get season in python assignment expert

Find the Season from a Timestamp in Python: Exploring 2 Methods

In this article, lets us understand how one can determine the season that is – winter, spring, summer, and autumn from provided timestamp or the date in Python programming language using the datetime module and the date built-in class of the same module. The date class is utilized for date formats, presuming the Gregorian calendar is currently in use. The following is the order of the attributes: Year, month, and day. Using the datetime module, users might very well work with a variety of classes that seamlessly works on the date as well as time data inputs and can integrate both date and time.

Methods to Determine Season from Timestamp in Python

Let us look at two different approaches both with their own pros and cons to determine the season for the provided date.

1. Using Simple Mathematical Formula to Determine Season

Simple maths is used in this approach, only the month is required to determine the season. This is quite a straightforward way, one cannot simply modify the seasons associated with the month. To do so new mathematical formula is required. The advantage of this method is that it is easy to implement, with no need to pre-define anything, just one line mathematical formula month % 12 // 3 + 1 is used.

Pros and Cons of this approach

• This method is suitable when we are considering the standard four-season spring from March to May, summer from June to August, Autumn from September to November, and Winter from December to February.
• The drawback of using this method is that if we consider seasons according to another hemisphere, for example in India the seasons described are three- Summer, Monsoon, and Winter, using this same mathematical formula will not work, one has to come up with different maths every time for every different situation which might not be very efficient at times.

2. Using Combined Month and Day Integer Values to Determine Season

To determine the season the year is not necessarily required. One needs to know the date: month and day, and that’s enough. In this approach, we are converting the month and day into a single integer by combining both values for example 3/20 is march 20th we will transform it into 3+ 20 = 320 similarly 12/10 is December 10th will get converted into 12 + 10 = 1210. Next, we define a function to determine the season using these combined values. Here in this example, four seasons are set.

• The major benefit of using this method is that one can modify the ‘if’ and ‘elif’ statements according to their requirements and can set seasons differently. The time period of seasons is to be set by the programmer and hence customization is possible in this approach.
• The only drawback here is that one to pre-define the seasons in the function.

To understand the difference between the ‘/’ and ‘//’ operators in the python programming language, please click here.

Comparing Both Approaches and Final Thoughts

The datetime module in the python programming language is a great help when it comes to date and time data inputs. The module has many built-in classes which can be combined and used in efficient ways. In this article, we studied two approaches to determine the season from provided input date using the datetime module and the “date” package of the same module.

Official Document

How To Find Seasonality Using Python

Parsing seasonality from time series data can often be useful in data analytics. It helps with analyzing seasonality for decision making as well as for more accurate forecasts. Python can be used to separate out these trend and seasonal components.

The time series data we’ll be analyzing is the Kansas City Crime Summary - more specifically the number of Breaking & Entering crimes per month. Do you think there will be any seasonal components to this data? Let’s find out.

Initial Visualization

To start, the original data is visualized below using Power BI - this helps to see any seasonality that can be spotted visually. There appears to be an overall downward trend and what looks like some seasonality as well - February is often the lowest point, while crimes increase in the summer and into the Holidays.

However, how much is related to seasonality vs. trend and how would we automate separation of seasonality components ?

Seasonal Decomposition

The statsmodels library in Python has a seasonal_decompose function that does just this. Given a time series of data, the function splits into separate trend, seasonality, and residual (noise) components.

After loading and reformatting the data, the date and metric will be fed into this function to parse out the separate pieces.

To get the data in the right shape, there are 4 main steps to take:

• Read in the data : Data will be read into a pandas dataframe using the pandas.read_csv function.
• Pull out just the date and metric columns : We only need the date component (monthly for this dataset) and metric (the Burglary/Breaking and Entering column).
• Normalize the data : Since each month has a different number of days, dividing the monthly totals by number of days in the month gives a more comparable average daily count to use.
• Set the dataframe date index : Setting our date column to be the index of the pandas dataframe allows for an easier setup when using the seasonal decompose function. Additional examples on setting an index can be found here .

The code for these four steps is as follows:

There is one custom function used as a helper, set date index , to abstract away date formatting into a separate function.

It creates a copy of the dataframe (to leave the original data intact), sets the date column to a datetime type, and finally sorts and sets the index.

Trend vs. Seasonality

The next piece is actually running the seasonal decomposition. The dataframe is passed in as an argument as well as period=12 to represent our monthly data and find year-over-year seasonality.

One additional helper function was used to simply add the results to our original dataframe as new columns.

Finally the results were written to a local csv file to be visualized in Power BI. If this was a recurring process, we could setup an API or use cloud services to host our model; however, for a one-time run a local csv file will do just fine.

Visualize Results

Visualizing the final result, the new seasonal (dark blue) and trend (orange) lines are added to the original chart.

