Merge pull request #302 from CamDavidsonPilon/ix-loc

remove ix from the api

CHANGELOG.md

@@ -1,7 +1,8 @@
 ### Changelogs
 
-#### 0.10.2
+#### 0.11.0
  - introduce a new `.plot` function to a fitted `CoxPHFitter` instance. This plots the hazard coefficients and their confidence intervals. 
+ - in all plot methods, the `ix` kwarg has been deprecated in favour of a new `loc` kwarg. This is to align with Pandas deprecating `ix`
 
 #### 0.10.1
  - fix in internal normalization for `CoxPHFitter` predict methods.

docs/Intro to lifelines.rst

@@ -572,14 +572,14 @@ gets smaller (as seen by the decreasing rate of change). Let's break the
 regimes down between democratic and non-democratic, during the first 20
 years:
 
-.. note::  We are using the ``ix`` argument in the call to ``plot`` here: it accepts a ``slice`` and plots only points within that slice.
+.. note::  We are using the ``loc`` argument in the call to ``plot`` here: it accepts a ``slice`` and plots only points within that slice.
 
 .. code:: python
 
     naf.fit(T[dem], event_observed=C[dem], label="Democratic Regimes")
-    ax = naf.plot(ix=slice(0,20))
+    ax = naf.plot(loc=slice(0,20))
     naf.fit(T[~dem], event_observed=C[~dem], label="Non-democratic Regimes")
-    naf.plot(ax=ax, ix=slice(0,20))
+    naf.plot(ax=ax, loc=slice(0,20))
     plt.title("Cumulative hazard function of different global regimes");
 
 

lifelines/fitters/aalen_additive_fitter.py

@@ -414,12 +414,12 @@ def predict_expectation(self, X):
         t = self.cumulative_hazards_.index
         return pd.DataFrame(trapz(self.predict_survival_function(X)[index].values.T, t), index=index)
 
-    def plot(self, ix=None, iloc=None, columns=[], legend=True, **kwargs):
+    def plot(self, loc=None, iloc=None, columns=[], legend=True, **kwargs):
         """"
         A wrapper around plotting. Matplotlib plot arguments can be passed in, plus:
 
           ix: specify a time-based subsection of the curves to plot, ex:
-                   .plot(ix=slice(0.,10.)) will plot the time values between t=0. and t=10.
+                   .plot(loc=slice(0.,10.)) will plot the time values between t=0. and t=10.
           iloc: specify a location-based subsection of the curves to plot, ex:
                    .plot(iloc=slice(0,10)) will plot the first 10 time points.
           columns: If not empty, plot a subset of columns from the cumulative_hazards_. Default all.
@@ -433,13 +433,13 @@ def shaded_plot(ax, x, y, y_upper, y_lower, **kwargs):
             fill_between_steps(x, y_lower, y2=y_upper, ax=ax, alpha=0.25,
                                color=base_line.get_color(), linewidth=1.0)
 
-        assert (ix is None or iloc is None), 'Cannot set both ix and iloc in call to .plot'
+        assert (loc is None or iloc is None), 'Cannot set both loc and iloc in call to .plot'
 
-        get_method = "ix" if ix is not None else "iloc"
-        if iloc == ix is None:
+        get_method = "loc" if loc is not None else "iloc"
+        if iloc == loc is None:
             user_submitted_ix = slice(0, None)
         else:
-            user_submitted_ix = ix if ix is not None else iloc
+            user_submitted_ix = loc if loc is not None else iloc
         get_loc = lambda df: getattr(df, get_method)[user_submitted_ix]
 
         if len(columns) == 0:

lifelines/plotting.py

@@ -235,11 +235,11 @@ def set_kwargs_drawstyle(kwargs):
     kwargs['drawstyle'] = kwargs.get('drawstyle', 'steps-post')
 
 
-def create_dataframe_slicer(iloc, ix):
-    user_did_not_specify_certain_indexes = (iloc is None) and (ix is None)
-    user_submitted_slice = slice(None) if user_did_not_specify_certain_indexes else coalesce(ix, iloc)
+def create_dataframe_slicer(iloc, loc):
+    user_did_not_specify_certain_indexes = (iloc is None) and (loc is None)
+    user_submitted_slice = slice(None) if user_did_not_specify_certain_indexes else coalesce(loc, iloc)
 
-    get_method = "ix" if ix is not None else "iloc"
+    get_method = "loc" if loc is not None else "iloc"
     return lambda df: getattr(df, get_method)[user_submitted_slice]
 
 
@@ -248,12 +248,12 @@ def plot_loglogs(cls):
     Specifies a plot of the log(-log(SV)) versus log(time) where SV is the estimated survival function.
     """
 
-    def _plot_loglogs(ix=None, iloc=None, show_censors=False, censor_styles=None, **kwargs):
+    def _plot_loglogs(loc=None, iloc=None, show_censors=False, censor_styles=None, **kwargs):
 
         loglog = lambda s: np.log(-np.log(s))
 
