Much of the data that we generate today is in the form of time-series data. And analysis of this data often relies on representing the timestamps of the data in a structure that is amenable to time-based slicing and dicing. In standard Python and popular data analysis libraries such as Numpy and Pandas, there are dedicated data types to store time-based information. However, incoming timestamps are often strings with different formats. And parsing these strings into time-based data types is a time-consuming and sometimes tedious process.
In standard Python, a common way of parsing timestamp strings that have a known format is the time module’s strptime method (similar interface to C’s strptime).
However, since most data scientists have to do much more with a dataset than parse timestamp strings, powerful libraries like Pandas have become very popular. And in Pandas, the most common way of parsing timestamp strings is the to_datetime method. This method provides a lot of flexibility and it can even infer formats automatically. Therefore, many people use it almost blindly.
In this article, we’ll examine the performance and applicability of different timestamp parsing methods on different types of datasets. We’ll see when to blindly use Pandas and when to use something else.
In this analysis, we’re going to compare six common ways of parsing a collection of timestamp strings.
For timestamp strings with a known format, Python’s time module provides this method to convert a string to a Python Datetime object.
2. Pandas.to_datetime without inferring
This method within the Pandas library can convert a collection of timestamp strings even without a pre-known format.
Note how the list of timestamp strings ts_str_list has timestamps in different formats. Pandas automatically infers out the format of each timestamp string before converting it.
3. Pandas.to_datetime with inferring
The same to_datetime method in Pandas has several optional arguments. One of these arguments is infer_datetime_format. By default, it is set to False. However, by setting it to True, the method infers the format of the first timestamp string in a collection, and then tries to use that format to parse the rest of the strings. If the inferred format doesn’t match any subsequent strings in the collection, the method falls back on the behaviour of infer_datetime_format = False.
The advantage of this method is that it saves a lot of time when parsing a collection of strings that have a consistent format.
4. Pandas.to_datetime with a specified format argument
Another argument accepted by the to_datetime method is format. Similar to time.strptime, this lets us explicitly define a format for parsing a collection of timestamp strings. As we will see later, the advantage of this method is that it is quite a bit faster than letting Pandas infer the datetime on its own. However, the pre-requisite is that the collection of timestamp strings has a consistent and pre-known format.
5. time.strptime with memoization
Memoization is a technique to store results of operations such that no operation has to be repeated. Using memos for the time.strptime method can ensure that in datasets that have duplicate timestamps, we don’t waste any time parsing the same string more than once. Of course, in datasets without any duplicates, this method will not have a benefit over the plain time.strptime method.
6. Pre-built lookup mapping
Another method to parse a long list of timestamps of a pre-known format and pre-known time-range would be to create a mapping of strings to Datetime objects. Then, we can use Python’s map method to obtain a list of Datetime objects that correspond to each timestamp string.
1. List of timestamps with a standard format
ISO-8601 is a widely accepted international standard for time-related information exchange. In addition to timestamps that follow the ISO-8601 standard, a few others are also a “standard” format as far as Pandas is concerned. This means that there is some set of timestamp formats that Pandas can parse very efficiently. An exhaustive list of these is not available (as far as I know) but in general, timestamp formats with all parts of the date and ones that start with the year seem to fall under this category.
So now, let us see how these methods perform when given timestamps of a known standard format. The formats of the timestamps are consistent throughout each dataset. We test the performance with datasets of different sizes given to the applicable methods.
The results show that Pandas.to_datetime significantly outperforms time.strptime in this instance. The pre-built lookup method also marginally outperforms the time.strptime method. However, it is still well short of the performance that Pandas delivers.
2. List of timestamps with a non-standard format
Now, if we run the same tests with datasets that have a non-standard timestamp format (e.g. 13–11–2000 04:50:32), we see some differences.
We notice here that Pandas.to_datetime with a specified format performs the best and a plain time.strptime loop comes in second place. The pre-built lookup method spends too much time building the map and therefore, its performance suffers. Pandas.to_datetime without the infer option also takes a long time because of the repeated format-inference of each timestamp string.