A slight downwards trend has been identified along with a seasonal component (adding or subtracting up to 4 crimes per day from the trend).

The neat part of this methodology is that the trend, seasonal, and residual components are additive back to the original time series. You’ll see in the visualization below, adding the trend and seasonal components more or less gets back to the original dataset (residual/noise is the remaining piece).

Crimes seem to be lowest in February due to seasonality (cold weather?), and ramp up throughout the summer months and into the holidays.

Using the statsmodels library in Python, we were able to separate out a time series into seasonal and trend components. This can be useful for forecasting - for example, extending a trend and then adding back the same seasonal ups and downs into the future. It can also be helpful when analyzing degree seasonality is important - ex: if you wanted to see what time of year to focus resources.

Interested in your thoughts, if you found this approach helpful or have used different approaches in the past to solve similar issues - comment below!

All examples and files available on Github .

• Machine Learning

For a deeper dive into some of the concepts related to this article, check out the following books:

Python Exercise: Prints the season for that month and day

Python conditional: exercise - 37 with solution.

Write a Python program that reads two integers representing a month and day and prints the season for that month and day.

Pictorial Presentation:

Sample Solution:

Python Code:

Sample Output:

Flowchart :

Python Code Editor:

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IPL Analysis with Python

• December 23, 2020
• Machine Learning

Cricket is an important part of the culture in India and the Indian Premier League (IPL) matches are one of the most important events in India. In this article, I will introduce you to a data science project on IPL analysis with Python.

Data Science Project on IPL Analysis

IPL is a major professional cricket league in India, contested by eight teams representing different cities in India. This IPL analysis task focuses on analyzing the performance of the eight competing IPL teams.

Also, Read – 100+ Machine Learning Projects Solved and Explained.

Team performance is visualized graphically using the Plotly library in Python to render interpretation efficiently. Performance data using visual analysis help select players for future matches and provides additional information about the player as well as team profiles.

Now let’s start the task of IPL analysis with Python by importing the necessary Python libraries and the dataset:

In December 2018 the team changed their name from Delhi Daredevils to Delhi Capitals and Sunrisers Hyderabad replaced Deccan Chargers in 2012 and debuted in 2013. But I consider them to be the same in this IPL analysis task. Now let’s start with some data preparation:

Let’s start with looking at the number of matches played in every season of the IPL:

The year 2013 has the most matches, possibly due to super overs. Also, there are 10 teams in 2011, 9 in 2012 and 2013, this is another reason for the increase in the number of matches.

Matches Played Vs Wins

Now let’s have a look at the number of matches played by each team and the number matches won by them:

Now let’s analyze the winning percentage of all IPL teams:

So MI, SRH and RCB are the top three teams with the highest winning percentage. Let’s look at the winning percentage of these three teams:

The next step in IPL analysis is to have a look at the venues where the most number of matches have been played:

So Eden Gardens, M Chinnaswamy, Wankhede and Feroz Shah Kotla are stadiums with most matches because most of each season’s eliminators, playoffs and finals were there. Now let’s have a look the most prefered decision taken by teams after winning the toss:

Runs Per Season

In this section of IPL analysis with Python, we will analyze the runs per season. Let’s start by looking at the average and total runs of all the seasons:

Now let’s have a look the distributions of runs over the years which will be distributed among three categories; 6s, 4s and remaining runs:

We can see just a slight increase in runs by boundaries over the years. At last, we will look at the highest runs scored by teams over the years:

I hope you liked this article on a data science project on IPL analysis with Python. Feel free to ask your valuable questions in the comments section below.

Aman Kharwal

Data Strategist at Statso. My aim is to decode data science for the real world in the most simple words.

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Hi. Could you please provide a code for the Total and Average runs per Seasons graph. I am referring to Runs Per Season section

Please update on my query regarding code for the Total and Average runs per Seasons graph.

Sure, I will come up with a new tutorial soon to cover everything in more detail

Actually I need a code, for the 2 graphs you generated above for “Total and Average runs per Seasons”.

Hope I clarify my query. Please advise at your earliest. Thanks in advance

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Augmented Assignment (Frozen Sets)

• Discussion (2)

00:00 Besides frozen sets being immutable and regular sets being mutable, there’s also another difference. Augmented assignment works differently for normal sets and frozen sets.

00:10 You would think augmented assignment would not work for frozen sets because it mutates regular sets, but it actually does work. Here we see intersection update ( &= ) and difference update ( -= ) and symmetric difference update ( ^= ). These all work differently for frozen sets.

00:25 These are actually the same as their expanded out counterparts. So x &= {1} is actually the same as x = x & {1} .

00:37 So, these are actually the same for frozen sets and they’re not the same for regular sets. Here’s our side-by-side comparison. We have x = frozenset({1}) , y = x , so now they’re pointing to the same frozenset .

