:py:mod:`ineqpy.api` ==================== .. py:module:: ineqpy.api .. autoapi-nested-parse:: API's module. Extend pandas.DataFrames with the main functions from statistics and inequality modules. .. !! processed by numpydoc !! Module Contents --------------- Classes ~~~~~~~ .. autoapisummary:: ineqpy.api.Convey ineqpy.api.Survey .. py:class:: Convey(data=None, index=None, columns=None, weights=None, group=None, **kw) Convey. .. !! processed by numpydoc !! .. py:property:: _constructor .. py:method:: _attach_method(instance) :classmethod: .. py:class:: Survey(data=None, index=None, columns=None, weights=None, group=None, **kw) Survey it's a data structure that handles survey data. :Attributes: **df** : pandas.DataFrame .. **weights** : str .. **group** : str .. .. rubric:: Methods =============================================================================== ========== **atkinson(income=None, weights=None, e=0.5)** Calculate Atkinson's index. **avg_tax_rate(total_tax=None, total_base=None, weights=None)** Calculate average tax rate. **c_moment(variable=None, weights=None, order=2, param=None, ddof=0)** Calculate central momment. **coef_variation(variable=None, weights=None)** Calculate coefficient of variation. **concentration(income=None, weights=None, sort=True)** Calculate concentration's index. **density(variable=None, weights=None, groups=None)** Calculate density. **gini(income=None, weights=None, sort=True)** Calculate Gini's index. **kakwani(tax=None, income_pre_tax=None, weights=None)** Calculate Kakwani's index. **kurt(variable=None, weights=None)** Calculate Kurtosis. **lorenz(income=None, weights=None)** Calculate Lorenz curve. **mean(variable=None, weights=None)** Calculate mean. **percentile(variable=None, weights=None, p=50, interpolate="lower")** Calculate percentile. **reynolds_smolensky(income_pre_tax=None, income_post_tax=None, weights=None)** Calculate Reynolds-Smolensky's index. **skew(variable=None, weights=None)** Calculate Skew. **std_moment(variable=None, weights=None, param=None, order=3, ddof=0)** Calculate standard deviation. **theil(income=None, weights=None)** Calculate Theil's index. **var(variable=None, weights=None, ddof=0)** Calculate variance. =============================================================================== ========== .. !! processed by numpydoc !! .. py:method:: c_moment(variable, weights=None, order=2, param=None, ddof=0) Calculate central momment. Calculate the central moment of `x` with respect to `param` of order `n`, given the weights `w`. :Parameters: **variable** : 1d-array Variable **weights** : 1d-array Weights **order** : int, optional Moment order, 2 by default (variance) **param** : int or array, optional Parameter for which the moment is calculated, the default is None, implies use the mean. **ddof** : int, optional Degree of freedom, zero by default. :Returns: **central_moment** : float .. .. rubric:: Notes - The cmoment of order 1 is 0 - The cmoment of order 2 is the variance. Source : https://en.wikipedia.org/wiki/Moment_(mathematics) .. !! processed by numpydoc !! .. py:method:: percentile(variable, weights=None, p=50, interpolate='lower') Calculate the value of a quantile given a variable and his weights. :Parameters: **data** : pd.DataFrame, optional pd.DataFrame that contains all variables needed. **variable** : str or array .. **weights** : str or array .. **q** : float Quantile level, if pass 0.5 means median. **interpolate** : bool .. :Returns: **percentile** : float or pd.Series .. .. !! processed by numpydoc !! .. py:method:: std_moment(variable, weights=None, param=None, order=3, ddof=0) Calculate the standardized moment. Calculate the standardized moment of order `c` for the variable` x` with respect to `c`. :Parameters: **data** : pd.DataFrame, optional pd.DataFrame that contains all variables needed. **variable** : 1d-array Random Variable **weights** : 1d-array, optional Weights or probability **order** : int, optional Order of Moment, three by default **param** : int or float or array, optional Central trend, default is the mean. **ddof** : int, optional Degree of freedom. :Returns: **std_moment** : float Returns the standardized `n` order moment. .. rubric:: References - https://en.wikipedia.org/wiki/Moment_(mathematics)#Significance_ of_the_moments - https://en.wikipedia.org/wiki/Standardized_moment .. only:: latex .. !! processed by numpydoc !! .. py:method:: mean(variable, weights=None) Calculate the mean of `variable` given `weights`. :Parameters: **variable** : array-like