We also see some curious behaviour with the results of Pandas.to_datetime with infer. We see that it performs exceptionally well until it hits a dataset size of close to 20000. And then it performs the same way as Pandas.to_datetime without infer. What is going on here?!
This behaviour happens to be a side-effect of the dataset used in these experiments but it illustrates an important point. The dataset used in these experiments is a list of timestamps that starts with 12:00AM on January 1, 2000 and progresses consistently with an interval of 1 second. The format of the timestamp used is dd-mm-yyyy hh:MM:ss. Therefore, when Pandas tries to infer the first timestamp in the list, there is ambiguity about the format. This is because the timestamp string 01–01–2000 00:01:00 could be either in the format dd-mm-yyyy hh:MM:ss or mm-dd-yyyy hh:MM:ss!
So, when we have a dataset that starts with an ambiguous timestamp but has an unambiguous timestamp towards the end of the list, Pandas may realize that its inference is incorrect when it reaches the end of the list. It would then fall back to the behaviour of inferring the datetime format for each timestamp string individually. This would cause the performance of the operation to be similar to the case when infer_datetime_format = False.
The datasets used thus far have had no duplicates in them. However, in the real world, we are often dealing with datasets that have repeated timestamps or multiple datasets from the same time period. In the industrial intelligence domain (in which I currently work), it is not uncommon to process scores of datasets together from the same time-range and therefore, there is a lot of duplicated timestamp strings between all of them.
In the following experiments, we’ll see how our choice of timestamp parsing may change based on how much duplication we have within our dataset.
For the following experiments, all datasets contained 1 million timestamp strings. During a test, different numbers of duplicates were infused into each dataset while keeping the dataset size fixed. The lowest number of duplicates infused was 0 (all unique), and the highest number of duplicates infused was 100 (each timestamp in the dataset had 99 other copies).
Experiment with timestamp strings of a standard format (and consistent throughout dataset)
We see here that Pandas.to_datetime is an easy choice when dealing with a standard format. And as expected, memoization and pre-built lookup mapping improves as the number of duplicates in a dataset increases.
Experiment with timestamp strings of a non-standard format (and consistent throughout dataset)
But when the format of the timestamps is not standard and there are some duplicates in the dataset, memoization and pre-built lookup mapping both perform significantly better. In fact, I recently used the pre-built lookup mapping method to parse a large collection of timestamp strings and it saved me over 8 hours!
For data without too many duplicates:
- Timestamps with a consistent known format
Use Pandas and specify the format.
- Timestamps with a consistent but unknown format
Use Pandas with infer_datetime_format = True.
- Timestamps without a consistent format
Use Pandas with infer_datetime_format=False.
For data with a lot of duplicates:
For data with duplicates, the format of the timestamps matter. Therefore, here is a handy table to help you choose.
We use the datetime64 dt accessor and the very flexible strftime formatter to convert your datetime stamps to a string. This convert the stamps datetime column to a string with format dd/mm/yyyy – note that after formatting with strftime, the Series / column dtype is object.How to convert pandas timestamp to datetime? ›
To convert a Timestamp object to a native Python datetime object, use the timestamp. to_pydatetime() method.Can you do time series analysis in Python? ›
Time Series Analysis in Python considers data collected over time might have some structure; hence it analyses Time Series data to extract its valuable characteristics. Consider the running of a bakery. Given the data of the past few months, you can predict what items you need to bake at what time.How do you slice time series data in Python? ›
- import numpy as np import matplotlib.pyplot as plt import pandas as pd from timeseries import read_data Copy. ...
- # Load input data index = 2 data = read_data('data_2D.txt', index) Copy.
Converting timestamp to datetime
We may use the datetime module's fromtimestamp() method to convert the timestamp back to a datetime object. It returns the POSIX timestamp corresponding to the local date and time, as returned by time. time().
- 2.1. Standard Format. The simplest way to parse a String to a Timestamp is its valueOf method: Timestamp.valueOf("2018-11-12 01:02:03.123456789") ...
