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COM-211-1-INTRODUCTION TO DATA SCIENCE-Fall 2026
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Which one or more of the following are typical tasks in data cleaning?
What will the layout of the subplots be given the code below:
plt.subplot(221)plt.plot(a)plt.subplot(222)plt.plot(b)plt.subplot(223)plt.plot(c)plt.show()
In numpy, this will successfully create a 2-D array:
a = np.linspace(4, 14, 11).reshape(3,4)
Given this dataframe
A B C D0 a1 NaN c1 d11 a2 b2 NaN d22 a3 b3 NaN d33 a4 b4 c4 NaN
How many columns will contain data after applying the command:
df.dropna(inplace=True)?
Given this dataframe called df:
A B C D0 a1 NaN c1 d11 a2 b2 NaN d22 a3 b3 NaN d33 a4 b4 c4 NaNdf.isna().sum(axis=0) will show the number of NaN's in each:
Which one or more of the following statistics can be used on nominal data?
In numpy, given a = np.array([[1,2,3],[4,5,6],[7,8,9]])print(a[(a < 2) | (a > 5)]) will show:
In numpy, given this_array = np.array([[1,2,3],[4,5,6], [1,1,1]])len(this_array) results in:
You read a data file into a dataframe and queried it to find how many NaN's are in each row and each column. At what minimum percentage of NaN's should a column or row automatically be deleted?