Formatters work by putting in one or more replacement fields and placeholders defined by a pair of curly braces { } into a string and calling the str.format(). The value we wish to put into the placeholders and concatenate with the string passed as parameters into the format function.


Single Formatter

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