- 2.2. Alternative Formats. Now, if it isn't in JDBC timestamp format, then luckily, valueOf also takes a LocalDateTime instance.
- import pandas as pd import datetime. Create the timestamp in Pandas.
- timestamp = pd.Timestamp(datetime.datetime(2021, 10, 10)) Display the Timestamp.
- print("Timestamp: ", timestamp) Getting the current date and time.
- res = timestamp.today() Example.
Timestamp is the pandas equivalent of python's Datetime and is interchangeable with it in most cases. It's the type used for the entries that make up a DatetimeIndex, and other timeseries oriented data structures in pandas.How to convert datetime string to Timestamp in Python? ›
- Import the datetime library.
- Use the datetime. ...
- Use the strptime method to convert a string datetime to a object datetime.
- Finally, use the timestamp method to get the Unix epoch time as a float.
There are multiple time-series analysis techniques like AR (AutoRegressive), MA (Moving Average), ARIMA (Auto-Regressive Integrated Moving Average), Seasonal AutoRegressive Integrated Moving Average (SARIMA), etc.
pmdarima is a Python library for statistical analysis of time series data. It is based on the ARIMA model and provides a variety of tools for analyzing, forecasting, and visualizing time series data.How do you Analyse time series data? ›
A time series analysis consists of two steps: (1) building a model that represents a time series (2) validating the model proposed (3) using the model to predict (forecast) future values and/or impute missing values.How do I get data between two timestamps in Python? ›
Time Difference between two timestamps in Python
Next, use the fromtimestamp() method to convert both start and end timestamps to datetime objects. We convert these timestamps to datetime because we want to subtract one timestamp from another. Next, use the total_seconds() method to get the difference in seconds.
- Define a dataframe.
- Apply pd.to_datetime() function inside df['datetime'] and select date using dt.date then save it as df['date']
- Apply pd.to_datetime() function inside df['datetime'] and select time using dt.time then save it as df['time']
Pandas has a built-in function called to_datetime()that converts date and time in string format to a DateTime object. As you can see, the 'date' column in the DataFrame is currently of a string-type object. Thus, to_datetime() converts the column to a series of the appropriate datetime64 dtype.How do I add a timestamp column in pandas? ›
In this example, we create a DataFrame with a `date` column containing string values. We then use the `to_datetime()` function to convert the `date` column to a datetime data type and assign it to a new column called `timestamp`. The resulting DataFrame has the original `date` column and a new `timestamp` column.What is the difference between datetime and timestamp? ›
The DATETIME type is used for values that contain both date and time parts. MySQL retrieves and displays DATETIME values in ' YYYY-MM-DD hh:mm:ss ' format. The supported range is '1000-01-01 00:00:00' to '9999-12-31 23:59:59' . The TIMESTAMP data type is used for values that contain both date and time parts.How to convert list of timestamp to date in Python? ›
Import the “datetime” file to start timestamp conversion into a date. Create an object and initialize the value of the timestamp. Use the ” fromtimestamp ()” method to place either data or object. Print the date after conversion of the timestamp.How to parse DateTime into string? ›
Convert DateTime to String using the ToString() Method
ToString() method to convert the date object to string with the local culture format. The value of the DateTime object is formatted using the pattern defined by the DateTimeFormatInfo. ShortDatePattern property associated with the current thread culture.
parseTimestamp() Parses a string into a timestamp. This function is important for creating parsers, as it is used to parse the timestamp for an incoming event. Before parsing the timestamp, the part of the log containing the timestamp should be captured into a field.
