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Grouper Not 1-dimensional

author
Elaine Sutton
• Saturday, 02 January, 2021
• 77 min read

Groupby() doesn't need to care about of or 'fruit' or 'color' or Nemo, group by() only cares about one thing, a lookup table that tells it which of.index is mapped to which label (i.e. In this case, for example, the dictionary passed to the group by() is instructing the group by() to: if you see index 11, then it is a “mine”, put the row with that index in the group named “mine”.

(Source: python-scripts.com)

Contents

I've tried to search the internet and Stack Overflow for this error, but got no results. Just like a lot of cryptic pandas errors, this one too stems from having two columns with the same name.

Amolkahat added a commit to amolkahat/pandas that referenced this issue Nov 26, 2016 Every once in a while it is useful to take a step back and look at pandas’ functions and see if there is a new or better way to do things.

I was recently working on a problem and noticed that pandas had a Grouper function that I had never used before. I looked into how it can be used and it turns out it is useful for the type of summary analysis I tend to do on a frequent basis.

In addition to functions that have been around a while, pandas continues to provide new and improved capabilities with every release. The updated AGG function is another very useful and intuitive tool for summarizing data.

This article will walk through how and why you may want to use the Grouper and AGG functions on your own data. Pandas’ origins are in the financial industry so it should not be a surprise that it has robust capabilities to manipulate and summarize time series data.

grouper encapsulation versus combination mesh
(Source: archimatix.com)

Just look at the extensive time series documentation to get a feel for all the options. These strings are used to represent various common time frequencies like days vs. weeks vs. years.

Since group by is one of my standard functions, this approach seems simpler to me and it is more likely to stick in my brain. The nice benefit of this capability is that if you are interested in looking at data summarized in a different time frame, just change the freq parameter to one of the valid offset aliases.

If your annual sales were on a non-calendar basis, then the data can be easily changed by modifying the freq parameter. When dealing with summarizing time series data, this is incredibly handy.

It is certainly possible (using pivot tables and custom grouping) but I do not think it is nearly as intuitive as the pandas approach. In pandas 0.20.1, there was a new AGG function added that makes it a lot simpler to summarize data in a manner similar to the group by API.

Fortunately we can pass a dictionary to AGG and specify what operations to apply to each column. I find this approach really handy when I want to summarize several columns of data.

grouper field dialogue structure level select
(Source: wiki.almworks.com)

In the past, I would run the individual calculations and build up the resulting data frame a row at a time. For instance, I frequently find myself needing to aggregate data and use a mode function that works on text.

This specification will select a column via the key parameter, or if the level and/or axis parameters are given, a level of the index of the target object. Convention {‘start’, ‘end’, ‘e’, ‘s’} If grouper is PeriodIndex and freq parameter is passed.

Base int, default 0 Only when freq parameter is passed. For frequencies that evenly subdivide 1 day, the “origin” of the aggregated intervals.

Loffset STR, Dateset, time delta object Only when freq parameter is passed. Dropna built, default True If True, and if group keys contain NA values, NA values together with row/column will be dropped.

To replace the use of the deprecated base argument, you can now use offset, in this example it is equivalent to have base=2 : In this tutorial, we'll discuss how to create a multidimensional Arrays in Java.

representation pairwise stereo alignment pdb code sam po4 publication
(Source: www.researchgate.net)

Suppose we want to represent a graph with 3 vertices, numbered 0 to 2. So, each point in this 3-D space will be represented by three coordinates, say, X, Y, and Z.

In addition to that, let's imagine each of those points will have a color, either Red, Green, Blue, or Yellow. Now, we can add colors to points in space.

Note that the index of elements in our spaceArrayList represents the X coordinate, while each 2-D Arrays, present at that index, represents the (Y, Z) coordinates. In this article, we discussed how to create a multidimensional Arrays in Java.

Moreover, we also explored how to represent 3-D space coordinates using a 3-D Arrays. Similarly, to create an N-Dimensional Arrays, we can extend the same concept.

The full implementation of this tutorial can be found on GitHub. Class lux.core.frame. LuxDataFrame (*arms, **kw) A subclass of pd. DataFrame that supports all data frame operations while housing other variables and functions for generating visual recommendations.

dimensional grouper enameling glazed brooch fish
(Source: www.rubylane.com)

(other)Get Addition of data frame and other, element-wise (binary operator add). (other) Append rows of other to the end of caller, returning a new object.

