From 4105851cf19574d6f308e25e2c215301ce65688b Mon Sep 17 00:00:00 2001
From: Chavithra PARANA <chavithra@gmail.com>
Date: Sat, 22 May 2021 19:40:05 +0000
Subject: [PATCH] degiro integration : pyupgrade unrelated files

---
 gamestonk_terminal/discovery/marketbeat_view.py               | 4 ++--
 .../fundamental_analysis/business_insider_view.py             | 2 +-
 gamestonk_terminal/helper_funcs.py                            | 4 ++--
 gamestonk_terminal/prediction_techniques/arima_view.py        | 2 +-
 gamestonk_terminal/residuals_analysis/residuals_model.py      | 2 +-
 5 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/gamestonk_terminal/discovery/marketbeat_view.py b/gamestonk_terminal/discovery/marketbeat_view.py
index c2f3c58007d..dcca665a898 100644
--- a/gamestonk_terminal/discovery/marketbeat_view.py
+++ b/gamestonk_terminal/discovery/marketbeat_view.py
@@ -57,13 +57,13 @@ def ratings_view(other_args: List[str]):
         )
 
         df_ratings["clean_current_price"] = df_ratings["current_price"].apply(
-            lambda x: "".join([c if c.isdigit() or c == "." else "" for c in str(x)])
+            lambda x: "".join(c if c.isdigit() or c == "." else "" for c in str(x))
         )
         df_ratings["clean_current_price"] = df_ratings["clean_current_price"].astype(
             float
         )
         df_ratings["clean_target_price"] = df_ratings["new_target"].apply(
-            lambda x: "".join([c if c.isdigit() or c == "." else "" for c in str(x)])
+            lambda x: "".join(c if c.isdigit() or c == "." else "" for c in str(x))
         )
         df_ratings["clean_target_price"] = df_ratings["clean_target_price"].astype(
             float
diff --git a/gamestonk_terminal/fundamental_analysis/business_insider_view.py b/gamestonk_terminal/fundamental_analysis/business_insider_view.py
index bb0d4db3d6c..eaf0d0db2ab 100644
--- a/gamestonk_terminal/fundamental_analysis/business_insider_view.py
+++ b/gamestonk_terminal/fundamental_analysis/business_insider_view.py
@@ -104,7 +104,7 @@ def management(other_args: List[str], ticker: str):
         for ind in df_management.index:
             s_name = f"{ind}{(max([len(x) for x in df_management.index])-len(ind))*' '}"
             df_mgmt_title = df_management["Title"]
-            spaces = max([len(x) for x in df_mgmt_title]) - len(df_mgmt_title[ind])
+            spaces = max(len(x) for x in df_mgmt_title) - len(df_mgmt_title[ind])
             s_title = f"{df_mgmt_title[ind]}{spaces * ' '}"
             s_management = f"""{s_name} {s_title} {df_management['Info'][ind]}"""
             print(s_management)
diff --git a/gamestonk_terminal/helper_funcs.py b/gamestonk_terminal/helper_funcs.py
index eca018d4973..08e4598e1df 100644
--- a/gamestonk_terminal/helper_funcs.py
+++ b/gamestonk_terminal/helper_funcs.py
@@ -326,10 +326,10 @@ def text_adjustment_join_unicode(self, lines, sep=""):
 def text_adjustment_adjoin(self, space, *lists, **kwargs):
     # Add space for all but the last column:
     pads = ([space] * (len(lists) - 1)) + [0]
-    max_col_len = max([len(col) for col in lists])
+    max_col_len = max(len(col) for col in lists)
     new_cols = []
     for col, pad in zip(lists, pads):
-        width = max([self.len(s) for s in col]) + pad
+        width = max(self.len(s) for s in col) + pad
         c = self.justify(col, width, mode="left")
         # Add blank cells to end of col if needed for different col lens:
         if len(col) < max_col_len:
diff --git a/gamestonk_terminal/prediction_techniques/arima_view.py b/gamestonk_terminal/prediction_techniques/arima_view.py
index e0153ef5927..2ccd1048cf4 100644
--- a/gamestonk_terminal/prediction_techniques/arima_view.py
+++ b/gamestonk_terminal/prediction_techniques/arima_view.py
@@ -150,7 +150,7 @@ def arima(other_args: List[str], s_ticker: str, df_stock: pd.DataFrame):
 
         # Machine Learning model
         if ns_parser.s_order:
-            t_order = tuple([int(ord) for ord in ns_parser.s_order.split(",")])
+            t_order = tuple(int(ord) for ord in ns_parser.s_order.split(","))
             model = ARIMA(df_stock["5. adjusted close"].values, order=t_order).fit()
             l_predictions = model.predict(
                 start=len(df_stock["5. adjusted close"]) + 1,
diff --git a/gamestonk_terminal/residuals_analysis/residuals_model.py b/gamestonk_terminal/residuals_analysis/residuals_model.py
index 3c19d8bfc11..44b65fb9acd 100644
--- a/gamestonk_terminal/residuals_analysis/residuals_model.py
+++ b/gamestonk_terminal/residuals_analysis/residuals_model.py
@@ -119,7 +119,7 @@ def arima(
             return
 
         if ns_parser.s_order:
-            t_order = tuple([int(ord) for ord in list(ns_parser.s_order)])
+            t_order = tuple(int(ord) for ord in list(ns_parser.s_order))
             model_fit = ARIMA(stock.values, order=t_order).fit()
             model = pd.Series(model_fit.fittedvalues[1:], index=stock.index[1:])
             model_name = f"ARIMA {t_order}"