RM FlashCard Code

Pandas_Basics QUESTION: What are the two primary data structures provided by the Pandas library? OPTIONS: A) Lists and Dictionaries B) Series and DataFrames C) Arrays and Tuples D) Sets and Matrices ?? SOLUTION: B) Series and DataFrames Pandas is built around these two fundamental data structures. Series are one-dimensional labeled arrays, and DataFrames are two-dimensional labeled data structures.

Pandas_Series QUESTION: Create a Pandas Series from the following dictionary: data = {'a': 10, 'b': 20, 'c': 30}. OPTIONS: A) pd.Series({'a': 10, 'b': 20, 'c': 30}) B) pd.DataFrame(data) C) pd.Series(data.values()) D) pd.Series(data, index=['x', 'y', 'z']) ?? SOLUTION: A) pd.Series({'a': 10, 'b': 20, 'c': 30}) When a dictionary is passed to pd.Series(), the keys become the index and the values become the data.

Pandas_DataFrame QUESTION: How can you display the top 5 rows of a Pandas DataFrame named df? OPTIONS: A) df.top(5) B) df.head(5) C) df.first(5) D) df.show(5) ?? SOLUTION: B) df.head(5) The head() method is used to display the first n rows of a DataFrame. The default is 5 if no number is specified.

Pandas_DataFrame_Info QUESTION: Which method provides a concise summary of a DataFrame, including data types and non-null values? OPTIONS: A) df.describe() B) df.info() C) df.summary()** D) df.inspect() ?? SOLUTION: B) df.info() The info() method prints information about the DataFrame, such as the number of rows, columns, data types, and memory usage.

Pandas_Selection QUESTION: Given a DataFrame df with columns ‘Name’ and ‘Age’, how do you select only the ‘Age’ column? OPTIONS: A) df.Age B) df['Age'] C) df.loc['Age'] D) Both A and B ?? SOLUTION: D) Both A and B Both df.Age (attribute access) and df['Age'] (bracket notation) can be used to select a single column. Bracket notation is generally preferred as it handles column names with spaces or special characters.

Pandas_Filtering QUESTION: How do you filter a DataFrame df to select rows where the ‘Age’ column is greater than 30? OPTIONS: A) df[df.Age > 30] B) df[df['Age'] > 30] C) df.filter(Age > 30) D) Both A and B ?? SOLUTION: D) Both A and B Boolean indexing is used for filtering. The condition df['Age'] > 30 creates a Boolean Series, which is then used to select rows where the condition is True.

Pandas_Missing_Data QUESTION: How can you fill missing values (NaN) in a DataFrame df with the value 0? OPTIONS: A) df.fill(0) B) df.fillna(0) C) df.replaceNaN(0) D) df.missing(0) ?? SOLUTION: B) df.fillna(0) The fillna() method is used to replace NaN values with a specified value or using a specified method (e.g., forward fill, backward fill).

Pandas_IO QUESTION: What function is used to read data from a CSV file into a Pandas DataFrame? OPTIONS: A) pd.read_csv() B) pd.load_csv() C) pd.import_csv() D) pd.from_csv() ?? SOLUTION: A) pd.read_csv() pd.read_csv() is the standard function for reading comma-separated value files.

Pandas_IO_Writing QUESTION: What method is used to save a DataFrame to a CSV file? Use df as the DataFrame name and ‘output.csv’ as the file name. OPTIONS: A) df.write_csv('output.csv') B) df.to_csv('output.csv') C) df.save_csv('output.csv') D) pd.save(df, 'output.csv') ?? SOLUTION: B) df.to_csv('output.csv') df.to_csv() writes the DataFrame to a CSV file. The index=False argument can be used to prevent writing the index.

Pandas_Concatenation QUESTION: You have two DataFrames, df1 and df2. How do you concatenate them vertically (stack them on top of each other)? OPTIONS: A) pd.concat([df1, df2], axis=1) B) pd.concat([df1, df2], axis=0) C) pd.merge([df1, df2]) D) df1.append(df2) ?? SOLUTION: B) pd.concat([df1, df2], axis=0) pd.concat() with axis=0 concatenates along the row axis (vertically).

