Pandas has 4 functions that allow for direct access and modification of the underlying values of the data:
```python df.iloc[3]``` This will select the data in column 3. We can also use integer slicing, similar to numpy to access more specific values, ```python df.iloc[3:5, 0:2] ```

We can also access using labels, ```python df.loc[dates[0]]``` or ```python df.loc[:, ["A", "B"]]```.

Data can also be accessed using boolean values, ```python df[df > 0]``` will select all values greater than 0. ```python df[df["A"] > 0]``` will select all values in column labeled "A" with values greater than 0. 

We can also use df.at and df.iat to access data directly. Usage is very similar to .loc and .iloc. All data accesses are pass by references, which means that modifications made using ```python df["A"] = 0``` will result in setting all values to 0.

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Selecting Data can be done very easily as data stored in Pandas data frames have a coordinate pair associated with it and also have a string based indexing, which utilizes the column names and index values. 

To select an entire column, use ``` python df["column_name"]```. To select a range of columns use ``` python df[0:3] ```. This will select the values ranging from column 0 to column 3. 

Data selection can also be done using the rows names, or selecting a range of values using string or integer names, for example: ``` python df["20130102":"20130104"]```
```
                  A         B         C         D
2013-01-02  1.212112 -0.173215  0.119209 -1.044236
2013-01-03 -0.861849 -2.104569 -0.494929  1.071804
2013-01-04  0.721555 -0.706771 -1.039575  0.271860
```

You can also directly access a specific index using one of the following 4 functions in the Child node.

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