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<speak> We don't have any string values. <break strength="x-strong"/>Let's check the mean median and average for all these columns.<break strength="x-strong"/> We use the pandas describe method, to view some basic statistical details like percentile, mean, std, etc. <break strength="x-strong"/> Let's run the cell. <break strength="x-strong"/> So, we have pregnancies. <break strength="x-strong"/>So the maximum a person has 17 pregnancies.<break strength="x-strong"/> I mean, a lady becomes pregnant 17 times, and she is a diabetic patient. <break strength="x-strong"/>We observe a minimum of 0 times.  <break strength="x-strong"/> Then we have 75 percent of ladies around six times they become pregnant.<break strength="x-strong"/>  And then we see glucose values. <break strength="x-strong"/> Then we have blood pressure, <break strength="strong"/>and the maximum blood pressure is 122.<break strength="x-strong"/> Here <break strength="strong"/>we see minimum blood pressure is zero, and skin thickness is maximum is 99, and the minimum is 0.<break strength="x-strong"/> The insulin is maximum <break strength="strong"/>and is 846.<break strength="x-strong"/> The minimum is 0.<break strength="x-strong"/> Here we have age, <break strength="x-strong"/>and the highest age of the patients is 81.<break strength="x-strong"/> And the minimum age is 21.  <break strength="x-strong"/> I hope every one of you able to understand the data set.<break strength="x-strong"/> Let's check the transpose of this data. <break strength="x-strong"/>Here in this way, we are unable to see details about all columns. <break strength="x-strong"/>We are not able to observe the outcome and the age features.<break strength="x-strong"/> We use the transpose method<break strength="strong"/> to change rows into columns and columns into rows. <break strength="x-strong"/> We can check all these columns at once using this method.<break strength="x-strong"/> </speak>