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Which of the following statements correctly describes a Pandas Series?
A one-dimensional labeled array capable of holding any data type.
A two-dimensional labeled data structure with columns of potentially different types.
A collection of rows and columns where each column can be of a different type.
An immutable sequence of values.
What will be the output of the following code? pd.Series([1, 2, 3], index=['a', 'b', 'c'])['b']
1
2
3
KeyError
Which method is used to concatenate two DataFrames along the rows?
pd.concat()
pd.append()
pd.merge()
pd.join()
If you have a DataFrame df with a column 'A', how can you create a new column 'B' that is the square of 'A'?
df['B'] = df['A'] ** 2
df['B'] = np.square(df['A'])
df['B'] = df['A'].apply(lambda x: x**2)
df['B'] = df['A'].map(lambda x: x**2)
What is the result of pd.DataFrame({'A': [1, 2], 'B': [3, 4]})['A'] + pd.DataFrame({'A': [5, 6], 'B': [7, 8]})['A']?
Series([6, 8])
Series([6, 8], index=[0, 1])
Series([6, 8], index=[0, 1], dtype=int)
ValueError
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4 Modules