flasc.flasc_dataframe.FlascDataFrame

class flasc.flasc_dataframe.FlascDataFrame(*args, channel_name_map=None, long_data_columns=None, **kwargs)

Bases: DataFrame

Subclass of pandas.DataFrame for working with FLASC data.

Stores data in preferred Flasc format, or user format, with option to convert between the two.

Want handling to go between long and wide.

Methods

abs	Return a Series/DataFrame with absolute numeric value of each element.
add	Get Addition of dataframe and other, element-wise (binary operator add).
add_prefix	Prefix labels with string prefix.
add_suffix	Suffix labels with string suffix.
agg	Aggregate using one or more operations over the specified axis.
aggregate	Aggregate using one or more operations over the specified axis.
align	Align two objects on their axes with the specified join method.
all	Return whether all elements are True, potentially over an axis.
any	Return whether any element is True, potentially over an axis.
apply	Apply a function along an axis of the DataFrame.
applymap	Apply a function to a Dataframe elementwise.
asfreq	Convert time series to specified frequency.
asof	Return the last row(s) without any NaNs before where.
assign	Assign new columns to a DataFrame.
astype	Cast a pandas object to a specified dtype dtype.
at_time	Select values at particular time of day (e.g., 9:30AM).
backfill	Fill NA/NaN values by using the next valid observation to fill the gap.
between_time	Select values between particular times of the day (e.g., 9:00-9:30 AM).
bfill	Fill NA/NaN values by using the next valid observation to fill the gap.
bool	Return the bool of a single element Series or DataFrame.
boxplot	Make a box plot from DataFrame columns.
check_flasc_format	Raise an error if the data is not in FLASC format.
clip	Trim values at input threshold(s).
combine	Perform column-wise combine with another DataFrame.
combine_first	Update null elements with value in the same location in other.
compare	Compare to another DataFrame and show the differences.
convert_dtypes	Convert columns to the best possible dtypes using dtypes supporting pd.NA.
convert_time_to_datetime	Convert the time column to a datetime representation.
convert_to_flasc_format	Convert the DataFrame to the format that FLASC expects.
convert_to_user_format	Convert the DataFrame to the format that the user expects, given the channel_name_map.
copy	Make a copy of this object's indices and data.
copy_metadata	Copy metadata from another FlascDataFrame to self.
corr	Compute pairwise correlation of columns, excluding NA/null values.
corrwith	Compute pairwise correlation.
count	Count non-NA cells for each column or row.
cov	Compute pairwise covariance of columns, excluding NA/null values.
cummax	Return cumulative maximum over a DataFrame or Series axis.
cummin	Return cumulative minimum over a DataFrame or Series axis.
cumprod	Return cumulative product over a DataFrame or Series axis.
cumsum	Return cumulative sum over a DataFrame or Series axis.
describe	Generate descriptive statistics.
diff	First discrete difference of element.
div	Get Floating division of dataframe and other, element-wise (binary operator truediv).
divide	Get Floating division of dataframe and other, element-wise (binary operator truediv).
dot	Compute the matrix multiplication between the DataFrame and other.
drop	Drop specified labels from rows or columns.
drop_duplicates	Return DataFrame with duplicate rows removed.
droplevel	Return Series/DataFrame with requested index / column level(s) removed.
dropna	Remove missing values.
duplicated	Return boolean Series denoting duplicate rows.
eq	Get Equal to of dataframe and other, element-wise (binary operator eq).
equals	Test whether two objects contain the same elements.
eval	Evaluate a string describing operations on DataFrame columns.
ewm	Provide exponentially weighted (EW) calculations.
expanding	Provide expanding window calculations.
explode	Transform each element of a list-like to a row, replicating index values.
export_to_windup_format	Convert the DataFrame to the format that wind-up expects.
ffill	Fill NA/NaN values by propagating the last valid observation to next valid.
fillna	Fill NA/NaN values using the specified method.
filter	Subset the dataframe rows or columns according to the specified index labels.