Pandas_Merging QUESTION: What function is used to perform database-style joins on DataFrames in Pandas? OPTIONS: A) pd.join() B) pd.merge() C) pd.combine() D) pd.concat() ?? SOLUTION: B) pd.merge() pd.merge() is used for SQL-like joins based on common columns or indices.

Pandas_GroupBy QUESTION: How would you group a DataFrame df by the ‘Category’ column and calculate the sum of the ‘Value’ column for each category? OPTIONS: A) df.groupby('Category').sum('Value') B) df.group('Category').sum()['Value'] C) df.groupby('Category')['Value'].sum() D) df.groupby('Category').aggregate('Value', 'sum') ?? SOLUTION: C) df.groupby('Category')['Value'].sum() This groups the DataFrame by ‘Category’, then selects the ‘Value’ column, and finally calculates the sum for each group.

Pandas_Apply QUESTION: Apply the function lambda x: x * 2 to every element in the ‘Value’ column of DataFrame df. OPTIONS: A) df[‘Value’].apply(lambda x: x2) B) df.apply(lambda x: x2, column=‘Value’) C) df[‘Value’].map(lambda x: x*2) D) Both A and C ?? SOLUTION: D) Both A and C Both apply() and map can be used on a Series to apply a function element-wise.

Pandas_Dates QUESTION: What is the correct Pandas function and its arguments to generate a sequence of 5 daily dates starting from ‘2025-01-01’? OPTIONS: A) pd.date_range(start='2025-01-01', end='2025-01-05', freq='D') B) pd.date_range(start='2025-01-01', periods=5, freq='D') C) pd.date_range('2025-01-01', '2025-01-05') D) pd.dates('2025-01-01', 5) ?? SOLUTION: B) pd.date_range(start='2025-01-01', periods=5, freq='D') pd.date_range() creates a fixed-frequency DatetimeIndex. start defines the start date, periods the number of periods, and freq='D' specifies daily frequency.

Pandas_Time_Series QUESTION: Given a DataFrame df with a datetime index, how do you resample the data to a monthly frequency, calculating the mean of each month? OPTIONS: A) df.resample('M').mean() B) df.monthly().mean() C) df.groupby(pd.Grouper(freq='M')).mean() D) Both A and C ?? SOLUTION: D) Both A and C resample('M') groups the data by month. .mean() then calculates the mean for each month. The Grouper option provides the same.

NumPy_Basics QUESTION: What is the primary data structure in NumPy? OPTIONS: A) List B) ndarray C) Tuple D) Dictionary ?? SOLUTION: B) ndarray The ndarray (n-dimensional array) is the core of NumPy, providing efficient storage and operations on numerical data.

NumPy_Creation QUESTION: Create a NumPy array of shape (2, 3) filled with zeros. OPTIONS: A) np.array([2, 3]) B) np.zeros(2, 3) C) np.zeros((2, 3)) D) np.empty((2, 3)) ?? SOLUTION: C) np.zeros((2, 3)) np.zeros() creates an array filled with zeros. The shape is specified as a tuple.

NumPy_Attributes QUESTION: What attribute of a NumPy array gives you the number of elements in the array? OPTIONS: A) shape B) size C) dtype D) nbytes ?? SOLUTION: B) size The size attribute returns the total number of elements in the array.

NumPy_Slicing QUESTION: Given a NumPy array arr = np.arange(10), how would you select the elements from index 2 up to (but not including) index 7? OPTIONS: A) arr[2:7] B) arr[2, 7] C) arr[2-7] D) arr[2 to 7] ?? SOLUTION: A) arr[2:7] This uses standard Python slicing notation.

NumPy_Broadcasting QUESTION: If you add a scalar value of 5 to a NumPy array, what happens due to broadcasting? OPTIONS: A) An error occurs. B) The value 5 is added to only the first element. C) The value 5 is added to every element of the array. D) The array is multiplied by 5. ?? SOLUTION: C) The value 5 is added to every element of the array. Broadcasting “stretches” the scalar to match the shape of the array, allowing element-wise addition.

NumPy_Linear_Algebra QUESTION: How do you calculate the dot product of two NumPy arrays, A and B? OPTIONS: A) A * B B) A.dot(B) C) np.dot(A, B) D) Both B and C ?? SOLUTION: D) Both B and C Both A.dot(B) (method call) and np.dot(A, B) (function call) compute the dot product. The @ operator can also be used (A @ B).

