clean_tweets_dataframe.py

import pandas as pd
import re
from datetime import datetime
class Clean_Tweets:
    """
    The PEP8 Standard AMAZING!!!
    """
    def __init__(self, df:pd.DataFrame):
        self.df = df
        print('Automation in Action...!!!')
        
    def drop_unwanted_column(self, df:pd.DataFrame)->pd.DataFrame:
        """
        remove rows that has column names. This error originated from
        the data collection stage.  
        """
        unwanted_rows = df[df['retweet_count'] == 'retweet_count' ].index
        df.drop(unwanted_rows , inplace=True)
        df = df[df['polarity'] != 'polarity']
        
        return df
    def drop_duplicate(self, df:pd.DataFrame)->pd.DataFrame:
        """
        drop duplicate rows
        """

        df = self.df.drop_duplicates()
        return df
        
        
    def convert_to_datetime(self, df:pd.DataFrame)->pd.DataFrame:
        """
        convert column to datetime
        """
        df['created_at'] = pd.to_datetime(df['created_at'], errors='coerce')

        df = df[df['created_at'] >= '2020-12-31' ]
        
        return df
    
    def convert_to_numbers(self, df:pd.DataFrame)->pd.DataFrame:
        """
        convert columns like polarity, subjectivity, retweet_count
        favorite_count etc to numbers
        """
        df['polarity'] = pd.to_numeric(df['polarity'], errors='coerce')
        df['subjectivity'] = pd.to_numeric(df['subjectivity'], errors='coerce')
        df['retweet_count'] = pd.to_numeric(
            df['retweet_count'], errors='coerce')
        df['favorite_count'] = pd.to_numeric(
            df['favorite_count'], errors='coerce')
        df['followers_count'] = pd.to_numeric(
            df['followers_count'], errors='coerce')
        return df
    
    def remove_non_english_tweets(self, df:pd.DataFrame)->pd.DataFrame:
        """
        remove non english tweets from lang
        """
        
        df = df.query("lang == 'en' ")

        return df
    def extract_twitter_source(self, source: str):
        """
        returnssource device from source text
        """
        res = re.split('<|>', source)[2].strip()
        return res
    def remove_place_characters(self, df: pd.DataFrame):
        """
        removes non-alphanumeric characters with the exception of underscore hyphen and space
        from the specified column
        """

        df["place"] = df["place"].apply(
            lambda text: re.sub("[^a-zA-Z0-9\s_-]", "", text))

        return df