Pandas

If you want to create a categorical variable, use pandas.Categorical. This variable takes on a limited number of possible values and can be ordered.
In the code above, I use pd.Categorical to create a list of ordered categories. This list can be sorted based on its specified order.
Link to previous tips on pandas.

A pivot table is useful to summarize and analyze the patterns in your data. If you want to turn your DataFrame into a pivot table, use pandas.pivot_table.
The image above shows how this method works.
Link to the source code.

If you want to use the previous value in a column or a row to fill the current missing value in a pandas DataFrame, use df.fillna(method=’ffill’). ffill stands for forward fill.
The code above shows how this method works.
Link to the source code.

If you want to get a random sample of items from each category in a column, use df.groupby.sample. To get 2 items from each category, use n=2.
This method is useful when you want to get a subset of a DataFrame while keeping all categories in a column.
Link to the source code.

If you want to apply different aggregations to different columns, insert a dictionary of column and aggregation methods to the DataFrame.agg method.
The code above shows an example of this method.
Link to the source code.

If you want to get the rows whose dates are before or after a specific date, use the comparison operator and a date string.
The image above shows an example of this method.
Link to the source code.

If you want to bin your Dataframe’s values into discrete intervals, use pd.cut.
The image above shows an example of this method.
Link to the source code.

When working with pandas DataFrame, if you want to transform each element in an iterable to a row, use explode.
The image above shows an example of this method.
Link to the source code.

If you want to iterate over rows of a pandas DataFrame, use df.iterrows(). Find an example of this method above.
Link to the source code.