What is the key difference between `ravel` and `flatten` when converting a multidimensional NumPy array to one dimension?
Explanation
Although both `ravel` and `flatten` can be used to create a 1D array from a higher-dimensional array, the main difference lies in memory management. `flatten` always allocates new memory and returns a copy, whereas `ravel` will return a view of the original array if possible, which is more memory-efficient.
Other questions
What is the result of subtracting `datetime(2008, 6, 24, 8, 15)` from `datetime(2011, 1, 7)` using Python's datetime module?
What does 'NaT' represent in the pandas library?
When indexing a pandas time series that has duplicate timestamps, what is the result if you use a duplicated timestamp as the indexer?
What is the primary function of `pandas.date_range`?
What is the effect of passing a frequency string to the `shift` method, such as `ts.shift(2, freq='M')`?
What is the result when two time series with different time zones are combined in pandas?
In pandas, what is the process of converting higher frequency time series data to a lower frequency called?
What is the purpose of the `tz_localize` method on a pandas time series?
How does the `expanding` operator differ from the `rolling` operator in pandas?
What does a `pandas.Period` object represent?
If a pandas `Period` object is defined as `p = pd.Period("2011", freq="A-DEC")`, what is the result of `p.asfreq("M", how="start")`?
In the context of `resample`, what does the `ohlc` aggregate function compute?
What is the purpose of the `span` parameter in the `ewm` (exponentially weighted moving) operator?
To perform a grouped resampling operation on a DataFrame with a time column and a key column, what object can be used with `groupby`?
According to the text, a pandas `Timestamp` can be substituted for a Python `datetime` object in most places. Is the reverse also true?
In the context of downsampling with `resample`, which two aspects must be considered about the time intervals or bins?
What is the result of applying the `rollforward` method of a `MonthEnd` offset to the date `datetime(2011, 11, 17)`?
When creating a `PeriodIndex` from separate year and quarter columns, such as `pd.PeriodIndex(year=data["year"], quarter=data["quarter"], freq="Q-DEC")`, what is the result?
To convert a time series indexed by Timestamps to one indexed by Periods, which method should be used?
What is the function of the `min_periods` argument in a `rolling` operation, for example `rolling(250, min_periods=10)`?
If you create a pandas date range using `pd.date_range("2000-01-01", "2000-12-01", freq="BM")`, what kind of dates will be in the resulting index?
Which method is used to convert a time series from a lower frequency to a higher frequency without aggregation, introducing missing values?
What does a call to `ts.resample('D')` on a time series `ts` return?
When using `pd.date_range` for a period between "2012-04-01" and "2012-06-01", what is the default frequency `freq`?
What is the difference in output between `p.asfreq("D", how="start")` and `p.asfreq("D", how="end")` for a quarterly period `p = pd.Period("2012Q4", freq="Q-JAN")`?
If `ts` is a pandas Series with a DatetimeIndex, what does the expression `ts[::2]` do?
When localizing a naive time series with `ts.tz_localize("America/New_York")`, what happens during a Daylight Saving Time (DST) transition?
How can you select a slice of a long time series `longer_ts` corresponding to the entire year 2001?
What does passing the `normalize=True` argument to `pd.date_range()` accomplish?
If `p = pd.Period("2011", freq="A-JUN")`, what is the result of `p.asfreq("M", how="end")`?
What is a key constraint for the target frequency when upsampling with periods?
How can you perform a binary moving window operation, such as a rolling correlation between two time series `returns["AAPL"]` and `spx_rets`?
What is the requirement for a user-defined function passed to the `apply` method of a rolling object?
What does a timedelta object such as `timedelta(12)` represent when added to a datetime object?
How can you select data from a time series `ts` for a specific date using a string, for example, January 10, 2011?
To convert a time series to a fixed daily frequency, you can call `ts.resample('D')`. What must be done after this call to get an aggregated result?
What is the key difference between the frequency strings 'M' and 'BM' in pandas?
When using `ts.shift(2)`, what values are introduced into the time series?
To convert a time-zone-aware pandas Timestamp from UTC to the 'America/New_York' time zone, which method is used?
If two periods have the same frequency, what does their difference represent?
In the context of upsampling a time series using `resample`, how can you fill the newly created `NaN` values with the last valid observation?
Which pandas function is used to create a regular range of `Period` objects?
If a time series has a frequency of 'W-WED' (weekly on Wednesday), resampling it to 'W-FRI' (weekly on Friday) is considered what type of operation?
What is the data type of scalar values from a pandas `DatetimeIndex`?
When resampling time series data with `ts.resample("5min")`, what does the default behavior for `closed` and `label` produce for a 00:00 to 00:05 interval?
To get a date range representing the third Friday of each month, what frequency string should be used?
When an operation is performed on two differently indexed time series, what is the fundamental principle of how pandas handles the operation?
To create a frequency of four hours in pandas, which string alias can be used?
A common use of the naive `shift` method is to compute consecutive percent changes. How is this expressed for a time series `ts`?