first	Select initial periods of time series data based on a date offset.
first_valid_index	Return index for first non-NA value or None, if no non-NA value is found.
floordiv	Get Integer division of dataframe and other, element-wise (binary operator floordiv).
from_dict	Construct DataFrame from dict of array-like or dicts.
from_records	Convert structured or record ndarray to DataFrame.
ge	Get Greater than or equal to of dataframe and other, element-wise (binary operator ge).
get	Get item from object for given key (ex: DataFrame column).
groupby	Group DataFrame using a mapper or by a Series of columns.
gt	Get Greater than of dataframe and other, element-wise (binary operator gt).
head	Return the first n rows.
hist	Make a histogram of the DataFrame's columns.
idxmax	Return index of first occurrence of maximum over requested axis.
idxmin	Return index of first occurrence of minimum over requested axis.
infer_objects	Attempt to infer better dtypes for object columns.
info	Print a concise summary of a DataFrame.
insert	Insert column into DataFrame at specified location.
interpolate	Fill NaN values using an interpolation method.
isetitem	Set the given value in the column with position loc.
isin	Whether each element in the DataFrame is contained in values.
isna	Detect missing values.
isnull	DataFrame.isnull is an alias for DataFrame.isna.
items	Iterate over (column name, Series) pairs.
iterrows	Iterate over DataFrame rows as (index, Series) pairs.
itertuples	Iterate over DataFrame rows as namedtuples.
join	Join columns of another DataFrame.
keys	Get the 'info axis' (see Indexing for more).
kurt	Return unbiased kurtosis over requested axis.
kurtosis	Return unbiased kurtosis over requested axis.
last	Select final periods of time series data based on a date offset.
last_valid_index	Return index for last non-NA value or None, if no non-NA value is found.
le	Get Less than or equal to of dataframe and other, element-wise (binary operator le).
lt	Get Less than of dataframe and other, element-wise (binary operator lt).
map	Apply a function to a Dataframe elementwise.
mask	Replace values where the condition is True.
max	Return the maximum of the values over the requested axis.
mean	Return the mean of the values over the requested axis.
median	Return the median of the values over the requested axis.
melt	Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.
memory_usage	Return the memory usage of each column in bytes.
merge	Merge DataFrame or named Series objects with a database-style join.
min	Return the minimum of the values over the requested axis.
mod	Get Modulo of dataframe and other, element-wise (binary operator mod).
mode	Get the mode(s) of each element along the selected axis.
mul	Get Multiplication of dataframe and other, element-wise (binary operator mul).
multiply	Get Multiplication of dataframe and other, element-wise (binary operator mul).
ne	Get Not equal to of dataframe and other, element-wise (binary operator ne).
nlargest	Return the first n rows ordered by columns in descending order.
notna	Detect existing (non-missing) values.
notnull	DataFrame.notnull is an alias for DataFrame.notna.
nsmallest	Return the first n rows ordered by columns in ascending order.
nunique	Count number of distinct elements in specified axis.
pad	Fill NA/NaN values by propagating the last valid observation to next valid.
pct_change	Fractional change between the current and a prior element.
pipe	Apply chainable functions that expect Series or DataFrames.
pivot	Return reshaped DataFrame organized by given index / column values.
pivot_table	Create a spreadsheet-style pivot table as a DataFrame.
pop	Return item and drop from frame.
pow	Get Exponential power of dataframe and other, element-wise (binary operator pow).
prod	Return the product of the values over the requested axis.
product	Return the product of the values over the requested axis.
quantile	Return values at the given quantile over requested axis.
query	Query the columns of a DataFrame with a boolean expression.
radd	Get Addition of dataframe and other, element-wise (binary operator radd).
rank	Compute numerical data ranks (1 through n) along axis.
rdiv	Get Floating division of dataframe and other, element-wise (binary operator rtruediv).
reindex	Conform DataFrame to new index with optional filling logic.