(freq)Convert Timeserver to specified frequency. (start_time, end_time, …)Select values between particular times of the day (e.g., 9:00-9:30 AM).

(other)Update null elements with value in the same location in other. (other, align_axis, int] = 1, …) Compare to another Database and show the differences.

(deep)Make a copy of this object’s indices and data. ()()Compute pairwise correlation of columns, excluding NA/null values.

(min_periods, DDAF)Compute pairwise covariance of columns, excluding NA/null values. (list)(*arms, **quarks)Generate descriptive statistics.

bonding assisted laser multidimensional interconnections heterogeneous devices technology etri
(Source: ettrends.etri.re.kr)

(periods, axis, int] = 0)First discrete difference of element. ()(other)Get Floating division of data frame and other, element-wise (binary operator true div).

(other)Get Floating division of data frame and other, element-wise (binary operator true div). (subset, Sequence, …) Return Database with duplicate rows removed.

(other)Get Equal to of data frame and other, element-wise (binary operator EQ). (exp)Evaluate a string describing operations on Database columns.

() Provide exponential weighted (EW) functions. ()()(column, Tuple], ignore_index)Transform each element of a list-like to a row, replicating index values.

()Subset the data frame rows or columns according to the specified index labels. (other)Get Integer division of data frame and other, element-wise (binary operator floor div).

(data) Convert structured or record array to Database. (other)Get Greater than or equal to of data frame and other, element-wise (binary operator GE).

()()()()Group Database using a mapper or by a Series of columns. (other)Get Greater than of data frame and other, element-wise (binary operator gt).

(other)Join columns of another Database. (other)Get Less than or equal to of data frame and other, element-wise (binary operator LE).

(other)Get Less than of data frame and other, element-wise (binary operator Lt). ()()(cold) Replace values where the condition is True.

() Unpivot a Database from wide to long format, optionally leaving identifiers set. ()Return the memory usage of each column in bytes.

(right) Merge Database or named Series objects with a database-style join. (other)Get Modulo of data frame and other, element-wise (binary operator mod).

(other)Get Multiplication of data frame and other, element-wise (binary operator mud). (other)Get Multiplication of data frame and other, element-wise (binary operator mud).

(other)Get Not equal to of data frame and other, element-wise (binary operator né). ()Percentage change between the current and a prior element.

() Create a spreadsheet-style pivot table as a Database. (other)Get Exponential power of data frame and other, element-wise (binary operator pow).

(exp)Query the columns of a Database with a boolean expression. (other)Get Addition of data frame and other, element-wise (binary operator add).

(other)Get Floating division of data frame and other, element-wise (binary operator trued). (change)() Alter axes labels.

(rule)Resample time-series data. (other)Get Integer division of data frame and other, element-wise (binary operator rfloordiv).

(other)Get Modulo of data frame and other, element-wise (binary operator rod). (other)Get Multiplication of data frame and other, element-wise (binary operator Raul).

(other)Get Exponential power of data frame and other, element-wise (binary operator row). (other)Get Subtraction of data frame and other, element-wise (binary operator sub).

(other)Get Floating division of data frame and other, element-wise (binary operator trued). () Return a random sample of items from an axis of object.

LuxDataFrame.set_executor_type (keys) Set the Database index using existing columns. (is)Set intent of the data frame as the Vi's(change)()Shift index by desired number of periods with an optional time freq.

(periods)Equivalent to shift without copying data. (other)Get Subtraction of data frame and other, element-wise (binary operator sub).

(other)Get Subtraction of data frame and other, element-wise (binary operator sub). (rec_infolist)(excel, SEP, **quarks)Copy object to the system clipboard.

(path, **quarks)Write a Database to the binary Feather format. (path_or_BUF, key, mode, comp level, …) Write the contained data to an HDF5 file using History.

(BUF, mode, index, **quarks)Print Database in Markdown-friendly format. ()(path, path lib. Path, IO], …) Write a Database to the binary parquet format.

(name, con) Write records stored in a Database to an SQL database. (path, path lib. Path, IO], …) Export Database object to State DTA format.

(BUF, path lib. Path, IO, …) Render a Database to a console-friendly tabular output. ()Cast to DatetimeIndex of timestamps, at beginning of period.

