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<speak> Now, let's check all the column names. <break strength="x-strong"/>We will write df.columns.<break strength="x-strong"/> So, here we have named the data frames as train_df. <break strength="x-strong"/>Let's run the cell.<break strength="x-strong"/> So, here we have Airline, Date of journey, Source, Destination, Route, Departure time and Arrival time, Duration, Total stops, Additional Info, and Price. <break strength="x-strong"/>So, these are all the column names present in this data frame. <break strength="x-strong"/>Now, let's check the information about this data frame. <break strength="x-strong"/>Now, let's check the information about this data frame.<break strength="x-strong"/> Let's see, what are the data types of these columns.<break strength="x-strong"/> Let’s run the cell. <break strength="x-strong"/>So, here we have object columns.<break strength="x-strong"/> So these are strings. <break strength="x-strong"/>Only prices are in integer (inte64) format. <break strength="x-strong"/> Other all these are in string format. <break strength="x-strong"/>So, here let's check more information about this price column. <break strength="x-strong"/>So, when we run this train df.describe. <break strength="x-strong"/>It will give us mean, average, and median of only price column.<break strength="x-strong"/> Because, it is only in number format other columns are in string format. <break strength="x-strong"/> Let's run the cell. <break strength="x-strong"/> so price total we have 10683 rows or samples. <break strength="x-strong"/> And the mean price is 9087.<break strength="x-strong"/> And the standard deviation is 4611.<break strength="x-strong"/> And the minimum price is 1759.<break strength="x-strong"/> And the maximum price is 79512 rupees. <break strength="x-strong"/> So 75% of data come under 12373 rupees. <break strength="x-strong"/> Now let's check whether we have any null values or not.<break strength="x-strong"/> We use is null method to check for null values.<break strength="x-strong"/> Let's run the cell.<break strength="x-strong"/> So here we can see that there are only very few null values. <break strength="x-strong"/>Let's check the total number of null values. <break strength="x-strong"/>We use is null dot sum method to find total number of values.<break strength="x-strong"/> So write train_ df. is null.sum. <break strength="x-strong"/>Here without dot sum, we got complete visualization of this data frame.<break strength="x-strong"/> Now after writing sum, we will get total values. <break strength="x-strong"/> <break strength="x-strong"/> </speak>