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For the last part of this exercise, the solution is
print(students.exam.value_counts())
but that results in this error:
Traceback (most recent call last):
File “script.py”, line 10, in
print(students.exam.value_counts())
File “/var/codecademy/runner_contexts/python/cc_python3_shared.py”, line 97, in series_str_with_print
return self._internal_str()
File “/usr/local/lib/python3.6/dist-packages/pandas/core/generic.py”, line 5179, in getattr
return object.getattribute(self, name)
AttributeError: ‘Series’ object has no attribute ‘_internal_str’
I struggled to understand this exercise for a few minutes before checking the solution code. It would be helpful to clarify that the pd.melt(frame=df_name…) function, apparently cannot be used alone to alter the specified dataframe in place (even though it requires you to specify the dataframe to melt as an argument). Instead, it requires assignment of the dataframe to itself in order to function, i.e. df_name = pd.melt(frame=df_name…).
This distinction was not immediately clear either from the lesson’s instructions or from the hint provided for the second step. The second step does state:
Use pd.melt() to create a new table (still called students ) that follows this structure.
However, it feels like a stretch to interpret that instruction as calling for the assignment when it was not discussed or illustrated anywhere else in the lesson. Hope that helps anyone else that might be confused here.
There is something wrong with the second.
I’ve written the code below and the system does not mark it as a right answer. Now I’ve got code solution and it is the same as I did.
students = pd.melt(frame=students, id_vars=[‘full_name’,‘gender_age’,‘grade’], value_vars=[‘fractions’, ‘probability’], value_name=‘score’, var_name=‘exam’)
I’m unsure why I get an error on Step 3 in Visual Studio Code and on here yet I get the green check mark on here.
import pandas as pd
from students import students
# Print out the columns of students.
for column in students.columns:
print(column)
# There is a column for the scores on the fractions exam,
# and a column for the scores on the probabilities exam.
# We want to make each row an observation, so we want to transform this table to look like:
# Use pd.melt() to create a new table (still called students) that follows this structure.
students = pd.melt(frame=students, id_vars=['full_name', 'gender_age', 'grade'], value_vars=[
'fractions', 'probability'], value_name="score", var_name="exam")
# Print the .head() and the .columns of students.
# Also, print out the .value_counts() of the column exam.
print(students.head())
print(students.columns)
print(students.value_count("exam"))
Traceback (most recent call last):
File “script.py”, line 19, in
print(students.value_count(“exam”))
File “/usr/local/lib/python3.6/dist-packages/pandas/core/generic.py”, line 5179, in getattr
return object.getattribute(self, name)
AttributeError: ‘DataFrame’ object has no attribute ‘value_count’
Print the .head() and the .columns of students .
Also, print out the .value_counts() of the column exam .
I think it’s sequenced full_name, gender_age, grade in the solution because that’s how the code is interpreted. When you write “pd.melt(frame=students, id_vars=[‘full_name’, ‘gender_age’, ‘grade’]…” it executes the creation of the “full_name”, 'gender_age", “grade” columns first. At least that’s how I understood it.
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