Which of the optimized groupby methods listed in Table 10-1 would you use to compute the cumulative sum of non-NA values within each group?
Explanation
This question tests the knowledge of the specific optimized methods available on GroupBy objects, as detailed in Table 10-1, focusing on the distinction between aggregation and transformation-like methods.
Other questions
According to the split-apply-combine strategy for group operations, what is the correct sequence of steps?
Which of the following is NOT described as a valid form for a grouping key in a pandas groupby operation?
When performing a groupby aggregation like .mean() on a DataFrame, what is a 'nuisance column'?
What is the key distinction between the .size() and .count() methods on a pandas GroupBy object?
When you iterate over a GroupBy object created with multiple keys, such as in 'for (k1, k2), group in df.groupby(["key1", "key2"]):', what is the type and content of the first element in the yielded tuple?
What is the difference in the object type returned by `df.groupby(["key1", "key2"])["data2"]` versus `df.groupby(["key1", "key2"])[["data2"]]`?
When grouping a DataFrame `people` by its columns using a dictionary mapping, `people.groupby(mapping, axis="columns")`, what happens to columns in the DataFrame that are not present as keys in the mapping dictionary?
To apply multiple aggregation functions, such as 'mean' and 'std', to a GroupBy Series object named `grouped_pct`, which syntax would produce a DataFrame with columns named after the functions?
How can you apply multiple aggregation functions to a GroupBy object and provide custom names for the resulting columns?
What is the primary function of the `as_index=False` parameter in a `groupby` operation?
Which method is described as the most general-purpose GroupBy method that splits an object, invokes a passed function on each piece, and attempts to concatenate the pieces?
When using `groupby(...).apply(my_function)`, how can you pass additional arguments to `my_function`, such as `n=1` and `column="total_bill"`?
What is the purpose of the `group_keys=False` parameter when used with `groupby(...).apply()`?
What is the primary characteristic of the `transform` method on a GroupBy object compared to the `apply` method?
Which of the following is NOT a constraint on a function used with the `transform` method?
What is meant by an 'unwrapped' group operation in pandas?
What is a pivot table, as described in the context of pandas?
In the pandas `pivot_table` method, what is the default aggregation function if `aggfunc` is not specified?
What does the `margins=True` argument do in the `pivot_table` and `crosstab` functions?
What is a cross-tabulation (crosstab), and which pandas function is used to create one?
Consider a DataFrame `df` with columns 'key1' and 'key2'. To get the number of occurrences of each unique pair of values from these columns, which GroupBy method is most direct?
In Chapter 10.1, a DataFrame `df` is created. If you execute `df.groupby('key1', dropna=False).size()`, how are the missing values in the 'key1' column handled?
If `grouped = df.groupby('key1')`, which code snippet will calculate the difference between the maximum and minimum of 'data1' for each group in 'key1'?
If `grouped = df.groupby('key1')`, what is the most direct way to calculate the difference between the maximum and minimum of the 'data1' column for each group?
To apply different aggregations to different columns of a grouped DataFrame `grouped`—for instance, `np.max` on the 'tip' column and 'sum' on the 'size' column—what is the correct syntax for the `.agg()` method?
In the example from Chapter 10.3 to fill missing state data, `data.groupby(group_key).apply(fill_mean)`, where `fill_mean` is `def fill_mean(group): return group.fillna(group.mean())`, how is the fill value determined for each missing value?
What is the result of using `groupby` in conjunction with `pd.cut` on a DataFrame column, as shown in the quantile analysis example?
In the group-wise linear regression example from Chapter 10.3, `by_year.apply(regress, yvar="AAPL", xvars=["SPX"])`, what does the `apply` function do?
Given a grouped Series `g`, what is the output of `g.transform('mean')`?
Why are built-in aggregate functions like 'mean' or 'sum' often much faster when used with `transform` compared to a general apply function?
In the `tips.pivot_table(index=["time", "day"], columns="smoker", values=["tip_pct", "size"])` example, what do the `index`, `columns`, and `values` arguments specify?
To create a cross-tabulation of `tips["time"]` and `tips["day"]` against `tips["smoker"]`, which is the correct `pd.crosstab` syntax?
What does invoking the `mean()` method on a `GroupBy` object, such as `df.groupby('key1').mean()`, actually compute?
Consider a DataFrame with a MultiIndex on its columns, named 'cty' and 'tenor'. How would you group the DataFrame by the 'cty' level of the column index?
Why are custom aggregation functions passed to `.agg()` generally much slower than the optimized functions listed in Table 10-1 (e.g., 'sum', 'mean')?
If you apply `.describe()` to a GroupBy object, what is the result?
What does the `fill_value` argument in the `pivot_table` method accomplish?
In the weighted average example, the function `get_wavg` is defined as `np.average(group["data"], weights=group["weights"])`. How is this function used to compute the weighted average for each category?
When grouping a DataFrame with a function, for example `people.groupby(len).sum()`, what is the function `len` applied to?
In the code `df.groupby(["key1", "key2"])[["data2"]].mean()`, what will the structure of the output be?
What does the `nsmallest` method, when used on a GroupBy object like `grouped["data1"].nsmallest(2)`, accomplish?
If you want to apply a list of functions `["count", "mean", "max"]` to two columns `["tip_pct", "total_bill"]` of a GroupBy object `grouped`, what is the structure of the resulting DataFrame?
To get a group-wise ranking of values in descending order using `transform`, you could use the function `def get_ranks(group): return group.rank(ascending=False)`. What will be the characteristics of the output of `g.transform(get_ranks)`?
What is the result of `df.groupby('key1')['key1'].count()`?
If a DataFrame `df` has 7 rows and you execute `df.groupby('key1').mean()`, and the result in `Out[25]` has two rows with key1 values 'a' and 'b', what can you infer about the 'key1' column in the original DataFrame?
In the random sampling example from Chapter 10.3, a deck of cards is grouped by suit using a function `get_suit`. The code `deck.groupby(get_suit).apply(draw, n=2)` is then used. What is the purpose of this operation?
Consider the code `tips.groupby(["day", "smoker"]).mean()`. To get the same aggregated data but in a 'flat' format where 'day' and 'smoker' are columns instead of the index, which is the most direct modification?
Consider the code `tips.groupby(["day", "smoker"]).mean()`. If you wanted the same aggregated data but in a 'flat' format where 'day' and 'smoker' are columns, what change would you make?
In the `pd.crosstab` example using the tips data, `pd.crosstab([tips["time"], tips["day"]], tips["smoker"])`, what do the rows and columns of the resulting table represent?
If `df.groupby('key2').mean()` is executed on the DataFrame from `In [15]`, why is the 'key1' column absent from the output shown in `Out[26]`?
What is a key benefit of using the `transform` method with a fast-path function like 'mean' and performing arithmetic on the results (an 'unwrapped' operation), compared to using `.apply` with a complex function?