00:52 We use the augmented assignment ( |= ) to union update x . We print out x , it’s the frozenset({1, 2}) . But when we print out y , it’s actually still the frozenset({1}) .

01:03 So this reassigned x . Versus y = x , x = x | {2} . This reassigned x to be a new frozenset({1, 2}) , and y is still frozenset({1}) .

01:18 Something to be careful with when using frozen sets is that you can use this augmented assignment, but you are not mutating it.

Minh Pham on March 25, 2020

Please include the code what you talk on the screen so that we can copy and try ouselves

James Uejio RP Team on April 4, 2020

Hi @Minh you can find the slides in the last video. It’s in PDF format but you can still copy and paste.

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Python is a high-level, interpreted, object-oriented programming language with dynamic semantics. It's a language that's known for its friendliness and ease of use, making it a favorite among both beginners and seasoned developers.

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• Regular Expressions: Regular expressions are powerful tools for pattern matching and text manipulation. Python's "re" module allows you to work with regular expressions.
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Using pandas to select specific seasons from a dataframe whose values are over a defined threshold

Apologies for the wall of code, but I can't shorten it further...

I want to sample climate data based on extreme seasons (seasons with temperatures greater or less than two standard deviations) in a deterministic way, i.e. so I can select a warm season, followed by a cold season, or multiple warms followed by multiple colds etc.

The sample code below should illustrate the problem

Import the necessary packages

Generate a dataframe with random values in a column

Define a season function and split data by season

If the month is December add 1 to the year value, so December January February are all in the same year

Calculate season means

Multi-index a new dataframe by season and year

Calculate the differences between seasons and season means

Visualise the seasonal differences and extremes based on standard deviations

It's clear from this that some seasons are above and below the 2 SD threshold, I want to be able to create a new deterministic sequence based on this, where I can select for example a normal spring followed by a hot summer followed by a hot autumn followed by a cold winter (or any permutations of this possible).

This function finds warm and cold seasons and appends 0 if it's not extreme and 1 if it's extreme to warm and cold columns respectively

Apply the function to the dataset:

add columns to the new dataframe which identify extremes

I want to generate a new climate sequence based on this data, where I can specify what kind of climate I want, i.e. I want to be able to select 8 random seasons in a row, then select a random spring, followed by a hot summer, followed by a random autumn, followed by a cold winter.

I need to be able to select whatever sequence I want, so far all I've managed to do is select a random sequence for x amount of years (50 in this case) using the following code:

The problem is this selects a completely random sequence, which is good as I want a random sequence, but I also want to be able to insert extreme seasons/years into this sequence e.g. 20 random years with 3 extreme years or 10 random years followed by 3 years with cold winters etc. and can't figure out how to do this.

2 Answers 2

Here's an example for selecting a normal spring followed by a warm summer (just using 1 std dev, not 2, for this example).

My random data is different than yours, so here are my values for 2036 and the std dev below that so that you can verify what the code is doing.

The following code creates a dataframe that has your year, season, temperature, two flag columns for unusually hot and cold weather this season, and two flag columns for unusually hot and cold weather last season.

First, duplicate your dataframe, and add flags for unusual weather to the new dataframe:

Then, drop your temperature column 'A', so that you have a "flags only" dataframe:

Now, concatenate your flags to your original temperature dataframe. By shifting the index of the flags as you concatenate, you can flag whether the unusual weather happened last season as opposed to this season.

In this case, seasdif2 adds flag columns for unusually warm and cold weather this season, while seasdif2.shift(-1) adds columns for unusually warm and cold weather the previous season:

Be careful when doing this, however, as you'll end up with multiple "warm" and "cold" flag columns. Make sure you rename the columns added by shift(-1) something like "cold_previous" and "warm_previous" respectively.

Now you can select rows where unusual weather occurred in two consecutive seasons. If you wanted to find whether a hot season is followed by cold season, you would just select dataframe rows where warm==1 and cold_previous==1, for example.

• Thanks this really helps - I'm sorry for not being clearer, but I don't want to identify consecutive seasons which may have been extreme, I just want to identify the extremes (which I can do thanks to your help) and then create a new climate sequence containing these extremes (e.g. 10 'normal' climate years followed by a year with an extremely cold and wet winter, followed by a hot summer etc), so I need to work out a way of selecting the all the data within an extreme season and putting it in a dataframe with the sequence of 'normal' observations... Thanks again! –  Pad Commented Mar 7, 2016 at 12:34

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Answer to Question #220754 in Python for Hari nadh babu

Weather condition

This Program name is Weather condition. Write a Python program to Weather condition, it has two test cases

The below link contains Weather condition question, explanation and test cases

https://drive.google.com/file/d/1scnLsEZFIGuWVs7r50zUIoPOl0UWzOJ0/view?usp=sharing

We need exact output when the code was run

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