NumPy_Statistical_Methods QUESTION: How to calculate the standard deviation of values in a NumPy array named arr? OPTIONS: A) arr.std() B) np.std(arr) C) arr.standard_deviation() D) Both A and B ?? SOLUTION: D) Both A and B Both arr.std() and np.std(arr) calculates the standard deviation.

NumPy_Random QUESTION: Generate a 2x2 NumPy array with random numbers drawn from a standard normal distribution. OPTIONS: A) np.random.rand(2, 2) B) np.random.randn(2, 2) C) np.random.normal(2, 2) D) np.random.uniform(2, 2) ?? SOLUTION: B) np.random.randn(2, 2) randn generates samples from the standard normal distribution (mean 0, variance 1).

NumPy_Axis QUESTION: In a 2D NumPy array, what does axis=0 typically refer to? OPTIONS: A) Columns B) Rows C) Depth D) Diagonal ?? SOLUTION: B) Rows axis=0 refers to operations down the rows (e.g., summing each column).

Matplotlib_Basics QUESTION: What function is used to display a plot in Matplotlib? OPTIONS: A) plt.display() B) plt.show() C) plt.render() D) plt.plot() ?? SOLUTION: B) plt.show() plt.show() is essential to display the created plot.

Matplotlib_Line_Plot QUESTION: Create a simple line plot with x-values [1, 2, 3] and y-values [4, 5, 6]. OPTIONS: A) plt.line([1, 2, 3], [4, 5, 6]) B) plt.plot([1, 2, 3], [4, 5, 6]) C) plt.scatter([1, 2, 3], [4, 5, 6]) D) plt.draw([1, 2, 3], [4, 5, 6]) ?? SOLUTION: B) plt.plot([1, 2, 3], [4, 5, 6]) plt.plot() is the primary function for creating line plots.

Matplotlib_Scatter_Plot QUESTION: How do you create a scatter plot in Matplotlib using plt? OPTIONS: A) plt.line(x, y) B) plt.scatter(x, y) C) plt.plot(x, y, 'o') D) Both B and C ?? SOLUTION: D) Both B and C Both plt.scatter(x, y) and plt.plot(x, y, 'o') create scatter plots. plt.scatter offers more control over marker properties.

Matplotlib_Subplots QUESTION: How would you create a figure with two subplots arranged vertically (one above the other)? OPTIONS: A) plt.subplots(2, 1) B) plt.subplot(2, 1) C) plt.figure(2) D) plt.plot(2, 1) ?? SOLUTION: A) plt.subplots(2, 1) plt.subplots(nrows, ncols) creates a figure and a grid of subplots.

Matplotlib_Customization QUESTION: How do you set the title of a plot in Matplotlib, using the plt interface? OPTIONS: A) plt.title = "My Title" B) plt.set_title("My Title") C) plt.title("My Title") D) plt.plot(title="My Title") ?? SOLUTION: C) plt.title("My Title") plt.title() sets the title of the current axes.

Matplotlib_Saving QUESTION: How can you save the current Matplotlib plot to a file named “my_plot.png”? OPTIONS: A) plt.save(“my_plot.png”) B) plt.save_figure(“my_plot.png”) C) plt.savefig(“my_plot.png”) D) plt.export(“my_plot.png”) ?? SOLUTION: C) plt.savefig(“my_plot.png”) plt.savefig() saves the current figure to a file. The file format is inferred from the extension.

Pandas_Series_Creation QUESTION: How can you create a Pandas Series with the values [1, 4, 9, 16] and the index [‘a’, ‘b’, ‘c’, ‘d’]? OPTIONS: A) pd.Series([1, 4, 9, 16], index=['a', 'b', 'c', 'd']) B) pd.Series(['a', 'b', 'c', 'd'], [1, 4, 9, 16]) C) pd.DataFrame({'values': [1, 4, 9, 16], 'index': ['a', 'b', 'c', 'd']}) D) pd.Series([1, 4, 9, 16], labels=['a', 'b', 'c', 'd']) ?? SOLUTION: A) pd.Series([1, 4, 9, 16], index=['a', 'b', 'c', 'd']) This correctly passes the data and the index to the pd.Series() constructor.

