Tokens_without_sw = įiltered_sentence = (" ").join(tokens_without_sw) Text = re.sub('' % re.escape(string.punctuation), '', text) # converting to lowercase, removing URL links, special characters, punctuations. I have done it in the following way: import pandas as pd I.ġ79106 More than 1,200 students test positive for #CO.ġ79107 I stop when I see a text, Length: 179108, dtype: object I have a dataframe df such that: print(df)Ġ If I smelled the scent of hand sanitizers toda.Ĥ 25 July : Media Bulletin on Novel #CoronaVirus.ġ79103 Thanks for nominating me for the 2020! The year of insanity! Lol! #COVID19 http.ġ79105 A powerful painting by Juan Lucena. Also, I want to know if there exists any dedicated python module to get the desired result easily. and stop-words.Īny criticisms and suggestions to improve the efficiency & readability of my code would be greatly appreciated. I wanted to find the top 10 most frequent words from the column excluding the URL links, special characters, punctuations. It compiles quite slowly due to the method of removing stop-words. I think the code could be written in a better and more compact form. Python Data Science Handbook: Essential Tools for Working with Data. Pandas for Everyone : Python Data Analysis. Hands-On Data Analysis with Pandas: Efficiently perform data collection, wrangling, analysis, and visualization using Python. Python for Data Analysis : Data Wrangling with Pandas, NumPy, and IPython (2nd ed.). ^ "NumFOCUS – pandas: a fiscally sponsored project".^ "Indexing and selecting data - pandas 1.4.1 documentation".^ "Reshaping and pivot tables - pandas 1.4.1 documentation".^ "Merge, join, concatenate and compare - pandas 1.4.1 documentation".^ "IO tools (Text, CSV, HDF5, …) - pandas 1.4.1 documentation"."Meet the man behind the most important tool in data science". Python for Data Analysis, Second Edition. "pandas: a Foundational Python Library for Data Analysis and Statistics" (PDF).
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