diff --git a/docs/examples/howto_logparser/plot_logparser_analyses.py b/docs/examples/howto_logparser/plot_logparser_analyses.py
index 66dc4ef10dabc8c9bdba0294b5f2c9340f36b489..339320751744a1adaa2c65480096b88b7eb22eac 100644
--- a/docs/examples/howto_logparser/plot_logparser_analyses.py
+++ b/docs/examples/howto_logparser/plot_logparser_analyses.py
@@ -40,8 +40,8 @@ df_records = pd.DataFrame(records)
 df_log = fill_ogs_context(df_records)
 
 # %%
-# Iterations per time step
-# ------------------------
+# Analysis of iterations per time step
+# ------------------------------------
 # Please see explanation in logparser
 # :ref:`sphx_glr_auto_examples_howto_logparser_plot_logparser_intro.py`.
 # (Section: Use predefined analyses)
@@ -52,8 +52,8 @@ df_ts_it  # noqa: B018
 
 
 # %%
-# Performance in separate parts by time step
-# ---------------------------------------------
+# Analysis of computational efficiency by time step
+# -------------------------------------------------
 # The resulting table presents the performance metrics for separate parts of the simulation,
 # organized by time step. Is uses :py:mod:`ogstools.logparser.analysis_time_step`.
 # Each row corresponds to a specific time step, displaying metrics such
@@ -66,8 +66,6 @@ df_ts = df_ts.loc[0]
 # - see advanced
 df_ts  # noqa: B018
 # %%
-# Performance of in separate parts by time step - plot
-# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 # We select only some metrics (3) and use pandas plot function.
 df_ts[["assembly_time", "dirichlet_time", "linear_solver_time"]].plot(
     logy=True, grid=True
@@ -85,8 +83,8 @@ analysis_convergence_newton_iteration(df_log)
 
 
 # %%
-# Staggered
-# ---------
+# Staggered - Analysis of convergence criteria - Newton iterations
+# ----------------------------------------------------------------
 # The resulting table provides convergence criteria for staggered coupled processes,
 # Each row represents convergence metrics such as `global component norm |x|`, `change of global component norm |dx|` (change between 2 iteration of non linear solver)
 # and `relative change of global component |dx|/|x|` at different time steps and coupling
diff --git a/ogstools/logparser/common_ogs_analyses.py b/ogstools/logparser/common_ogs_analyses.py
index 1ae1bd007e7fe8c802eff57d635199bf4781bb78..7737884b57ec413b6272c255e5965d231122b62a 100644
--- a/ogstools/logparser/common_ogs_analyses.py
+++ b/ogstools/logparser/common_ogs_analyses.py
@@ -206,11 +206,13 @@ def fill_ogs_context(df_raw_log: pd.DataFrame) -> pd.DataFrame:
     Pandas documentation : https://pandas.pydata.org/pandas-docs/stable/user_guide/
 
     Notes:
-    Some logs do not contain information about time_step and iteration. The information must be collected by context (by surrounding log lines from same mpi_process)
-    Logs are grouped by mpi_process to get only surrounding log lines from same mpi_process
+    Some logs do not contain information about time_step and iteration. The
+    information must be collected by context (by surrounding log lines from same mpi_process).
+    Logs are grouped by mpi_process to get only surrounding log lines from same mpi_process.
     There are log lines that give the current time step (when time step starts).
-    It can be assumed that in all following lines belong to this time steps, until next collected value of time step
-    Some columns that contain actual integer values are converted to float
+    It can be assumed that in all following lines belong to this time steps, until next
+    collected value of time step.
+    Some columns that contain actual integer values are converted to float.
     See https://pandas.pydata.org/pandas-docs/stable/user_guide/integer_na.html
     ToDo list of columns with integer values are known from regular expression