Pandas_DataFrame_Creation QUESTION: Create a Pandas DataFrame from the following dictionary: data = {'col1': [1, 2], 'col2': [3, 4]}. OPTIONS: A) pd.Series(data) B) pd.DataFrame(data) C) pd.DataFrame([1, 2], [3, 4]) D) pd.DataFrame({'col1': [1, 2]}, {'col2': [3, 4]}) ?? SOLUTION: B) pd.DataFrame(data) Passing a dictionary to pd.DataFrame() creates a DataFrame where keys are column names and values are column data.

Pandas_DataFrame_Tail QUESTION: How do you view the last 3 rows of a DataFrame named my_dataframe? OPTIONS: A) my_dataframe.last(3) B) my_dataframe.tail(3) C) my_dataframe.bottom(3) D) my_dataframe.end(3) ?? SOLUTION: B) my_dataframe.tail(3) The tail() method displays the last n rows of a DataFrame.

Pandas_Selection_Multiple QUESTION: Given a DataFrame df with columns ‘A’, ‘B’, and ‘C’, select columns ‘A’ and ‘C’. OPTIONS: A) df['A', 'C'] B) df[['A', 'C']] C) df.loc['A', 'C'] D) df.iloc['A', 'C'] ?? SOLUTION: B) df[['A', 'C']] Using a list of column names within brackets selects multiple columns.

Pandas_iloc QUESTION: How would you select the element in the second row and third column of a DataFrame df using integer-based indexing? OPTIONS: A) df.loc[1, 2] B) df.iloc[1, 2] C) df[1, 2] D) df.at[1, 2] ?? SOLUTION: B) df.iloc[1, 2] iloc is used for integer-based indexing. Remember that indexing starts at 0. So row 2 is index 1, and column 3 is index 2.

Pandas_loc QUESTION: If a DataFrame df has row labels ‘row1’, ‘row2’, ‘row3’, and you want to select ‘row2’, how would you do it using label-based indexing? OPTIONS: A) df.iloc['row2'] B) df.loc['row2'] C) df['row2'] D) df.ix['row2'] ?? SOLUTION: B) df.loc['row2'] loc is used for label-based indexing.

Pandas_Filtering_Multiple QUESTION: Select rows from DataFrame df where ‘Age’ is greater than 25 AND ‘City’ is ‘New York’. OPTIONS: A) df[(df['Age'] > 25) & (df['City'] == 'New York')] B) df[df['Age'] > 25 & df['City'] == 'New York'] C) df.filter(Age > 25 and City == 'New York') D) df.query("Age > 25 and City == 'New York'") ?? SOLUTION: A) df[(df['Age'] > 25) & (df['City'] == 'New York')] This uses boolean indexing with the & operator for combining conditions. Parentheses are crucial for correct operator precedence. Option D using .query() is also valid, but not mentioned in the provided PDF content.

Pandas_dropna QUESTION: How to remove all rows with any missing values (NaN) from DataFrame named df_nan? OPTIONS: A) df_nan.remove_na() B) df_nan.dropna() C) df_nan.fillna() D) df_nan.delete(NaN) ?? SOLUTION: B) df_nan.dropna() The dropna() method, by default, removes all rows containing at least one NaN value.

Pandas_to_excel QUESTION: You have a DataFrame df. Save it to an Excel file named “data.xlsx” without including the index. OPTIONS: A) df.to_excel(“data.xlsx”, include_index=False) B) df.to_excel(“data.xlsx”, index=False) C) pd.save_excel(df, “data.xlsx”) D) df.write_excel(“data.xlsx”, index=False) ?? SOLUTION: B) df.to_excel(“data.xlsx”, index=False) to_excel() writes to an Excel file, and index=False prevents writing the index.

Pandas_read_excel QUESTION: How to read sheet name ‘Sheet2’ from excel file ‘data.xlsx’ into DataFrame df_excel? OPTIONS: A) df_excel = pd.read_excel(‘data.xlsx’, sheetname=‘Sheet2’) B) df_excel = pd.read_excel(‘data.xlsx’, sheet_name=‘Sheet2’) C) df_excel = pd.read_excel(‘data.xlsx’, sheet=‘Sheet2’) D) df_excel = pd.read_excel(‘data.xlsx’)[Sheet2] ?? SOLUTION: B) df_excel = pd.read_excel(‘data.xlsx’, sheet_name=‘Sheet2’) The argument sheet_name takes a string for sheet name, an integer for sheet index, a list of sheet names, or None (default reads the first sheet).

