This method is used for label-based indexing. For example, 5 of 'a'. Notice that in this case 5 is a label of and index not a location. 
An array or list of labels can be passed to retrieve multiple rows, ['a', 'b', 'c']


Pandas DataFrame Indexing .loc

The `.iloc` property in `pandas` enables integer-location based indexing, allowing users to select or slice specific rows and columns by their numerical positions rather than their explicit labels. This functionality is particularly useful in data preparation for isolating subsets of a dataset, such as slicing columns to separate input features from target values.

pandas.DataFrame.iloc

Indexing is one of the ways that you can slice and dice your data frames into subsets. There are a couple choices on how to index, and a brief introduction is below. 

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Pandas DataFrame is two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). A Data frame is a two-dimensional data structure, i.e., data is aligned in a tabular fashion in rows and columns.

Pandas DataFrame

https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html

Pandas Indexing

To extract data from a Pandas DataFrame you can use slicing. Two of the most popular and useful slicing techniques are the .loc and .iloc features of Pandas. The main difference between .loc and .iloc is that .loc is label based while .iloc is integer position based. 

Pandas DataFrame Slicing

.groupby() is a useful function to sort a dataframe into groups based on certain criteria. For example, if you have a dataframe of team rosters, you can group by team name or college and each group will consist only of entries with the same team or college.

```python
#group by team
df.groupby(‘Team’)
#group by college
df.groupby(‘College’)
#group by team and college 
df.groupby([‘Team’, ‘College’])
```

Pandas GroupBy

- .drop (Removes values by row or column label)
- .dropna (Removes missing data)
- .drop_duplicates (Removes duplicate values)

Removing Values from DataFrame

Pandas DataFrame Indexing

The `pandas.get_dummies()` function is used to convert categorical variables into binary indicator columns. To apply categorical imputation, the `dummy_na=True` argument can be passed, which instructs the function to treat `NaN` as a valid category and generate a dedicated dummy column to explicitly flag missing entries.

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