reindex_like	Return an object with matching indices as other object.
rename	Rename columns or index labels.
rename_axis	Set the name of the axis for the index or columns.
reorder_levels	Rearrange index levels using input order.
replace	Replace values given in to_replace with value.
resample	Resample time-series data.
reset_index	Reset the index, or a level of it.
rfloordiv	Get Integer division of dataframe and other, element-wise (binary operator rfloordiv).
rmod	Get Modulo of dataframe and other, element-wise (binary operator rmod).
rmul	Get Multiplication of dataframe and other, element-wise (binary operator rmul).
rolling	Provide rolling window calculations.
round	Round a DataFrame to a variable number of decimal places.
rpow	Get Exponential power of dataframe and other, element-wise (binary operator rpow).
rsub	Get Subtraction of dataframe and other, element-wise (binary operator rsub).
rtruediv	Get Floating division of dataframe and other, element-wise (binary operator rtruediv).
sample	Return a random sample of items from an axis of object.
select_dtypes	Return a subset of the DataFrame's columns based on the column dtypes.
sem	Return unbiased standard error of the mean over requested axis.
set_axis	Assign desired index to given axis.
set_flags	Return a new object with updated flags.
set_index	Set the DataFrame index using existing columns.
shift	Shift index by desired number of periods with an optional time freq.
skew	Return unbiased skew over requested axis.
sort_index	Sort object by labels (along an axis).
sort_values	Sort by the values along either axis.
squeeze	Squeeze 1 dimensional axis objects into scalars.
stack	Stack the prescribed level(s) from columns to index.
std	Return sample standard deviation over requested axis.
sub	Get Subtraction of dataframe and other, element-wise (binary operator sub).
subtract	Get Subtraction of dataframe and other, element-wise (binary operator sub).
sum	Return the sum of the values over the requested axis.
swapaxes	Interchange axes and swap values axes appropriately.
swaplevel	Swap levels i and j in a MultiIndex.
tail	Return the last n rows.
take	Return the elements in the given positional indices along an axis.
to_clipboard	Copy object to the system clipboard.
to_csv	Write object to a comma-separated values (csv) file.
to_dict	Convert the DataFrame to a dictionary.
to_excel	Write object to an Excel sheet.
to_feather	Raise warning about lost information and save to feather format.
to_gbq	Write a DataFrame to a Google BigQuery table.
to_hdf	Write the contained data to an HDF5 file using HDFStore.
to_html	Render a DataFrame as an HTML table.
to_json	Convert the object to a JSON string.
to_latex	Render object to a LaTeX tabular, longtable, or nested table.
to_markdown	Print DataFrame in Markdown-friendly format.
to_numpy	Convert the DataFrame to a NumPy array.
to_orc	Write a DataFrame to the ORC format.
to_parquet	Write a DataFrame to the binary parquet format.
to_period	Convert DataFrame from DatetimeIndex to PeriodIndex.
to_pickle	Pickle (serialize) object to file.
to_records	Convert DataFrame to a NumPy record array.
to_sql	Write records stored in a DataFrame to a SQL database.
to_stata	Export DataFrame object to Stata dta format.
to_string	Render a DataFrame to a console-friendly tabular output.
to_timestamp	Cast to DatetimeIndex of timestamps, at beginning of period.
to_xarray	Return an xarray object from the pandas object.
to_xml	Render a DataFrame to an XML document.
transform	Call func on self producing a DataFrame with the same axis shape as self.
transpose	Transpose index and columns.
truediv	Get Floating division of dataframe and other, element-wise (binary operator truediv).
truncate	Truncate a Series or DataFrame before and after some index value.
tz_convert	Convert tz-aware axis to target time zone.
tz_localize	Localize tz-naive index of a Series or DataFrame to target time zone.
unstack	Pivot a level of the (necessarily hierarchical) index labels.
update	Modify in place using non-NA values from another DataFrame.
value_counts	Return a Series containing the frequency of each distinct row in the Dataframe.
var	Return unbiased variance over requested axis.
where	Replace values where the condition is False.
xs	Return cross-section from the Series/DataFrame.

