Q3d: value_counts() Using the value_counts() method, determine how many different possible values there are for the chocolate series in the df DataFrame and how many observations fall into each. Store the output in the object chocolate_values . Take a look at the output. Be sure you understand whether or not there are more chocolate ( chocolate == 1) or nonchocolate candies ( chocolate == 0 ) in the dataset from the output. In [26]: # YOUR CODE HERE #chocolate_values = df['chocolate'].value_counts()[0:10] #chocolate_values Out[26]: 0 48 1 37 Name: chocolate, dtype: int64 In [27]: assert chocolate_values.loc[0] == 48
Q3d: value_counts() Using the value_counts() method, determine how many different possible values there are for the chocolate series in the df DataFrame and how many observations fall into each. Store the output in the object chocolate_values . Take a look at the output. Be sure you understand whether or not there are more chocolate ( chocolate == 1) or nonchocolate candies ( chocolate == 0 ) in the dataset from the output. In [26]: # YOUR CODE HERE #chocolate_values = df['chocolate'].value_counts()[0:10] #chocolate_values Out[26]: 0 48 1 37 Name: chocolate, dtype: int64 In [27]: assert chocolate_values.loc[0] == 48
Computer Networking: A Top-Down Approach (7th Edition)
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in python
Using the value_counts() method, determine how many different possible values there are for the chocolate series in the df DataFrame and how many observations fall into each.
Store the output in the object chocolate_values.
Take a look at the output. Be sure you understand whether or not there are more chocolate (chocolate == 1) or nonchocolate candies (chocolate == 0) in the dataset from the output.
![Q3d: value_counts()
Using the value_counts() method, determine how many different possible values there are for the chocolate series in the df DataFrame and how
many observations fall into each.
Store the output in the object chocolate values.
Take a look at the output. Be sure you understand whether or not there are more chocolate ( chocolate ==
1 ) or nonchocolate candies ( chocolate
0) in the dataset from the output.
In [26]: # YOUR CODE HERE
#chocolate_values
#chocolate_values
df[ 'chocolate'].value_counts()[0:10]
%D
Out[26]: 0
48
1
37
Name: chocolate, dtype: int64
In [27]: assert chocolate_values.loc[0]
48
==](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fb8aafd3f-642f-4ab2-9984-0cfa89ac7a37%2Fffb0c382-b7da-46a4-abb0-a76844f28b20%2Ft3f4b7_processed.png&w=3840&q=75)
Transcribed Image Text:Q3d: value_counts()
Using the value_counts() method, determine how many different possible values there are for the chocolate series in the df DataFrame and how
many observations fall into each.
Store the output in the object chocolate values.
Take a look at the output. Be sure you understand whether or not there are more chocolate ( chocolate ==
1 ) or nonchocolate candies ( chocolate
0) in the dataset from the output.
In [26]: # YOUR CODE HERE
#chocolate_values
#chocolate_values
df[ 'chocolate'].value_counts()[0:10]
%D
Out[26]: 0
48
1
37
Name: chocolate, dtype: int64
In [27]: assert chocolate_values.loc[0]
48
==
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