-        if (ix is not None) and (iloc is not None):
-            raise ValueError('Cannot set both ix and iloc in call to .plot().')
+        if (loc is not None) and (iloc is not None):
+            raise ValueError('Cannot set both loc and iloc in call to .plot().')
 
         if censor_styles is None:
             censor_styles = {}
@@ -263,7 +263,7 @@ def _plot_loglogs(ix=None, iloc=None, show_censors=False, censor_styles=None, **
         set_kwargs_drawstyle(kwargs)
         kwargs['logx'] = True
 
-        dataframe_slicer = create_dataframe_slicer(iloc, ix)
+        dataframe_slicer = create_dataframe_slicer(iloc, loc)
 
         # plot censors
         ax = kwargs['ax']
@@ -311,8 +311,8 @@ def plot_estimate(cls, estimate):
                      the lines' labels to the legend. Default: False
           at_risk_counts: show group sizes at time points. See function
                           'add_at_risk_counts' for details. Default: False
-          ix: specify a time-based subsection of the curves to plot, ex:
-                   .plot(ix=slice(0.,10.))
+          loc: specify a time-based subsection of the curves to plot, ex:
+                   .plot(loc=slice(0.,10.))
               will plot the time values between t=0. and t=10.
           iloc: specify a location-based subsection of the curves to plot, ex:
                    .plot(iloc=slice(0,10))
@@ -324,16 +324,16 @@ def plot_estimate(cls, estimate):
           ax: a pyplot axis object
         """ % estimate
 
-    def plot(ix=None, iloc=None, show_censors=False,
+    def plot(loc=None, iloc=None, show_censors=False,
              censor_styles=None, ci_legend=False, ci_force_lines=False,
              ci_alpha=0.25, ci_show=True, at_risk_counts=False,
              bandwidth=None, **kwargs):
 
         if censor_styles is None:
             censor_styles = {}
 
-        if (ix is not None) and (iloc is not None):
-            raise ValueError('Cannot set both ix and iloc in call to .plot().')
+        if (loc is not None) and (iloc is not None):
+            raise ValueError('Cannot set both loc and iloc in call to .plot().')
 
         set_kwargs_ax(kwargs)
         set_kwargs_color(kwargs)
@@ -349,7 +349,7 @@ def plot(ix=None, iloc=None, show_censors=False,
             estimate_ = getattr(cls, estimate)
             confidence_interval_ = getattr(cls, 'confidence_interval_')
 
-        dataframe_slicer = create_dataframe_slicer(iloc, ix)
+        dataframe_slicer = create_dataframe_slicer(iloc, loc)
 
         # plot censors
         ax = kwargs['ax']

tests/test_estimation.py

@@ -1157,7 +1157,7 @@ def test_aalen_additive_fit_no_censor(self, block):
             ax = plt.subplot(d + 1, 1, i + 1)
             col = cumulative_hazards.columns[i]
             ax = cumulative_hazards[col].loc[:15].plot(legend=False, ax=ax)
-            ax = aaf.plot(ix=slice(0, 15), ax=ax, columns=[col], legend=False)
+            ax = aaf.plot(loc=slice(0, 15), ax=ax, columns=[col], legend=False)
         plt.show(block=block)
         return
 
@@ -1187,7 +1187,7 @@ def test_aalen_additive_fit_with_censor(self, block):
             ax = plt.subplot(d + 1, 1, i + 1)
             col = cumulative_hazards.columns[i]
             ax = cumulative_hazards[col].loc[:15].plot(legend=False, ax=ax)
-            ax = aaf.plot(ix=slice(0, 15), ax=ax, columns=[col], legend=False)
+            ax = aaf.plot(loc=slice(0, 15), ax=ax, columns=[col], legend=False)
         plt.show(block=block)
         return
 

tests/test_plotting.py

@@ -112,7 +112,7 @@ def test_naf_plotting_slice(self, block):
         data2 = np.random.exponential(1, size=(200, 1))
         naf = NelsonAalenFitter()
         naf.fit(data1)
-        ax = naf.plot(ix=slice(0, None))
+        ax = naf.plot(loc=slice(0, None))
         naf.fit(data2)
         naf.plot(ax=ax, ci_force_lines=True, iloc=slice(100, 180))
         self.plt.title('test_naf_plotting_slice')
@@ -151,7 +151,7 @@ def test_naf_plot_cumulative_hazard_bandwidth_2(self, block):
         data1 = np.random.exponential(5, size=(2000, 1))
         naf = NelsonAalenFitter()
         naf.fit(data1)
-        naf.plot_hazard(bandwidth=1., ix=slice(0, 7.))
+        naf.plot_hazard(bandwidth=1., loc=slice(0, 7.))
         self.plt.title('test_naf_plot_cumulative_hazard_bandwidth_2')
         self.plt.show(block=block)
         return