Improve ca NaN handling + update pre-commit (#980)

.pre-commit-config.yaml

@@ -27,11 +27,13 @@ repos:
     hooks:
       - id: mypy
         args: [ --ignore-missing-imports  ]
-  - repo: https://github.com/pre-commit/mirrors-pylint
-    rev: 'v3.0.0a4'
+  - repo: local
     hooks:
-      - id: pylint
-        entry: pylint terminal.py gamestonk_terminal tests
+       - id: pylint
+         name: pylint
+         entry: pylint terminal.py gamestonk_terminal tests discordbot
+         language: system
+         types: [python]
   - repo: https://github.com/Yelp/detect-secrets
     rev: v1.1.0
     hooks:

gamestonk_terminal/stocks/comparison_analysis/yahoo_finance_model.py

@@ -52,7 +52,7 @@ def get_historical(
     # where all tickers are columns.
     return yf.download(all_tickers, start=start, progress=False, threads=False)[
         d_candle_types[candle_type]
-    ].dropna(axis=1)
+    ]
 
 
 def get_1y_sp500() -> pd.DataFrame:
@@ -120,7 +120,7 @@ def get_sp500_comps_tsne(
             if company != ticker:
                 ax.annotate(company, (x, y), fontsize=9, alpha=0.75)
             else:
-                ax.scatter(x, y, s=20, c="r")
+                ax.scatter(x, y, s=50, c="r")
                 ax.annotate(company, (x, y), fontsize=9, alpha=1)
         fig.tight_layout()
         plt.show()

gamestonk_terminal/stocks/comparison_analysis/yahoo_finance_view.py

@@ -60,7 +60,10 @@ def display_historical(
     )
     # To plot with ticker first
     df_similar = df_similar[ordered_tickers]
-
+    if np.any(df_similar.isna()):
+        nan_tickers = df_similar.columns[df_similar.isna().sum() >= 1].to_list()
+        print(f"NaN values found in: {', '.join(nan_tickers)}.  Replacing with zeros.")
+        df_similar = df_similar.fillna(0)
     fig, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
     # This puts everything on 0-1 scale for visualizing
     if normalize:
@@ -170,7 +173,10 @@ def display_correlation(
     )
     # To plot with ticker first
     df_similar = df_similar[ordered_tickers]
-
+    if np.any(df_similar.isna()):
+        nan_tickers = df_similar.columns[df_similar.isna().sum() >= 1].to_list()
+        print(f"NaN values found in: {', '.join(nan_tickers)}.  Backfilling data")
+        df_similar = df_similar.fillna(method="bfill")
     mask = np.zeros((df_similar.shape[1], df_similar.shape[1]), dtype=bool)
     mask[np.triu_indices(len(mask))] = True