How To Access Columns From A Data Frame
We can access a column from a dataframe by using [] brackets
We can use single [] for accessing a single column or use [[]] for accessing single or multiple columns
Example 1
How To Delete A Column From A Data Frame
We can use drop() function or can use pop() or we can use del keyword also
drop()
drop() function can be used to remove rows as well as columns
So if i want to remove column by using drop() we have to use axis parameter as 1 (1 for rows 0 for columns)
Example 2
When trying to make changes to a Pandas dataframe using a function,like drop() here we use 'inplace=True' if we want to commit the changes to the orginal dataframe or else default will be inplace=False no changes to orginal dataframe
Example 3
pop()
By using pop() also we can remove a column
This functions directly affects the original data
Example 4
del keyword
We can use delete keyword to delete a column
Example 5
del can also be used to delete the entire dataframe
Example 6
How To Add A Column To An Existing Data Frame
By using simple accessing method we can add a column to a dataframe
First it will check whether that particular column label is there or not if it matches with any column label in the dataframe then it will replace the existing column values else creates a new column with the given values
Example 7
Example 8
if the appended column length does not matches with the dataframe column length then it will try to extend the appended column length with nan values to match with the dataframe length
Example 9
How To Select Rows From A Data Frame
iloc and loc indexing are two main technique to select rows from a dataframe
iloc -- integer based
loc -- label based
loc
loc is label-based, here we use labels (names) of columns and rows to filter it out
example, let’s say we are search for the rows whose index is 1, 5 or 15. Here we will get only the rows who's index are 1, 5 and 15 not 1 row 5th row or 15 row
By using loc we can filter the data by some condition
Example 10
Example 11
Using loc, we can also slice the pandas dataframe . In loc when we do slicing the end index is also included
Find all the rows based on any condition in a column
loc can also be used to filter out the rows based on the condition applied to columns
we can solve types of queries with a simple line of code using DataFrame.loc[]. We just need to pass the condition within the loc statement.
Example 12
**In the 1 ex above we filtered out all the rows in our dataset where age is greater than 25
**In the 2 ex above we filtered out all the rows in our dataset where sex is equal to m
Find all the rows with more than one condition
We can also use multiple conditions to filter our data
For example finding out all the rows in our dataset where age is greater than 25 and sex equal to m
Example13
Example 14
Example 15
** When we are applying two or more conditions pass each condition in this format
(condition1) & (condition2)
** other wise we will get and error trying to pass multiple conditons
** Use & and | symbols for "and" and for "or " condition
** we will get an error if use "and" or "for" keyword between each condition
Example 16
Select only the required columns with a condition
We can also select the columns that are required of the rows that satisfy our condition
For example, if our dataset contains many columns and we want to view only a few of them, then we can add a list of columns after the condition within the loc statement itself
Example 17
** pass it as a list
Example 18
Update the values of a particular column on selected rows
Some time we have to update some values in our dataset based on a certain condition. So here we we just need to specify the condition followed by the target column and then assign the value with which we want to update
Example 19
Example 20
Here we updated the sex column values with "f" where age > 25
Here we updated the sex and age column values with "f" and 50 where age > 25
iloc
iloc is integer-based, here we we have to specify rows and columns by their integer index.
example, let’s say we are searching for the rows whose index is 1, 5 or 15. Here we will get the 1 ,5 and 15 rows not he rows with label 1,5 and 15
Example 21
Example 22
Using iloc, we can also slice the pandas dataframe . In iloc when we do slicing the end index is not included
Example 23
With iloc we cant filter out the data
Select rows with indices using iloc
When we are using iloc, we need to specify the rows and columns by their integer index
Example 24
Select rows with particular indices and particular columns
if we wanted to select a few columns from the dataset We can also do this by using the iloc function. When we use iloc we need to provide the index number of the column instead of the column name
Example 25
Example 26
when we use iloc always we need to provide the index number of the column instead of the column name or else we get index error
