Dataframe attributes - Part 2
WE WILL USE NEW EXAMPLE FROM HERE.
6.new_data_frame.iat[] use to access(get or set) single value at
[row index,column index]
Here, in new_data_frame.iat[0,2] 0 is row index, 2 is country.
0th index=name
1st index=rank
2nd index=country.
7.new_data_frame.iloc[] result differs with the various type of inputs passed.
a) an integer - returns the entire row at specified integer(index value) along with column.
b)list of integers - new_data_frame.iloc[[0,1]] returns rows in 0th and 1st index.
c)Using slicing operator - new_data_frame.iloc[1:3] returns data in rows 1 and 2.
d)A list of boolean values matching length of dataframe -
Here length is 3, new_data_frame.iloc[[True,False,True]].
Now, 0th row and 2nd row will be printed.
e)Using lambda function - using lambda function.
f) Using double indexing - Access the value with row and column index.
8. new_data_frame.index - returns the row labels.
9. new_data_frame.loc - returns the mentioned row or rows.
10. new_data_frame.ndim - returns the array dimension of dataframe.
11. new_data_frame.size - returns the size of number of elements in dataframe.
12.new_data_frame.shape - returns the shape of dataframe.
3 rows and 4 columns in new_data_frame.
13.new_data_frame.style - styles the dataframe using html and css.
14. new_data_frame.to_numpy - changes elements to numpy array.
If you have come this far. You did a great job!
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