Data Cleaning and Preparation

By default, what is the behavior of the `dropna()` method when applied to a pandas DataFrame?

What is the effect of passing `how="all"` as an argument to the `data.dropna()` method on a DataFrame?

Suppose you want to keep only the rows in a DataFrame that have at least a certain number of non-missing values. Which argument should you use with the `dropna()` method?

When using the `fillna()` method on a DataFrame, what is accomplished by passing a dictionary to it?

Which method is considered the workhorse function for replacing missing values in a pandas DataFrame or Series?

What does the DataFrame method `duplicated()` return?

By default, the `duplicated()` and `drop_duplicates()` methods keep the first observed value combination. How can you modify this behavior to keep the last observed combination instead?

What is the primary use of the `map` method on a pandas Series in the context of data transformation?

Given the pandas Series `data = pd.Series([1., -999., 2., -999., -1000., 3.])`, what is the result of calling `data.replace(-999, np.nan)`?

If you want to replace multiple different values with a single substitute value in a pandas Series, how should you use the `replace` method?

How can you create a transformed version of a DataFrame with renamed index and column labels without modifying the original DataFrame?

What is the primary function of `pandas.cut`?

In the string representation of an interval returned by `pandas.cut`, such as `(18, 25]`, what does the square bracket `]` signify?

What is the main difference between the `pandas.cut` and `pandas.qcut` functions?

To select all rows in a DataFrame `data` that have a value in any of their columns exceeding 3 in absolute value, which line of code is correct?

What does the `numpy.random.permutation()` function produce when called with the length of an axis?

How can you select a random subset of 3 rows from a DataFrame `df` without replacement?

What is the purpose of the `pandas.get_dummies` function?

If a column in a DataFrame contains strings where multiple categories are separated by a delimiter (e.g., 'Animation|Children's|Comedy'), which method is specially designed to create dummy variables from it?

Why did pandas develop an extension type system, departing from its original reliance on NumPy types?

When creating a pandas Series of integers with a missing value using an extension type, what data type should be specified to avoid converting the Series to float64?

What is the primary difference between Python's built-in `find()` and `index()` string methods?

In the context of regular expressions in Python, why is it highly recommended to use the `re.compile()` function?

What is the difference between the `re.search()` and `re.match()` methods?

In pandas, how do you access array-oriented methods for string operations on a Series that correctly handle missing (NA) values?

Given a pandas Series `data` containing email addresses and NA values, what does the method `data.str.findall(pattern, flags=re.IGNORECASE)` return for a row containing an NA value?

What is the purpose of the `.str.extract()` method on a pandas Series?

In data warehousing, what is the best practice for representing a column with many repeated values, as described in the chapter?

When a pandas Series is converted to the 'category' dtype, what two main components does the underlying Categorical object have?

If you have an array of integer codes and an array of corresponding category labels from an external source, which constructor should you use to create a `pandas.Categorical` object?

How can you make an unordered categorical Series instance ordered in pandas?

Why can GroupBy operations be significantly faster when performed on categorical data compared to string data?

In a pandas Series `cat_s` with a categorical dtype, how do you access the categorical methods like `set_categories` or `remove_unused_categories`?

After filtering a large DataFrame, many of the original categories in a categorical column may no longer be present in the data. Which method can be used to trim these unobserved categories?

What is another term for creating dummy variables from categorical data, as mentioned in the section 'Creating dummy variables for modeling'?

Consider the Series `s = pd.Series(['a', 'b', 'c', 'd'] * 2, dtype='category')`. What will be the output of `pd.get_dummies(s)`?

In a pandas Series created with `pd.Series([1, 2, None], dtype='float64')`, what value is at index 2?

Given a DataFrame `df`, what is the result of `df.fillna(method="ffill", limit=2)`?

What does the `precision` argument in `pd.cut(data, 4, precision=2)` do?

Consider the code `data[data.abs() > 3] = np.sign(data) * 3`. What is its effect on the DataFrame `data`?

What is the difference between `data.replace()` and `data.str.replace()` for a pandas Series?

In regular expressions, what does the `findall` method return when the pattern contains capturing groups?

How can you slice substrings from each element in a pandas Series `data` in a vectorized way?

Consider the code `pd.get_dummies(pd.cut(values, bins))`. What is the useful application of this combination of functions?

If you have a pandas Series `cat_s2` with 5 defined categories ('a' through 'e') but the data only contains 'a', 'b', 'c', 'd', what will `cat_s2.value_counts()` show for category 'e'?

What is the return type of the `.codes` attribute of a pandas Categorical object?

Given `ages = [20, 22, 25, 27, 21, 23, 37, 31, 61, 45, 41, 32]` and `bins = [18, 25, 35, 60, 100]`, how many values fall into the `(18, 25]` bin when `pd.cut(ages, bins)` is called?

Which pandas method is specifically designed to perform a vectorized set membership check?

What does the pandas `value_counts()` method return?

How can you get an index array from an array of possibly non-distinct values into another array of distinct values, which is helpful for data alignment?