The toString() method of the java. sql. Timestamp class returns the JDBC escape format of the time stamp of the current Timestamp object as String variable. i.e. using this method you can convert a Timestamp object to a String.How to get current timestamp in pandas? ›
To obtain the current time in the local time zone in pandas, we use the now() function of Pandas. Timestamp (an equivalent of Python's datetime object).How do I add a timestamp to a Dataframe in Python? ›
The current timestamp can be added as a new column to spark Dataframe using the current_timestamp() function of the sql module in pyspark. The method returns the timestamp in the yyyy-mm-dd hh:mm:ss. nnn format.How to combine date and Timestamp pandas? ›
A Timestamp object in pandas is an equivalent of Python's datetime object. It is a combination of date and time fields. To combine date and time into a Timestamp object, we use the Timestamp. combine() function in pandas .What is the difference between Timestamp and datetime big query? ›
Datetime type: comprises both calendar date and time. It does not store time zone information: YYYY-MM-DD HH:MM:SS (e.g. ). Timestamp type: comprises date, time, and time zone information.What are the different datetime formats in pandas? ›
By default pandas datetime format is YYYY-MM-DD ( %Y-%m-%d ). In this article, I will explain how to convert this datetime to a String format for example to MM/DD/YYYY ( %m/%d/%Y ) and to any other string date pattern.How to format datetime timestamp in Python? ›
What is timestamp in Python? Timestamp is the date and time of occurrence of an event. In Python we can get the timestamp of an event to an accuracy of milliseconds. The timestamp format in Python returns the time elapsed from the epoch time which is set to 00:00:00 UTC for 1 January 1970.How to parse date in Python? ›
- import datetime.
- def convert(date_time):
- format = '%b %d %Y %I:%M%p'
- datetime_str = datetime.datetime.strptime(date_time, format)
- return datetime_str.
- date_time = 'Dec 7 2022 10:46AM'
Use to_timestamp() function to convert String to Timestamp (TimestampType) in PySpark. The converted time would be in a default format of MM-dd-yyyy HH:mm:ss.What is the best way to visualize time series data? ›
A line graph is the simplest way to represent time series data. It helps the viewer get a quick sense of how something has changed over time.
Python and R are both great programming languages for performing time series. However, R is unparalleled today for diverse time series applications except for applications that require LSTM and other deep learning models to be implemented, in which case Python works best.What is the best forecasting method for time series data? ›
AutoRegressive Integrated Moving Average (ARIMA) models are among the most widely used time series forecasting techniques: In an Autoregressive model, the forecasts correspond to a linear combination of past values of the variable.What is time series analysis in pandas? ›
In Python it is very popular to use the pandas package to work with time series. It offers a powerful suite of optimised tools that can produce useful analyses in just a few lines of code. A pandas. DataFrame object can contain several quantities, each of which can be extracted as an individual pandas.Which is the best DateTime package in Python? ›
Pendulum: Probably The Best Python DateTime Library.How to plot time series in Python? ›
- Create x and y points, using numpy.
- Plot the created x and y points using the plot() method.
- To display the figure, use the show() method.
- Trend component.
- Seasonal component.
- Cyclical component.
- Irregular component.
- converted the Month column from strings to datetime;
- set the transformed datetime column as the index;
- extracted year, month and weekday from the index and stored in new columns.
The length of time series can vary, but are generally at least 20 observations long, and many models require at least 50 observations for accurate estimation (McCleary et al., 1980, p. 20). More data is always preferable, but at the very least, a time series should be long enough to capture the phenomena of interest.What is the best way to compare timestamps in Python? ›
Use the strptime(date_str, format) function to convert a date string into a datetime object as per the corresponding format . For example, the %Y/%m/%d format codes are for yyyy-mm-dd . Use comparison operators (like < , > , <= , >= , != , etc.) to compare dates in Python.How to extract timestamp from string in Python? ›
To extract the date, simply use a regular expression and "datetime. datetime. strptime" to parse it. For example, if you have a date in the format YYYY−MM−DD in a string, you may extract and parse it using the code below.
In order to select rows between two dates in pandas DataFrame, first, create a boolean mask using mask = (df['InsertedDates'] > start_date) & (df['InsertedDates'] <= end_date) to represent the start and end of the date range. Then you select the DataFrame that lies within the range using the DataFrame. loc method.How to split timestamp in pandas? ›
- Step 1 - Import the library. import pandas as pd. ...
- Step 2 - Setting up the Data. We have created an empty dataframe then we have created a column 'date'. ...
