🛠️ patch fix series object has no attribute percentile

pandaswizard/aggregate.py

@@ -20,6 +20,9 @@ def percentile(n : float, outname : str = None, **kwargs) -> float:
  a type of [*quanitile*](https://en.wikipedia.org/wiki/Quantile)
  and can be interchangeably used.
 
+ Internally, the function uses the `pd.Series.quantile(n = n / 100)`
+ method to calculate the n-th percentile of the grouped series.
+
  :type n: int or float
  :param n: Percentage value to compute. Values must be between
  `[0, 100]` both inclusive.
@@ -36,7 +39,16 @@ def percentile(n : float, outname : str = None, **kwargs) -> float:
  use for estimating the percentile. There are many
  different methods of which some are unique to NumPy.
  Accepts any value as in `np.percentile(method = )`
- parameter, defaults to "linear" method.
+ parameter, defaults to "linear" method. However, for
+ the `pd.Series.quantile()` the argument `method` is
+ termed as `interpolation` and the values can be:
+ {'linear', 'lower', 'higher', 'midpoint', 'nearest'}.
+
+ * **interpolation** (*str*): Same as :attr:`method` the
+ method for quantile calculation as per pandas. Both the
+ attribute :attr:`method` and :attr:`interpolation` cannot
+ be passed at the same time, and raises `AssertionError`
+ if done so.
 
  Example and Usages
  ------------------
@@ -63,14 +75,18 @@ def percentile(n : float, outname : str = None, **kwargs) -> float:
  using the argument `outname`.
  """
 
- method = kwargs.get("method", "linear")
+ assert not all(["method" in kwargs, "interpolation" in kwargs]), \
+ "Either `method` or `interpolation` is required. Received both."
+
+ method = kwargs.get("method", kwargs.get("interpolation", "linear"))
 
  def percentile_(x : list) -> float:
- return x.percentile(n, method = method)
+ return x.quantile(n / 100, interpolation = method)
 
- percentile_.__name__ = outname or f"Q{n:.2f}"
+ percentile_.__name__ = outname or f"P{n:.2f}"
  return percentile_
 
+
 def quantile(n : float, outname : str = None, **kwargs) -> float:
  """
  Compute the n-th Quantile for the Grouped Data Series
@@ -81,6 +97,9 @@ def quantile(n : float, outname : str = None, **kwargs) -> float:
  sample in the same way. More information is available
  [here](https://en.wikipedia.org/wiki/Quantile).
 
+ Internally, the function uses the `pd.Series.quantile()` method
+ to calculate the n-th quantile of the grouped series.
+
  :type n: int or float
  :param n: Probability value for the quantiles to compute. The
  values must be between `[0, 1]` both inclusive.
@@ -97,7 +116,16 @@ def quantile(n : float, outname : str = None, **kwargs) -> float:
  use for estimating the quantile. There are many
  different methods of which some are unique to NumPy.
  Accepts any value as in `np.quantile(method = )`
- parameter, defaults to "linear" method.
+ parameter, defaults to "linear" method. However, for
+ the `pd.Series.quantile()` the argument `method` is
+ termed as `interpolation` and the values can be:
+ {'linear', 'lower', 'higher', 'midpoint', 'nearest'}.
+
+ * **interpolation** (*str*): Same as :attr:`method` the
+ method for quantile calculation as per pandas. Both the
+ attribute :attr:`method` and :attr:`interpolation` cannot
+ be passed at the same time, and raises `AssertionError`
+ if done so.
 
  Example and Usages
  ------------------
@@ -111,10 +139,10 @@ def quantile(n : float, outname : str = None, **kwargs) -> float:
  data = pd.DataFrame(data = {"G" : ["A", "B", "B"], "V" : [1, 2, 3]})
 
  # CASE-I: standalone usage, can be used on multiple features
- quantile = data.groupby("A").agg("V" : pdw.quantile(50))
+ quantile = data.groupby("A").agg("V" : pdw.quantile(0.5))
 
  # CASE-II: usage in conjunture of any other accepted function
- quantile = data.groupby("A").agg("V" : [sum, pdw.quantile(50)])
+ quantile = data.groupby("A").agg("V" : [sum, pdw.quantile(0.5)])
  ```
 
  Both the methods calculates the quantile for the grouped value.
@@ -124,10 +152,13 @@ def quantile(n : float, outname : str = None, **kwargs) -> float:
  using the argument `outname`.
  """
 
- method = kwargs.get("method", "linear")
+ assert not all(["method" in kwargs, "interpolation" in kwargs]), \
+ "Either `method` or `interpolation` is required. Received both."
+
+ method = kwargs.get("method", kwargs.get("interpolation", "linear"))
 
  def quantile_(x : list) -> float:
- return x.quantile(n, method = method)
+ return x.quantile(n, interpolation = method)
 
- quantile_.__name__ = outname or f"Q{n:.2f}"
+ quantile_.__name__ = outname or f"Q{n*100:.2f}"
  return quantile_