Fix linting (ignore imports for examples)

examples/tools/flatfiles-stock-trades/exchange-heatmap.py

@@ -3,11 +3,11 @@
 # trade flows, including opening hours and peak activity times, across the
 # exchanges. Please see https://polygon.io/blog/insights-from-trade-level-data
 #
-import pandas as pd
-import seaborn as sns
-import matplotlib.pyplot as plt
-import numpy as np
-import pytz
+import pandas as pd  # type: ignore
+import seaborn as sns  # type: ignore
+import matplotlib.pyplot as plt  # type: ignore
+import numpy as np  # type: ignore
+import pytz  # type: ignore
 
 # Replace '2024-04-05.csv' with the path to your actual file
 file_path = "2024-04-05.csv"

examples/tools/flatfiles-stock-trades/exchanges-seen.py

@@ -3,7 +3,7 @@
 # respective percentages of the total trades. Please see
 # https://polygon.io/blog/insights-from-trade-level-data
 #
-import pandas as pd
+import pandas as pd  # type: ignore
 
 # Replace '2024-04-05.csv' with the path to your actual file
 file_path = "2024-04-05.csv"

examples/tools/flatfiles-stock-trades/top-10-tickers.py

@@ -2,7 +2,7 @@
 # most traded stocks and calculates their respective percentages of the total
 # trades. Please see https://polygon.io/blog/insights-from-trade-level-data
 #
-import pandas as pd
+import pandas as pd  # type: ignore
 
 # Replace '2024-04-05.csv' with the path to your actual file
 file_path = "2024-04-05.csv"

examples/tools/flatfiles-stock-trades/trades-histogram.py

@@ -4,8 +4,8 @@
 # the distribution of trading volume across the day, from pre-market to after-
 # hours. Please see https://polygon.io/blog/insights-from-trade-level-data
 #
-import pandas as pd
-import matplotlib.pyplot as plt
+import pandas as pd  # type: ignore
+import matplotlib.pyplot as plt  # type: ignore
 
 # Replace '2024-04-05.csv' with the path to your actual file
 file_path = "2024-04-05.csv"