(other)Get Floating division of data frame and other, element-wise (binary operator true div). ()Truncate a Series or Database before and after some index value.

(TZ)Localize naive index of a Series or Database to target time zone. (other) Modify in place using non-NA values from another Database.

(subset, normalize, sort, ascending)Return a Series containing counts of unique rows in the Database. (cold) Replace values where the condition is False.

(key)Return cross-section from the Series/Database. Access a single value for a row/column label pair. Dictionary of global attributes on this object. Return a list representing the axes of the Database. The column labels of the Database. Return the types in the Database. Indicator whether Database is empty. Get selected visualizations as exported Vi's Distances a single value for a row/column pair by integer position. Purely integer-location based indexing for selection by position. The index (row labels) of the Database. Access a group of rows and columns by label(s) or a boolean array. Return an int representing the number of axes / array dimensions. Return a tuple representing the dimensionality of the Database. Return an int representing the number of elements in this object. Returns a Styler object. Return a Bumpy representation of the Database. Abs () FrameOrSeries Return a Series/Database with absolute numeric value of each element.

Absolute numeric values in a Series with complex numbers. Absolute numeric values in a Series with a Time delta element.

Select rows with data closest to certain value using resort (from Stack Overflow). Equivalent to data frame+other, but with support to substitute a fill_value for missing data in one of the inputs.

Subtract a list and Series by axis with operator version. Multiply a Database of different shape with operator version.

All (axis=0, built_only=None, skin=True, level=None, **quarks) Return whether all elements are True, potentially over an axis. Returns True unless there at least one element within a series or along a Data frame axis that is False or equivalent (e.g. zero or empty).

Default behavior checks if column-wise values all return True. Any (axis=0, built_only=None, skin=True, level=None, **quarks) Return whether any element is True, potentially over an axis.

Returns:Return type: conceit() General function to concatenate Database or Series objects. If a list of dict/series is passed and the keys are all contained in the Database’s index, the order of the columns in the resulting Database will be unchanged. Iteratively appending rows to a Database can be more computationally intensive than a single concatenate.

True : the passed function will receive array objects instead. If you are just applying a Bumpy reduction function this will achieve much better performance.

’reduce’ : returns a Series if possible rather than expanding list-like results. However, if the apply function returns a Series these are expanded to columns.

Returning a Series inside the function is similar to passing result_type='expand'. Passing result_type='broadcast' will ensure the same shape result, whether list-like or scalar is returned by the function, and broadcast it along the axis.

Applymap (fun) lux.core.frame. LuxDataFrame Apply a function to a Data frame element wise. This method applies a function that accepts and returns a scalar to every element of a Database.

Asfreq (freq, method=None, how: Optional = None, normalize: built = False, fill_value=None) FrameOrSeries Convert Timeserver to specified frequency. Optionally provide filling method to pad/backfill missing values.

Returns the original data conformed to a new index with the specified frequency. Resample is more appropriate if an operation, such as summarization, is necessary to represent the data at the new frequency.

The callable must not change input Database (though pandas doesn’t check it). If the values are not callable, (e.g. a Series, scalar, or array), they are simply assigned. Returns:A new Database with the new columns in addition to all the existing columns. Return type:DataFrameAssigning multiple columns within the same assign is possible.

Later items in ‘**quarks’ may refer to newly created or modified columns in ‘of’; items are computed and assigned into ‘of’ in order. Changed in version 0.23.0: Keyword argument order is maintained.

Alternatively, the same behavior can be achieved by directly referencing an existing Series or sequence: Alternatively, use {col: type, …}, where col is a column label and type is a bumpy.type or Python type to cast one or more of the Database’s columns to column-specific types.

Date times are localized to UTC first before converting to the specified timezone: DatetimeIndex.indexer_at_time() Get just the index locations for values at particular time of the day.

Backfill (axis=None, in place: built = False, limit=None, downcast=None) Optional Synonym for Database.filled() with method='bill'. Returns:Object with missing values filled or None if in place=True. Return type:{class} or None between_time (start_time, end_time, include_start: built = True, include_end: built = True, axis=None) FrameOrSeries Select values between particular times of the day (e.g., 9:00-9:30 AM).

Bfill (axis=None, in place: built = False, limit=None, downcast=None) Optional Synonym for Database.filled() with method='bill'. Returns:Object with missing values filled or None if in place=True. Return type:{class} or None built () Return the built of a single element Series or Database.

