lisa.datautils#

Dataframe utilities.

Globals

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Classes

`DataAccessor`	Proxy class that allows extending the `pandas.DataFrame` API.
`DataFrameAccessor`	Proxy class that allows extending the `pandas.DataFrame` API.
`SeriesAccessor`	Proxy class that allows extending the `pandas.DataFrame` API.
`SignalDesc`	Define a signal to be used by various signal-oriented APIs.
`Timestamp`	Nanosecond-precision timestamp. It inherits from `float` and as such can be manipulating as a floating point number of seconds. The `nanoseconds` attribute allows getting the exact timestamp regardless of the magnitude of the float, allowing for more precise computation.

Functions

`df_add_delta()`	Add a column containing the delta of the given other column.
`df_combine()`	Same as `pandas.DataFrame.combine()` on a list of series rather than just two.
`df_combine_duplicates()`	Combine the duplicated rows using `func` and remove the duplicates.
`df_convert_to_nullable()`	Convert the columns of the dataframe to their equivalent nullable dtype, when possible.
`df_deduplicate()`	Same as `series_deduplicate()` but for `pandas.DataFrame`.
`df_delta()`	pre_df and post_df containing paired/consecutive events indexed by time, df_delta() merges the two dataframes and adds a `delta` column containing the time spent between the two events. A typical usecase would be adding pre/post events at the entry/exit of a function.
`df_dereference()`	Similar to `series_dereference()`.
`df_filter()`	Filter the content of a dataframe.
`df_filter_task_ids()`	Filter a dataframe using a list of `lisa.analysis.tasks.TaskID`.
`df_find_redundant_cols()`	Find the columns that are redundant to `col`, i.e. that can be computed as `df[x] = df[col].map(dict(...))`.
`df_make_empty_clone()`	Make an empty clone of the given dataframe.
`df_merge()`	Merge a list of `pandas.DataFrame`, keeping the index sorted.
`df_refit_index()`	Same as `series_refit_index()` but acting on `pandas.DataFrame`.
`df_split_signals()`	Yield subset of `df` that only contain one signal, along with the signal identification values.
`df_squash()`	Slice a dataframe of deltas in [start:end] and ensure we have an event at exactly those boundaries.
`df_update_duplicates()`	Same as `series_update_duplicates()` but on a `pandas.DataFrame`.
`df_window()`	Same as `series_window()` but acting on a `pandas.DataFrame`.
`df_window_signals()`	Similar to `df_window()` with `method='pre'` but guarantees that each signal will have a values at the beginning of the window.
`series_align_signal()`	Align a signal to an expected reference signal using their cross-correlation.
`series_combine()`	Same as `pandas.Series.combine()` on a list of series rather than just two.
`series_convert()`	Convert a `pandas.Series` with a best effort strategy.
`series_deduplicate()`	Remove duplicate values in a `pandas.Series`.
`series_dereference()`	Replace each value in `series` by the value at the corresponding index by the source indicated by `series`’s value.
`series_derivate()`	Compute a derivative of a `pandas.Series` with respect to another series.
`series_envelope_mean()`	Compute the average between the mean of local maximums and local minimums of the series.
`series_integrate()`	Compute the integral of y with respect to x.
`series_local_extremum()`	Returns a series of local extremum.
`series_mean()`	Compute the average of y by integrating with respect to x and dividing by the range of x.
`series_refit_index()`	Slice a series using `series_window()` and ensure we have a value at exactly the specified boundaries, unless the signal started after the beginning of the required window.
`series_rolling_apply()`	Apply a function on a rolling window of a series.
`series_update_duplicates()`	Update a given series to avoid duplicated values.
`series_window()`	Select a portion of a `pandas.Series`.

lisa.datautils#

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