Skip to main content

To find a maximum number from a list, then finding the length of that maximum number and the total number of elements in the list using python.



  1. Now this is how we can find the maximum number from the list.
  2. Length of the maximum number from the list.
  3. The total number of elements from the list.



a = [54654,854,21,715732143241974,871,6354,9753,21]
print('the maximum number from the list is:', max(a))


def digits(a):
count = 0
while a != 0:
count+=1
a=a//10
return count
print('length of the maximum number from the list is:', digits(max(a)))

print('the total number of elements in the list are:', len(a))

Comments

Popular posts from this blog

Stock Market using Python

 "The stock market is a device for transferring money from the impatient to the patient." - Warren Buffett Today we'll look into few ways for accessing the stock market. And we'll do this using Python ! Now, as we know that there are 2 stock exchange in India; BSE and NSE So we'll get the data from both! To begin with let's access the data from BSE first. (P.S: I certainly like the 2nd and the 3rd method to access stock market!) * So, to import the BSE data we need to " pip install bsedata ". => And then import the module, => Create an object to store the Driver Class => Then we need to do " getQuote('script_code')" where we need to provide a script code of a company which we need to access. Just like here we have given; => And from here we can see that the script code was for the company named "V-MART". But we can't remember all the script code hence we need to download this script file from the BSE websi...

Predicting whether the bank will give loan to its customers based on their credit score (Logistic Regression)

"The best thing about data is that it tells a story." - Naveen Jain Today let's have a look at another machine learning model know as Logistic Regression. Logistic Regression is a statistical model where the outcome is predicted as binary such as YES or NO, based on the previous/train_dataset. ( Please open the images in a new tab or try to zoom-in ) import required models read your dataframe visualize your dataset create Logistic Regression model split your data into train and test dataset fit your train dataset into the regression model predict future values visualize your predicted values Github link for pdf:  click here

Machine Learning model to predict price_of_house

Today let's look at one of the machine learning model which can predict the prices of house based on the described data. We'll look at the linear regression model for which we'll follow a few steps such as: import required modules read the csv file plot the graph building the linear regression model to fit/train the data predict value with a parameter have a new data file for value prediction ( to view the images either open it in a new tab or zoom-in  please! )