Pandas_concat_axis1 QUESTION: What is the result of pd.concat([df1, df2], axis=1) if df1 and df2 are DataFrames? OPTIONS: A) df1 and df2 are stacked vertically. B) df1 and df2 are joined side-by-side (column-wise). C) An error occurs. D) The DataFrames are merged based on a common column. ?? SOLUTION: B) df1 and df2 are joined side-by-side (column-wise). axis=1 in pd.concat() performs concatenation along columns.

Pandas_merge_how QUESTION: In pd.merge(df1, df2, on='key', how='left'), what does how='left' specify? OPTIONS: A) An inner join B) A right join C) A left join D) An outer join ?? SOLUTION: C) A left join how='left' uses only the keys from the left DataFrame (df1 in this case), similar to a SQL left outer join.

Pandas_groupby_multiple QUESTION: Group DataFrame df by ‘Col1’ and ‘Col2’, then calculate the mean of ‘Col3’ for each group. OPTIONS: A) df.groupby(['Col1', 'Col2']).mean()['Col3'] B) df.groupby('Col1', 'Col2').mean('Col3') C) df.groupby(['Col1', 'Col2'])['Col3'].mean() D) Both A and C are incorrect ?? SOLUTION: C) df.groupby(['Col1', 'Col2'])['Col3'].mean() This groups by the specified columns, selects ‘Col3’, and then computes the mean.

Pandas_Date_range_frequency QUESTION: Which frequency string should be used with pd.date_range to generate a range of dates representing the last day of each month? OPTIONS: A) ‘MS’ B) ‘M’ C) ‘D’ D) ‘W’ ?? SOLUTION: B) ‘M’ ‘M’ represents month end frequency.

Pandas_Date_range_timezone QUESTION: How to create a date range with timezone set to ‘UTC’ using pd.date_range? OPTIONS: A) Set timezone parameter B) Set tz parameter to ‘UTC’ C) Set time_zone parameter D) It is not possible ?? SOLUTION: B) Set tz parameter to ‘UTC’ pd.date_range(start=‘2024-01-01’, periods=5, freq=‘D’, tz=‘UTC’)

NumPy_Array_Creation_arange QUESTION: Create a NumPy array containing the sequence of numbers from 5 to 15 (exclusive) with a step of 2. OPTIONS: A) np.arange(5, 15, 2) B) np.linspace(5, 15, 2) C) np.array(5, 15, 2) D) np.range(5, 15, 2) ?? SOLUTION: A) np.arange(5, 15, 2) np.arange(start, stop, step) generates a sequence.

NumPy_reshape QUESTION: You have a 1D NumPy array arr with 12 elements. Reshape it into a 3x4 2D array. OPTIONS: A) arr.reshape(4, 3) B) arr.resize(3, 4) C) arr.reshape(3, 4) D) np.reshape(arr, 3, 4) ?? SOLUTION: C) arr.reshape(3, 4) The reshape() method returns a new array with the specified shape.

NumPy_Boolean_Indexing QUESTION: Given arr = np.array([1, 2, 3, 4, 5]), how would you select elements greater than 2? OPTIONS: A) arr[arr > 2] B) arr.select(arr > 2) C) arr[arr.greater_than(2)] D) arr(arr > 2) ?? SOLUTION: A) arr[arr > 2] This uses a boolean array (arr > 2) as an index to select elements where the condition is True.

NumPy_Linear_Algebra_Inverse QUESTION: How do you find the inverse of a square matrix A in NumPy? OPTIONS: A) A.inverse() B) np.linalg.inv(A) C) A.invert() D) np.inverse(A) ?? SOLUTION: B) np.linalg.inv(A) The np.linalg.inv() function computes the matrix inverse.

NumPy_ufunc QUESTION: What is a NumPy “universal function” (ufunc)? OPTIONS: A) A function that operates on ndarrays element-by-element. B) A function that can only handle 1D arrays. C) A function that converts lists to arrays. D) A function for generating random numbers. ?? SOLUTION: A) A function that operates on ndarrays element-by-element. Ufuncs (e.g., np.sin, np.exp, np.add) are vectorized functions that apply operations efficiently to each element of an array.

