Increase the usage of augmented assignment statements

[elfring]

:eyes: Some source code analysis tools can help to find opportunities for improving software components. :thought_balloon: I propose to increase the usage of augmented assignment statements accordingly.

diff --git a/dataprep/clean/clean_date.py b/dataprep/clean/clean_date.py
index 41d07fd7..ff8ba367 100644
--- a/dataprep/clean/clean_date.py
+++ b/dataprep/clean/clean_date.py
@@ -908,7 +908,7 @@ def _transform_year(result_str: str, year_token: str, year: int) -> str:
             if len(year_token) == 4:
                 result = result.replace(year_token, str(year))
             else:
-                year = year - 2000
+                year -= 2000
                 if year < 10:
                     result = result.replace(year_token, f"{0}{year}")
                 else:
@@ -1005,7 +1005,7 @@ def _transform_hms(result_str: str, hms_token: str, ispm: bool, hms_value: int)
                 result = result.replace(hms_token, "-")
         else:
             if ispm:
-                hms_value = hms_value - 12
+                hms_value -= 12
             if len(hms_token) == 2:
                 if hms_value < 10:
                     result = result.replace(hms_token, f"{0}{hms_value}", 1)
diff --git a/dataprep/eda/diff/compute/multiple_df.py b/dataprep/eda/diff/compute/multiple_df.py
index b8b1a322..46c4c8be 100644
--- a/dataprep/eda/diff/compute/multiple_df.py
+++ b/dataprep/eda/diff/compute/multiple_df.py
@@ -380,7 +380,7 @@ def _calc_line_dt(srs: Srs, x: str, unit: str) -> Tuple[List[pd.DataFrame], str]
         df.columns = [x, "freq"]
         df["pct"] = df["freq"] / len(df) * 100
 
-        df[x] = df[x] - pd.to_timedelta(6, unit="d") if unit == "week" else df[x]
+        df[x] -= pd.to_timedelta(6, unit="d") if unit == "week" else df[x]
         df["lbl"] = df[x].dt.to_period("S").dt.strftime(DTMAP[unit][1])
 
     return (dfr, DTMAP[unit][3])
diff --git a/dataprep/eda/diff/render.py b/dataprep/eda/diff/render.py
index dffe842c..dceeb67c 100644
--- a/dataprep/eda/diff/render.py
+++ b/dataprep/eda/diff/render.py
@@ -471,7 +471,7 @@ def render_correlation_single_heatmaps(
     for meth in ["Pearson", "Spearman", "KendallTau"]:
         corr[meth] = []
         for i, df in enumerate(df_list):
-            df[meth]["x"] = df[meth]["x"] + "_" + str(i + 1)
+            df[meth]["x"] += "_" + str(i + 1)
             corr[meth].append(df[meth])
     tabs: List[Panel] = []
     tooltips = [("y", "@y"), ("correlation", "@correlation{1.11}")]
@@ -728,7 +728,7 @@ def render_comparison_continous(itmdt: Intermediate, cfg: Config) -> Dict[str, A
     for panel in tabs:
         panel.child.children[0].frame_width = int(plot_width * 0.9)
     if cfg.correlations.enable:
-        tabs = tabs + (
+        tabs += (
             render_correlation_single_heatmaps(data["corr"], col, plot_width, plot_height, cfg)
         )
 
diff --git a/dataprep/eda/distribution/compute/bivariate.py b/dataprep/eda/distribution/compute/bivariate.py
index d9f7261f..d12a9a45 100644
--- a/dataprep/eda/distribution/compute/bivariate.py
+++ b/dataprep/eda/distribution/compute/bivariate.py
@@ -429,7 +429,7 @@ def _calc_box_dt(df: dd.DataFrame, unit: str) -> Tuple[pd.DataFrame, List[str],
         )
     ).T
     # If grouping by week, make the label for the week the beginning Sunday
-    df.index = df.index - pd.to_timedelta(6, unit="d") if unit == "week" else df.index
+    df.index -= pd.to_timedelta(6, unit="d") if unit == "week" else df.index
     df.index.name = "grp"
     df = df.reset_index()
     df["grp"] = df["grp"].dt.to_period("S").dt.strftime(DTMAP[unit][2])
diff --git a/dataprep/eda/distribution/render.py b/dataprep/eda/distribution/render.py
index 826cfaf0..df8c960a 100644
--- a/dataprep/eda/distribution/render.py
+++ b/dataprep/eda/distribution/render.py
@@ -1151,7 +1151,7 @@ def stacked_viz(
             var ttl_bar = 0
             for (let i = 0; i < columns.length; i++) {
                 if (columns[i] != 'index'){
-                    ttl_bar = ttl_bar + source.data[columns[i]][cur_bar]
+                    ttl_bar += source.data[columns[i]][cur_bar]
                 }
             }
             const cur_val = source.data[special_vars.name][cur_bar]
diff --git a/dataprep/eda/missing/render.py b/dataprep/eda/missing/render.py
index ef5adea2..62f255dd 100644
--- a/dataprep/eda/missing/render.py
+++ b/dataprep/eda/missing/render.py
@@ -472,7 +472,7 @@ def render_bar_chart(
         var ttl_bar = 0
         for (let i = 0; i < columns.length; i++) {
             if (columns[i] != 'index'){
-                ttl_bar = ttl_bar + source.data[columns[i]][cur_bar]
+                ttl_bar += source.data[columns[i]][cur_bar]
             }
         }
         const cur_val = source.data[special_vars.name][cur_bar]
diff --git a/dataprep/eda/utils.py b/dataprep/eda/utils.py
index f654de4b..8280bd5e 100644
--- a/dataprep/eda/utils.py
+++ b/dataprep/eda/utils.py
@@ -245,8 +245,8 @@ def _calc_box_otlrs(df: dd.DataFrame) -> Tuple[List[str], List[float]]:
     outy: List[float] = []  # list for the outlier values
     for ind in df.index:
         otlrs = df.loc[ind]["otlrs"]
-        outx = outx + [df.loc[ind]["grp"]] * len(otlrs)
-        outy = outy + otlrs
+        outx += [df.loc[ind]["grp"]] * len(otlrs)
+        outy += otlrs
 
     return outx, outy
 
@@ -313,7 +313,7 @@ def _calc_line_dt(
                 dfr = srs[x].to_frame().groupby(grouper).size().reset_index()
             dfr.columns = [x, agg]
             # if grouping by week, make the label for the week the beginning Sunday
-            dfr[x] = dfr[x] - pd.to_timedelta(6, unit="d") if unit == "week" else dfr[x]
+            dfr[x] -= pd.to_timedelta(6, unit="d") if unit == "week" else dfr[x]
             # format the label
             dfr["lbl"] = dfr[x].dt.to_period("S").dt.strftime(DTMAP[unit][1])
             hist_lst.append((list(dfr[agg]), list(dfr[x]), list(dfr["lbl"])))
@@ -331,7 +331,7 @@ def _calc_line_dt(
     else:  # aggregate over a second column
         dfr = df.groupby(grouper)[df.columns[1]].agg(agg).reset_index()
         dfr.columns = [x, agg]
-    dfr[x] = dfr[x] - pd.to_timedelta(6, unit="d") if unit == "week" else dfr[x]
+    dfr[x] -= pd.to_timedelta(6, unit="d") if unit == "week" else dfr[x]
     dfr["lbl"] = dfr[x].dt.to_period("S").dt.strftime(DTMAP[unit][1])
 
     return (dfr, DTMAP[unit][3], miss_pct) if agg is None else (dfr, DTMAP[unit][3])