Create Hotel_Reviews_Tags.py

6-NLP/4-Hotel-Reviews-1/Hotel_Reviews_Tags.py

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+# This code explores the Tag column of the Hotel_Reviews dataset.
+# It is not integral to the NLP aspect of the lesson, but useful for learning pandas and EDA
+# The goal was to identify what tags were worth keeping
+import pandas as pd
+
+# Load the hotel reviews from CSV (you can )
+df = pd.read_csv('Hotel_Reviews_Filtered.csv')
+
+# We want to find the most useful tags to keep
+# Remove opening and closing brackets
+df.Tags = df.Tags.str.strip("[']")
+# remove all quotes too
+df.Tags = df.Tags.str.replace(" ', '", ",", regex = False)
+
+# removing this to take advantage of the 'already a phrase' fact of the dataset 
+# Now split the strings into a list
+tag_list_df = df.Tags.str.split(',', expand = True)
+
+# Remove leading and trailing spaces
+df["Tag_1"] = tag_list_df[0].str.strip()
+df["Tag_2"] = tag_list_df[1].str.strip()
+df["Tag_3"] = tag_list_df[2].str.strip()
+df["Tag_4"] = tag_list_df[3].str.strip()
+df["Tag_5"] = tag_list_df[4].str.strip()
+df["Tag_6"] = tag_list_df[5].str.strip()
+
+# Merge the 6 columns into one with melt
+df_tags = df.melt(value_vars=["Tag_1", "Tag_2", "Tag_3", "Tag_4", "Tag_5", "Tag_6"])
+
+# Get the value counts
+tag_vc = df_tags.value.value_counts()
+# print(tag_vc)
+print("The shape of the tags with no filtering:", str(df_tags.shape))
+# Drop rooms, suites, and length of stay, mobile device and anything with less count than a 1000
+df_tags = df_tags[~df_tags.value.str.contains("Standard|room|Stayed|device|Beds|Suite|Studio|King|Superior|Double", na=False, case=False)]
+tag_vc = df_tags.value.value_counts().reset_index(name="count").query("count > 1000")
+# Print the top 10 (there should only be 9 and we'll use these in the filtering section)
+print(tag_vc[:10])
+
+#                         index   count
+# 0                Leisure trip  417778
+# 1                      Couple  252294
+# 2               Solo traveler  108545
+# 3               Business trip   82939
+# 4                       Group   65392
+# 5  Family with young children   61015
+# 6  Family with older children   26349
+# 7      Travelers with friends    2143
+# 8                  With a pet    1405