Database.as type() Change the data type of Database, including to boolean. Boxplot (column=None, by=None, ax=None, font size=None, rot=0, grid=True, fig size=None, layout=None, return_type=None, backend=None, **quarks) Make a box plot from Database columns.

A box plot is a method for graphically depicting groups of numerical data through their quartiles. The box extends from the Q1 to Q3 quartile values of the data, with a line at the median (Q2).

’dict’ returns a dictionary whose values are the matplotlib Lines of the box plot. If return_type is None, a Bumpy array of axes with the same shape as layout is returned.

Additional formatting can be done to the box plot, like suppressing the grid (grid=False), rotating the labels in the x-axis (i.e. rot=45) or changing the font size (i.e. font size=15): The parameter return_type can be used to select the type of element returned by box plot.

Clip (lower=None, upper=None, axis=None, in place: built = False, *arms, **quarks) FrameOrSeries Trim values at input threshold(s). Thresholds can be singular values or array like, and in the latter case the clipping is performed element-wise in the specified axis.

Returns:Same type as calling object with the values outside the clip boundaries replaced. Return type: Series.clip() Trim values at input threshold in series.

Clips using specific lower and upper thresholds per column element: The row and column indexes of the resulting Database will be the union of the two.

Return type: Database.combine_first() Combine two Database objects and default to non-null values in frame calling the method. Combine using a simple function that chooses the smaller column. Using fill_value fills None's prior to passing the column to the merge function.

Example that demonstrates the use of overwrite and behavior when the axis differ between the data frames. The row and column indexes of the resulting Database will be the union of the two.

Compare (other: lux.core.frame. LuxDataFrame, align_axis: Union = 1, keep_shape: built = False, keep_equal: built = False) lux.core.frame. LuxDataFrame Compare to another Database and show the differences. 0, or ‘index’ : Resulting differences are stacked vertically with rows drawn alternately from self and other.

The resulting index will be a Multitude with ‘self’ and ‘other’ stacked alternately at the inner level. Return type: Series.compare() Compare with another Series and show differences. Matching Fans will not appear as a difference.

By using the options convert_string, convert_integer, and convert_boolean, it is possible to turn off individual conversions to StringDtype, the integer extension types or BooleanDtype, respectively. For object-dtyped columns, if infer_objects is True, use the inference rules as during normal Series/Database construction.

Then, if possible, convert to StringDtype, BooleanDtype or an appropriate integer extension type, otherwise leave as object. Start with a Series of strings and missing data represented by NP.Nan.

Modifications to the data or indices of the copy will not be reflected in the original object (see notes below). When deep=False, a new object will be created without copying the calling object’s data or index (only references to the data and index are copied).

Since Index is immutable, the underlying data can be safely shared and a copy is not needed. Shallow copy shares data and index with original.

Updating a nested data object will be reflected in the deep copy. Corr (method='Pearson', min_periods=1) lux.core.frame. LuxDataFrame Compute pairwise correlation of columns, excluding NA/null values.

Database.shape() Number of Database rows and columns (including NA elements). Cov (min_periods: Optional = None, DDAF: Optional = 1) lux.core.frame. LuxDataFrame Compute pairwise covariance of columns, excluding NA/null values.

The returned data frame is the covariance matrix of the columns of the Database. Both NA and null values are automatically excluded from the calculation.

Core.window. Rolling.COV() Rolling sample covariance. Returns the covariance matrix of the Database’s time series. For Databases that have Series that are missing data (assuming that data is missing at random) the returned covariance matrix will be an unbiased estimate of the variance and covariance between the member Series.

Cummax (axis=None, skin=True, *arms, **quarks) Return cumulative maximum over a Database or Series axis. Returns a Database or Series of the same size containing the cumulative maximum.

Return type: core.window. Expanding.max() Similar functionality but ignores Nan values. By default, iterates over rows and finds the maximum in each column.

Cummin (axis=None, skin=True, *arms, **quarks) Return cumulative minimum over a Database or Series axis. Returns a Database or Series of the same size containing the cumulative minimum.

Return type: core.window. Expanding.min() Similar functionality but ignores Nan values. By default, iterates over rows and finds the minimum in each column.

