This community-built FAQ covers the “Calculating Aggregate Functions I” exercise from the lesson “Aggregates in Pandas”.
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This exercise can be found in the following Codecademy content:
Data Science
Data Analysis with Pandas
FAQs on the exercise Calculating Aggregate Functions I
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Hi, how should look like this loop script: “We want to get an average grade for each student across all assignments. We could do some sort of loop”. What is this loop? I have no idea.
Why does df.groupby('column1').column2.measurement() return a series data type and not a data frame?
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The final question where we are determining the type of the object. Is the object a series? I must review my object types.
Additionally, I am having problems knowing when to use parenthesis verses brackets. I understand that brackets are for lists but in many of the functions the positioning can be confusing.
Thank You
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I examined data types at several levels:
print(type(orders.groupby('shoe_type')))
# <class 'pandas.core.groupby.DataFrameGroupBy'>
print(type(orders.groupby('shoe_type').price))
# <class 'pandas.core.groupby.SeriesGroupBy'>
print(type(orders.groupby('shoe_type').price.max()))
# <class 'pandas.core.series.Series'>
print(type(orders.groupby('shoe_type').max()))
# <class 'pandas.core.frame.DataFrame'>
It seems that .groupby() method returns a DataFrameGroupBy object and df.groupby('column1').column2 returns a SeriesGroupBy object. Then .max() attribute of SeriesGroupBy object returns a series. On the other hand, .max() attribute of DataFrameGroupBy object returns a DataFrame. I don’t know why, but it seems to have such specifications.
If we want to convert the series pricey_shoes to a DataFrame, we can use pd.DataFrame():
print(type(pricey_shoes))
# <class 'pandas.core.series.Series'>
pricey_shoes_dataframe = pd.DataFrame(pricey_shoes)
print(type(pricey_shoes_dataframe))
# <class 'pandas.core.frame.DataFrame'>
In this case, it seems the shoe_type values become index of this new DataFrame.
Another way is introduced in the next exercise. By using .reset_index(), we can create a DataFrame with shoe_type added as a column.
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what is the difference between a Panda series and a normal python array? when I print the type it says panda series and that got me a bit confuse. Also, why is not a data frame?
numpy array: You can think of it as a python list, but it has more useful function, like numerical operation, or being reshaped.
pandas series: similar to 1d numpy array, but it has additional functionality that allows values in the series to be indexed using label. (I use the explanation from the codecademy tutorial.)
dataframe: similar to series, but it is composed of multiple series.
More detailed info can be found here:
Introduction to Numpy and Pandas
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Got curious based on the line in the lesson:
We want to get an average grade for each student across all assignments. We could do some sort of loop, but Pandas gives us a much easier option: the method .groupby
Here is my attempt on pulling the same data from the dataframe given using a loop, to demonstrate how much easier the groupby method works. The output in my case is a dictionary, but it could be easily converted to a series or a dataframe.
pricey_shoes_dict = {}
for index, row in orders.iterrows():
shoe_type = row["shoe_type"]
price = row["price"]
if shoe_type not in pricey_shoes_dict or price > pricey_shoes_dict[shoe_type]:
pricey_shoes_dict[shoe_type] = price
print(pricey_shoes_dict)
You would need to copy and paste it into the lesson to see it run properly in order for the orders.csv to be loaded.
