fix call to pivot function with pandas 2.0

sdpython · sdpython · commit 1ca08907be03 · 2023-04-30T11:15:14.000+02:00
diff --git a/examples/plot_benchmark_associative.py b/examples/plot_benchmark_associative.py
@@ -67,7 +67,7 @@
     obs.append(t2)
 
 df = DataFrame(obs)
-piv = df.pivot("size", "formula", "average")
+piv = df.pivot(index="size", columns="formula", values="average")
 piv
 
 ###########################################
diff --git a/examples/plot_benchmark_dot.py b/examples/plot_benchmark_dot.py
@@ -89,9 +89,11 @@
 cc['N'] = cc['x_name']
 
 fig, ax = plt.subplots(1, 2, figsize=(10, 4))
-cc[cc.N <= 1100].pivot('N', 'fct', 'average').plot(
+cc[cc.N <= 1100].pivot(
+        index='N', columns='fct', values='average').plot(
     logy=True, logx=True, ax=ax[0])
-cc[cc.fct != 'pydot'].pivot('N', 'fct', 'average').plot(
+cc[cc.fct != 'pydot'].pivot(
+        index='N', columns='fct', values='average').plot(
     logy=True, logx=True, ax=ax[1])
 ax[0].set_title("Comparison of dot implementations")
 ax[1].set_title("Comparison of dot implementations\nwithout python")
diff --git a/examples/plot_benchmark_dot_cython.py b/examples/plot_benchmark_dot_cython.py
@@ -94,11 +94,14 @@ def get_vectors(fct, n, h=100, dtype=numpy.float64):
 cc['N'] = cc['x_name']
 
 fig, ax = plt.subplots(2, 2, figsize=(10, 10))
-cc[cc.N <= 1100].pivot('N', 'fct', 'average').plot(
+cc[cc.N <= 1100].pivot(
+        index='N', columns='fct', values='average').plot(
     logy=True, logx=True, ax=ax[0, 0])
-cc[cc.fct != 'dot_product'].pivot('N', 'fct', 'average').plot(
+cc[cc.fct != 'dot_product'].pivot(
+        index='N', columns='fct', values='average').plot(
     logy=True, ax=ax[0, 1])
-cc[cc.fct != 'dot_product'].pivot('N', 'fct', 'average').plot(
+cc[cc.fct != 'dot_product'].pivot(
+        index='N', columns='fct', values='average').plot(
     logy=True, logx=True, ax=ax[1, 1])
 ax[0, 0].set_title("Comparison of cython ddot implementations")
 ax[0, 1].set_title("Comparison of cython ddot implementations"
@@ -130,9 +133,9 @@ def get_vectors(fct, n, h=100, dtype=numpy.float64):
 cc['N'] = cc['x_name']
 
 fig, ax = plt.subplots(1, 2, figsize=(10, 4))
-cc.pivot('N', 'fct', 'average').plot(
+cc.pivot(index='N', columns='fct', values='average').plot(
     logy=True, ax=ax[0])
-cc.pivot('N', 'fct', 'average').plot(
+cc.pivot(index='N', columns='fct', values='average').plot(
     logy=True, logx=True, ax=ax[1])
 ax[0].set_title("Comparison of cython sdot implementations")
 ax[1].set_title("Comparison of cython sdot implementations")
diff --git a/examples/plot_benchmark_dot_cython_omp.py b/examples/plot_benchmark_dot_cython_omp.py
@@ -118,14 +118,15 @@ def ddot_omp_cpp_16(va, vb):
 cc['N'] = cc['x_name']
 
 fig, ax = plt.subplots(2, 2, figsize=(10, 10))
-cc[cc.N <= 1000].pivot('N', 'fct', 'average').plot(
+cc[cc.N <= 1000].pivot(index='N', columns='fct', values='average').plot(
     logy=True, ax=ax[0, 0])
-cc.pivot('N', 'fct', 'average').plot(
+cc.pivot(index='N', columns='fct', values='average').plot(
     logy=True, ax=ax[0, 1])
-cc.pivot('N', 'fct', 'average').plot(
+cc.pivot(index='N', columns='fct', values='average').plot(
     logy=True, logx=True, ax=ax[1, 1])
 cc[((cc.fct.str.contains('omp') | (cc.fct == 'ddot_array')) &
-    ~cc.fct.str.contains('dyn'))].pivot('N', 'fct', 'average').plot(
+    ~cc.fct.str.contains('dyn'))].pivot(
+        index='N', columns='fct', values='average').plot(
     logy=True, ax=ax[1, 0])
 ax[0, 0].set_title("Comparison of cython ddot implementations")
 ax[0, 1].set_title("Comparison of cython ddot implementations"
diff --git a/examples/plot_benchmark_dot_mul.py b/examples/plot_benchmark_dot_mul.py
@@ -149,9 +149,9 @@
     'N', 'fct', 'average').plot(logy=True, logx=True, ax=ax[1, 0])
 cc[ccnp | (~cct & ~cca0)].pivot(
     'N', 'fct', 'average').plot(logy=True, logx=True, ax=ax[1, 1])
-cc[ccnp | cca0].pivot('N', 'fct', 'average').plot(
+cc[ccnp | cca0].pivot(index='N', columns='fct', values='average').plot(
     logy=True, logx=True, ax=ax[2, 0])
-cc[ccnp | ~cca0].pivot('N', 'fct', 'average').plot(
+cc[ccnp | ~cca0].pivot(index='N', columns='fct', values='average').plot(
     logy=True, logx=True, ax=ax[2, 1])
 fig.suptitle("Comparison of matrix multiplication implementations")
 
diff --git a/examples/plot_benchmark_filter.py b/examples/plot_benchmark_filter.py
@@ -91,14 +91,18 @@ def numpy_filter(va, mx):
 cc['N'] = cc['x_name']
 
 fig, ax = plt.subplots(2, 2, figsize=(10, 10))
-cc[cc.N <= 1100].pivot('N', 'fct', 'average').plot(
+cc[cc.N <= 1100].pivot(
+        index='N', columns='fct', values='average').plot(
     logy=True, ax=ax[0, 0])
-cc[cc.fct != 'pyfilter_dmax'].pivot('N', 'fct', 'average').plot(
+cc[cc.fct != 'pyfilter_dmax'].pivot(
+        index='N', columns='fct', values='average').plot(
     logy=True, ax=ax[0, 1])
-cc[cc.fct != 'pyfilter_dmax'].pivot('N', 'fct', 'average').plot(
+cc[cc.fct != 'pyfilter_dmax'].pivot(
+        index='N', columns='fct', values='average').plot(
     logy=True, logx=True, ax=ax[1, 1])
 cc[(cc.fct.str.contains('cfilter') |
-    cc.fct.str.contains('numpy'))].pivot('N', 'fct', 'average').plot(
+    cc.fct.str.contains('numpy'))].pivot(
+        index='N', columns='fct', values='average').plot(
     logy=True, ax=ax[1, 0])
 ax[0, 0].set_title("Comparison of filter implementations")
 ax[0, 1].set_title("Comparison of filter implementations\n"
diff --git a/td3a_cpp/tutorial/td_mul_cython.cpp b/td3a_cpp/tutorial/td_mul_cython.cpp