Cumprod (axis=None, skin=True, *arms, **quarks) Return cumulative product over a Database or Series axis. Returns a Database or Series of the same size containing the cumulative product.

Return type: core.window. Expanding.prod() Similar functionality but ignores Nan values. By default, iterates over rows and finds the product in each column.

Cumsum (axis=None, skin=True, *arms, **quarks) Return cumulative sum over a Database or Series axis. Returns a Database or Series of the same size containing the cumulative sum.

Return type: core.window. Expanding.sum() Similar functionality but ignores Nan values. By default, iterates over rows and finds the sum in each column.

Describe (*arms, **quarks) Generate descriptive statistics. Descriptive statistics include those that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding Nan values.

Analyzes both numeric and object series, as well as Database column sets of mixed data types. A list-like of types : Limits the results to the provided data types.

A list-like of types : Excludes the provided data types from the result. To exclude object columns submit the data type bumpy.object.

Returns:Summary statistics of the Series or Data frame provided. Database.select_types() Subset of a Database including/excluding columns based on their type. For numeric data, the result’s index will include count, mean, std, min, max as well as lower, 50 and upper percentiles.

For object data (e.g. strings or timestamps), the result’s index will include count, unique, top, and freq. For mixed data types provided via a Database, the default is to return only an analysis of numeric columns.

If the data frame consists only of object and categorical data without any numeric columns, the default is to return an analysis of both the object and categorical columns. If include='all' is provided as an option, the result will include a union of attributes of each type.

Return type: Data frame.pct_change() Percent change over given the number of periods. Data frame.shift() Shift index by desired number of periods with an optional time freq.

Div (other, axis='columns', level=None, fill_value=None) Get Floating division of data frame and other, element-wise (binary operator true div). Equivalent to data frame/other, but with support to substitute a fill_value for missing data in one of the inputs.

Subtract a list and Series by axis with operator version. Multiply a Database of different shape with operator version.

Divide (other, axis='columns', level=None, fill_value=None) Get Floating division of data frame and other, element-wise (binary operator true div). Equivalent to data frame/other, but with support to substitute a fill_value for missing data in one of the inputs.

Subtract a list and Series by axis with operator version. Multiply a Database of different shape with operator version.

Remove rows or columns by specifying label names and corresponding axis, or by specifying directly index or column names. Returns:Database without the removed index or column labels.

Database.drop_duplicates() Return Database with duplicate rows removed, optionally only considering certain columns. Series.drop() Return Series with specified index labels removed.

By default, it removes duplicate rows based on all columns. Returns:Database with requested index / column level(s) removed.

Changed in version 1.0.0: Pass tuple or list to drop on multiple axes. ’any’ : If any NA values are present, drop that row or column.

Duplicated (subset: Union, None] = None, keep: Union = 'first') pandas.core.series. Series Return boolean Series denoting duplicate rows. Database.drop_duplicates() Remove duplicate values from Database. Consider dataset containing ramen rating.

Among flexible wrappers (EQ, né, LE, Lt, GE, gt) to comparison operators. , , <, >=, > with support to choose axis (rows or columns) and level for comparison.

Database.GE() Compare Databases for greater than inequality or equality element wise. Database.gt() Compare Databases for strictly greater than inequality element wise. Mismatched indices will be unioned together.

When other is a Series, the columns of a Database are aligned with the index of other and broadcast: This function allows two Series or Databases to be compared against each other to see if they have the same shape and elements.

Provides an easy interface to ignore inequality in types, indexes and precision among others. However, the column labels do not need to have the same type, as long as they are still considered equal.

Databases of and exactly_equal have the same types and values for their elements and column labels, which will return True. Databases of and different_data_type have different types for the same values for their elements, and will return False even though their column labels are the same values and types.

Eval (exp, in place=False, **quarks) Evaluate a string describing operations on Database columns. This allows evil to run arbitrary code, which can make you vulnerable to code injection if you pass user input to this function.

Database.query() Evaluates a boolean expression to query the columns of a frame. Assignment is allowed though by default the original Database is not modified.

Ewm (com=None, span=None, half life=None, alpha=None, min_periods=0, adjust=True, ignore_Na=False, axis=0, times=None) Provide exponential weighted (EW) functions. Available EW functions: mean(), var(), std(), corr(), COV().

When ignore_Na=True (reproducing pre-0.15.0 behavior), weights are based on relative positions. If STR, the name of the column in the Database representing the times.

