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

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.

pandas.get_dummies

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.

University of Michigan - Ann Arbor

In computer programming, pandas is a software library written for the Python programming language for data manipulation and analysis. It is primarily used for data cleaning and preparation through the use of dataframes and series. In particular, it offers data structures and operations for manipulating numerical tables and time series. It is a free software anyone can download.

Pandas

All references relating to the pandas library of Python.

Pandas References

Pandas includes two main data structures: 
- Series
- DataFrame

Pandas Data Structures

Pandas DataFrame

The to_numpy() method in pandas is used to convert a DataFrame into a NumPy array. By passing the dtype argument, such as dtype=float, you can ensure that all extracted numerical data is uniformly formatted as floating-point numbers. This is a common preprocessing step before loading tabular data into deep learning tensors.

pandas.DataFrame.to_numpy()

In data science, missing values (often represented programmatically as `NaN`) occur when entries in a dataset are empty or undefined, resulting from missing data collection or parsing errors. These incomplete records are a persistent challenge in data preprocessing and must be deliberately handled before training a model, typically through either imputation (estimating replacement values) or deletion (discarding the incomplete rows or columns).

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