Indexing
What is Indexing in Pandas?
Indexing in Pandas means selecting particular rows and columns from a DataFrame
Indexing in Pandas is same as we did for a Python List and a NumPy array
Pandas has two known operators used for indexing.
There are two different methods of indexing in Pandas:
loc - label based selection
iloc - index based selection
Index Based Selection
Index - based selection is to select data based on its numerical position in dataframe.
iloc is used for selecting data based on numerical position
The syntax for using iloc operator is
df.iloc[ ], where df is a dataframe name. You can pass the numerical positions of rows and columns to select in the square bracket.Do you remember the indexing in a NumPy array? If not, don’t worry, you will soon see its implementation on a dataset.
Import Pandas Library and load ‘exam_scores.csv’ file
Selecting data based on its numerical position. Used operator - iloc
Selecting data based on its numerical position. Used operator - iloc
Selecting data based on its numerical position. Used operator - iloc
You can also pass list of indexes
Selecting data based on its numerical position. Used operator - iloc
You can also pass negative indexes
Label Based Selection
Label based selection selects data based on the column or row names/index. This becomes important while selecting data from a dataframe
Label based selection is done with loc
Do you remember the python default indexing of the dataframe and the indexes/names that you changed in the previous topics?
loc and iloc are conceptually similar. The difference is that iloc considers the default indexing while loc ignores the default indexing
loc is used for selecting data based on the data index value/name, not the numerical positions.
The syntax for loc is similar to iloc:
df.loc[ ], df is a dataframe.We will learn about its implementation on a dataset soon.
Selecting data from a dataframe using column and row names/index. Used operator - loc
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