misc small docstring fixes

Author: gdementen

larray/core/array.py

@@ -3277,6 +3277,7 @@ def wrapper(self, *args, **kwargs):
     @_decorate_agg_method(np.all, commutative=True, long_name="AND reduction")
     def all(self, *args, **kwargs):
         """{signature}
+
         Test whether all selected elements evaluate to True.
 
         {parameters}
@@ -3347,6 +3348,7 @@ def all(self, *args, **kwargs):
     @_decorate_agg_method(np.all, commutative=True, by_agg=True, long_name="AND reduction")
     def all_by(self, *args, **kwargs):
         """{signature}
+
         Test whether all selected elements evaluate to True.
 
         {parameters}
@@ -3414,6 +3416,7 @@ def all_by(self, *args, **kwargs):
     @_decorate_agg_method(np.any, commutative=True, long_name="OR reduction")
     def any(self, *args, **kwargs):
         """{signature}
+
         Test whether any selected elements evaluate to True.
 
         {parameters}
@@ -3484,6 +3487,7 @@ def any(self, *args, **kwargs):
     @_decorate_agg_method(np.any, commutative=True, by_agg=True, long_name="OR reduction")
     def any_by(self, *args, **kwargs):
         """{signature}
+
         Test whether any selected elements evaluate to True.
 
         {parameters}
@@ -3553,6 +3557,7 @@ def any_by(self, *args, **kwargs):
     @_decorate_agg_method(np.sum, np.nansum, commutative=True, extra_kwargs=['dtype'])
     def sum(self, *args, **kwargs):
         """{signature}
+
         Computes the sum of array elements along given axes/groups.
 
         {parameters}
@@ -3617,6 +3622,7 @@ def sum(self, *args, **kwargs):
     @_decorate_agg_method(np.sum, np.nansum, commutative=True, by_agg=True, extra_kwargs=['dtype'], long_name="sum")
     def sum_by(self, *args, **kwargs):
         """{signature}
+
         Computes the sum of array elements for the given axes/groups.
 
         {parameters}
@@ -3679,6 +3685,7 @@ def sum_by(self, *args, **kwargs):
     @_decorate_agg_method(np.prod, np_nanprod, commutative=True, extra_kwargs=['dtype'], long_name="product")
     def prod(self, *args, **kwargs):
         """{signature}
+
         Computes the product of array elements along given axes/groups.
 
         {parameters}
@@ -3744,6 +3751,7 @@ def prod(self, *args, **kwargs):
                           long_name="product")
     def prod_by(self, *args, **kwargs):
         """{signature}
+
         Computes the product of array elements for the given axes/groups.
 
         {parameters}
@@ -3805,6 +3813,7 @@ def prod_by(self, *args, **kwargs):
     @_decorate_agg_method(np.min, np.nanmin, commutative=True, long_name="minimum", action_verb="search")
     def min(self, *args, **kwargs):
         """{signature}
+
         Get minimum of array elements along given axes/groups.
 
         {parameters}
@@ -3868,6 +3877,7 @@ def min(self, *args, **kwargs):
     @_decorate_agg_method(np.min, np.nanmin, commutative=True, by_agg=True, long_name="minimum", action_verb="search")
     def min_by(self, *args, **kwargs):
         """{signature}
+
         Get minimum of array elements for the given axes/groups.
 
         {parameters}
@@ -3928,6 +3938,7 @@ def min_by(self, *args, **kwargs):
     @_decorate_agg_method(np.max, np.nanmax, commutative=True, long_name="maximum", action_verb="search")
     def max(self, *args, **kwargs):
         """{signature}
+
         Get maximum of array elements along given axes/groups.
 
         {parameters}
@@ -3991,6 +4002,7 @@ def max(self, *args, **kwargs):
     @_decorate_agg_method(np.max, np.nanmax, commutative=True, by_agg=True, long_name="maximum", action_verb="search")
     def max_by(self, *args, **kwargs):
         """{signature}
+
         Get maximum of array elements for the given axes/groups.
 
         {parameters}
@@ -4051,6 +4063,7 @@ def max_by(self, *args, **kwargs):
     @_decorate_agg_method(np.mean, np.nanmean, commutative=True, extra_kwargs=['dtype'])
     def mean(self, *args, **kwargs):
         """{signature}
+
         Computes the arithmetic mean.
 
         {parameters}
@@ -4116,6 +4129,7 @@ def mean(self, *args, **kwargs):
     @_decorate_agg_method(np.mean, np.nanmean, commutative=True, by_agg=True, extra_kwargs=['dtype'], long_name="mean")
     def mean_by(self, *args, **kwargs):
         """{signature}
+
         Computes the arithmetic mean.
 
         {parameters}
@@ -4178,6 +4192,7 @@ def mean_by(self, *args, **kwargs):
     @_decorate_agg_method(np.median, np.nanmedian, commutative=True)
     def median(self, *args, **kwargs):
         """{signature}
+
         Computes the arithmetic median.
 