or str Variable on which the mean is estimated. **weights** : array-like or str Weights of the `x` variable. **data** : pandas.DataFrame Is possible pass a DataFrame with variable and weights, then you must pass as `variable` and `weights` the column name stored in `data`. :Returns: **mean** : array-like or float .. .. !! processed by numpydoc !! .. py:method:: density(variable, weights=None, groups=None) Calculate density in percentage. This make division of variable inferring width in groups as max - min. :Parameters: **data** : pd.DataFrame, optional pandas.DataFrame that contains all variables needed. **variable** : array-like, optional .. **weights** : array-like, optional .. **groups** : array-like, optional .. :Returns: **density** : array-like .. .. rubric:: References Histogram. (2017, May 9). In Wikipedia, The Free Encyclopedia. Retrieved: https://en.wikipedia.org/w/index.php?title=Histogram .. only:: latex .. !! processed by numpydoc !! .. py:method:: var(variable, weights=None, ddof=0) Calculate the population variance of `variable` given `weights`. :Parameters: **data** : pd.DataFrame, optional pd.DataFrame that contains all variables needed. **variable** : 1d-array or pd.Series or pd.DataFrame Variable on which the quasivariation is estimated **weights** : 1d-array or pd.Series or pd.DataFrame Weights of the `variable`. :Returns: **variance** : 1d-array or pd.Series or float Estimation of quasivariance of `variable` .. rubric:: Notes If stratificated sample must pass with groupby each strata. .. rubric:: References Moment (mathematics). (2017, May 6). In Wikipedia, The Free Encyclopedia. Retrieved 14:40, May 15, 2017, from https://en.wikipedia.org/w/index.php?title=Moment_(mathematics) .. only:: latex .. !! processed by numpydoc !! .. py:method:: coef_variation(variable, weights=None) Calculate the coefficient of variation. The coefficient of variation is the square root of the variance of the incomes divided by the mean income. It has the advantages of being mathematically tractable and is subgroup decomposable, but is not bounded from above. :Parameters: **data** : pandas.DataFrame .. **variable** : array-like or str .. **weights** : array-like or str .. :Returns: **coefficient_variation** : float .. .. rubric:: References Coefficient of variation. (2017, May 5). In Wikipedia, The Free Encyclopedia. Retrieved 15:03, May 15, 2017, from https://en.wikipedia.org/w/index.php?title=Coefficient_of_variation .. only:: latex .. !! processed by numpydoc !! .. py:method:: kurt(variable, weights=None) Calculate the asymmetry coefficient. :Parameters: **variable** : 1d-array .. **w** : 1d-array .. :Returns: **kurt** : float Kurtosis coefficient. .. rubric:: Notes It is an alias of the standardized fourth-order moment. .. rubric:: References Moment (mathematics). (2017, May 6). In Wikipedia, The Free Encyclopedia. Retrieved 14:40, May 15, 2017, from https://en.wikipedia.org/w/index.php?title=Moment_(mathematics) .. only:: latex .. !! processed by numpydoc !! .. py:method:: skew(variable, weights=None) Return the asymmetry coefficient of a sample. :Parameters: **data** : pandas.DataFrame .. **variable** : array-like, str .. **weights** : array-like, str .. :Returns: **skew** : float .. .. rubric:: Notes It is an alias of the standardized third-order moment. .. rubric:: References Moment (mathematics). (2017, May 6). In Wikipedia, The Free Encyclopedia. Retrieved 14:40, May 15, 2017, from https://en.wikipedia.org/w/index.php?title=Moment_(mathematics)& oldid=778996402 .. only:: latex .. !! processed by numpydoc !! .. py:method:: concentration(income, weights=None, sort=True) Calculate concentration index. This function calculate the concentration index, according to the notation used in [Jenkins1988]_ you can calculate the: C_x = 2 / x · cov(x, F_x) if x = g(x) then C_x becomes C_y when there are taxes: y = g(x) = x - t(x) :Parameters: **income** : array-like .. **weights** : array-like .. **data** : pandas.DataFrame .. **sort** : bool .. :Returns: **concentration** : array-like .. .. rubric:: References Jenkins, S. (1988). Calculating income distribution indices from micro-data. National Tax Journal. http://doi.org/10.2307/41788716 .. only:: latex .. !! processed by numpydoc !! .. py:method:: lorenz(income, weights=None) Calculate lorenz curve. In economics, the Lorenz curve is a graphical representation of the distribution of income or of wealth. It was developed by Max O. Lorenz in 1905 for representing grouped of the wealth distribution. This