Core Pandas Vocabulary: 10 practice questions

Question 1 of 10

What does the Index identify in a DataFrame?

1. AThe column names after array conversion
2. BThe labels associated with its rows
3. CThe values stored in every selected cell
4. DOne dtype shared by all columns

Question 2 of 10

Which attribute reports the row and column lengths of a DataFrame?

1. Adtype
2. Bindex
3. Cshape
4. Dcolumns

Question 3 of 10

What does selecting one column from a DataFrame produce?

1. AA NumPy array that retains both label axes
2. BA scalar with every column label
3. CA Series that retains the row Index
4. DAn Index that stores the selected values

Question 4 of 10

Which description correctly distinguishes dtype from dtypes?

1. AA Series has one dtype; DataFrame dtypes describes each column
2. BBoth names contain the Index and column labels together
3. CA Series has many dtypes; DataFrame dtype describes every row
4. DBoth names return only the DataFrame row and column counts

Question 5 of 10

What labels are created when a Series is built from a list without an explicit index?

1. AA two-level Index containing rows and columns
2. BA RangeIndex from zero through one less than the length
3. CNo labels because list input disables the Index
4. DColumn labels copied from every value in the list

Question 6 of 10

Which statement describes the container relationship among pandas objects?

1. AA scalar contains Series, and each Series contains separate DataFrames
2. BA DataFrame contains Series, and each Series contains scalar values
3. CA Series contains DataFrames, and each DataFrame contains one label
4. DAn Index contains DataFrames, and each row contains a complete package

Question 7 of 10

What information is lost when DataFrame.to_numpy() creates an array representation?

1. AThe ability of the array to hold any values
2. BThe DataFrame's row and column labels
3. CThe dimensions represented by the array values
4. DThe current number of rows and columns

Question 8 of 10

A table has customer IDs as row labels, four named variables, and a different type in each variable. You must inspect both axis lengths and all variable types. Which checks meet both constraints?

1. ARead columns for row count and shape for every column type
2. BRead shape for axis lengths and dtypes for column types
3. CRead dtype for axis lengths and Index for column types
4. DConvert to NumPy for labels and read Index for all value types

Question 9 of 10

A downstream function requires a NumPy array, but your analysis also depends on customer and variable labels. What is the key tradeoff when converting a heterogeneous DataFrame?

1. AThe Index becomes the array values and column labels become its dtype
2. BThe array keeps separate column dtypes and preserves every pandas label
3. CThe array may use a common dtype and will omit both label axes
4. DThe DataFrame becomes one Series while retaining both labeled dimensions

Question 10 of 10

You need one named variable across many labeled observations and must preserve those row labels when selecting it from a wider table. Which object should the selection yield?

1. AA one-dimensional Series with the original row Index
2. BAn unlabeled NumPy array with a separate dtype per column
3. CA two-dimensional DataFrame with every original variable
4. DA scalar whose label represents all observations together