- Step 3 - Creating features of Date Time Stamps. We have to split the date time stamp into few features like Year, Month, Day, Hour, Minute and Seconds.
Method 1: Use SPLIT Function
Since the timestamp is composed of Date and Time, we can use the SPLIT function to extract the Date to one cell and Time to another cell. Here are the steps: On cell B2, type =SPLIT(A2, “ ”). This will automatically write the date in cell B2 and the time in cell C2.
Select Text to Columns and choose Space for the Separated By field. By default, the Tab option will be enabled for the Separated By field, so you'll need to uncheck that after choosing Space. Choose the Collection Time column and then select Date (MDY) from the Column type drop-down. Once you're done, click OK.What is Parse_dates in pandas? ›
We can use the parse_dates parameter to convince pandas to turn things into real datetime types. parse_dates takes a list of columns (since you could want to parse multiple columns into datetimes ).How to convert pandas Timestamp to DateTime? ›
To convert a Timestamp object to a native Python datetime object, use the timestamp. to_pydatetime() method.Does pandas support DateTime? ›
pandas supports converting integer or float epoch times to Timestamp and DatetimeIndex . The default unit is nanoseconds, since that is how Timestamp objects are stored internally.Can we convert timestamp to string? ›
The toString() method of the java. sql. Timestamp class returns the JDBC escape format of the time stamp of the current Timestamp object as String variable. i.e. using this method you can convert a Timestamp object to a String.How to get string from timestamp Python? ›
- We imported datetime class from the datetime module. ...
- The datetime object containing current date and time is stored in now variable.
- The strftime() method can be used to create formatted strings.
Use the datetime. strptime() function(formats a time stamp in string format into a date-time object) to convert the timestamp to datetime object by passing the input timestamp and format as arguments to it. Print resultant datetime object.
Problem: How to convert the Spark Timestamp column to String on DataFrame column? Solution: Using <em>date_format</em>() Spark SQL date function, we can convert Timestamp to the String format. Spark support all Java Data formatted patterns for conversion.How do I format a date as a timestamp string? ›
The default format of the timestamp contained in the string is yyyy-mm-dd hh:mm:ss.How to convert timestamp string to seconds in Python? ›
To convert a datetime to seconds, subtracts the input datetime from the epoch time. For Python, the epoch time starts at 00:00:00 UTC on 1 January 1970. Subtraction gives you the timedelta object. Use the total_seconds() method of a timedelta object to get the number of seconds since the epoch.How do I change the timestamp format in pandas? ›
To change the datetime format from YYYY-MM-DD to DD-MM-YYYY use the dt. strftime('%d-%m-%Y') function.How to convert timestamp list to date in Python? ›
Import the “datetime” file to start timestamp conversion into a date. Create an object and initialize the value of the timestamp. Use the ” fromtimestamp ()” method to place either data or object. Print the date after conversion of the timestamp.What is the timestamp type in pandas? ›
Timestamp is the pandas equivalent of python's Datetime and is interchangeable with it in most cases. It's the type used for the entries that make up a DatetimeIndex, and other timeseries oriented data structures in pandas.How to convert timestamp column to string in Python? ›
You can use the strftime() function provided by the datetime module to convert a timestamp to a date format in Python. The strftime() function lets you format a date and time object into a string representation of the date in the specified format.How to convert timestamp to formatted date in Python? ›
strptime() converts a timestamp in the form of a string to a datetime object which gives us a lot of extra functionalities. This function expects a string and the format of the timestamp. The string 21-02-2021 18:46:00 is converted to a suitable datetime using the format specified.How to convert timestamp to UTC string in Python? ›
You can use the datetime module to convert a datetime to a UTC timestamp in Python. If you already have the datetime object in UTC, you can the timestamp() to get a UTC timestamp. This function returns the time since epoch for that datetime object.How do I add a timestamp to a DataFrame? ›
In this example, we create a DataFrame with a `date` column containing string values. We then use the `to_datetime()` function to convert the `date` column to a datetime data type and assign it to a new column called `timestamp`. The resulting DataFrame has the original `date` column and a new `timestamp` column.