function compute the lorenz curve and returns a DF with two columns of axis x and y. :Parameters: **data** : pandas.DataFrame A pandas.DataFrame that contains data. **income** : str or 1d-array, optional Population or wights, if a DataFrame is passed then `income` should be a name of the column of DataFrame, else can pass a pandas.Series or array. **weights** : str or 1d-array Income, monetary variable, if a DataFrame is passed then `y`is a name of the series on this DataFrame, however, you can pass a pd.Series or np.array. :Returns: **lorenz** : pandas.Dataframe Lorenz distribution in a Dataframe with two columns, labeled x and y, that corresponds to plots axis. .. rubric:: References Lorenz curve. (2017, February 11). In Wikipedia, The Free Encyclopedia. Retrieved 14:34, May 15, 2017, from https://en.wikipedia.org/w/index.php?title=Lorenz_curve&oldid=764853675 .. only:: latex .. !! processed by numpydoc !! .. py:method:: gini(income, weights=None, sort=True) Calculate Gini's index. The Gini coefficient (sometimes expressed as a Gini ratio or a normalized Gini index) is a measure of statistical dispersion intended to represent the income or wealth distribution of a nation's residents, and is the most commonly used measure of grouped. It was developed by Corrado Gini. The Gini coefficient measures the grouped among values of a frequency distribution (for example, levels of income). A Gini coefficient of zero expresses perfect equality, where all values are the same (for example, where everyone has the same income). A Gini coefficient of 1 (or 100%) expresses maximal grouped among values (e.g., for a large number of people, where only one person has all the income or consumption, and all others have none, the Gini coefficient will be very nearly one). :Parameters: **data** : pandas.DataFrame DataFrame that contains the data. **income** : str or np.array, optional Name of the monetary variable `x` in` df` **weights** : str or np.array, optional Name of the series containing the weights `x` in` df` **sorted** : bool, optional If the DataFrame is previously ordered by the variable `x`, it's must pass True, but False by default. :Returns: **gini** : float Gini Index Value. .. rubric:: Notes The calculation is done following (discrete probability distribution): G = 1 - [∑_i^n f(y_i)·(S_{i-1} + S_i)] where: - y_i = Income - S_i = ∑_{j=1}^i y_i · f(y_i) .. !! processed by numpydoc !! .. py:method:: atkinson(income, weights=None, e=0.5) Calculate Atkinson index. More precisely labelled a family of income grouped measures, the theoretical range of Atkinson values is 0 to 1, with 0 being a state of equal distribution. An intuitive interpretation of this index is possible: Atkinson values can be used to calculate the proportion of total income that would be required to achieve an equal level of social welfare as at present if incomes were perfectly distributed. For example, an Atkinson index value of 0.20 suggests that we could achieve the same level of social welfare with only 1 – 0.20 = 80% of income. The theoretical range of Atkinson values is 0 to 1, with 0 being a state of equal distribution. :Parameters: **income** : array or str If `data` is none `income` must be an 1D-array, when `data` is a pd.DataFrame, you must pass the name of income variable as string. **weights** : array or str, optional If `data` is none `weights` must be an 1D-array, when `data` is a pd.DataFrame, you must pass the name of weights variable as string. **e** : int, optional Epsilon parameter interpreted by atkinson index as grouped adversion, must be a number between 0 to 1. **data** : pd.DataFrame, optional data is a pd.DataFrame that contains the variables. :Returns: **atkinson** : float .. .. !! processed by numpydoc !! .. py:method:: kakwani(tax, income_pre_tax, weights=None) Calculate kakwani's index. The Kakwani (1977) index of tax progressivity is defined as twice the area between the concentration curves for taxes and pre-tax income, or equivalently, the concentration index for t(x) minus the Gini index for x, i.e. K = C(t) - G(x) = (2/t) cov [t(x), F(x)] - (2/x) cov [x, F(x)]. :Parameters: **data** : pandas.DataFrame This variable is a DataFrame that contains all data required in columns. **tax_variable** : array-like or str This variable represent tax payment of person, if pass array-like then data must be None, else you pass str-name column in `data`. **income_pre_tax** : array-like or str This variable represent income of person, if pass array-like then data must