Ffill (axis=None, in place: built = False, limit=None, downcast=None) Optional Synonym for Database.filled() with method='fill'. Returns:Object with missing values filled or None if in place=True. Return type:{class} or None filled (value=None, method=None, axis=None, in place=False, limit=None, downcast=None) Optional Fill NA/Nan values using the specified method.

In other words, if there is a gap with more than this number of consecutive Fans, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where Fans will be filled.

Returns:Object with missing values filled or None if in place=True. Return type:Fill Nan values using interpolation. Conform object to new index. Convert Timeserver to specified frequency.

Filter (items=None, like: Optional = None, regex: Optional = None, axis=None) FrameOrSeries Subset the data frame rows or columns according to the specified index labels. By default, this is the info axis, ‘index’ for Series, ‘columns’ for Database.

Add a scalar with operator version which return the same results. Subtract a list and Series by axis with operator version.

Multiply a Database of different shape with operator version. Creates Database object from dictionary by columns or by index allowing type specification.

Data can be provided as a list of tuples with corresponding columns: Among flexible wrappers (EQ, né, LE, Lt, GE, gt) to comparison operators.

Return type: Database.EQ() Compare Databases for equality element wise. Database.GE() Compare Databases for greater than inequality or equality element wise.

If False, NA values will also be treated as the key in groups Return type:Convenience method for frequency conversion and resampling of time series.

Among flexible wrappers (EQ, né, LE, Lt, GE, gt) to comparison operators. , , <, >=, > with support to choose axis (rows or columns) and level for comparison.

Database.GE() Compare Databases for greater than inequality or equality element wise. Database.gt() Compare Databases for strictly greater than inequality element wise. Mismatched indices will be unioned together.

When other is a Series, the columns of a Database are aligned with the index of other and broadcast: Head (n: int = 5) This function returns the first n rows for the object based on position.

Hist (column: Union]] = None, by=None, grid: built = True, xlabelsize: Optional = None, rot: Optional = None, ylabelsize: Optional = None, rot: Optional = None, ax=None, share: built = False, share: built = False, fig size: Optional] = None, layout: Optional] = None, bins: Union] = 10, backend: Optional = None, legend: built = False, **quarks) Make a histogram of the Database’s. This function calls matplotlib.pilot.hist(), on each series in the Database, resulting in one histogram per column.

Series.IMAX() Return index of the maximum element. This method is the Database version of array.aroma. Consider a dataset containing food consumption in Argentina.

By default, it returns the index for the maximum value in each column. To return the index for the maximum value in each row, use axis=”columns”.

Series.admin() Return index of the minimum element. This method is the Database version of array.Armin. Consider a dataset containing food consumption in Argentina.

By default, it returns the index for the minimum value in each column. To return the index for the minimum value in each row, use axis=”columns”.

Infer_objects () FrameOrSeries Attempt to infer better types for object columns. Info (*arms, **quarks) Print a concise summary of a Database.

Without deep introspection a memory estimation is made based in column type and number of rows assuming values consume the same memory amount for corresponding types. Return type: Database.describe() Generate descriptive statistics of Database columns.

The memory_usage parameter allows deep introspection mode, specially useful for big Databases and fine-tune memory optimization: Raises a ValueError if column is already contained in the Database, unless allow_duplicates is set to True.

’time’: Works on daily and higher resolution data to interpolate given length of interval. ’nearest’, ‘zero’, ‘linear’, ‘quadratic’, ‘cubic’, ‘spline’, ‘barometric’, ‘polynomial’: Passed to city.interpolate.interp1d.

Both ‘polynomial’ and ‘spline’ require that you also specify an order (int), e.g. of.interpolate(method='polynomial', order=5). ’Prof’, ‘piecewise_polynomial’, ‘spline’, ‘chip’, ‘Yakima’, ‘cubic spline’: Wrappers around the City interpolation methods of similar names.

If limit is specified: If ‘method’ is ‘pad’ or ‘fill’, ‘limit_direction’ must be ‘forward’. Changed in version 1.1.0: raises ValueError if limit_direction is ‘forward’ or ‘both’ and method is ‘backfill’ or ‘bill’.

Raises ValueError if limit_direction is ‘backward’ or ‘both’ and method is ‘pad’ or ‘fill’. ’inside’: Only fill Fans surrounded by valid values (interpolate).