NumPy_mean_axis QUESTION: Calculate the mean of each column in a 2D NumPy array my_array. OPTIONS: A) np.mean(my_array, axis=0) B) np.mean(my_array, axis=1) C) my_array.mean(columns=True) D) my_array.mean(axis=columns) ?? SOLUTION: A) np.mean(my_array, axis=0) axis=0 specifies that the mean should be computed along the rows (resulting in a mean for each column).

NumPy_random_seed QUESTION: What is the purpose of np.random.seed(42)? OPTIONS: A) Generates a random number between 0 and 42. B) Sets the size of random arrays to 42. C) Ensures reproducibility of random number generation. D) Creates an array with 42 random elements. ?? SOLUTION: C) Ensures reproducibility of random number generation. Setting the seed allows you to get the same sequence of “random” numbers each time the code is run.

NumPy_linspace QUESTION: Generate 5 evenly spaced numbers between 0 and 1 (inclusive) using NumPy. OPTIONS: A) np.arange(0, 1, 5) B) np.linspace(0, 1, 5) C) np.random(0, 1, 5) D) np.array([0, 0.25, 0.5, 0.75, 1]) ?? SOLUTION: B) np.linspace(0, 1, 5) np.linspace(start, stop, num) generates num evenly spaced samples.

Matplotlib_Figure_Axes QUESTION: Explain the relationship between “figure” and “axes” in Matplotlib. OPTIONS: A) They are the same thing. B) A figure can contain multiple axes, and axes are where the plots are drawn. C) An axes can contain multiple figures. D) Axes are used for 3D plots, while figures are for 2D plots. ?? SOLUTION: B) A figure can contain multiple axes, and axes are where the plots are drawn. The figure is the top-level container; axes are individual plots within the figure.

Matplotlib_Labels QUESTION: Add x and y axis labels to a plot using the object-oriented interface with an axes object named ax. OPTIONS: A) ax.xlabel("X Label"); ax.ylabel("Y Label") B) ax.set_xlabels("X Label"); ax.set_ylabels("Y Label") C) ax.set_xlabel("X Label"); ax.set_ylabel("Y Label") D) plt.xlabel("X Label", ax); plt.ylabel("Y Label", ax) ?? SOLUTION: C) ax.set_xlabel("X Label"); ax.set_ylabel("Y Label") The set_xlabel and set_ylabel methods are used with the axes object.

Matplotlib_Legends QUESTION: Add a legend to a plot using the object-oriented interface with an axes object called ax. OPTIONS: A) ax.add_legend() B) ax.legend() C) plt.legend(ax) D) ax.show_legend() ?? SOLUTION: B) ax.legend() The legend() method displays the legend on the specified axes. The labels are usually defined within the plot() call.

Matplotlib_grid QUESTION: How to add grid lines to your plot using the plt interface? OPTIONS: A) plt.gridlines() B) plt.grid(True) C) plt.show_grid() D) plt.axes().grid() ?? SOLUTION: B) plt.grid(True) plt.grid(True) enables grid lines; plt.grid(False) disables them.

Matplotlib_styles QUESTION: Change the plot style to the ‘ggplot’ style. OPTIONS: A) plt.style('ggplot') B) plt.set_style('ggplot') C) plt.style.use('ggplot') D) plt.rc('style', 'ggplot') ?? SOLUTION: C) plt.style.use('ggplot') plt.style.use() sets the plotting style.

Matplotlib_twinx QUESTION: What is the purpose of the twinx() method in Matplotlib? OPTIONS: A) To create a copy of the current plot. B) To create a second axes that shares the x-axis, allowing plots with different y-scales. C) To create a second axes that shares the y-axis. D) To zoom in on a section of the plot. ?? SOLUTION: B) To create a second axes that shares the x-axis, allowing plots with different y-scales. twinx() is useful for plotting two datasets with the same x-values but different y-ranges.

Matplotlib_tight_layout QUESTION: What does plt.tight_layout() do? OPTIONS: A) Compresses the plot image file size. B) Automatically adjusts subplot parameters to provide reasonable spacing. C) Makes the plot lines thicker. D) Removes whitespace around the figure. ?? SOLUTION: B) Automatically adjusts subplot parameters to provide reasonable spacing. plt.tight_layout() helps prevent labels and titles from overlapping.