         {parameters}
@@ -4247,6 +4262,7 @@ def median(self, *args, **kwargs):
     @_decorate_agg_method(np.median, np.nanmedian, commutative=True, by_agg=True, long_name="mediane")
     def median_by(self, *args, **kwargs):
         """{signature}
+
         Computes the arithmetic median.
 
         {parameters}
@@ -4317,6 +4333,7 @@ def median_by(self, *args, **kwargs):
     # signature as other aggregate functions (extra argument)
     def percentile(self, q, *args, **kwargs):
         """{signature}
+
         Computes the qth percentile of the data along the specified axis.
 
         {parameters}
@@ -4404,6 +4421,7 @@ def percentile(self, q, *args, **kwargs):
 
     def percentile_by(self, q, *args, **kwargs):
         """{signature}
+
         Computes the qth percentile of the data for the specified axis.
 
         {parameters}
@@ -4489,6 +4507,7 @@ def percentile_by(self, q, *args, **kwargs):
 
     def ptp(self, *args, **kwargs):
         """{signature}
+
         Returns the range of values (maximum - minimum).
 
         The name of the function comes from the acronym for ‘peak to peak’.
@@ -4553,6 +4572,7 @@ def ptp(self, *args, **kwargs):
     @_decorate_agg_method(np.var, np.nanvar, extra_kwargs=['dtype', 'ddof'], long_name="variance")
     def var(self, *args, **kwargs):
         """{signature}
+
         Computes the unbiased variance.
 
         Normalized by N-1 by default. This can be changed using the ddof argument.
@@ -4616,6 +4636,7 @@ def var(self, *args, **kwargs):
     @_decorate_agg_method(np.var, np.nanvar, by_agg=True, extra_kwargs=['dtype', 'ddof'], long_name="variance")
     def var_by(self, *args, **kwargs):
         """{signature}
+
         Computes the unbiased variance.
 
         Normalized by N-1 by default. This can be changed using the ddof argument.
@@ -4679,6 +4700,7 @@ def var_by(self, *args, **kwargs):
     @_decorate_agg_method(np.std, np.nanstd, extra_kwargs=['dtype', 'ddof'], long_name="standard deviation")
     def std(self, *args, **kwargs):
         """{signature}
+
         Computes the sample standard deviation.
 
         Normalized by N-1 by default. This can be changed using the ddof argument.
@@ -4743,6 +4765,7 @@ def std(self, *args, **kwargs):
                           long_name="standard deviation")
     def std_by(self, *args, **kwargs):
         """{signature}
+
         Computes the sample standard deviation.
 
         Normalized by N-1 by default. This can be changed using the ddof argument.
@@ -5472,10 +5495,10 @@ def append(self, axis, value, label=None):
         Parameters
         ----------
         axis : axis reference
-            Axis along which to append input array (`value`).
+            Axis along which to append `value`.
         value : scalar or LArray
             Scalar or array with compatible axes.
-        label : str, optional
+        label : scalar, optional
             Label for the new item in axis
 
         Returns
@@ -5906,6 +5929,8 @@ def drop(self, labels=None):
     def transpose(self, *args):
         """Reorder axes.
 
+        By default, reverse axes, otherwise permute the axes according to the list given as argument.
+
         Parameters
         ----------
         *args
@@ -6688,7 +6713,7 @@ def diff(self, axis=-1, d=1, n=1, label='upper'):
 
         Returns
         -------
-        LArray :
+        LArray
             The n-th order differences. The shape of the output is the same as `a` except for `axis` which is smaller
             by `n` * `d`.
 

larray/inout/csv.py

@@ -34,12 +34,12 @@ def read_csv(filepath_or_buffer, nb_axes=None, index_col=None, sep=',', headerse
     ----------
     filepath_or_buffer : str or any file-like object
         Path where the csv file has to be read or a file handle.
-    nb_axes : int, optional
+    nb_axes : int or None, optional
         Number of axes of output array. The first ``nb_axes`` - 1 columns and the header of the CSV file will be used
         to set the axes of the output array. If not specified, the number of axes is given by the position of the
         first column header including a ``\`` character plus one. If no column header includes a ``\`` character,
         the array is assumed to have one axis. Defaults to None.
-    index_col : list, optional
+    index_col : list or None, optional
         Positions of columns for the n-1 first axes (ex. [0, 1, 2, 3]). Defaults to None (see nb_axes above).
     sep : str, optional
         Separator.
@@ -57,9 +57,10 @@ def read_csv(filepath_or_buffer, nb_axes=None, index_col=None, sep=',', headerse
         Whether or not to assume the array is stored in "wide" format.
         If False, the array is assumed to be stored in "narrow" format: one column per axis plus one value column.
         Defaults to True.
-    dialect : 'classic' | 'larray' | 'liam2', optional
+    dialect : {'classic', 'larray', 'liam2'}, optional
         Name of dialect. Defaults to 'larray'.
     **kwargs
+        Extra keyword arguments are passed on to pandas.read_csv
 
     Returns
     -------