Scipy.interpolate. CubicSpline() Cubic spline data interpolator. The ‘Prof’, ‘piecewise_polynomial’, ‘spline’, ‘chip’ and ‘Yakima’ methods are wrappers around the respective City implementations of similar names. Filling in Nan in a Series via polynomial interpolation or splines: Both ‘polynomial’ and ‘spline’ methods require that you also specify an order (int).

If values is a dict, the keys must be the column names, which must match. Series.STR.contains() Test if pattern or regex is contained within a string of a Series or Index.

When values is a Series or Database the index and column must match. Note that ‘falcon’ does not match based on the number of legs in df2.

Isna () lux.core.frame. LuxDataFrame Return a boolean same-sized object indicating if the values are NA. Characters such as empty strings '' or bumpy.inf are not considered NA values (unless you set pandas.options.mode.use_inf_as_Na=True).

Returns:Mask of built values for each element in Database that indicates whether an element is not an NA value. Return type:DataFrame.is null() Alias of SNA. Database.drop() Omit axes labels with missing values. Top-level SNA. Show which entries in a Database are NA.

Isnull () lux.core.frame. LuxDataFrame Return a boolean same-sized object indicating if the values are NA. Characters such as empty strings '' or bumpy.inf are not considered NA values (unless you set pandas.options.mode.use_inf_as_Na=True).

Returns:Mask of built values for each element in Database that indicates whether an element is not an NA value. Return type:DataFrame.is null() Alias of SNA. Database.drop() Omit axes labels with missing values. Top-level SNA. Show which entries in a Database are NA.

To preserved types while iterating over the rows, it is better to use which returns named tuples of the values and which is generally faster than terrors. Depending on the data types, the iterator returns a copy and not a view, and writing to it will have no effect.

On python versions < 3.7 regular tuples are returned for Databases with many columns (>254). Efficiently join multiple Database objects by index at once by passing a list.

Return type: Database.merge() For column(s)-on-columns(s) operations. Parameters on, suffix, and suffix are not supported when passing a list of Database objects. Support for specifying index levels as the on parameter was added in version 0.23.0.

Among flexible wrappers (EQ, né, LE, Lt, GE, gt) to comparison operators. , , <, >=, > with support to choose axis (rows or columns) and level for comparison.

Database.GE() Compare Databases for greater than inequality or equality element wise. Database.gt() Compare Databases for strictly greater than inequality element wise. Mismatched indices will be unioned together.

When other is a Series, the columns of a Database are aligned with the index of other and broadcast: Among flexible wrappers (EQ, né, LE, Lt, GE, gt) to comparison operators.

Return type: Database.EQ() Compare Databases for equality element wise. Database.GE() Compare Databases for greater than inequality or equality element wise.

Mask (cold, other=Nan, in place=False, axis=None, level=None, errors='raise', try_cast=False) Replace values where the condition is True. If cold is callable, it is computed on the Series/Database and should return boolean Series/Database or array.

The column will have a Categorical type with the value of “left_only” for observations whose merge key only appears in the left Database, “right_only” for observations whose merge key only appears in the right Database, and “both” if the observation’s merge key is found in both Databases. ”one_to_one” or “1:1”: check if merge keys are unique in both left and right datasets.

Merge Databases df1 and df2 with specified left and right suffixes appended to any overlapping columns. Min (axis=None, skin=None, level=None, numeric_only=None, **quarks) Return the minimum of the values for the requested axis.

Subtract a list and Series by axis with operator version. Multiply a Database of different shape with operator version.

By default, missing values are not considered, and the mode of wings is both 0 and 2. Setting drop=Fallen values are considered, and they can be the mode (like for wings).

To compute the mode over columns and not rows, use the axis parameter: Equivalent to dataframe×other, but with support to substitute a fill_value for missing data in one of the inputs.

Subtract a list and Series by axis with operator version. Multiply a Database of different shape with operator version.

Multiply (other, axis='columns', level=None, fill_value=None) Get Multiplication of data frame and other, element-wise (binary operator mud). Equivalent to dataframe×other, but with support to substitute a fill_value for missing data in one of the inputs.

Subtract a list and Series by axis with operator version. Multiply a Database of different shape with operator version.

