techykajal

# Code for accented characters removal
def accented_characters_removal(text):
    # this is a docstring
    """
    The function will remove accented characters from the 
    text contained within the Dataset.
       
    arguments:
        input_text: "text" of type "String". 
                    

techykajal

def remove_whitespace(text):
    """ This function will remove 
        extra whitespaces from the text
    arguments:
        input_text: "text" of type "String". 
                    
    return:
        value: "text" after extra whitespaces removed .
        
    Example:

techykajal

def remove_links(text):
    """
    This function will remove all the occurrences of links.
    
    arguments:
        input_text: "text" of type "String". 
                    
    return:
        value: "text" after removal of all types of links.

techykajal

def strip_html_tags(text):
    """ 
    This function will remove all the occurrences of html tags from the text.
    
    arguments:
        input_text: "text" of type "String". 
                    
    return:
        value: "text" after removal of html tags.
        

techykajal

def remove_newlines_tabs(text):
    """
    This function will remove all the occurrences of newlines, tabs, and combinations like: \\n, \\.
    
    arguments:
        input_text: "text" of type "String". 
                    
    return:
        value: "text" after removal of newlines, tabs, \\n, \\ characters.
        

techykajal

#Command line script for youtube to gif maker
git clone https://github.com/techykajal/youtube-to-gif-maker.git

cd downloads/youtube-to-gif

youtube-dl -o input.mkv https://www.youtube.com/watch?v=AzjS8k5TEsY
  
ffmpeg -i input.mkv -ss 76 -t 4 output.mp4

ffmpeg -i output.mp4 -vf drawtext="fontfile=/path/to/font.ttf: \

techykajal

import pandas as pd
import matplotlib.pyplot as plt

Df = pd.read_csv("/content/Market.csv", encoding = 'latin')
Df.head()

#Location wise Data Science Jobs
Df.Location.apply(pd.Series).stack().str.strip().value_counts()[:10].plot.pie(figsize=(12,10),startangle=50,autopct='%1.1f%%',fontsize=15)
plt.title("Location Wise Data scientist Jobs",fontsize=30)
centre_circle = plt.Circle((0,0),0.72,color='gray', fc='white',linewidth=1.25)

techykajal

pip install --upgrade ShopifyAPI
import shopify
import pandas as pd
df = pd.read_csv('THIS_IS_FINAL.csv', encoding = 'latin-1')
df.head()

shop_url = "https://1da77cd6765932aa:sh2ad37dd07bb32aa@techy-kajal.myshopify.com/admin/api/2020-07" 
shopify.ShopifyResource.set_site(shop_url)

# Get the current shop

techykajal

replies=[]
for tweet in tweepy.Cursor(api.search,q='to:'+name, result_type='recent', timeout=999999).items(100):
    if hasattr(tweet, 'in_reply_to_status_id_str'):
        if (tweet.in_reply_to_status_id_str==tweet_id):
            replies.append(tweet)

with open('trump_data.csv', 'a+') as f:
    csv_writer = csv.DictWriter(f, fieldnames=('user', 'text'))
    csv_writer.writeheader()
    for tweet in replies:

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labels =  ['Positive_Trump', 'Positive_Biden'] 
sizes = lis_pos
explode = (0.1, 0.1)

fig1, ax1 = plt.subplots()
ax1.pie(sizes, explode=explode, labels = labels, autopct = '%1.1f%%', shadow = True, startangle=90)
ax1.set_title('Positive tweets on both the handles')
plt.show()