import pandas as pd

# retrieve bigrams for each topic and select only the word columns
topic_0 = pd.DataFrame(model.get_topic(0), columns=["Topic_0_word", "Topic_0_prob"])[["Topic_0_word"]]
topic_1 = pd.DataFrame(model.get_topic(1), columns=["Topic_1_word", "Topic_1_prob"])[["Topic_1_word"]]
topic_2 = pd.DataFrame(model.get_topic(2), columns=["Topic_2_word", "Topic_2_prob"])[["Topic_2_word"]]
topic_3 = pd.DataFrame(model.get_topic(3), columns=["Topic_3_word", "Topic_3_prob"])[["Topic_3_word"]]
topic_4 = pd.DataFrame(model.get_topic(4), columns=["Topic_4_word", "Topic_4_prob"])[["Topic_4_word"]]
topic_5 = pd.DataFrame(model.get_topic(5), columns=["Topic_5_word", "Topic_5_prob"])[["Topic_5_word"]]
topic_6 = pd.DataFrame(model.get_topic(6), columns=["Topic_6_word", "Topic_6_prob"])[["Topic_6_word"]]
topic_7 = pd.DataFrame(model.get_topic(7), columns=["Topic_7_word", "Topic_7_prob"])[["Topic_7_word"]]

# concatenate the dataframes
topics_df = pd.concat([topic_0, topic_1, topic_2, topic_3, topic_4, 
                       topic_5, topic_6,topic_7], axis=1)