This community-built FAQ covers the "Review " 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
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How would I go about deleting the numbers in front of the months if I wanted to make the final pivot table look a little cleaner? For example, take the "1 - " out of “1 - January”.
2 Likes
How can I do the opposite? From pivoted dataframe to panel like dataframe?
1 Like
I think you could apply a lambda function to each string value in the month column (using .apply()) as follows:
tidy_months = lambda x: x.split('- ')[-1]
3 Likes
I tried to write a function to do it.
def unpivot(pivoted_dataframe, second_column_name, third_column_name):
data = []
for i, value1 in pivoted_dataframe.iloc[:, 0].iteritems():
for value2 in pivoted_dataframe.columns[1:]:
data.append([value1, value2, pivoted_dataframe.loc[i, value2]])
return pd.DataFrame(data, columns=[pivoted_dataframe.columns[0], second_column_name, third_column_name])
click_source_by_month_unpivot = unpivot(click_source_by_month_pivot, 'month', 'visits')
The names of the second and third columns (month and visits in this example) are missing in the pivoted DataFrame, so I think we need to give them. (Probably the previous DataFrame, click_source_by_month, has a third column named id, but I thought this name would be better to be changed to visits or count.)
If you have any comments or find a better way please let me know.
Recently I learned that there is a method .melt() to reshape a DataFrame to an “unpivoted” one.
click_source_by_month_melt = pd.melt(click_source_by_month_pivot, id_vars='utm_source', var_name='month', value_name='visits')
1 Like
What’s the difference between
click_source_by_month = user_visits.groupby('utm_source')['month'].count().reset_index()
and
click_source_by_month = user_visits.groupby(['utm_source', 'month']).count().reset_index()
1 Like
