.loc is label based, so you must use the row or column labels to access the data.


Given the pictured dataframe, 


If you want to access all of the “viper” values you would run... 
```python
df.loc["viper"]
```
This returns a Pandas Series of the “viper” row. 


If you want to extract multiple rows, you can include all of the row names in a list like... 
```python
df.loc[["cobra", "viper"]]
```
This returns a Pandas DataFrame of all of the values from both the “cobra” and “viper” rows. 


To get a single value such as the max_speed of a sidewinder, you can use...
```python
df.loc["sidewinder", "max_speed"]
```
This would return a single integer value of 7. 


The syntax for the above example is... 
```python
df.loc[row_name, column_name]
```


Pandas DataFrame .loc Slicing

.iloc is integer position based, so you must use the row or column integer position to access the data.


Given the pictured dataframe, 


If you want to access all of the “viper” values you would run... 
```python
df.iloc[1]
```
This would return a Pandas Series of the “viper” row.


If you want to extract multiple rows, you can include the integer values in a list like... 
```python
df.iloc[[0,1]]
```
This returns a Pandas Dataframe of all of the values from both the “cobra” and “viper” rows.


To get a single value such as the max_speed of a sidewinder, you can use...
```python
df.iloc[2,0]
```
This would return a single integer value of 7. 


The syntax for the above example is... 
```python
df.iloc[row_number, column_number]
``` 

Remember that Python is a zero-based indexing language so a row or column in the first position has a row or column number of 0 and not 1.


Pandas DataFrame .iloc Slicing

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. 

University of Michigan - Ann Arbor

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/docs/index.html

Pandas documentation

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

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. 

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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