In this blog post I will show you how to join two 2D Python lists together.
The code is in the screenshot below.
Lines 1 – 2 are two lists that are going to be joined, line 3 is an empty list where the output will be appended to.
Lines 4 – 5 are two loops (one nested inside the other) which cycle through the records in both lists, line 6 checks whether the first items (index 0) in the two records from each list that are currently i & j match. If yes: the Key, Capital and Country are appended to our new list.
Lines 9 – 10 show the output record by record showing the join has worked successfully.
This code can be expanded for 3 lists, the code would have a 3rd for loop and an extra check in the if statement to find the correct record in the 3rd list to join.
Joining lists together in Python is useful when there is data in different lists and it would be beneficial if it were combined.
In this blog post, I will run through 3 different ways to execute a delayed Python loop. In these examples, we will aim to run the loop once every minute.
To show the delay, we will print out the current datetime using the datetime module.
1 – Sleep
The sleep function from Python’s time module pauses the Python execution by the number of seconds inputted. The example below pauses the script for 60 seconds.
The above script has a 60 second delay between the end of a loop and the start of the next. However, what if we wanted to execute the next loop 60 seconds after the start of the previous loop.
In other words, how do we start the loop at the same time every minute.
2 – Calculate Sleep
To do this we calculate how long the loop should sleep for.
We will print the datetime every time the minutes changes. At the start of the loop we lookup the number of seconds that have passed so far this minute.
The number of seconds that have passed this minute is calculated from date_time[-2:]. Subtracting this from 60 gives the length of time in seconds for which the loop should sleep for, to execute when the next minute starts.
Once the loop has slept for the required number of seconds, we lookup the datetime again and print it out.
3 – Task Scheduler
The previous two options are good for executing a loop a few times, ten in our case. If we wanted to execute a python script continuously without expiring, we could use the above examples with an infinite appending loop.
However, if one loop errors the script will stop. Therefore, we want to execute the entire Python script once a minute using an external trigger. This is where we can use Task Scheduler.
Task Scheduler can execute a python script from source but it is often easier to use a batch file. The batch file includes the location of the python application (python.exe) and the location of the python script (.py). For more detail on using Task Scheduler and batch files to run Python scripts, please see the following datatofish post – https://datatofish.com/python-script-windows-scheduler
Our batch file is:
To demonstrate Task Scheduler, I’m going to run the following Python code every minute.
This code uses Pandas to produce a blank CSV file, but the name of CSV file is the datetime the script was run.
These following screenshots show the triggers and actions used.
This produced the following CSV files, we can see that the files takes 1 – 4 seconds to create.
In summary we have seen three different ways to delay a Python loop, two using loops inside Python and one using Task Scheduler. All can be used depending on what kind of delay is best.
Pi is 3.14159 to 5 decimal places.
To work out Pi, we will be using Leibniz’s formula:
X = 4 – 4/3 + 4/5 – 4/7 + 4/9 – …
This series converges to Pi, the more terms that are added to the series, the closer the value is to Pi.
For the proof on why this series converges to Pi – https://proofwiki.org/wiki/Leibniz’s_Formula_for_Pi
There are several points to note about the series:
- It’s infinite, we need to find a way to continue adding term after term.
- The denominator of the fraction increases by 2 every term.
- The terms alternate between positive and negative.
Firstly, let’s create a function called pi.
To continue adding terms, let’s use a for loop. Every time the loop executes another term is added.
range(1,10) will produce the numbers 1, 2, … 9, 10.
However, before the loops starts, our variables’ initial values need to be set.
The pi series will start from 0.
n represents the numerator of our fractions which is the constant 4.
d represents the denominator of our fractions which starts as 1.
d needs to increase by 2 every loop, let’s use sum equals to do this.
Pi will also use a sum equals, a denotes our positive/negative function which we will get on to:
The only problem left is how to get a to alternate between 1 and -1.
This is where we introduce modulo.
Modulo outputs the remainder of a division and is denoted by %.
This example shows from numbers 1 to 4, modulo 2 alternates between 0 and 1.
If we multiple by 2 and minus 1 it will alternate between 1 and -1 which is what we require.
Putting everything together gives:
This isn’t close to 3.14159 at all.
However, we are only executing the loop 10 times, hence only 10 terms are being used to calculate Pi.
Increasing the number of loops to a million will change this:
There it is! A function written from scratch to calculate Pi.
To get a value even closer to Pi just increase the number of loops.
Finally, if you do wish to use Pi in python the easiest way is to use the numpy library, which has a pi constant stored.