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Questions and Answers
What does np.arange(7) return?
What does np.arange(7) return?
- [0, 1, 2, 3, 4, 5, 6] (correct)
- [1, 2, 3, 4, 5, 6, 7]
- [1, 2, 3, 4, 5, 6]
- [0, 2, 4, 6]
Given arr = np.array([[1, 2, 3], [4, 5, 6]]), what will arr.sum(axis=0) output?
Given arr = np.array([[1, 2, 3], [4, 5, 6]]), what will arr.sum(axis=0) output?
- [1, 2, 3]
- [3, 6]
- [6, 15]
- [5, 7, 9] (correct)
Which NumPy function will generate a 4x4 matrix with all elements as zeros?
Which NumPy function will generate a 4x4 matrix with all elements as zeros?
- np.ones((4, 4))
- np.zeros((4, 4)) (correct)
- np.identity(4)
- np.diag([0, 0, 0, 0])
What does np.random.randint(1, 10, 3) do?
What does np.random.randint(1, 10, 3) do?
Given a = np.array([1, 2, 3]) and b = np.array([2, 2, 2]), what will np.dot(a, b) output?
Given a = np.array([1, 2, 3]) and b = np.array([2, 2, 2]), what will np.dot(a, b) output?
Which method, given an array arr, can be used to insert a new column at a specific index?
Which method, given an array arr, can be used to insert a new column at a specific index?
Given df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}), what does df['B'].sum() return?
Given df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}), what does df['B'].sum() return?
Given a DataFrame df, how can you select all rows where column 'A' is greater than 2 and column 'B' is less than 5?
Given a DataFrame df, how can you select all rows where column 'A' is greater than 2 and column 'B' is less than 5?
Flashcards
What does np.arange(5) return in NumPy?
What does np.arange(5) return in NumPy?
The np.arange(5) function in NumPy generates a sequence of numbers starting from 0 (inclusive) and ending at 5 (exclusive), incrementing by 1.
How does arr.sum(axis=1) work in NumPy?
How does arr.sum(axis=1) work in NumPy?
The arr.sum(axis=1) function calculates the sum of elements along the rows (axis=1) of a NumPy array. In this case, it adds the elements of each row, returning an array with the sum of each row.
How do you create a 3x3 identity matrix in NumPy?
How do you create a 3x3 identity matrix in NumPy?
The np.identity(3) function creates a 3x3 identity matrix, which has 1s along the main diagonal and 0s elsewhere.
Which NumPy function produces random numbers between 0 and 1?
Which NumPy function produces random numbers between 0 and 1?
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What does np.dot(a, b) do in NumPy?
What does np.dot(a, b) do in NumPy?
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How do you add a row to a NumPy array?
How do you add a row to a NumPy array?
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How do you sum values in a Pandas DataFrame column?
How do you sum values in a Pandas DataFrame column?
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How do you check for missing values in a Pandas DataFrame?
How do you check for missing values in a Pandas DataFrame?
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Study Notes
NumPy Functions
np.arange(5)returns[0, 1, 2, 3, 4]sum(axis=1)calculates the row-wise sums in a 2D array
NumPy Identity Matrix
np.identity(3)generates a 3x3 identity matrix
Random Number Generation
np.random.random()generates random numbers between 0 and 1
NumPy Array Operations
np.append()adds a new row to the end of a NumPy array
Pandas DataFrame Operations
-
The
sum()method on a Pandas Series calculates the sum of the values. -
df.isnull()checks for missing or NaN values in a DataFrame. -
df.groupby()groups data based on column values, enabling aggregation and transformation -
df.dropna()removes rows containing missing values. -
To select rows where 'A' is greater than 2, use
df.loc[df['A'] > 2],df.query('A > 2'), ordf[df['A'] > 2]
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