Attributes

T	The transpose of the DataFrame.
at	Access a single value for a row/column label pair.
attrs	Dictionary of global attributes of this dataset.
axes	Return a list representing the axes of the DataFrame.
columns	The column labels of the DataFrame.
dtypes	Return the dtypes in the DataFrame.
empty	Indicator whether Series/DataFrame is empty.
flags	Get the properties associated with this pandas object.
iat	Access a single value for a row/column pair by integer position.
iloc	Purely integer-location based indexing for selection by position.
in_flasc_format	Return True if the data is in FLASC format, False otherwise.
index	The index (row labels) of the DataFrame.
loc	Access a group of rows and columns by label(s) or a boolean array.
n_turbines	Return the number of turbines in the dataset.
ndim	Return an int representing the number of axes / array dimensions.
shape	Return a tuple representing the dimensionality of the DataFrame.
size	Return an int representing the number of elements in this object.
style	Returns a Styler object.
values	Return a Numpy representation of the DataFrame.

_metadata: list[str] = ['channel_name_map', '_user_format', '_long_data_columns']

property in_flasc_format

Return True if the data is in FLASC format, False otherwise.

property _constructor

Used when a manipulation result has the same dimensions as the original.

_repr_html_()

Printout when displaying results in jupyter notebook.

property n_turbines

Return the number of turbines in the dataset.

check_flasc_format()

Raise an error if the data is not in FLASC format.

copy_metadata(other)

Copy metadata from another FlascDataFrame to self.

Parameters:

other (FlascDataFrame) -- DataFrame to copy metadata from.

convert_to_user_format(inplace=False)

Convert the DataFrame to the format that the user expects, given the channel_name_map.

Parameters:

inplace (bool) -- If True, modify the DataFrame in place. If False, return a new DataFrame.

Returns:

FlascDataFrame in user format if inplace is False, None otherwise.

Return type:

FlascDataFrame

convert_time_to_datetime(inplace=False)

Convert the time column to a datetime representation.

Parameters:

inplace (bool) -- If True, modify the DataFrame in place. If False, return a new DataFrame.

Returns:

FlascDataFrame with time column as datetime object if inplace is False, None otherwise

Return type:

FlascDataFrame

convert_to_flasc_format(inplace=False)

Convert the DataFrame to the format that FLASC expects.

Parameters:

inplace (bool) -- If True, modify the DataFrame in place. If False, return a new DataFrame.

Returns:

FlascDataFrame in FLASC format if inplace is False, None otherwise

Return type:

FlascDataFrame

# TODO: could consider converting "time" to datetime type here. If so, will want to keep # the original "time" column for back-conversion if needed. # Similarly, we could sort on time, but perhaps both are too meddlesome

_convert_long_to_wide(df_)

Convert a long format DataFrame to a wide format DataFrame.

Parameters:

df (FlascDataFrame) -- Long format FlascDataFrame

Returns:

Wide format FlascDataFrame

Return type:

FlascDataFrame

_convert_wide_to_long(df_)

Convert a wide format DataFrame to a long format DataFrame.

Parameters:

df (FlascDataFrame) -- Wide format FlascDataFrame

Returns:

Long format FlascDataFrame

Return type:

FlascDataFrame

to_feather(path, **kwargs)

Raise warning about lost information and save to feather format.

_mgr: BlockManager | ArrayManager

_attrs: dict[Hashable, Any]

_cache: dict[str, Any]

export_to_windup_format(turbine_names: list[str] | None = None, time_col: str = 'time', power_col: str = 'pow', windspeed_col: str = 'ws', winddirection_col: str = 'wd', normal_operation_col: str | None = None, pitchangle_col: str | None = None, genrpm_col: str | None = None, downtimecounter_col: str | None = None, turbine_num_digits: int = 3)

Convert the DataFrame to the format that wind-up expects.

Parameters:

• turbine_names (list[str] | None)

• time_col (str)

• power_col (str)

• windspeed_col (str)

• winddirection_col (str)

• normal_operation_col (str | None)

• pitchangle_col (str | None)

• genrpm_col (str | None)

• downtimecounter_col (str | None)

• turbine_num_digits (int)