Among flexible wrappers (EQ, né, LE, Lt, GE, gt) to comparison operators. , , <, >=, > with support to choose axis (rows or columns) and level for comparison.

Database.GE() Compare Databases for greater than inequality or equality element wise. Database.gt() Compare Databases for strictly greater than inequality element wise. Mismatched indices will be unioned together.

When other is a Series, the columns of a Database are aligned with the index of other and broadcast: Return the first n rows with the largest values in columns, in descending order.

In the following example, we will use largest to select the three rows having the largest values in column “population”. Return a boolean same-sized object indicating if the values are not NA.

Characters such as empty strings '' or bumpy.inf are not considered NA values (unless you set pandas.options.mode.use_inf_as_Na=True). Returns:Mask of built values for each element in Database that indicates whether an element is not an NA value. Return type:DataFrame.not null() Alias of Nona.

Database.drop() Omit axes labels with missing values. Top-level Nona. Show which entries in a Database are not NA. Return a boolean same-sized object indicating if the values are not NA.

Characters such as empty strings '' or bumpy.inf are not considered NA values (unless you set pandas.options.mode.use_inf_as_Na=True). Returns:Mask of built values for each element in Database that indicates whether an element is not an NA value. Return type:DataFrame.not null() Alias of Nona.

Database.drop() Omit axes labels with missing values. Top-level Nona. Show which entries in a Database are not NA. Return the first n rows with the smallest values in columns, in ascending order.

In the following example, we will use smallest to select the three rows having the smallest values in column “population”. Nunique (axis=0, drop=True) pandas.core.series. Series Count distinct observations over requested axis.

Returns:Object with missing values filled or None if in place=True. Return type:{class} or None pct_change (periods=1, fill_method='pad', limit=None, freq=None, **quarks) FrameOrSeries Percentage change between the current and a prior element. Computes the percentage change from the immediately previous row by default.

Return type: Series.diff() Compute the difference of two elements in a Series. Percentage change in French franc, Deutsche Mark, and Italian lira from 1980-01-01 to 1980-03-01.

Reshape data (produce a “pivot” table) based on column values. Uses unique values from specified index / columns to form axes of the resulting Database.

This function does not support data aggregation, multiple values will result in a Multitude in the columns. Changed in version 1.1.0: Also accept list of index names.

Changed in version 1.1.0: Also accept list of columns names. Changed in version 0.23.0: Also accept list of column names.

Return type: Database.pivot() Pivot without aggregation that can handle non-numeric data. The next example aggregates by taking the mean across multiple columns.

We can also calculate multiple types of aggregations for any given value column. Equivalent to data frame**other, but with support to substitute a fill_value for missing data in one of the inputs.

Subtract a list and Series by axis with operator version. Multiply a Database of different shape with operator version.

If q is a float, a Series will be returned where the index is the columns of self and the values are the quantiles. Specifying numeric_only=False will also compute the quantile of date time and time delta data.

This way you can also escape names that start with a digit, or those that are a Python keyword. New in version 1.0.0: Expanding functionality of back tick quoting for more than only spaces.

Return type:Evaluate a string describing operations on Database columns. Database.evil() Evaluate a string describing operations on Database columns. The result of the evaluation of this expression is first passed to Database.LOC and if that fails because of a multidimensional key (e.g., a Database) then the result will be passed to Database.__get item__().

The query() method uses a slightly modified Python syntax by default. For example, the & and | (bitwise) operators have the precedence of their boolean cousins, and or.

You can change the semantics of the expression by passing the keyword argument parser='python'. Likewise, you can pass engine='python' to evaluate an expression using Python itself as a backend.

The Database.index and Database.columns attributes of the Database instance are placed in the query namespace by default, which allows you to treat both the index and columns of the frame as a column in the frame. During parsing a number of disallowed characters inside the back tick quoted string are replaced by strings that are allowed as a Python identifier.

These characters include all operators in Python, the space character, the question mark, the exclamation mark, the dollar sign, and the euro sign. For other characters that fall outside the ASCII range (U+0001. U+007F) and those that are not further specified in PEP 3131, the query parser will raise an error.

In a special case, quotes that make a pair around a back tick can confuse the parser. For example, `it's`>`that's` will raise an error, as it forms a quoted string ('s>`that') with a back tick inside.

Add a scalar with operator version which return the same results. Subtract a list and Series by axis with operator version.

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Sources
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