be None, else you pass str-name column in `data`. **weights** : array-like or str This variable represent weights of each person, if pass array-like then data must be None, else you pass str-name column in `data`. :Returns: **kakwani** : float .. .. rubric:: References Jenkins, S. (1988). Calculating income distribution indices from micro-data. National Tax Journal. http://doi.org/10.2307/41788716 .. only:: latex .. !! processed by numpydoc !! .. py:method:: reynolds_smolensky(income_pre_tax, income_post_tax, weights=None) Calculate Reynolds-Smolensky's index. The Reynolds-Smolensky (1977) index of the redistributive effect of taxes, which can also be interpreted as an index of progressivity (Lambert 1985), is defined as: L = Gx - Gy = [2/x]cov[x,F(x)] - [2/ybar] cov [y, F(y)]. :Parameters: **data** : pandas.DataFrame This variable is a DataFrame that contains all data required in it's columns. **income_pre_tax** : array-like or str This variable represent tax payment of person, if pass array-like then data must be None, else you pass str-name column in `data`. **income_post_tax** : array-like or str This variable represent income of person, if pass array-like then data must be None, else you pass str-name column in `data`. **weights** : array-like or str This variable represent weights of each person, if pass array-like then data must be None, else you pass str-name column in `data`. :Returns: **reynolds_smolensky** : float .. .. rubric:: References Jenkins, S. (1988). Calculating income distribution indices from micro-data. National Tax Journal. http://doi.org/10.2307/41788716 .. only:: latex .. !! processed by numpydoc !! .. py:method:: theil(income, weights=None) Calculate theil index. The Theil index is a statistic primarily used to measure economic grouped and other economic phenomena. It is a special case of the generalized entropy index. It can be viewed as a measure of redundancy, lack of diversity, isolation, segregation, grouped, non-randomness, and compressibility. It was proposed by econometrician Henri Theil. :Parameters: **data** : pandas.DataFrame This variable is a DataFrame that contains all data required in it's columns. **income** : array-like or str This variable represent tax payment of person, if pass array-like then data must be None, else you pass str-name column in `data`. **weights** : array-like or str This variable represent weights of each person, if pass array-like then data must be None, else you pass str-name column in `data`. :Returns: **theil** : float .. .. rubric:: References Theil index. (2016, December 17). In Wikipedia, The Free Encyclopedia. Retrieved 14:17, May 15, 2017, from https://en.wikipedia.org/w/index.php?title=Theil_index&oldid=755407818 .. only:: latex .. !! processed by numpydoc !! .. py:method:: avg_tax_rate(total_tax, total_base, weights=None) Compute the average tax rate given a base income and a total tax. :Parameters: **total_base** : str or numpy.array .. **total_tax** : str or numpy.array .. **data** : pd.DataFrame .. :Returns: **avg_tax_rate** : float or pd.Series Is the ratio between mean the tax income and base of income. .. !! processed by numpydoc !! .. py:method:: top_rest(income, weights=None, data=None, top_percentage=10) Calculate the 10:90 Ratio. Calculates the quotient between the number of contributions from the top 10% of contributors divided by the number contributions made by the other 90%. The ratio is 1 if the total contributions by the top contributors are equal to the cotnributions made by the rest; less than zero if the top 10% contributes less than the rest; and greater that 1 if the top 10% contributes more than the other ninety percent. :Parameters: **income** : array-like or str This variable represent tax payment of person, if pass array-like then data must be None, else you pass str-name column in `data`. **weights** : array-like or str This variable represent weights of each person, if pass array-like then data must be None, else you pass str-name column in `data`. All-ones by default **data** : pandas.DataFrame This variable is a DataFrame that contains all data required in it's columns. **top_percentage** : float The richest x percent to consider. (10 percent by default) It must be a number between 0 and 100 :Returns: **ratio** : float .. .. rubric:: References Participation Inequality in Wikis: A Temporal Analysis Using WikiChron. Serrano, Abel & Arroyo, Javier & Hassan, Samer. (2018). DOI: 10.1145/3233391.3233536. .. only:: latex .. !! processed by numpydoc !!